Sacred Heritage and Environmental Sustainability: Carbon and Biodiversity Insights from Taiwan’s Fo Guang Shan Monastery and Buddha Museum

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This study uses remote sensing and spatial analysis to evaluate the carbon storage potential and ecological resilience of the Fo Guang Shan Buddha Museum in Kaohsiung, Taiwan. Between 2016 and 2022, carbon storage increased by 17.8%, with tree canopy contributing over 1,300 tons of carbon. Despite a recent decline in sequestration rate, habitat connectivity remains high, while biodiversity indices indicate growing species richness and evenness. Economic valuation estimates the site’s carbon sequestration potential at USD 16–19 million, aligned with Taiwan’s 2024 Carbon Fee Policy. Comparative landscape metrics suggest that temple-managed green spaces mitigate fragmentation more effectively than passive conservation models. These findings support the integration of faith-driven sacred landscapes into carbon offset programs and nature-based climate solutions. The study highlights the policy relevance of religious sites as multifunctional heritage spaces contributing to ecological sustainability and national climate strategies. Sacred landscapes Cultural ecosystem services Carbon sequestration Remote sensing Sustainable heritage management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Religious landscapes have historically played a vital role in cultural, spiritual, and environmental stewardship, serving as centers for social cohesion, ecological conservation, and long-term sustainability practices (1, 2). While their significance has been well-documented in heritage studies, their contributions to carbon sequestration and climate change mitigation remain underexplored in contemporary environmental discourse (3). As global frameworks such as the United Nations Sustainable Development Goals (SDGs) and the Paris Agreement emphasize the urgent need for carbon sequestration strategies, sacred landscapes—including monastery forests, temple ecosystems, and church woodlands—present a unique but underutilized opportunity for nature-based climate solutions (3, 4). Studies have increasingly highlighted the biodiversity conservation value of religious landscapes. For example, recent research has emphasized the role of sacred natural sites in maintaining ecological integrity, with studies documenting how these landscapes serve as biodiversity hotspots and contribute to ecosystem resilience (5, 6). Furthermore, monastic forest management practices in regions such as Southeast Asia have demonstrated significant contributions to long-term habitat preservation and carbon sequestration (7). These findings underscore the broader conservation benefits of religious landscapes, reinforcing their potential for integration into global climate adaptation strategies. Ethiopian church forests function as biodiversity refuges, where native species are preserved under religious protection, even amidst widespread deforestation pressures in the region (8, 9). Similarly, sacred groves in India have been maintained for centuries through religious taboos and traditional ecological knowledge, demonstrating high levels of plant diversity and passive carbon storage (10). Beyond Africa and South Asia, Tibetan Buddhist sacred landscapes, including Mount Kawa Karpo and Iran’s Zagros forests, illustrate how religious worldviews influence sustainable ecological practices (1, 6) Despite these precedents, there remains limited empirical research quantifying the carbon sequestration potential of Buddhist temple ecosystems in East Asia. This study addresses this gap by integrating high-resolution remote sensing, landscape carbon modeling, and comparative conservation frameworks to assess the climate benefits of Buddhist temple landscapes. Unlike previous research that has primarily focused on qualitative assessments or regional biodiversity surveys, this study employs advanced remote sensing techniques and standardized carbon measurement methodologies to provide a rigorous, data-driven evaluation of the sequestration potential of religious landscapes. Particularly in the context of contemporary environmental policy and carbon accounting frameworks (5). This study evaluates Fo Guang Shan Monastery and Fo Guang Shan Buddha Museum—two interconnected religious sites in Kaohsiung, Taiwan—to assess the carbon sequestration potential of Buddhist temple landscapes (Fig. 1). Fo Guang Shan Monastery, established in 1967, serves as the spiritual and administrative headquarters of Fo Guang Shan Buddhism, comprising a monastery, pilgrimage zones, and extensive forested land (11). Fo Guang Shan Buddha Museum, completed in 2011, is one of the largest Buddhist cultural institutions in East Asia, featuring extensive gardens, tree-lined pathways, and managed green spaces designed to harmonize religious heritage with environmental conservation (12). These sites create an extensive religious and ecological landscape, seamlessly integrating afforestation, cultural heritage preservation, and sustainable landscape management. Unlike Ethiopian church forests (2, 9), which rely on passive conservation, and Indian sacred groves, which are community-driven (13-15), the Fo Guang Shan Monastery and Buddha Museum represent an actively managed conservation model, combining afforestation, reforestation, and strategic landscape planning to ensure long-term ecological sustainability. This study employs a geospatial and remote sensing-based approach combined with ecological modeling techniques to evaluate the carbon sequestration potential of Fo Guang Shan Monastery and Buddha Museum. Vegetative health and canopy coverage are assessed through the Normalized Difference Vegetation Index (NDVI), providing insights into the distribution and density of tree cover within the study area (16, 17). i-Tree Canopy Model estimates aboveground carbon storage by categorizing land-use types and their contributions. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model further refines this analysis by incorporating vegetation biomass and soil composition, offering a quantitative assessment of long-term carbon sequestration potential (18). In addition to modeling vegetation and soil carbon, we analyze landscape fragmentation using FRAGSTATS, a widely used tool for quantifying spatial configuration and ecological connectivity (19-23). This model evaluates key fragmentation metrics, such as Patch Density (PD) and Edge Density (ED), to assess the spatial arrangement of afforested areas and green spaces within the study site (22, 24). FRAGSTATS helps determine how spatial heterogeneity influences carbon retention efficiency by measuring habitat connectivity and fragmentation (19, 25). This analysis is essential because landscape fragmentation can disrupt carbon sequestration by increasing edge effects, reducing biomass continuity, and altering microclimatic conditions. The inclusion of FRAGSTATS in this study provides quantitative evidence of how green corridors and afforested zones contribute to optimizing carbon storage efficiency (26). Integrating these methodological tools ensures a comprehensive assessment of carbon sequestration across the Fo Guang Shan Monastery and Buddha Museum landscapes, positioning the study within a broader comparative framework of religious conservation strategies worldwide. The findings of this study carry significant implications for global climate policy, heritage conservation, and sustainable site management. The research provides empirical evidence of carbon sequestration within Buddhist temple ecosystems, reinforcing the potential for integrating religious landscapes into carbon offset programs. This issue is particularly relevant to Taiwan's 2024 Carbon Fee Policy, which underscores the need for nature-based solutions in climate mitigation (27). Furthermore, this study aligns with international conservation frameworks, particularly the UNESCO World Heritage Convention, which recognizes cultural landscapes' ecological significance and heritage value (28). UNESCO policies emphasize the protection and sustainable management of sacred natural sites, integrating biodiversity conservation with cultural preservation. These policies support the inclusion of religious landscapes in environmental conservation initiatives, acknowledging their role in carbon sequestration and climate adaptation strategies. Additionally, these policies align with emerging carbon credit markets, advocating for recognizing cultural landscapes as carbon sinks. advocating for the recognition of cultural landscapes as carbon sinks. This study builds on the methodological and policy framework outlined above to address key research questions that highlight the role of Buddhist temple landscapes in carbon sequestration and sustainable heritage management. Doing so aims to provide empirical insights into the intersection of religious heritage and climate mitigation strategies. The following key research questions guide this study: 1. How do Buddhist temple landscapes contribute to carbon sequestration, and how do they compare with other religious conservation models? 2. What is the carbon sequestration potential of Fo Guang Shan Monastery and Buddha Museum, and how does it align with global climate action goals? 3. How do institutional conservation strategies at Buddhist sites influence carbon storage efficiency compared to community-driven or naturally preserved religious landscapes? Results Vegetation Health and Growth Trends: NDVI Analysis NDVI Analysis (2016–2022) and Vegetation Health in Buddhist Temple Landscapes The Normalized Difference Vegetation Index (NDVI) is a key metric for assessing vegetation health and ecological stability in Buddhist temple landscapes (Fig. 6). Table 1 presents NDVI variations across 2016, 2019, and 2022, reflecting vegetation coverage and quality fluctuations. Table 1 NDVI values and vegetation growth trends in 2016, 2019, and 2022 Year Minimum NDVI Maximum NDVI Mean NDVI (± SD) Growth Rate (%) 2016 -0.24 0.80 0.30 ± 0.22 Baseline (0.00) 2019 -0.17 0.71 0.27 ± 0.17 -12.41% (Decline) 2022 -0.06 0.74 0.31 ± 0.19 +15.78% (Recovery) Source: InVEST Model (47, 52), and i-Tree Canopy (4, 53). Between 2016 and 2019, NDVI values declined by 12.41%, with a decrease in maximum NDVI from 0.80 to 0.71, and an increase in minimum NDVI from -0.24 to -0.17. From 2019 to 2022, mean NDVI rebounded by 15.78%, with minimum NDVI improving to -0.06. The maximum NDVI in 2022 (0.74) remained slightly lower than in 2016. These trends indicate periods of vegetation decline (2016–2019) followed by partial recovery (2019–2022), with an overall NDVI increase of 3.37% from 2016 to 2022. Carbon Sequestration in Faith-Managed Forests (InVEST, IPCC, and i-Tree Canopy Assessments) Carbon Storage Estimates (2016–2022) Using Multiple Methodologies The carbon sequestration capacity of Buddhist-managed forests was quantified using three methodologies: InVEST Model – Evaluates ecosystem service-based carbon sequestration across diverse land cover types. IPCC Methodology – Provides standardized estimates of biomass accumulation, root-to-shoot ratios, and carbon stock per hectare. i-Tree Canopy Analysis – Assesses tree canopy contributions and sequestration efficiency in urbanized sacred landscapes. Figure 7 shows a net increase in total carbon stock over the six-year period, as estimated by the InVEST model. Total carbon storage increased from 3,788.09 tons in 2016 to 4,502.39 tons in 2019 before stabilizing at 4,462.48 tons in 2022. CO₂ equivalent sequestration followed a similar trend, reaching 16,508.76 tons in 2019 before stabilizing at 16,362.43 tons in 2022 (Table 2). These findings suggest natural biomass accumulation and potential land-use effects on carbon retention capacity. Table 2 Estimated carbon storage and CO₂ sequestration using the InVEST model (2016–2022) Year Total Carbon Stock (tons) CO₂ Equivalent Storage (tons) 2016 3,788.09 13,889.66 2019 4,502.39 16,508.76 2022 4,462.48 16,362.43 IPCC-based calculations estimate carbon stock at 67.36 tons per hectare, with a CO₂ equivalent of 4,218.35 tons (Table 3). These values integrate biomass expansion factors (BEF = 1.40), root-to-shoot ratios (R = 0.24), and carbon fractions (CF = 0.4691), ensuring consistency with international carbon estimation protocols (68). Table 3 IPCC-based carbon storage parameters and estimates Item Timber Volume (m³/ha) Basic Wood Density (ton/m³) Biomass Expansion Factor (BEF) Root-to-Shoot Ratio (R) Carbon Fraction (CF) Carbon Stock (tons/ha) Total Carbon (tons) CO₂ Equivalent (tons) Buddha Museum 147.7 0.56 1.40 0.24 0.4691 67.36 1,150.46 4,218.35 The i-Tree Canopy analysis (Table 4) refines these estimates by quantifying tree-specific carbon sequestration. Findings indicate that trees within the 25.25-ha study area store 1,312.55 ± 63.94 tons of Carbon, translating to 4,812.67 ± 234.44 tons of CO₂ equivalent sequestration. Additionally, the annual sequestration rate is 52.26 ± 2.55 tons of Carbon (191.63 ± 9.34 tons CO₂ equivalent), reinforcing the role of Buddhist sacred forests as continuous carbon sinks. Table 4 Carbon storage, sequestration benefits, and market valuation of trees Item Area (ha) Total Carbon Stock (tons) CO₂ Equivalent sequestration (tons) Estimated Market Value (USD) Annual Carbon Sequestration 25.25 52.26 ± 2.55 191.63 ± 9.34 316,469 Cumulative Carbon Stock 25.25 1,312.55 ± 63.94 4,812.67± 234.44 7,947,731 Source: i-Tree Canopy Model (4, 53) and National Greenhouse Gas Inventory Report (68). Tree Canopy Coverage and Land Cover Contribution to Carbon Storage The i-Tree Canopy land cover classification provides insights into the distribution of vegetation and its role in carbon sequestration. The analysis identifies tree and shrub cover as the dominant carbon sink, occupying 17.08 ha of the total 25.25 ha study area(Table 5, Fig. 8). Grasslands cover 8.17 ha, providing moderate carbon sequestration potential. Table 5 Land cover classification and area estimates based on i-Tree Canopy analysis Abbreviation Land Cover Type Description Area (ha) T Tree/Shrub Woody vegetation with canopy 17.08 ± 0.83 H Grass/Herbaceous Low-lying vegetative cover 8.17 ± 0.64 IO Impervious Other Paved and non-vegetated surfaces 10.75 ± 0.71 IR Impervious Road Roads and transportation areas 10.81 ± 0.71 S Soil/Bare Ground Exposed soil or barren land 1.67 ± 0.31 IB Impervious Buildings Structures and built environments 9.14 ± 0.67 W Water Water bodies or aquatic features 0.23 ± 0.12 Total 57.85 In contrast, impervious surfaces (10.75 ha) and bare soil (1.67 ha) offer minimal contributions to carbon retention, as these land types lack the biomass necessary for long-term sequestration. The classification also highlights roads (10.81 ha) and built environments (9.14 ha), further restricting sequestration potential due to their non-vegetative nature. These findings suggest that expanding tree-dominated areas within temple landscapes could enhance overall sequestration efficiency while minimizing impervious land use and mitigating carbon loss. Economic Valuation of Carbon Storage in Faith-Managed Forests We assessed the economic impact of carbon sequestration under three global carbon credit trading mechanisms: European Energy Exchange (EEX) California Cap-and-Trade Program (CARB) Australian Carbon Credit Units (ACCU) As presented in Table 6, the financial valuation of stored Carbon demonstrates an upward trend over time. In 2016, the total market value ranged from $13.84 million (ACCU) to $16.22 million (EEX). By 2019, this valuation peaked at $16.88 million (ACCU) and $19.44 million (EEX) before stabilizing in 2022, settling between $16.74 million (ACCU) and $19.26 million (EEX). Table 6 Estimated carbon sequestration value under different carbon market scenarios Year Total Carbon Stock (tons) CO₂ Equivalent Storage (tons) EEX Market Value (USD) CARB Market Value (USD) ACCU Market Value (USD) 2016 3,788.09 13,889.66 16,221,000.00 14,563,000.00 13,842,000.00 2019 4,502.39 16,508.76 19,440,000.00 17,480,000.00 16,882,000.00 2022 4,462.48 16,362.43 19,260,000.00 17,320,000.00 16,740,000.00 Additionally, the i-Tree Canopy economic valuation (Table 7) estimates that the tree covers carbon sequestration at $408,853 (EEX), $234,084 (CARB), and $229,672 (ACCU). These figures highlight the monetary significance of faith-managed forests, demonstrating the potential for integration into Carbon offset markets. Table 7 Carbon storage and market valuation of tree and grassland areas Area (ha) Total CO₂ Storage (tons) EEX Market Value (USD) CARB Market Value (USD) ACCU Market Value (USD) 25.25 191.63 ± 9.34 408,853.72 234,084.52 229,672.39 To ensure cross-market comparability, we standardized all financial values using 2025 currency exchange rates (1 EUR = 1.046 USD; 1 AUD = 0.6397 USD). This approach enables a consistent assessment of faith-managed sequestration potential within international carbon pricing frameworks. The multi-method assessment of carbon sequestration confirms that faith-managed forests exhibit a stable and increasing sequestration trajectory, with notable gains from 2016 to 2019, followed by a slight plateau in 2022. The land cover analysis underscores the importance of tree canopy expansion, while the financial valuation highlights the potential integration of Buddhist conservation forests into Carbon offset markets. These results reinforce the significance of sacred landscapes in global climate change mitigation strategies. Land Cover, Biodiversity, and Habitat Connectivity in Temple Forests The FRAGSTATS model analysis comprehensively assesses land cover changes, biodiversity contributions, and habitat connectivity within FGSM and FGBM landscape from 2016 to 2022. This section presents land cover classifications, evaluates the role of temple forests in supporting biodiversity, and examines landscape fragmentation trends and their impact on ecosystem stability. Land Cover Classification (Tree Canopy, Grasslands, Impervious Surfaces) The Land Use and Land Cover (LULC) classification (Fig. 9) identifies seven primary land cover types within the study area, each influencing carbon storage and biodiversity conservation (Table 8): Temple structures – 82.83 ha (60.17% PLAND), forming the dominant spatial feature. Tree cover – 17.54 ± 2.24 ha (12.75% PLAND), acting as the primary carbon sink and habitat zone. Herbaceous land – 7.16 ha (5.20% PLAND) contributes to species diversity and stabilization of ground cover. Road networks – 19.80 ha (14.39% PLAND), increasing landscape fragmentation and limiting habitat connectivity. Built environments (non-temple) – 4.11 ha (2.99% PLAND), reducing available natural spaces. Barren land and paths – 6.21 ha (4.51% PLAND), with minimal ecological contributions. Table 8 Landscape metrics for biodiversity and carbon storage assessment Land Use CA (ha) PLAND (%) LPI (%) LSI AI (%) NLSI Barren 1.58 ± 2.18 1.15 ± 1.58 0.16 ± 0.15 19.20± 24.67 74.61± 18.86 0.25 ± 0.19 Building 4.11 ± 0.95 2.99 ± 0.69 0.26 ± 0.07 23.17± 16.37 78.78± 13.19 0.21 ± 0.13 Herbaceous 7.16 ± 1.19 5.20 ± 0.87 0.68 ± 0.08 26.62± 14.89 80.91± 10.18 0.19 ± 0.10 Path 4.63 ± 0.10 3.36 ± 0.07 1.14 ± 0.06 21.00± 10.44 81.11± 10.05 0.19 ± 0.10 Road 19.80± 3.79 14.39± 2.75 4.62 ± 1.10 32.51± 15.81 85.19± 8.88 0.15 ± 0.09 Temple 82.83± 1.06 60.17± 0.77 56.60± 1.14 7.71 ± 1.71 98.52± 0.39 0.02 ± 0.01 Tree 17.54± 2.24 12.75± 1.62 3.18 ± 1.00 24.49± 9.84 88.73± 4.49 0.11 ± 0.04 Source: FRAGSTATS Model (20, 26, 61). Tree cover is characterized by a Largest Patch Index (LPI) of 3.18, suggesting that tree patches are spatially dominant but not fully connected. The Landscape Shape Index (LSI) 24.49 reflects moderate complexity in tree patch configurations, which may facilitate diverse edge habitats. In contrast, expanding impervious surfaces, particularly roads and buildings, contributes to higher habitat fragmentation, potentially reducing ecological connectivity. Role of Faith-Based Conservation Areas in Promoting Biodiversity and Habitat Integrity Temple forests are critical biodiversity reserves, sustaining native flora and fauna through stable habitat formations. The high Aggregation Index (AI) of 88.73 ± 4.49 for tree cover confirms that tree patches remain clustered, ensuring spatial continuity essential for species survival. However, the gradual decline in AI from 93 (2016) to 87 (2022) indicates a weakening of habitat connectivity, which, if continued, may disrupt species migration and genetic diversity (Fig. 10). Tree cover maintains a Percentage of Landscape (PLAND) at 12.75%, reinforcing its ecological significance despite increasing land-use pressures. The Landscape Shape Index (LSI) for trees has risen from 20 in 2016 to 24.49 in 2022, reflecting growing complexity in patch structure, which may support more excellent microhabitat formation and species adaptability. Herbaceous land, with an LSI increase from 20 to 40 over the study period, complements tree-dominated areas by providing ground cover, supporting pollinators, and stabilizing the soil structure. Despite occupying a smaller fraction of the landscape (5.20% PLAND), it contributes to enhanced biodiversity richness and functional ecosystem services. Temple structures, while non-vegetative, shape landscape organization and habitat stability. The Largest Patch Index (LPI) of 56.60 ± 1.14 confirms their dominance and their role as stable landscape anchors supports species persistence by reducing landscape disruption from urban expansion. Landscape Fragmentation Trends and Their Effects on Ecosystem Stability in Temple Grounds Landscape fragmentation is a growing concern within the study area, as indicated by key FRAGSTATS metrics (Fig. 11) : Patch Density (PD) increased from 1,000 in 2019 to 6,000 in 2022, indicating a substantial rise in fragmentation levels. Contagion Index (CONTAG) decreased from 62 (2019) to 55 (2022), suggesting a reduction in habitat connectivity and more excellent isolation of ecological patches. Shannon's Diversity Index (SHDI) rose from 1.225 in 2019 to 1.325 in 2022, indicating increased habitat heterogeneity and a more even distribution of land cover types. Simpson's Diversity Index (SIDI) increased from 0.585 to 0.610, reinforcing species diversity and potential instability due to shifting habitat distributions. Aggregation Index (AI) for trees declined from 93 in 2016 to 87 in 2022, signaling a reduction in tree patch connectivity and increased spatial isolation. Ecosystem Services of Buddhist Temple Green Spaces: Air Quality and Climate Regulation The Buddha Museum’s forested area plays a significant role in urban air quality improvement and climate regulation, with measurable impacts on pollutant removal and temperature moderation. This section presents the quantification of air purification benefits, the climate-buffering function of temple groves, and the long-term resilience offered by faith-driven forest preservation. Quantification of Pollutant Removal in Faith-Protected Forests The temple forest (17.08 ha) effectively captures and removes key atmospheric pollutants, including carbon monoxide (CO), nitrogen dioxide (NO₂), ozone (O₃), sulfur dioxide (SO₂), delicate particulate matter (PM₂.₅), and coarse particulate matter (PM₁₀). Over the study period, the total pollutant removal exceeded 1,477 kg annually, generating an estimated economic value of $17,752 per year (Table 9). Among the removed pollutants, PM₂.₅ and PM₁₀ account for the highest economic benefits, emphasizing the role of temple forests in reducing urban air pollution and associated respiratory health risks. Notably, ozone removal exceeded 927 kg per year, reinforcing the importance of temple forests in mitigating smog formation and maintaining atmospheric balance. Removing gaseous pollutants (CO, NO₂, and SO₂) suggests that temple forests also reduce vehicle and industrial emissions. Table 9 Air purification benefits of trees: Pollutant removal and economic valuation Gas Area (ha) Total Dust (kg) Unit Dust (kg/ha) Total Value (USD) Unit Value (USD/ha) CO 17.08 17.13 ± 0.83 1.00 129 ± 6 55.87 ± 5.32 NO₂ 17.08 88.82 ± 4.33 5.20 22 ± 1 101.09 ± 9.31 O₃ 17.08 927.67 ± 45.19 54.31 1,468 ± 72 4,652.92 ± 426.98 SO₂ 17.08 75.09 ± 3.66 4.40 5 ± 0 14.63 ± 1.33 PM₂.₅ 17.08 45.45 ± 2.21 2.66 3,036 ± 148 9,740.80 ± 893.87 PM₁₀ 17.08 323.18 ±15.74 18.92 12,791 ± 623 3,187.08 ± 292.64 Total 17.08 1,477.33 ± 71.97 86.49 17,451 ± 850 17,752.39 ± 1,628.12 Figure 12 illustrates the amount of pollutants removed (in kg) by trees in the study area, ranked from highest to lowest efficiency. O₃ (Ozone) exhibits the highest removal rate, followed by PM₁₀ (Particulate Matter 10) and NO₂ (Nitrogen Dioxide). SO₂ (Sulfur Dioxide), PM₂.₅ (Fine Particulate Matter), and CO (Carbon Monoxide) show lower removal rates. The data highlight the essential role of urban trees in air purification and environmental health. Discussion The analysis of NDVI values from 2016 to 2022 reveals significant fluctuations in vegetation health, reflecting the interplay between environmental stressors and conservation efforts. The decline in mean NDVI by 12.41% between 2016 and 2019, coupled with a decrease in maximum NDVI from 0.80 to 0.71, suggests a reduction in vegetation density and canopy coverage (Table 4). Although minimum NDVI improved slightly from -0.24 to -0.17, the overall trend indicates ecological stress, likely attributable to urban encroachment, land-use modifications, and the natural aging of tree stands. Conversely, the subsequent period from 2019 to 2022 exhibited a notable NDVI recovery of 15.78%, indicative of successful regrowth efforts. Minimum NDVI improved to -0.06, signifying the revitalization of previously degraded patches. However, the maximum NDVI in 2022 remained slightly below its 2016 level (0.74 vs. 0.80), indicating that while afforestation initiatives have contributed to ecosystem restoration, the expansion of FGBM and FGSM under new building construction and land use changes has led to lower NDVI values, suggesting that the ecosystem has not yet fully recovered (Fig. 6). These findings parallel broader trends observed in faith-managed conservation areas, where periodic vegetation stress is mitigated through localized stewardship and long-term conservation commitments (28). The observed fluctuations in NDVI correspond closely with the conservation practices undertaken by Buddhist monasteries. These findings underscore the function of religious institutions as ecological sanctuaries, wherein faith-based environmental stewardship actively counteracts urbanization pressures and fosters ecosystem resilience (11, 37). The post-2019 NDVI rebound is likely the result of structured afforestation programs, sustainable forestry management, and conservation awareness initiatives spearheaded by monasteries. Other faith-managed conservation systems have reported similar patterns, such as : In China, indigenous ecological beliefs and sacred conservation practices sustain higher NDVI values in Fengshui forests compared to adjacent state-managed areas (68). Due to long-term religious protection, Ethiopian church forests exhibit enhanced vegetation stability and biodiversity compared to surrounding landscapes (28). The significant NDVI recovery (2019–2022) suggests that monastic afforestation initiatives have successfully counterbalanced prior vegetation losses. Reforestation efforts, including selective planting of native species and soil rehabilitation programs, have likely contributed to this recovery. These findings align with studies on temple forests in Thailand, where minimal human intervention and sacred status ensure sustained ecological restoration (7). In a comparative study of faith-managed reforestation efforts, Ouyang et al. (2024) found that Japanese temple forests exhibit long-term NDVI stability, a phenomenon attributed to low-impact forest management and religious conservation ethics (54). These comparisons highlight the importance of faith-based conservation models in sustaining long-term ecological integrity, particularly in regions experiencing rapid land-use transformations. The decline in NDVI between 2016 and 2019 aligns with well-documented trends in land-use transformations. The expansion of impervious surfaces, infrastructure development, and rising anthropogenic pressures near Buddhist temple landscapes have directly contributed to vegetation loss. Furthermore, as shown in Figure 7, the land use and land cover of FGBM in 2022 reveal a notable increase in barren areas (brown color), indicating continued landscape alterations that further suppress vegetation recovery. Research indicates that vegetation loss in urbanizing regions often results from progressive land-use changes, a trend corroborated by Nowak et al. (2013), who reported a 10–15% NDVI reduction within five years in urban forestry zones due to infrastructure expansion and foot traffic (58). The observed increase in minimum NDVI values suggests improving the resilience of degraded landscapes. This trend is consistent with findings by Huo and Wang (33), who demonstrated that faith-managed forests mitigate microclimate fluctuations and maintain long-term ecosystem stability (33). Since monastic landscapes are relatively insulated from large-scale disturbances, they serve as strongholds of ecological resilience, buffering against climate variability and external environmental stressors. A comparative evaluation of Buddhist temple forests and other faith-managed conservation areas further reinforces the efficacy of sacred landscapes in biodiversity preservation and ecosystem stability (Table 10) (2, 6, 13). Studies on Ethiopian church forests and indigenous sacred groves demonstrate that religiously protected landscapes tend to sustain higher NDVI values over time than state-managed reserves (9). Table 10 Comparative analysis of NDVI trends and conservation practices in sacred landscapes Sacred Landscape NDVI Trends Key Findings Fo Guang Shan Forests (2016–2022) Initial decline (2016–2019), followed by recovery (2019–2022) Monastic afforestation programs counteract urban expansion effects Ethiopian Church Forests Stable NDVI over decades Protection from deforestation due to religious significance Japanese Temple Forests Minimal NDVI fluctuations Strong cultural commitment to long-term conservation These comparisons indicate that faith-based conservation models outperform state parks in maintaining vegetation stability, mainly due to religious protection from deforestation, lower infrastructure development, and community-driven stewardship. Buddhist temple forests function as significant long-term carbon sinks, with sequestration trends from 2016 to 2022 demonstrating sustained carbon accumulation. The InVEST model estimated an increase in total carbon stock from 3,788.09 tons in 2016 to 4,502.39 tons in 2019, followed by stabilization at 4,462.48 tons in 2022. CO₂ sequestration mirrored this pattern, peaking at 16,508.76 tons in 2019 before leveling at 16,362.43 tons in 2022. This trend suggests a shift in biomass accumulation dynamics, similar to those observed in other sacred conservation areas. A comparative approach using the IPCC methodology estimated 67.36 tons of Carbon per hectare, corresponding to a CO₂ equivalent of 4,218.35 tons. This estimation, derived from standardized biomass expansion factors and root-to-shoot ratios, ensures compatibility with international carbon stock estimation protocols (69). Additionally, the i-Tree Canopy assessment, which provides a fine-scale analysis of sequestration potential, estimated that the 25.25-ha study area contained 1,312.55 ± 63.94 tons of Carbon, translating to a CO₂ equivalent of 4,812.67 ± 234.44 tons. The annual sequestration rate, recorded at 52.26 ± 2.55 tons of Carbon, reinforces the role of tree-dominated landscapes in maintaining continuous carbon absorption (70). These multi-method assessments underscore Buddhist temple forests' robust carbon sequestration potential, validating their role in nature-based climate mitigation strategies (71). The observed stabilization of carbon sequestration post-2019 suggests that the system is approaching equilibrium due to biophysical and climatic factors. One potential driver is aboveground biomass saturation, as the InVEST model suggests (71, 72). Miyawaki forests exhibit similar patterns, where rapid biomass accumulation slows as carbon storage capacity reaches its limit (73). Without additional afforestation or canopy expansion, sequestration rates naturally plateau. Additionally, soil carbon fluxes and microbial decomposition influence belowground carbon retention (74). While the IPCC approach accounts for biomass-derived sequestration, it does not fully capture soil-carbon interactions. Research indicates that microbial decomposition and soil respiration can partially offset carbon storage gains, particularly in humid environments where organic matter decay accelerates (75). Climate variability further impacts sequestration potential. Findings from the i-Tree Canopy analysis emphasize that tree canopy density regulates sequestration efficiency (70). Disruptions in monsoon cycles and increased seasonal droughts may have constrained tree growth rates, reducing the system's ability to sustain additional sequestration gains (73, 76). These findings emphasize the necessity of adaptive conservation frameworks considering biomass management and soil carbon stabilization (75). Buddhist temple forests are essential biodiversity refuges, sustaining native flora and fauna in urbanized landscapes. However, habitat fragmentation poses increasing challenges to their ecological stability (26). The FRAGSTATS model analysis highlights a substantial increase in Patch Density (PD) from 1,000 (2019) to 6,000 (2022), reflecting rising landscape fragmentation (64). Simultaneously, the Aggregation Index (AI) declined from 93 (2016) to 87 (2022), indicating a reduction in habitat connectivity, which may disrupt species migration, genetic diversity, and ecosystem resilience (25, 26). Tree cover, which accounts for 12.75% of the study area (PLAND), remains a critical ecological component despite urban expansion pressures (20). The Landscape Shape Index (LSI) for trees increased from 20 in 2016 to 24.49 in 2022, signifying a transition toward irregular patch configurations (64). While this can support edge-adapted species, it reduces interior habitat stability, negatively affecting forest-dependent taxa (77). The decline in the Contagion Index (CONTAG) from 62 (2019) to 55 (2022) further reinforces habitat isolation, necessitating targeted conservation interventions (25, 26). Buddhist temple forests retain more biodiversity than state-managed reserves due to religious land protection and long-term ecological stability. However, analyzing other sacred and secular conservation landscapes reveals parallels and key vulnerabilities. Ethiopian Church Forests, similarly embedded within human-dominated landscapes, exhibit increasing fragmentation, with patch density rising from 5 patches/km² in 1990 to 12 patches/km² in 2020 and a concurrent decline in the Aggregation Index from 0.85 to 0.60 (9). These patterns closely mirror those in Buddhist temple forests, emphasizing the need for connectivity restoration efforts to prevent further habitat isolation. African Sacred Groves face similar fragmentation pressures, with studies reporting a 20% decline in forest cover within a 1-km radius over a decade, leading to biodiversity loss and ecosystem degradation (78). Fragmentation of temple forests reduces functional diversity and threatens long-term species stability without effective conservation strategies (9, 79). European Monastic Forests, historically semi-managed conservation reserves, have experienced a tree density reduction from 300 trees/ha to 200 trees/ha, contributing to significant declines in avifaunal populations within fragmented patches. This trend reinforces findings from Buddhist temple forests, where fragmentation has altered habitat availability for specific taxa (79-81). Indigenous Sacred Groves in India, known for strong belowground carbon sequestration potential, have recorded a 30% decline in pollinator visitation rates in fragmented groves, impacting fruit set and seed production (82). A similar pattern may emerge in Buddhist temple forests, where habitat isolation could disrupt pollination networks and associated trophic interactions. While faith-managed forests demonstrate superior biodiversity conservation compared to state-managed reserves, rising fragmentation trends indicate an urgent need for conservation strategies to ensure their continued ecological functionality. Buddhist temple forests are pivotal to urban air quality management, functioning as highly efficient natural air filters. In our study, the Buddha Museum's temple forest—spanning 17.08 ha—removes approximately 1,477 kg of airborne pollutants annually, with an estimated economic benefit of $17,752 annually. Notably, ozone (O₃) removal is particularly effective, averaging 927.67 kg per year, substantially contributing to reducing ground-level smog and mitigating associated respiratory health risks. Equally important is the absorption of particulate matter, with PM₁₀ and PM₂.₅ being removed at rates of 323 kg and 45 kg per year, respectively—critical processes given the established links between these particulates and cardiovascular and respiratory diseases. Furthermore, removing nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) underscores the capacity of temple forests to lower emissions from traffic and industry, thereby mitigating acid rain and related environmental hazards. When normalized per hectare, the highest removal efficiencies are observed for ozone (54.31 kg/ha) and PM₁₀ (18.92 kg/ha), reinforcing the ecological significance of these sacred green spaces as nature-based solutions for urban air pollution (70). The air purification function of Buddhist temple forests is not an isolated phenomenon; instead, it is consistent with the performance of other religiously managed landscapes worldwide. For example, Fengshui forests in China have demonstrated comparable pollutant sequestration capabilities, yet they are seldom incorporated into formal air quality policies (54). Similarly, studies show that urban temple forests in Bangkok significantly reduce particulate concentrations in high-canopy areas (83). Ethiopian church forests, characterized by dense and relatively undisturbed vegetation, provide robust pollutant absorption and carbon sequestration, further validating the ecological merits of faith-managed green spaces (9). Although subject to varying degrees of urban encroachment, European monastic forests also retain significant ecosystem service functions; however, their contributions to air quality are often underrepresented in policy frameworks (Müller et al., 2022). These comparative insights highlight that faith-managed landscapes, by their long-term protection from deforestation, often exhibit superior air quality benefits compared to conventional urban parks, yet they remain underrecognized in environmental policy (68). Urban expansion, particularly the increase in impervious surfaces, has exacerbated habitat isolation (24, 40, 84). Road networks, which now cover 19.80 ha (14.39%), present significant barriers to species dispersal, increasing the risk of genetic bottlenecks (21, 24, 84). Despite doubling the herbaceous land LSI from 20 to 40, which supports pollinator populations and soil stabilization, the spatial disconnect between herbaceous zones and core forest patches reduces their effectiveness as functional ecological buffers (85). The contradiction between rising biodiversity indices (SHDI, SIDI) and increasing fragmentation (PD increase, AI decline) presents a complex conservation scenario. While more significant landscape heterogeneity can support generalist species, continued fragmentation may disrupt interior-dependent taxa, leading to long-term biodiversity instability (21, 26, 86). If habitat connectivity continues to decline, Buddhist temple forests may transition toward semi-urban ecological structures, reducing their overall capacity to function as compelling conservation landscapes (19, 20, 84). A comparative evaluation reveals that Buddhist temple forests exhibit carbon sequestration characteristics consistent with other faith-managed conservation areas. Ethiopian church forests, which experience low deforestation rates due to community-led conservation efforts, display sequestration trends similar to IPCC-derived biomass estimates (9). Indigenous sacred groves in India, known for their deep-rooted vegetation, exhibit higher belowground carbon retention, aligning with i-Tree Canopy findings that emphasize the role of root systems in sequestration dynamics (13). A broader comparison underscores that faith-managed landscapes outperform urban parks in sequestration efficiency. While urban parks store an average of 15.3 tons of Carbon per hectare, religious forests surpass 67 tons per hectare (4). This significant disparity highlights the potential of sacred landscapes as high-density carbon sinks and their importance in climate resilience strategies. In addition to their role in air purification, Buddhist temple forests serve as effective urban climate regulators. The removal of PM₂.₅ and PM₁₀ not only improves air quality but also reduces atmospheric heat absorption, thereby mitigating radiative forcing and attenuating urban heat island effects (56). Similarly, ozone and nitrogen dioxide absorption limits smog formation and reduces temperature variability caused by chemical pollutants. Moreover, temple forests' inherent dense biomass and mature canopy structures enhance thermal regulation, which can decrease the frequency and intensity of extreme heat events—a benefit corroborated by studies on urban forestry in regions such as Canada (87). Such multifaceted climate mitigation functions underscore the role of temple forests as nature-based solutions that provide sustainable alternatives to energy-intensive cooling systems. Buddhist temple forests play a vital role in biodiversity conservation and climate mitigation, yet they face vegetation health fluctuations due to urban pressures. The NDVI decline observed in FGSM and FGBM from 2016–2019, followed by partial recovery (2019–2022), reflects trends seen in Ethiopian church forests, where afforestation efforts counteract habitat fragmentation. Targeted afforestation programs using native high-carbon sequestration species should be prioritized to enhance long-term resilience, as demonstrated in Chinese Fengshui forests (88). Beyond afforestation, carbon sequestration stagnation is a growing concern in old-growth temple forests, where biomass saturation limits further carbon absorption. Similar patterns in Chinese temple forests and Indigenous sacred groves suggest that sequestration equilibrium is inevitable without active management (60). Strategies such as: Soil carbon stabilization techniques (biochar application, microbial soil enhancement) (3, 60). Implementing selective thinning and light management enhances biomass renewal (36, 51, 89). Despite their high sequestration potential, Buddhist temple forests remain excluded from carbon markets, missing a significant financial opportunity. The carbon stock valuation of Fo Guang Shan increased from $16.74M (2016) to $19.26M (2022), yet lack of certification prevents market participation. Studies on Nepalese and Thai religious forests indicate that including faith-managed forests in voluntary carbon markets can generate conservation funding (7, 90). Aligning temple forests with Taiwan’s 2024 carbon pricing policy could enable them to participate in global offset schemes, ensuring financial sustainability (91). Ecotourism presents an alternative revenue model for conservation. Chinese sacred groves have successfully integrated faith-based conservation tourism, where visitors support heritage protection and biodiversity conservation (31, 32, 92). Buddhist temple forests could adopt similar models by integrating guided ecological tours, sustainable visitor facilities, and community engagement (93). However, careful zoning regulations and impact assessments are needed to prevent over-commercialization, as seen in overdeveloped sacred landscapes in China (32, 35, 37, 94). A significant challenge in conserving Buddhist temple forests is their lack of formal policy recognition, restricting legal protection and funding access. Faith-managed forests in Ethiopia, China, and India face similar exclusions despite demonstrated ecological benefits. We recommend the following policy measures to address this issue : Legal Protection as Conservation Reserves – Temple forests should be designated as protected ecological reserves, ensuring long-term financial and legal safeguards (28, 44, 95). Institutional Collaboration – Partnerships between monasteries, government agencies, and conservation organizations should establish co-managed forest governance, as seen in Chinese Fengshui forests (88). Integration into National Green Infrastructure – Temple forests should be incorporated into urban resilience planning, mirroring China’s recognition of faith-managed landscapes as climate buffers (70). Community-Led Conservation – Local communities should actively engage in forest management, following successful Indigenous sacred forest models (28, 96). Further, fostering collaborative research partnerships between monastic institutions and environmental scientists can significantly enhance conservation efforts (93, 97). Integrating GIS-based biodiversity monitoring allows for precisely tracking vegetation changes and habitat health (96). At the same time, remote sensing analysis provides large-scale insights into forest cover dynamics and environmental stressors (70). Additionally, data-driven afforestation strategies can optimize reforestation by selecting suitable native species and ensuring long-term ecosystem stability (70, 96). By merging scientific methodologies with faith-based conservation practices, Buddhist temple forests can be more effectively protected and sustainably managed as resilient ecological sanctuaries in rapidly urbanizing landscapes (98, 99). This study comprehensively assesses the carbon sequestration capacity, biodiversity conservation potential, and environmental benefits of the FGSM and FGBM temple forest, highlighting the intersection of cultural heritage management and climate resilience. Through quantitative geospatial analysis and ecological modeling, the findings confirm that faith-managed landscapes serve as critical carbon sinks while enhancing biodiversity stability and urban sustainability (32, 37, 45). Between 2016 and 2022, carbon storage increased from 3,788.09 to 4,462.48 tons, with an annual sequestration rate of 52.26 ± 2.55 tons of Carbon (191.63 ± 9.34 tons CO₂ equivalent). However, a slight decline of 0.89% in sequestration between 2019 and 2022 suggests that biomass saturation, soil carbon flux limitations, and land-use modifications require strategic intervention. The i-Tree Canopy and IPCC models corroborate these estimates, reinforcing the temple forest's function as a long-term carbon reservoir with an average carbon stock of 67.36 tons per hectare. Beyond carbon sequestration, the FRAGSTATS landscape analysis revealed that temple forests contribute to habitat connectivity, with an Aggregation Index (AI) of 88.73%, facilitating species migration and ecological continuity. However, the declining Contagion Index (CONTAG) suggests increasing habitat fragmentation, which may disrupt ecological integrity. In contrast, Shannon’s Diversity Index (SHDI) and Simpson’s Diversity Index (SIDI) improvements indicate species diversification and enhanced ecological resilience, further emphasizing the ecological significance of Buddhist temple forests as biodiversity sanctuaries (37, 67). From an economic perspective, the conservation initiatives at Fo Guang Shan align with Taiwan's 2024 Carbon Fee Policy, with carbon market valuations increasing from $16.74 million (2016) to $19.26 million (2022). These findings highlight the potential for Buddhist temple forests to be formally integrated into global carbon trading markets, creating sustainable revenue streams for conservation efforts (27, 100). Comparative analysis of Ethiopian church forests and Indian sacred groves underscores Fo Guang Shan's proactive conservation model, characterized by afforestation, sustainable landscape management, and carbon credit potential (91). Despite these ecological and economic benefits, temple forests remain underrepresented in formal environmental and urban planning policies. Their exclusion from urban air quality management, climate mitigation policies, and carbon finance mechanisms limits their full impact. Addressing these gaps requires strategic conservation planning, policy integration, and scientific monitoring. Future efforts should focus on: Expanding afforestation and reforestation programs to enhance carbon sequestration and mitigate fragmentation (72, 91). Implementing soil carbon stabilization mechanisms prevents sequestration stagnation and maximizes long-term carbon storage potential (70, 71). Strengthening conservation finance models, integrating temple forests into carbon markets and green infrastructure frameworks (95, 100). This study underscores the urgent need for policy frameworks incorporating religious landscapes into climate mitigation strategies. As cultural heritage sites increasingly contribute to biodiversity resilience and climate adaptation, targeted conservation strategies—including afforestation, reforestation, and enhanced ecosystem monitoring—are essential to ensure ecological and economic sustainability. By aligning faith-based conservation with global climate policy, Buddhist temple forests can continue functioning as vital ecological assets, reinforcing their role in urban sustainability, climate change mitigation, and long-term biodiversity conservation. While this study enhances the understanding of religious landscapes as carbon sinks and biodiversity refuges, several limitations must be acknowledged. These constraints provide a foundation for future research to refine methodologies, expand comparative frameworks, and strengthen policy integration. One of the primary limitations lies in data resolution and temporal scope. Although high-resolution satellite imagery and geospatial models offer valuable insights, the absence of field-based validation techniques such as tree-core sampling, LiDAR mapping, and soil carbon analysis limits the precision of sequestration estimates. The six-year study period (2016–2022) also provides useful short-term trends. However, longitudinal analyses spanning multiple decades would yield more substantial insights into the long-term effects of climate variability, biomass saturation, and ecosystem adaptation. A further challenge stems from uncertainties in carbon estimation models. While i-Tree Canopy, InVEST, and IPCC models provide reliable sequestration estimates, regional variability in biomass expansion factors may introduce discrepancies. Developing a calibrated, site-specific biomass model tailored to Buddhist temple forests would improve estimation accuracy, particularly when considering species composition, soil carbon flux, and age-dependent sequestration variations. Additionally, biodiversity monitoring in this study relies primarily on landscape-level FRAGSTATS metrics, which capture habitat connectivity and fragmentation trends but lack direct species assessments. Future research should integrate faunal surveys, camera trapping, and soil microbial diversity analysis to corroborate species richness patterns and assess ecological resilience in Buddhist temple forests. From an economic and policy perspective, while this study monetizes carbon sequestration values, the actual financial viability of temple forests in carbon markets depends on policy implementation, legal recognition, and market accessibility. Future research should explore government incentives, regulatory frameworks, and voluntary carbon credit mechanisms to facilitate the inclusion of religious conservation landscapes in global climate finance. Finally, while this study compares Buddhist temple forests with Ethiopian church forests and Indian sacred groves, a broader comparative framework is needed to assess regional conservation strategies and policy adaptations. Expanding research to Buddhist temple forests in Japan, Thailand, and China would provide a more comprehensive understanding of faith-based conservation models across different cultural and ecological contexts. Addressing these limitations through expanded temporal analyses, direct biodiversity monitoring, improved carbon modeling, and global comparative studies will further strengthen the role of religious landscapes in climate mitigation and ecological conservation. Methods Study Area The Fo Guang Shan Monastery (FGSM) and Fo Guang Shan Buddha Museum (FGBM), located in Dashu District, Kaohsiung, Taiwan, form an extensive religious, cultural, and ecological complex. Established in 1967 by Venerable Master Hsing Yun, FGSM serves as the spiritual and administrative headquarters of the Fo Guang Shan Buddhist Order. The FGBM, inaugurated in 2011, extends this mission by integrating Buddhist teachings, cultural heritage preservation, and religious tourism. These sites exemplify a holistic approach to monastic practice, heritage conservation, and sustainable landscape management (11, 12). FGSM and FGBM sit at 22.7276° N and 120.4042° E within a tropical monsoon climate zone, where hot, humid summers and mild winters prevail. The area receives an average annual rainfall of 2,000 mm between May and September. This climate fosters lush vegetation and diverse ecological landscapes, reinforcing the sacred atmosphere of the complex while supporting its environmental conservation initiatives. Land Use and Architectural Features The Fo Guang Shan Buddha Museum spans approximately 100 hectares, integrating traditional Buddhist architectural elements with contemporary cultural and educational functions. The Buddha Memorial Center (BMC), positioned at the heart of the museum, enshrines a sacred Buddha tooth relic, serving as a focal point for devotional practice and pilgrimage. Eight pagodas surround the central structure, each symbolizing an aspect of the Noble Eightfold Path, which reinforces the philosophical and didactic dimensions of the museum. The Great Buddha Hall, dominated by a 108-meter bronze Buddha statue, is a defining feature of the complex, reflecting architectural grandeur and religious symbolism (Fig. 2). The Fo Guang Shan Buddha Temple spans 50 hectares, incorporating monastic residences, administrative buildings, meditation halls, and sacred shrines. Although the combined area of the temple and its associated museum covers 150 hectares, official tourism maps indicate that only 55 hectares are designated for public access, tourism, and pilgrimage (Fig. 3). This distribution suggests that most of the site is allocated for monastic activities, ecological conservation, and restricted areas, preserving its spiritual and environmental integrity. Ecological Significance and Green Space Distribution Although precise woodland data is unavailable, qualitative insights from key informant interviews indicate that built-up structures occupy less than 20% of the total area. In contrast, greenspace, including landscaped gardens, forested areas, and vegetative land covers, comprises more than 50%. These green spaces contribute significantly to carbon sequestration, biodiversity conservation, and microclimate regulation, aligning with Buddhist ecological ethics emphasizing harmonious coexistence between human activities and nature (1, 2, 6, 14, 29). Beyond its monastic and architectural significance, FGSM and FGBM are interdisciplinary research hubs, fostering academic inquiries into heritage conservation, sustainable tourism, and environmental management (12). The museum's integration of cultural heritage preservation with contemporary sustainability practices positions it as a model for heritage site conservation and religious landscape management. The surrounding forested and landscaped areas, designed by Buddhist ecological principles, play a crucial role in carbon sequestration, habitat preservation, and climate resilience. Geospatial Analysis and Research Considerations To provide spatial clarity, Figure 4 presents high-resolution satellite imagery of FGSM and FGBM, illustrating the distribution of built structures, green spaces, and ecological buffer zones. This geospatial analysis enables a quantitative evaluation of the site's cultural and ecological functions, ensuring analytical coherence with broader conservation and sustainability research (30, 31). In this study, GIS-based classification and remote sensing techniques—including NDVI (Normalized Difference Vegetation Index) and supervised land cover classification—were applied to refine estimates of green space coverage and assess its ecological contributions (32-35). These analyses provide further insights into the role of religious landscapes in sustainable environmental stewardship and heritage conservation. Remote Sensing and Land Cover Analysis Satellite Data Selection This study employs Pleiades-1A and Pleiades-1B high-resolution satellite imagery, operated by Airbus Defence and Space, due to their 0.5-meter spatial resolution and stereo imaging capabilities, which facilitate detailed land-use classification and vegetation analysis (36). Compared to alternative satellite sensors, such as Sentinel-2 (10m resolution) and Landsat-8 (30m resolution), Pleiades provides superior spatial precision, enabling the accurate detection of micro-scale land-use changes and vegetation dynamics. This higher resolution is particularly beneficial for assessing subtle variations in vegetation density, anthropogenic land modifications, and conservation effectiveness. Three datasets—2016, 2019, and 2022—were selected to assess temporal changes in vegetation cover, land-use transitions, and conservation practices within the study site (Fig. 5, Table 11). The selection of 2016 as the baseline year was determined by data availability, as this was the earliest Pleiades satellite imagery accessible from the Center for Space and Remote Sensing Research, NCU, Taiwan. To ensure a comprehensive longitudinal assessment, subsequent datasets from 2019 and 2022 were incorporated, allowing for the analysis of long-term environmental trends in relation to heritage site management. This temporal framework enables a detailed evaluation of the impact of sustainable conservation strategies, afforestation efforts, and climate variability on landscape stability and ecological resilience (37). As the analysis was concluded in 2024, this dataset selection provides a methodologically robust and temporally consistent basis for monitoring heritage landscapes through remote sensing techniques. Table 11 Pleiades satellite imagery datasets for remote sensing analysis Date Image ID 2016.12.14 P1B_H1M_20161214_024525 2019.12.19 P1A_H1M_20191219_023401 2022.12.01 P1A_H1M_20221201_024138 (Source: CNES (2016, 2019, 2022), Distributed by Airbus DS) These datasets provide multi-temporal insights into vegetation health, urban expansion, and conservation effectiveness, supporting a comprehensive geospatial analysis of the FGSM and FGBM landscape evolution. Vegetation Analysis: Normalized Difference Vegetation Index (NDVI) This study employed the Normalized Difference Vegetation Index (NDVI) to evaluate vegetation health and detect land-cover changes (38). This widely used spectral vegetation index quantifies plant vigor by measuring the difference in reflectance between near-infrared (NIR) and red light (39). Healthy vegetation exhibits strong reflectance in the NIR spectrum (700–1100 nm) while absorbing most red light (620–750 nm) for photosynthesis (40). The NDVI calculation follows the standard formula (41, 42): NDVI=(NIR+Red)(NIR−Red) (1) The index produces values ranging from -1 to 1, where higher values indicate greater vegetation density and vitality. NDVI values above 0.6 typically correspond to dense, healthy vegetation, while values between 0.2 and 0.6 indicate moderate vegetation cover. Values below 0.2 suggest sparse vegetation or non-vegetated surfaces. By offering a quantitative measure of vegetation health, NDVI is instrumental in assessing ecological stability, monitoring land degradation, and evaluating climate-induced shifts in vegetation patterns (43). InVEST Carbon Storage and Sequestration Model Carbon Storage and Sequestration Modeling This study employed the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Carbon Storage and Sequestration Model, developed by the Natural Capital Project, to assess carbon stock dynamics within the FGSM and FGBM. The model provides a spatially explicit assessment of carbon storage based on land cover classifications, integrating four major carbon pools: aboveground biomass, belowground biomass, soil organic carbon, and dead organic matter (31, 44, 45). The model quantifies carbon sequestration potential by analyzing temporal variations in land cover and vegetation, supporting heritage conservation strategies and climate adaptation policies (18, 46). Data Collection and Processing Land Cover Classification and Input Preparation This study utilized high-resolution Pleiades-1A and Pleiades-1B satellite imagery to generate land cover maps for 2016, 2019, and 2022, employing Support Vector Machine (SVM) classification within ArcGIS Pro (3.0) for data processing (32, 35). The classification system identified key vegetation types, including evergreen broad-leaved forests and grasslands, both of which function as primary carbon sinks (33, 34). To enhance spatial accuracy and boundary delineation, administrative boundary data and conservation zoning layers were integrated into the analysis of the FGSM-FGBM site. Carbon Pool Parameterization Researchers assigned carbon stock values to each land cover class based on IPCC (2019) guidelines, supplementing them with regional ecological datasets for improved estimation accuracy. Each land cover type was attributed carbon pool coefficients, representing aboveground biomass, belowground biomass, soil organic carbon, and dead organic matter (47-51). These coefficients were applied uniformly across the study area, enabling the calculation of total carbon storage per unit area and facilitating spatial comparisons over time. Carbon Storage Estimation The total carbon storage ( C i ) for each land parcel was computed as the sum of the four primary carbon pools (52): C i = C i a + C i b + C i s + C i d (2) Where: C i represents total Carbon stored in land parcel i (t/ha), C i a is aboveground biomass carbon (t/ha), C i b is belowground biomass carbon (t/ha), C i s is soil organic carbon (t/ha), and C i d is dead organic matter carbon (t/ha). The cumulative carbon storage across the study area was obtained by aggregating individual land parcel values: C total = (3) Where: C total : Total Carbon stored in the study area. S i : Area of each land cover type (ha). Table 12 Carbon storage parameters by land cover type Land cover C i a C i b C i s C i d Evergreen broad-leaved forests 67.36 70 35 12 Grassland 15 35 30 4 (Units: tons per hectare (t/ha)) Carbon Sequestration Estimation We calculated the net carbon sequestration (ΔC) between the selected periods as follows: ΔC=C t2 −C t1 Where: C t1 represents carbon storage in the initial year (2016), C t2 represents carbon storage in the final year (2022), and ΔC quantifies the net change in carbon stocks (tons CO₂ per year). This approach detects land-use-induced carbon gains or losses, providing insights into afforestation, deforestation, and conservation-driven carbon sequestration dynamics (49, 50). The InVEST carbon model was applied to estimate carbon storage and sequestration using IPCC Tier 1 default values and regional datasets. The resulting carbon stock estimates were analyzed using GIS-based spatial mapping techniques, producing high-resolution carbon distribution maps to visualize sequestration patterns across the FGSM-FGBM landscape. i-Tree Canopy Model for Vegetation and Carbon Sequestration Assessment Data Collection and Processing The i-Tree Canopy Model, developed by the USDA Forest Service, was used to estimate tree canopy coverage, impervious surface extent, and vegetation composition within FGSM and FGBM. The model employs randomized point sampling of high-resolution aerial imagery, coupled with manual classification and validation, to ensure higher accuracy in land cover estimation (53). GIS software delineated a geo-referenced boundary of the 57.85-hectare study area and imported it into i-Tree Canopy. We systematically generated 1,016 randomized sampling points within this boundary and used high-resolution aerial imagery from Google Earth for classification (54). Land Cover Classification and Validation We manually classified each sampling point into four primary land cover types: tree canopy, grassland/shrubland, impervious surfaces, and water bodies (53). Classification accuracy was ensured through systematic verification and cross-referencing with high-resolution satellite imagery and ancillary spatial data (9, 55). Any discrepancies were resolved using cross-validation techniques and expert judgment, ensuring consistency and reliability in land cover identification. The model automatically calculated the percent land cover for each category, estimating vegetation distribution and urbanization extent (53). Carbon Sequestration Estimation We calculated carbon sequestration potential using the i-Tree Canopy tool, incorporating region-specific parameters calibrated to reflect local tree species and environmental conditions in Taiwan (55, 56). To ensure consistency in carbon stock assessments, we compared sequestration estimates from i-Tree with those obtained from the InVEST Carbon Storage and Sequestration Model. Cross-validating these two models enhanced methodological rigor and accuracy in our carbon stock estimations (57). IPCC Carbon Estimation Methodology Biomass Carbon Stock Estimation Carbon storage and sequestration were estimated using the Intergovernmental Panel on Climate Change (IPCC) Tier 1 methodology, a standardized framework for quantifying greenhouse gas (GHG) emissions and removals (58). This approach ensures comparability with global carbon assessments and aligns with climate policy reporting requirements. We calculated the total biomass carbon stock (C) using the equation recommended by the IPCC (59) : C = Vt × BD × BEF × (1 + R ) × CF (4) Where: C = Total Carbon stored in biomass (tC) Vt = Aboveground biomass volume (m³/ha), derived from regional land cover classifications BD = Biomass bulk density (t/m³), obtained from regional ecological datasets BEF = Biomass Expansion Factor, accounting for unmeasured biomass components such as branches and leaves R = Root-to-shoot ratio, used to estimate belowground biomass CF = Carbon fraction of biomass (0.47 as per IPCC default) We used regional forest inventory data and land cover classifications to estimate Vt for different vegetation types, including broadleaf forests and grasslands (7). Biomass density values were obtained from Taiwan’s National Forest Carbon Database, ensuring the application of region-specific values in carbon stock calculations. Economic Valuation of Carbon Sequestration The financial value of carbon sequestration was estimated based on international carbon market prices, incorporating data from three primary carbon trading mechanisms: European Union Emission Trading System (EU ETS): €63.50/tCO₂ California Cap-and-Trade Market (USA): $39.80/tCO₂ Australian Carbon Credit Units (ACCU): $39.20/tCO₂ We determined the total economic value ( V ) of carbon sequestration as: V = Csequestered × Pcarbon Where: V = Total economic value of carbon sequestration Csequestered = Total Carbon sequestered (tCO₂/yr), derived from IPCC and InVEST model estimates Pcarbon = Carbon price per metric ton, adjusted for currency conversion Ecosystem-Specific Carbon Valuation Carbon sequestration rates were analyzed separately for forests, grasslands, and urban vegetation to ensure an ecosystem-based assessment, reflecting variation in sequestration potential across land cover types (47, 51, 60). Carbon sequestration values were aggregated within each land cover class to produce a spatially explicit valuation of carbon storage within the heritage landscape. Validation and Accuracy Assessment We obtained all carbon pricing data from verified carbon trading platforms and government agencies to ensure the accuracy and reliability of monetary valuation. To maintain currency precision, we retrieved exchange rates from the Bank of Taiwan (2025). We conducted a comparative analysis using carbon sequestration estimates derived from the InVEST model to ensure consistency with IPCC-derived values. To validate the final results, we cross-referenced them with Taiwan's National Carbon Inventory, confirming methodological consistency and reinforcing the robustness of the economic valuation. Landscape Structure Analysis Using FRAGSTATS Data Acquisition and Preprocessing High-resolution Pleiades satellite imagery (2016, 2019, and 2022) was acquired and preprocessed to analyze landscape structural changes and biodiversity implications within the FGSM and FGBM. We employed supervised classification techniques using a support vector machine (SVM) in ArcGIS Pro (3.0) to classify land cover, ensuring consistency in detecting vegetation cover changes, habitat connectivity, and fragmentation patterns (61). Classified images were converted into raster format, allowing for comparative analysis of spatial patterns across study years. Landscape Metrics Computation and Biodiversity Assessment The FRAGSTATS model was used to compute key landscape metrics associated with biodiversity conservation, fragmentation, and spatial organization (20). The analysis was conducted at three hierarchical levels (class, patch, and landscape) to assess habitat distribution and ecological integrity (21): Class-Level Metrics (assessing habitat extent and fragmentation): Class Area (CA, ha) and Percentage of Landscape (PLAND, %) quantified each land cover type's total area and proportion, mainly focusing on tree cover and green spaces essential for biodiversity. The most extensive Patch Index (LPI, %) identified the dominance of the largest contiguous natural habitat, which is critical for species movement and ecosystem stability. Aggregation Index (AI, %) measured the degree of clustering within land cover classes, where lower values indicate higher fragmentation and habitat isolation. Normalized Landscape Shape Index (NLSI) assesses habitat shape complexity, as irregular and fragmented patches can impact wildlife dispersal and biodiversity richness. Patch-Level Metrics (evaluating habitat fragmentation and complexity): Patch Density (PD, patches/ha) measured the number of habitat patches, with higher values suggesting increased fragmentation and potential loss of contiguous habitat for species survival. The Landscape Shape Index (LSI) quantified the morphological complexity of habitat patches, where more irregular patches may indicate disturbance or land-use pressures on biodiversity. Landscape-Level Metrics (assessing ecosystem diversity and connectivity): Contagion Index (CONTAG) evaluated habitat continuity, where lower values indicate higher landscape fragmentation and loss of significant, connected habitats essential for biodiversity corridors. Shannon’s Diversity Index (SHDI) and Simpson’s Diversity Index (SIDI) measured overall habitat diversity, with higher values indicating a more heterogeneous and ecologically diverse landscape supporting multiple species. Landscape metrics were computed for each study year (2016, 2019, and 2022) to identify temporal trends in habitat loss, biodiversity-supporting landscape stability, and land-use modifications (62-65). We analyzed landscape composition and fragmentation using FRAGSTATS and summarized key metrics in Table 13. Table 13 Summary of landscape metrics used for spatial and biodiversity analysis Metric Description Ecological Relevance CA (ha) Total area of land cover class Indicates dominant habitat size PLAND (%) Percentage of the total landscape Measures spatial distribution of habitats LPI (%) Largest contiguous patch Reflects habitat dominance AI (%) Aggregation Index Higher values = better connectivity NLSI Normalized Landscape Shape Complexity of land cover boundaries PD (patches/ha) Patch Density Higher values = increased fragmentation CONTAG Contagion Index Measures clustering of land cover types SHDI Shannon’s Diversity Index Represents habitat diversity SIDI Simpson’s Diversity Index Measures species richness and evenness Fragmentation and Habitat Connectivity Assessment To further assess habitat fragmentation and connectivity, temporal trends in AI, PD, LSI, and CONTAG were analyzed. A decrease in AI and CONTAG, combined with an increase in PD and LSI, would indicate a shift toward higher fragmentation and reduced habitat connectivity, which can negatively impact biodiversity and species movement across the landscape (40, 66, 67). Integration of Landscape Metrics for Biodiversity and Conservation Planning The results from FRAGSTATS landscape metrics were integrated into biodiversity conservation planning to support the sustainable management of green spaces within the FGSM-FGBM site (23, 26). By quantifying changes in habitat structure, fragmentation, and spatial diversity, this study provides empirical evidence for land-use policies to preserve ecological integrity (19, 22, 23). The findings contribute to decision-making in heritage landscape conservation, ensuring that cultural and ecological values remain balanced over time (65). Declarations Data Availability All data generated or analyzed during this study are included in this published article and its supplementary information files. Additional geospatial data (e.g., NDVI, LULC maps) and carbon modeling outputs are available from the corresponding author upon reasonable request. Code Availability Not applicable. Acknowledgements The authors express their gratitude to the Fo Guang Shan Monastery and Fo Guang Shan Buddha Museum for their collaboration and data access. Special thanks to the Center for Space and Remote Sensing Research (CSRSR), National Central University, for providing satellite imagery and technical support. Author Contributions Chih-Lin Liu led the research and contributed most substantially to the work. He conceptualized the study, collected and analyzed the data, wrote the original draft, and completed all revisions in response to editorial and reviewer feedback. 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Economic Valuation of Carbon Storage and Sequestration in Retezat National Park, Romania. Forests. 2021;12(1). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Jul, 2025 Read the published version in npj Heritage Science → Version 1 posted Editorial decision: Revision requested 29 Apr, 2025 Reviews received at journal 29 Apr, 2025 Reviewers agreed at journal 07 Apr, 2025 Reviews received at journal 06 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviewers invited by journal 05 Apr, 2025 Submission checks completed at journal 04 Apr, 2025 First submitted to journal 09 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5513941","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":447414699,"identity":"6af5c306-ce65-41a8-8834-b713e766e8cf","order_by":0,"name":"Chih-Lin Liu","email":"","orcid":"","institution":"National Chung Hsing University","correspondingAuthor":false,"prefix":"","firstName":"Chih-Lin","middleName":"","lastName":"Liu","suffix":""},{"id":447414700,"identity":"a541ba8d-62ea-4721-937a-a9694742a767","order_by":1,"name":"Wan-Yu Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYFAC5oYDDBUMDAYgNg9xWhiBWs4gtEgQpYWBsY0ULQY3EhsP8867I28ukcD44G0bQ53BAcJaGg7zbntmuHNGArPh3DYGCYJazCBaDicY3Ehgk+YFajEjTsscsBb23yRoaYDYwkyUFvszDxsOzjl22HDDmYfNknPOSUjuJ6RFsj358Ic3NYflDY4nH/zwpsyGX7KBgBYGgQQYixGklpiY5CfkjlEwCkbBKBgFABIXRo0AtQkmAAAAAElFTkSuQmCC","orcid":"","institution":"National Chung Hsing University","correspondingAuthor":true,"prefix":"","firstName":"Wan-Yu","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-11-24 12:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5513941/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5513941/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s40494-025-01836-2","type":"published","date":"2025-07-03T15:58:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82099230,"identity":"5dad39ca-45e5-40fc-aded-97bf12fd603b","added_by":"auto","created_at":"2025-05-06 18:21:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9557747,"visible":true,"origin":"","legend":"\u003cp\u003eBird’s-eye view of the study sites: (a) Fo Guang Shan Monastery and (b) Fo Guang Shan Buddha Museum. The images illustrate the spatial layout, green spaces, and surrounding landscape features relevant to the analysis. \u003cem\u003eSource: Fo Guang Shan Buddha Museum\u003c/em\u003e (https://www.fgsbmc.org.tw/EN/index.aspx).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5513941/v1/5ec073171ff146d870092145.png"},{"id":82099227,"identity":"ba2413ec-3d81-4182-bde7-40164066e90f","added_by":"auto","created_at":"2025-05-06 18:21:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":7316722,"visible":true,"origin":"","legend":"\u003cp\u003eCultural and ecological landmarks of Fo Guang Shan, including the 108-meter Bronze Buddha, Main Shrine, Sacred Buddha Tooth Relic, and Buddhist Botanical Garden. (a) The 108-meter Bronze Buddha statue (佛光大佛), a monumental structure symbolizing wisdom, compassion, and enlightenment. (b) The Main Shrine of Fo Guang Shan (佛光山大雄寶殿), serving as the spiritual and cultural center of Buddhist practice. (c) The Sacred Buddha Tooth Relic (佛牙舍利), a revered relic embodying spiritual legacy and devotion. (d) The Buddhist Botanical Garden (佛教植物園), a serene landscape dedicated to the preservation of sacred plant species and ecological harmony.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource: Life News Agency; Fo Guang Shan Buddha Museum\u003c/em\u003e \u003cu\u003e(\u003c/u\u003ehttps://www.lnanews.com/home\u003cu\u003e,\u0026nbsp;\u003c/u\u003ehttps://www.fgsbmc.org.tw/EN/index.aspx\u003cu\u003e)\u003c/u\u003e.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5513941/v1/b2859c733d23744d0dadbcf7.png"},{"id":82100315,"identity":"fedb365f-ed99-41bb-8d64-3ac2bfd56779","added_by":"auto","created_at":"2025-05-06 18:37:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24412652,"visible":true,"origin":"","legend":"\u003cp\u003eTour map of Fo Guang Shan, illustrating the spatial organization and key landmarks within the site. This map was used to delineate the study boundary.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5513941/v1/b103f2d7e71124dc2195c4fb.png"},{"id":82099251,"identity":"7d53a30a-7b64-43e4-a61f-3d426d1da172","added_by":"auto","created_at":"2025-05-06 18:21:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6586041,"visible":true,"origin":"","legend":"\u003cp\u003eGeospatial representation of Fo Guang Shan Monastery (FGSM) and Fo Guang Shan Buddha Museum (FGBM) with delineated study boundaries (red outline). The inset maps provide regional context, showing the site's location in Taiwan and its relation to Kaohsiung. This map serves as a reference for defining the study area.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5513941/v1/262c91bcd5e61fda5e8e7ce0.png"},{"id":82099246,"identity":"5f1aec69-aed1-4357-99eb-55bb3602c775","added_by":"auto","created_at":"2025-05-06 18:21:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6390983,"visible":true,"origin":"","legend":"\u003cp\u003eSatellite imagery of Fo Guang Shan captured by Pleiades-1A and Pleiades-1B, illustrating its spatial extent, landscape features, and surrounding environment as a geospatial reference for the study.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5513941/v1/c10eefcb3a95101630528468.png"},{"id":82100314,"identity":"9808ba47-f959-4844-8517-f5e415772648","added_by":"auto","created_at":"2025-05-06 18:37:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":6580209,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized Difference Vegetation Index (NDVI) of the study area, showing vegetation distribution and density changes over different years.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5513941/v1/71acfd6bf0f9cc1b0ffe914a.png"},{"id":82099240,"identity":"314dd21e-6e0a-4f4c-8cc1-a9f853e0ada5","added_by":"auto","created_at":"2025-05-06 18:21:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3237508,"visible":true,"origin":"","legend":"\u003cp\u003eLand Use and Land Cover (LULC) classification for the InVEST carbon module, highlighting tree and grassland areas as key carbon storage contributors in the study area.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5513941/v1/e37c16c8fe4ae1696d6c1784.png"},{"id":82100022,"identity":"8d9c0d16-ec59-4324-b55f-21dc73ee4807","added_by":"auto","created_at":"2025-05-06 18:29:42","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":337684,"visible":true,"origin":"","legend":"\u003cp\u003eLand use classifications derived from the i-Tree Canopy model, illustrating vegetation and land cover types relevant to ecological assessment in the Fo Guang Shan study area.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5513941/v1/ebab0a45da2d29e72145dc1b.png"},{"id":82100027,"identity":"03e73037-3dcf-4c9b-8044-3f7a984cfa57","added_by":"auto","created_at":"2025-05-06 18:29:42","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4118554,"visible":true,"origin":"","legend":"\u003cp\u003eLand use and land cover (LULC) classification of the study area, serving as the basis for FRAGSTATS analysis to assess landscape patterns and spatial characteristics.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-5513941/v1/6f312b9b7ae7ab06640bde85.png"},{"id":82099237,"identity":"d2b68a06-ada2-48bd-ae65-3228c0e5b28b","added_by":"auto","created_at":"2025-05-06 18:21:42","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":213697,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trends of landscape metrics across different land use categories, illustrating variations in AI (Aggregation Index), CA (Class Area), PLAND (Percentage of Landscape), LPI (Largest Patch Index), LSI (Landscape Shape Index), and NLSI (Normalized Landscape Shape Index) over time. Each sub-panel represents a distinct landscape metric, with different colors indicating various land use types. The Y-axis is scaled independently for each metric to facilitate comparison, and trend lines are included to highlight changes in landscape patterns. The legend at the bottom defines the land use classifications.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-5513941/v1/d9d5a089f777ae98b67bcc8c.png"},{"id":82100316,"identity":"cacd0f9f-24dd-4dc4-9e4e-71cef302754d","added_by":"auto","created_at":"2025-05-06 18:37:42","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":200556,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trends of key and supporting landscape metrics at three time points: 16 (2016), 19 (2019), and 22 (2022). The top panel presents key metrics (PD, LSI, CONTAG, PRD, SHDI) that highlight significant landscape structure changes, while the bottom panel displays supporting metrics (SIDI, SHEI, SIEI, AI) to provide additional insights into spatial heterogeneity. X-axis labels are included only in the bottom panel to improve clarity and readability. The Y-axis scales vary by metric to optimize trend visualization, reflecting structural landscape shifts influenced by ecological and anthropogenic factors.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-5513941/v1/32be95a74858cbbed73dc26b.png"},{"id":82100030,"identity":"465823dd-9c0f-4001-b1b7-5048cefcf629","added_by":"auto","created_at":"2025-05-06 18:29:42","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":398552,"visible":true,"origin":"","legend":"\u003cp\u003eAir purification benefits of trees analyzed using the i-Tree Canopy model, illustrating their role in removing pollutants and improving air quality within the study area.\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-5513941/v1/fe768c2b7752b9ed65440dbf.png"},{"id":86181160,"identity":"888c6070-d83e-4a69-8b98-aece88ccffff","added_by":"auto","created_at":"2025-07-07 16:23:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":68154874,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5513941/v1/92004df6-6e10-41e6-ad9a-a6b7ab314eef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sacred Heritage and Environmental Sustainability: Carbon and Biodiversity Insights from Taiwan’s Fo Guang Shan Monastery and Buddha Museum","fulltext":[{"header":"Introduction","content":"\u003cp\u003eReligious landscapes have historically played a vital role in cultural, spiritual, and environmental stewardship, serving as centers for social cohesion, ecological conservation, and long-term sustainability practices\u0026nbsp;(1, 2). While their significance has been well-documented in heritage studies, their contributions to carbon sequestration and climate change mitigation remain underexplored in contemporary environmental discourse\u0026nbsp;(3). As global frameworks such as the United Nations Sustainable Development Goals (SDGs) and the Paris Agreement emphasize the urgent need for carbon sequestration strategies, sacred landscapes\u0026mdash;including monastery forests, temple ecosystems, and church woodlands\u0026mdash;present a unique but underutilized opportunity for nature-based climate solutions\u0026nbsp;(3, 4).\u003c/p\u003e\n\u003cp\u003eStudies have increasingly highlighted the biodiversity conservation value of religious landscapes. For example, recent research has emphasized the role of sacred natural sites in maintaining ecological integrity, with studies documenting how these landscapes serve as biodiversity hotspots and contribute to ecosystem resilience\u0026nbsp;(5, 6). Furthermore, monastic forest management practices in regions such as Southeast Asia have demonstrated significant contributions to long-term habitat preservation and carbon sequestration\u0026nbsp;(7). These findings underscore the broader conservation benefits of religious landscapes, reinforcing their potential for integration into global climate adaptation strategies. Ethiopian church forests function as biodiversity refuges, where native species are preserved under religious protection, even amidst widespread deforestation pressures in the region\u0026nbsp;(8, 9). Similarly, sacred groves in India have been maintained for centuries through religious taboos and traditional ecological knowledge, demonstrating high levels of plant diversity and passive carbon storage (10). Beyond Africa and South Asia, Tibetan Buddhist sacred landscapes, including Mount Kawa Karpo and Iran\u0026rsquo;s Zagros forests, illustrate how religious worldviews influence sustainable ecological practices\u0026nbsp;(1, 6)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite these precedents, there remains limited empirical research quantifying the carbon sequestration potential of Buddhist temple ecosystems in East Asia. This study addresses this gap by integrating high-resolution remote sensing, landscape carbon modeling, and comparative conservation frameworks to assess the climate benefits of Buddhist temple landscapes. Unlike previous research that has primarily focused on qualitative assessments or regional biodiversity surveys, this study employs advanced remote sensing techniques and standardized carbon measurement methodologies to provide a rigorous, data-driven evaluation of the sequestration potential of religious landscapes. Particularly in the context of contemporary environmental policy and carbon accounting frameworks (5).\u003c/p\u003e\n\u003cp\u003eThis study evaluates Fo Guang Shan Monastery and Fo Guang Shan Buddha Museum\u0026mdash;two interconnected religious sites in Kaohsiung, Taiwan\u0026mdash;to assess the carbon sequestration potential of Buddhist temple landscapes (Fig. 1).\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eFo Guang Shan Monastery, established in 1967, serves as the spiritual and administrative headquarters of Fo Guang Shan Buddhism, comprising a monastery, pilgrimage zones, and extensive forested land\u0026nbsp;(11).\u003c/li\u003e\n \u003cli\u003eFo Guang Shan Buddha Museum, completed in 2011, is one of the largest Buddhist cultural institutions in East Asia, featuring extensive gardens, tree-lined pathways, and managed green spaces designed to harmonize religious heritage with environmental conservation (12).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese sites create an extensive religious and ecological landscape, seamlessly integrating afforestation, cultural heritage preservation, and sustainable landscape management. Unlike Ethiopian church forests (2, 9), which rely on passive conservation, and Indian sacred groves, which are community-driven (13-15), the Fo Guang Shan Monastery and Buddha Museum represent an actively managed conservation model, combining afforestation, reforestation, and strategic landscape planning to ensure long-term ecological sustainability.\u003c/p\u003e\n\u003cp\u003eThis study employs a geospatial and remote sensing-based approach combined with ecological modeling techniques to evaluate the carbon sequestration potential of Fo Guang Shan Monastery and Buddha Museum. Vegetative health and canopy coverage are assessed through the Normalized Difference Vegetation Index (NDVI), providing insights into the distribution and density of tree cover within the study area\u0026nbsp;(16, 17). i-Tree Canopy Model estimates aboveground carbon storage by categorizing land-use types and their contributions. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model further refines this analysis by incorporating vegetation biomass and soil composition, offering a quantitative assessment of long-term carbon sequestration potential\u0026nbsp;(18).\u003c/p\u003e\n\u003cp\u003eIn addition to modeling vegetation and soil carbon, we analyze landscape fragmentation using FRAGSTATS, a widely used tool for quantifying spatial configuration and ecological connectivity\u0026nbsp;(19-23). This model evaluates key fragmentation metrics, such as Patch Density (PD) and Edge Density (ED), to assess the spatial arrangement of afforested areas and green spaces within the study site\u0026nbsp;(22, 24). FRAGSTATS helps determine how spatial heterogeneity influences carbon retention efficiency by measuring habitat connectivity and fragmentation\u0026nbsp;(19, 25). This analysis is essential because landscape fragmentation can disrupt carbon sequestration by increasing edge effects, reducing biomass continuity, and altering microclimatic conditions. The inclusion of FRAGSTATS in this study provides quantitative evidence of how green corridors and afforested zones contribute to optimizing carbon storage efficiency\u0026nbsp;(26).\u003c/p\u003e\n\u003cp\u003eIntegrating these methodological tools ensures a comprehensive assessment of carbon sequestration across the Fo Guang Shan Monastery and Buddha Museum landscapes, positioning the study within a broader comparative framework of religious conservation strategies worldwide.\u003c/p\u003e\n\u003cp\u003eThe findings of this study carry significant implications for global climate policy, heritage conservation, and sustainable site management. The research provides empirical evidence of carbon sequestration within Buddhist temple ecosystems, reinforcing the potential for integrating religious landscapes into carbon offset programs. This issue is particularly relevant to Taiwan\u0026apos;s 2024 Carbon Fee Policy, which underscores the need for nature-based solutions in climate mitigation (27). Furthermore, this study aligns with international conservation frameworks, particularly the UNESCO World Heritage Convention, which recognizes cultural landscapes\u0026apos; ecological significance and heritage value (28). UNESCO policies emphasize the protection and sustainable management of sacred natural sites, integrating biodiversity conservation with cultural preservation. These policies support the inclusion of religious landscapes in environmental conservation initiatives, acknowledging their role in carbon sequestration and climate adaptation strategies. Additionally, these policies align with emerging carbon credit markets, advocating for recognizing cultural landscapes as carbon sinks. advocating for the recognition of cultural landscapes as carbon sinks.\u003c/p\u003e\n\u003cp\u003eThis study builds on the methodological and policy framework outlined above to address key research questions that highlight the role of Buddhist temple landscapes in carbon sequestration and sustainable heritage management. Doing so aims to provide empirical insights into the intersection of religious heritage and climate mitigation strategies.\u003c/p\u003e\n\u003cp\u003eThe following key research questions guide this study:\u003c/p\u003e\n\u003cp\u003e1. How do Buddhist temple landscapes contribute to carbon sequestration, and how do they compare with other religious conservation models?\u003c/p\u003e\n\u003cp\u003e2. What is the carbon sequestration potential of Fo Guang Shan Monastery and Buddha Museum, and how does it align with global climate action goals?\u003c/p\u003e\n\u003cp\u003e3. How do institutional conservation strategies at Buddhist sites influence carbon storage efficiency compared to community-driven or naturally preserved religious landscapes?\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eVegetation Health and Growth Trends: NDVI Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNDVI Analysis (2016\u0026ndash;2022) and Vegetation Health in Buddhist Temple Landscapes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Normalized Difference Vegetation Index (NDVI) is a key metric for assessing vegetation health and ecological stability in Buddhist temple landscapes (Fig. 6). Table 1 presents NDVI variations across 2016, 2019, and 2022, reflecting vegetation coverage and quality fluctuations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e NDVI values and vegetation growth trends in 2016, 2019, and 2022\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMinimum NDVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMaximum NDVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean NDVI (\u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGrowth Rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.30 \u0026plusmn; 0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBaseline (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.27 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-12.41% (Decline)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.31 \u0026plusmn; 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+15.78% (Recovery)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: InVEST Model (47, 52), and i-Tree Canopy (4, 53).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBetween 2016 and 2019, NDVI values declined by 12.41%, with a decrease in maximum NDVI from 0.80 to 0.71, and an increase in minimum NDVI from -0.24 to -0.17. From 2019 to 2022, mean NDVI rebounded by 15.78%, with minimum NDVI improving to -0.06. The maximum NDVI in 2022 (0.74) remained slightly lower than in 2016.\u003cbr\u003e\u0026nbsp;These trends indicate periods of vegetation decline (2016\u0026ndash;2019) followed by partial recovery (2019\u0026ndash;2022), with an overall NDVI increase of 3.37% from 2016 to 2022.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCarbon Sequestration in Faith-Managed Forests (InVEST, IPCC, and i-Tree Canopy Assessments)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCarbon Storage Estimates (2016\u0026ndash;2022) Using Multiple Methodologies\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe carbon sequestration capacity of Buddhist-managed forests was quantified using three methodologies:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eInVEST Model \u0026ndash; Evaluates ecosystem service-based carbon sequestration across diverse land cover types.\u003c/li\u003e\n \u003cli\u003eIPCC Methodology \u0026ndash; Provides standardized estimates of biomass accumulation, root-to-shoot ratios, and carbon stock per hectare.\u003c/li\u003e\n \u003cli\u003ei-Tree Canopy Analysis \u0026ndash; Assesses tree canopy contributions and sequestration efficiency in urbanized sacred landscapes.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFigure 7 shows a net increase in total carbon stock over the six-year period, as estimated by the InVEST model. Total carbon storage increased from 3,788.09 tons in 2016 to 4,502.39 tons in 2019 before stabilizing at 4,462.48 tons in 2022. CO₂ equivalent sequestration followed a similar trend, reaching 16,508.76 tons in 2019 before stabilizing at 16,362.43 tons in 2022 (Table 2). These findings suggest natural biomass accumulation and potential land-use effects on carbon retention capacity.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Estimated carbon storage and CO₂ sequestration using the InVEST model (2016\u0026ndash;2022)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eTotal Carbon Stock\u003c/p\u003e\n \u003cp\u003e(tons)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eCO₂ Equivalent Storage (tons)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e3,788.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e13,889.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e4,502.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e16,508.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e4,462.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e16,362.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIPCC-based calculations estimate carbon stock at 67.36 tons per hectare, with a CO₂ equivalent of 4,218.35 tons (Table 3). These values integrate biomass expansion factors (BEF = 1.40), root-to-shoot ratios (R = 0.24), and carbon fractions (CF = 0.4691), ensuring consistency with international carbon estimation protocols (68).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e IPCC-based carbon storage parameters and estimates\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eTimber Volume (m\u0026sup3;/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eBasic Wood Density (ton/m\u0026sup3;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eBiomass Expansion Factor (BEF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eRoot-to-Shoot Ratio (R)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eCarbon Fraction (CF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eCarbon Stock (tons/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eTotal Carbon (tons)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eCO₂ Equivalent (tons)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eBuddha Museum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e147.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.4691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e67.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1,150.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4,218.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe i-Tree Canopy analysis (Table 4) refines these estimates by quantifying tree-specific carbon sequestration. Findings indicate that trees within the 25.25-ha study area store 1,312.55 \u0026plusmn; 63.94 tons of Carbon, translating to 4,812.67 \u0026plusmn; 234.44 tons of CO₂ equivalent sequestration. Additionally, the annual sequestration rate is 52.26 \u0026plusmn; 2.55 tons of Carbon (191.63 \u0026plusmn; 9.34 tons CO₂ equivalent), reinforcing the role of Buddhist sacred forests as continuous carbon sinks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Carbon storage, sequestration benefits, and market valuation of trees\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003cp\u003e(ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal Carbon Stock\u003c/p\u003e\n \u003cp\u003e(tons)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCO₂\u003c/p\u003e\n \u003cp\u003eEquivalent sequestration (tons)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEstimated Market Value (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnnual Carbon Sequestration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.26 \u0026plusmn; 2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e191.63 \u0026plusmn; 9.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e316,469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCumulative Carbon Stock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,312.55 \u0026plusmn; 63.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,812.67\u0026plusmn; 234.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7,947,731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: i-Tree Canopy Model (4, 53) and National Greenhouse Gas Inventory Report (68).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTree Canopy Coverage and Land Cover Contribution to Carbon Storage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe i-Tree Canopy land cover classification provides insights into the distribution of vegetation and its role in carbon sequestration. The analysis identifies tree and shrub cover as the dominant carbon sink, occupying 17.08 ha of the total 25.25 ha study area(Table 5, Fig. 8). Grasslands cover 8.17 ha, providing moderate carbon sequestration potential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e Land cover classification and area estimates based on i-Tree Canopy analysis\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eLand Cover Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eTree/Shrub\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eWoody vegetation with canopy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e17.08 \u0026plusmn; 0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eGrass/Herbaceous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eLow-lying vegetative cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e8.17 \u0026plusmn; 0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eIO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eImpervious Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003ePaved and non-vegetated surfaces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e10.75 \u0026plusmn; 0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eImpervious Road\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eRoads and transportation areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e10.81 \u0026plusmn; 0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eSoil/Bare Ground\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eExposed soil or barren land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.67 \u0026plusmn; 0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eImpervious Buildings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eStructures and built environments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e9.14 \u0026plusmn; 0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eWater bodies or aquatic features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.23 \u0026plusmn; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e57.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn contrast, impervious surfaces (10.75 ha) and bare soil (1.67 ha) offer minimal contributions to carbon retention, as these land types lack the biomass necessary for long-term sequestration. The classification also highlights roads (10.81 ha) and built environments (9.14 ha), further restricting sequestration potential due to their non-vegetative nature.\u003c/p\u003e\n\u003cp\u003eThese findings suggest that expanding tree-dominated areas within temple landscapes could enhance overall sequestration efficiency while minimizing impervious land use and mitigating carbon loss.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEconomic Valuation of Carbon Storage in Faith-Managed Forests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe assessed the economic impact of carbon sequestration under three global carbon credit trading mechanisms:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eEuropean Energy Exchange (EEX)\u003c/li\u003e\n \u003cli\u003eCalifornia Cap-and-Trade Program (CARB)\u003c/li\u003e\n \u003cli\u003eAustralian Carbon Credit Units (ACCU)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAs presented in Table 6, the financial valuation of stored Carbon demonstrates an upward trend over time. In 2016, the total market value ranged from $13.84 million (ACCU) to $16.22 million (EEX). By 2019, this valuation peaked at $16.88 million (ACCU) and $19.44 million (EEX) before stabilizing in 2022, settling between $16.74 million (ACCU) and $19.26 million (EEX).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e Estimated carbon sequestration value under different carbon market scenarios\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal Carbon Stock (tons)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCO₂ Equivalent Storage (tons)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEEX Market Value (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCARB Market Value (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eACCU Market Value (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3,788.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13,889.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16,221,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14,563,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13,842,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,502.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16,508.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19,440,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17,480,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16,882,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,462.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16,362.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19,260,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17,320,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16,740,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAdditionally, the i-Tree Canopy economic valuation (Table 7) estimates that the tree covers carbon sequestration at $408,853 (EEX), $234,084 (CARB), and $229,672 (ACCU). These figures highlight the monetary significance of faith-managed forests, demonstrating the potential for integration into Carbon offset markets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7\u003c/strong\u003e Carbon storage and market valuation of tree and grassland areas\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eTotal CO₂ Storage (tons)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eEEX Market Value (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eCARB Market Value (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eACCU Market Value (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e25.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e191.63 \u0026plusmn; 9.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e408,853.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e234,084.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e229,672.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTo ensure cross-market comparability, we standardized all financial values using 2025 currency exchange rates (1 EUR = 1.046 USD; 1 AUD = 0.6397 USD). This approach enables a consistent assessment of faith-managed sequestration potential within international carbon pricing frameworks.\u003c/p\u003e\n\u003cp\u003eThe multi-method assessment of carbon sequestration confirms that faith-managed forests exhibit a stable and increasing sequestration trajectory, with notable gains from 2016 to 2019, followed by a slight plateau in 2022. The land cover analysis underscores the importance of tree canopy expansion, while the financial valuation highlights the potential integration of Buddhist conservation forests into Carbon offset markets. These results reinforce the significance of sacred landscapes in global climate change mitigation strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLand Cover, Biodiversity, and Habitat Connectivity in Temple Forests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe FRAGSTATS model analysis comprehensively assesses land cover changes, biodiversity contributions, and habitat connectivity within FGSM and FGBM landscape from 2016 to 2022. This section presents land cover classifications, evaluates the role of temple forests in supporting biodiversity, and examines landscape fragmentation trends and their impact on ecosystem stability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLand Cover Classification (Tree Canopy, Grasslands, Impervious Surfaces)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Land Use and Land Cover (LULC) classification (Fig. 9) identifies seven primary land cover types within the study area, each influencing carbon storage and biodiversity conservation (Table 8):\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eTemple structures \u0026ndash; 82.83 ha (60.17% PLAND), forming the dominant spatial feature.\u003c/li\u003e\n \u003cli\u003eTree cover \u0026ndash; 17.54 \u0026plusmn; 2.24 ha (12.75% PLAND), acting as the primary carbon sink and habitat zone.\u003c/li\u003e\n \u003cli\u003eHerbaceous land \u0026ndash; 7.16 ha (5.20% PLAND) contributes to species diversity and stabilization of ground cover.\u003c/li\u003e\n \u003cli\u003eRoad networks \u0026ndash; 19.80 ha (14.39% PLAND), increasing landscape fragmentation and limiting habitat connectivity.\u003c/li\u003e\n \u003cli\u003eBuilt environments (non-temple) \u0026ndash; 4.11 ha (2.99% PLAND), reducing available natural spaces.\u003c/li\u003e\n \u003cli\u003eBarren land and paths \u0026ndash; 6.21 ha (4.51% PLAND), with minimal ecological contributions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8\u003c/strong\u003e Landscape metrics for biodiversity and carbon storage assessment\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eLand Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eCA (ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003ePLAND (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eLPI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eLSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eAI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eNLSI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eBarren\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.58 \u0026plusmn; 2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.15 \u0026plusmn; 1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.16 \u0026plusmn; 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e19.20\u0026plusmn; 24.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e74.61\u0026plusmn; 18.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.25 \u0026plusmn; 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eBuilding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e4.11 \u0026plusmn; 0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e2.99 \u0026plusmn; 0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.26 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e23.17\u0026plusmn; 16.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e78.78\u0026plusmn; 13.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.21 \u0026plusmn; 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eHerbaceous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e7.16 \u0026plusmn; 1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e5.20 \u0026plusmn; 0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.68 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e26.62\u0026plusmn; 14.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e80.91\u0026plusmn; 10.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.19 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e4.63 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e3.36 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.14 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e21.00\u0026plusmn; 10.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e81.11\u0026plusmn; 10.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.19 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eRoad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e19.80\u0026plusmn; 3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e14.39\u0026plusmn; 2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e4.62 \u0026plusmn; 1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e32.51\u0026plusmn; 15.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e85.19\u0026plusmn; 8.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.15 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eTemple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e82.83\u0026plusmn; 1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e60.17\u0026plusmn; 0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e56.60\u0026plusmn; 1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e7.71 \u0026plusmn; 1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e98.52\u0026plusmn; 0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.02 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eTree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e17.54\u0026plusmn; 2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e12.75\u0026plusmn; 1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e3.18 \u0026plusmn; 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e24.49\u0026plusmn; 9.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e88.73\u0026plusmn; 4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.11 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: FRAGSTATS Model (20, 26, 61).\u003c/p\u003e\n\u003cp\u003eTree cover is characterized by a Largest Patch Index (LPI) of 3.18, suggesting that tree patches are spatially dominant but not fully connected. The Landscape Shape Index (LSI) 24.49 reflects moderate complexity in tree patch configurations, which may facilitate diverse edge habitats. In contrast, expanding impervious surfaces, particularly roads and buildings, contributes to higher habitat fragmentation, potentially reducing ecological connectivity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of Faith-Based Conservation Areas in Promoting Biodiversity and Habitat Integrity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTemple forests are critical biodiversity reserves, sustaining native flora and fauna through stable habitat formations. The high Aggregation Index (AI) of 88.73 \u0026plusmn; 4.49 for tree cover confirms that tree patches remain clustered, ensuring spatial continuity essential for species survival. However, the gradual decline in AI from 93 (2016) to 87 (2022) indicates a weakening of habitat connectivity, which, if continued, may disrupt species migration and genetic diversity (Fig. 10).\u003c/p\u003e\n\u003cp\u003eTree cover maintains a Percentage of Landscape (PLAND) at 12.75%, reinforcing its ecological significance despite increasing land-use pressures. The Landscape Shape Index (LSI) for trees has risen from 20 in 2016 to 24.49 in 2022, reflecting growing complexity in patch structure, which may support more excellent microhabitat formation and species adaptability.\u003c/p\u003e\n\u003cp\u003eHerbaceous land, with an LSI increase from 20 to 40 over the study period, complements tree-dominated areas by providing ground cover, supporting pollinators, and stabilizing the soil structure. Despite occupying a smaller fraction of the landscape (5.20% PLAND), it contributes to enhanced biodiversity richness and functional ecosystem services.\u003c/p\u003e\n\u003cp\u003eTemple structures, while non-vegetative, shape landscape organization and habitat stability. The Largest Patch Index (LPI) of 56.60 \u0026plusmn; 1.14 confirms their dominance and their role as stable landscape anchors supports species persistence by reducing landscape disruption from urban expansion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLandscape Fragmentation Trends and Their Effects on Ecosystem Stability in Temple Grounds\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLandscape fragmentation is a growing concern within the study area, as indicated by key FRAGSTATS metrics (Fig. 11) :\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003ePatch Density (PD) increased from 1,000 in 2019 to 6,000 in 2022, indicating a substantial rise in fragmentation levels.\u003c/li\u003e\n \u003cli\u003eContagion Index (CONTAG) decreased from 62 (2019) to 55 (2022), suggesting a reduction in habitat connectivity and more excellent isolation of ecological patches.\u003c/li\u003e\n \u003cli\u003eShannon\u0026apos;s Diversity Index (SHDI) rose from 1.225 in 2019 to 1.325 in 2022, indicating increased habitat heterogeneity and a more even distribution of land cover types.\u003c/li\u003e\n \u003cli\u003eSimpson\u0026apos;s Diversity Index (SIDI) increased from 0.585 to 0.610, reinforcing species diversity and potential instability due to shifting habitat distributions.\u003c/li\u003e\n \u003cli\u003eAggregation Index (AI) for trees declined from 93 in 2016 to 87 in 2022, signaling a reduction in tree patch connectivity and increased spatial isolation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eEcosystem Services of Buddhist Temple Green Spaces: Air Quality and Climate Regulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Buddha Museum\u0026rsquo;s forested area plays a significant role in urban air quality improvement and climate regulation, with measurable impacts on pollutant removal and temperature moderation. This section presents the quantification of air purification benefits, the climate-buffering function of temple groves, and the long-term resilience offered by faith-driven forest preservation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantification of Pollutant Removal in Faith-Protected Forests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe temple forest (17.08 ha) effectively captures and removes key atmospheric pollutants, including carbon monoxide (CO), nitrogen dioxide (NO₂), ozone (O₃), sulfur dioxide (SO₂), delicate particulate matter (PM₂.₅), and coarse particulate matter (PM₁₀). Over the study period, the total pollutant removal exceeded 1,477 kg annually, generating an estimated economic value of $17,752 per year (Table 9).\u003c/p\u003e\n\u003cp\u003eAmong the removed pollutants, PM₂.₅ and PM₁₀ account for the highest economic benefits, emphasizing the role of temple forests in reducing urban air pollution and associated respiratory health risks. Notably, ozone removal exceeded 927 kg per year, reinforcing the importance of temple forests in mitigating smog formation and maintaining atmospheric balance. Removing gaseous pollutants (CO, NO₂, and SO₂) suggests that temple forests also reduce vehicle and industrial emissions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9\u003c/strong\u003e Air purification benefits of trees: Pollutant removal and economic valuation\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eGas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eTotal Dust (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eUnit Dust (kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eTotal Value (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eUnit Value (USD/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e17.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e17.13 \u0026plusmn; 0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e129 \u0026plusmn; 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e55.87 \u0026plusmn; 5.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eNO₂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e17.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e88.82 \u0026plusmn; 4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e5.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e22 \u0026plusmn; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e101.09 \u0026plusmn; 9.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eO₃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e17.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e927.67 \u0026plusmn; 45.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e54.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1,468 \u0026plusmn; 72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e4,652.92 \u0026plusmn; 426.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eSO₂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e17.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e75.09 \u0026plusmn; 3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e4.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e5 \u0026plusmn; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e14.63 \u0026plusmn; 1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePM₂.₅\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e17.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e45.45 \u0026plusmn; 2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e3,036 \u0026plusmn; 148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e9,740.80 \u0026plusmn; 893.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePM₁₀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e17.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e323.18 \u0026plusmn;15.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e18.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e12,791 \u0026plusmn; 623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e3,187.08 \u0026plusmn; 292.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e17.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1,477.33 \u0026plusmn; 71.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e86.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e17,451 \u0026plusmn; 850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e17,752.39 \u0026plusmn; 1,628.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFigure 12 illustrates the amount of pollutants removed (in kg) by trees in the study area, ranked from highest to lowest efficiency.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eO₃ (Ozone) exhibits the highest removal rate, followed by PM₁₀ (Particulate Matter 10) and NO₂ (Nitrogen Dioxide).\u003c/li\u003e\n \u003cli\u003eSO₂ (Sulfur Dioxide), PM₂.₅ (Fine Particulate Matter), and CO (Carbon Monoxide) show lower removal rates.\u003c/li\u003e\n \u003cli\u003eThe data highlight the essential role of urban trees in air purification and environmental health.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe analysis of NDVI values from 2016 to 2022 reveals significant fluctuations in vegetation health, reflecting the interplay between environmental stressors and conservation efforts. The decline in mean NDVI by 12.41% between 2016 and 2019, coupled with a decrease in maximum NDVI from 0.80 to 0.71, suggests a reduction in vegetation density and canopy coverage (Table 4). Although minimum NDVI improved slightly from -0.24 to -0.17, the overall trend indicates ecological stress, likely attributable to urban encroachment, land-use modifications, and the natural aging of tree stands.\u003c/p\u003e\n\u003cp\u003eConversely, the subsequent period from 2019 to 2022 exhibited a notable NDVI recovery of 15.78%, indicative of successful regrowth efforts. Minimum NDVI improved to -0.06, signifying the revitalization of previously degraded patches. However, the maximum NDVI in 2022 remained slightly below its 2016 level (0.74 vs. 0.80), indicating that while afforestation initiatives have contributed to ecosystem restoration, the expansion of FGBM and FGSM under new building construction and land use changes has led to lower NDVI values, suggesting that the ecosystem has not yet fully recovered (Fig. 6). These findings parallel broader trends observed in faith-managed conservation areas, where periodic vegetation stress is mitigated through localized stewardship and long-term conservation commitments (28).\u003c/p\u003e\n\u003cp\u003eThe observed fluctuations in NDVI correspond closely with the conservation practices undertaken by Buddhist monasteries. These findings underscore the function of religious institutions as ecological sanctuaries, wherein faith-based environmental stewardship actively counteracts urbanization pressures and fosters ecosystem resilience (11, 37). The post-2019 NDVI rebound is likely the result of structured afforestation programs, sustainable forestry management, and conservation awareness initiatives spearheaded by monasteries.\u003c/p\u003e\n\u003cp\u003eOther faith-managed conservation systems have reported similar patterns, such as :\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eIn China, indigenous ecological beliefs and sacred conservation practices sustain higher NDVI values in Fengshui forests compared to adjacent state-managed areas (68).\u003c/li\u003e\n \u003cli\u003eDue to long-term religious protection, Ethiopian church forests exhibit enhanced vegetation stability and biodiversity compared to surrounding landscapes (28).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe significant NDVI recovery (2019\u0026ndash;2022) suggests that monastic afforestation initiatives have successfully counterbalanced prior vegetation losses. Reforestation efforts, including selective planting of native species and soil rehabilitation programs, have likely contributed to this recovery. These findings align with studies on temple forests in Thailand, where minimal human intervention and sacred status ensure sustained ecological restoration (7). In a comparative study of faith-managed reforestation efforts, Ouyang et al. (2024) found that Japanese temple forests exhibit long-term NDVI stability, a phenomenon attributed to low-impact forest management and religious conservation ethics (54).\u003c/p\u003e\n\u003cp\u003eThese comparisons highlight the importance of faith-based conservation models in sustaining long-term ecological integrity, particularly in regions experiencing rapid land-use transformations.\u003c/p\u003e\n\u003cp\u003eThe decline in NDVI between 2016 and 2019 aligns with well-documented trends in land-use transformations. The expansion of impervious surfaces, infrastructure development, and rising anthropogenic pressures near Buddhist temple landscapes have directly contributed to vegetation loss. Furthermore, as shown in Figure 7, the land use and land cover of FGBM in 2022 reveal a notable increase in barren areas (brown color), indicating continued landscape alterations that further suppress vegetation recovery.\u003c/p\u003e\n\u003cp\u003eResearch indicates that vegetation loss in urbanizing regions often results from progressive land-use changes, a trend corroborated by Nowak et al. (2013), who reported a 10\u0026ndash;15% NDVI reduction within five years in urban forestry zones due to infrastructure expansion and foot traffic (58).\u003c/p\u003e\n\u003cp\u003eThe observed increase in minimum NDVI values suggests improving the resilience of degraded landscapes. This trend is consistent with findings by Huo and Wang (33), who demonstrated that faith-managed forests mitigate microclimate fluctuations and maintain long-term ecosystem stability (33). Since monastic landscapes are relatively insulated from large-scale disturbances, they serve as strongholds of ecological resilience, buffering against climate variability and external environmental stressors.\u003c/p\u003e\n\u003cp\u003eA comparative evaluation of Buddhist temple forests and other faith-managed conservation areas further reinforces the efficacy of sacred landscapes in biodiversity preservation and ecosystem stability (Table 10) (2, 6, 13). Studies on Ethiopian church forests and indigenous sacred groves demonstrate that religiously protected landscapes tend to sustain higher NDVI values over time than state-managed reserves (9).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 10\u003c/strong\u003e Comparative analysis of NDVI trends and conservation practices in sacred landscapes\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eSacred Landscape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eNDVI Trends\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eKey Findings\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eFo Guang Shan Forests (2016\u0026ndash;2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eInitial decline (2016\u0026ndash;2019), followed by recovery (2019\u0026ndash;2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eMonastic afforestation programs counteract urban expansion effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eEthiopian Church Forests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eStable NDVI over decades\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eProtection from deforestation due to religious significance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eJapanese Temple Forests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eMinimal NDVI fluctuations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eStrong cultural commitment to long-term conservation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThese comparisons indicate that faith-based conservation models outperform state parks in maintaining vegetation stability, mainly due to religious protection from deforestation, lower infrastructure development, and community-driven stewardship.\u003c/p\u003e\n\u003cp\u003eBuddhist temple forests function as significant long-term carbon sinks, with sequestration trends from 2016 to 2022 demonstrating sustained carbon accumulation. The InVEST model estimated an increase in total carbon stock from 3,788.09 tons in 2016 to 4,502.39 tons in 2019, followed by stabilization at 4,462.48 tons in 2022. CO₂ sequestration mirrored this pattern, peaking at 16,508.76 tons in 2019 before leveling at 16,362.43 tons in 2022. This trend suggests a shift in biomass accumulation dynamics, similar to those observed in other sacred conservation areas.\u003c/p\u003e\n\u003cp\u003eA comparative approach using the IPCC methodology estimated 67.36 tons of Carbon per hectare, corresponding to a CO₂ equivalent of 4,218.35 tons. This estimation, derived from standardized biomass expansion factors and root-to-shoot ratios, ensures compatibility with international carbon stock estimation protocols (69). Additionally, the i-Tree Canopy assessment, which provides a fine-scale analysis of sequestration potential, estimated that the 25.25-ha study area contained 1,312.55 \u0026plusmn; 63.94 tons of Carbon, translating to a CO₂ equivalent of 4,812.67 \u0026plusmn; 234.44 tons. The annual sequestration rate, recorded at 52.26 \u0026plusmn; 2.55 tons of Carbon, reinforces the role of tree-dominated landscapes in maintaining continuous carbon absorption (70).\u003c/p\u003e\n\u003cp\u003eThese multi-method assessments underscore Buddhist temple forests\u0026apos; robust carbon sequestration potential, validating their role in nature-based climate mitigation strategies (71).\u003c/p\u003e\n\u003cp\u003eThe observed stabilization of carbon sequestration post-2019 suggests that the system is approaching equilibrium due to biophysical and climatic factors.\u003c/p\u003e\n\u003cp\u003eOne potential driver is aboveground biomass saturation, as the InVEST model suggests (71, 72). Miyawaki forests exhibit similar patterns, where rapid biomass accumulation slows as carbon storage capacity reaches its limit (73). Without additional afforestation or canopy expansion, sequestration rates naturally plateau.\u003c/p\u003e\n\u003cp\u003eAdditionally, soil carbon fluxes and microbial decomposition influence belowground carbon retention (74). While the IPCC approach accounts for biomass-derived sequestration, it does not fully capture soil-carbon interactions. Research indicates that microbial decomposition and soil respiration can partially offset carbon storage gains, particularly in humid environments where organic matter decay accelerates (75).\u003c/p\u003e\n\u003cp\u003eClimate variability further impacts sequestration potential. Findings from the i-Tree Canopy analysis emphasize that tree canopy density regulates sequestration efficiency (70). Disruptions in monsoon cycles and increased seasonal droughts may have constrained tree growth rates, reducing the system\u0026apos;s ability to sustain additional sequestration gains (73, 76). These findings emphasize the necessity of adaptive conservation frameworks considering biomass management and soil carbon stabilization (75).\u003c/p\u003e\n\u003cp\u003eBuddhist temple forests are essential biodiversity refuges, sustaining native flora and fauna in urbanized landscapes. However, habitat fragmentation poses increasing challenges to their ecological stability (26). The FRAGSTATS model analysis highlights a substantial increase in Patch Density (PD) from 1,000 (2019) to 6,000 (2022), reflecting rising landscape fragmentation (64). Simultaneously, the Aggregation Index (AI) declined from 93 (2016) to 87 (2022), indicating a reduction in habitat connectivity, which may disrupt species migration, genetic diversity, and ecosystem resilience (25, 26).\u003c/p\u003e\n\u003cp\u003eTree cover, which accounts for 12.75% of the study area (PLAND), remains a critical ecological component despite urban expansion pressures (20). The Landscape Shape Index (LSI) for trees increased from 20 in 2016 to 24.49 in 2022, signifying a transition toward irregular patch configurations (64). While this can support edge-adapted species, it reduces interior habitat stability, negatively affecting forest-dependent taxa (77). The decline in the Contagion Index (CONTAG) from 62 (2019) to 55 (2022) further reinforces habitat isolation, necessitating targeted conservation interventions (25, 26).\u003c/p\u003e\n\u003cp\u003eBuddhist temple forests retain more biodiversity than state-managed reserves due to religious land protection and long-term ecological stability. However, analyzing other sacred and secular conservation landscapes reveals parallels and key vulnerabilities.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eEthiopian Church Forests, similarly embedded within human-dominated landscapes, exhibit increasing fragmentation, with patch density rising from 5 patches/km\u0026sup2; in 1990 to 12 patches/km\u0026sup2; in 2020 and a concurrent decline in the Aggregation Index from 0.85 to 0.60 (9). These patterns closely mirror those in Buddhist temple forests, emphasizing the need for connectivity restoration efforts to prevent further habitat isolation.\u003c/li\u003e\n \u003cli\u003eAfrican Sacred Groves face similar fragmentation pressures, with studies reporting a 20% decline in forest cover within a 1-km radius over a decade, leading to biodiversity loss and ecosystem degradation (78). Fragmentation of temple forests reduces functional diversity and threatens long-term species stability without effective conservation strategies (9, 79).\u003c/li\u003e\n \u003cli\u003eEuropean Monastic Forests, historically semi-managed conservation reserves, have experienced a tree density reduction from 300 trees/ha to 200 trees/ha, contributing to significant declines in avifaunal populations within fragmented patches. This trend reinforces findings from Buddhist temple forests, where fragmentation has altered habitat availability for specific taxa (79-81).\u003c/li\u003e\n \u003cli\u003eIndigenous Sacred Groves in India, known for strong belowground carbon sequestration potential, have recorded a 30% decline in pollinator visitation rates in fragmented groves, impacting fruit set and seed production (82). A similar pattern may emerge in Buddhist temple forests, where habitat isolation could disrupt pollination networks and associated trophic interactions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhile faith-managed forests demonstrate superior biodiversity conservation compared to state-managed reserves, rising fragmentation trends indicate an urgent need for conservation strategies to ensure their continued ecological functionality.\u003c/p\u003e\n\u003cp\u003eBuddhist temple forests are pivotal to urban air quality management, functioning as highly efficient natural air filters. In our study, the Buddha Museum\u0026apos;s temple forest\u0026mdash;spanning 17.08 ha\u0026mdash;removes approximately 1,477 kg of airborne pollutants annually, with an estimated economic benefit of $17,752 annually. Notably, ozone (O₃) removal is particularly effective, averaging 927.67 kg per year, substantially contributing to reducing ground-level smog and mitigating associated respiratory health risks. Equally important is the absorption of particulate matter, with PM₁₀ and PM₂.₅ being removed at rates of 323 kg and 45 kg per year, respectively\u0026mdash;critical processes given the established links between these particulates and cardiovascular and respiratory diseases. Furthermore, removing nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) underscores the capacity of temple forests to lower emissions from traffic and industry, thereby mitigating acid rain and related environmental hazards. When normalized per hectare, the highest removal efficiencies are observed for ozone (54.31 kg/ha) and PM₁₀ (18.92 kg/ha), reinforcing the ecological significance of these sacred green spaces as nature-based solutions for urban air pollution (70).\u003c/p\u003e\n\u003cp\u003eThe air purification function of Buddhist temple forests is not an isolated phenomenon; instead, it is consistent with the performance of other religiously managed landscapes worldwide. For example, Fengshui forests in China have demonstrated comparable pollutant sequestration capabilities, yet they are seldom incorporated into formal air quality policies (54). Similarly, studies show that urban temple forests in Bangkok significantly reduce particulate concentrations in high-canopy areas (83). Ethiopian church forests, characterized by dense and relatively undisturbed vegetation, provide robust pollutant absorption and carbon sequestration, further validating the ecological merits of faith-managed green spaces (9). Although subject to varying degrees of urban encroachment, European monastic forests also retain significant ecosystem service functions; however, their contributions to air quality are often underrepresented in policy frameworks (M\u0026uuml;ller et al., 2022). These comparative insights highlight that faith-managed landscapes, by their long-term protection from deforestation, often exhibit superior air quality benefits compared to conventional urban parks, yet they remain underrecognized in environmental policy (68).\u003c/p\u003e\n\u003cp\u003eUrban expansion, particularly the increase in impervious surfaces, has exacerbated habitat isolation (24, 40, 84). Road networks, which now cover 19.80 ha (14.39%), present significant barriers to species dispersal, increasing the risk of genetic bottlenecks (21, 24, 84). Despite doubling the herbaceous land LSI from 20 to 40, which supports pollinator populations and soil stabilization, the spatial disconnect between herbaceous zones and core forest patches reduces their effectiveness as functional ecological buffers (85).\u003c/p\u003e\n\u003cp\u003eThe contradiction between rising biodiversity indices (SHDI, SIDI) and increasing fragmentation (PD increase, AI decline) presents a complex conservation scenario. While more significant landscape heterogeneity can support generalist species, continued fragmentation may disrupt interior-dependent taxa, leading to long-term biodiversity instability (21, 26, 86). If habitat connectivity continues to decline, Buddhist temple forests may transition toward semi-urban ecological structures, reducing their overall capacity to function as compelling conservation landscapes (19, 20, 84).\u003c/p\u003e\n\u003cp\u003eA comparative evaluation reveals that Buddhist temple forests exhibit carbon sequestration characteristics consistent with other faith-managed conservation areas.\u003c/p\u003e\n\u003cp\u003eEthiopian church forests, which experience low deforestation rates due to community-led conservation efforts, display sequestration trends similar to IPCC-derived biomass estimates (9). Indigenous sacred groves in India, known for their deep-rooted vegetation, exhibit higher belowground carbon retention, aligning with i-Tree Canopy findings that emphasize the role of root systems in sequestration dynamics (13).\u003c/p\u003e\n\u003cp\u003eA broader comparison underscores that faith-managed landscapes outperform urban parks in sequestration efficiency. While urban parks store an average of 15.3 tons of Carbon per hectare, religious forests surpass 67 tons per hectare (4). This significant disparity highlights the potential of sacred landscapes as high-density carbon sinks and their importance in climate resilience strategies.\u003c/p\u003e\n\u003cp\u003eIn addition to their role in air purification, Buddhist temple forests serve as effective urban climate regulators. The removal of PM₂.₅ and PM₁₀ not only improves air quality but also reduces atmospheric heat absorption, thereby mitigating radiative forcing and attenuating urban heat island effects (56). Similarly, ozone and nitrogen dioxide absorption limits smog formation and reduces temperature variability caused by chemical pollutants. Moreover, temple forests\u0026apos; inherent dense biomass and mature canopy structures enhance thermal regulation, which can decrease the frequency and intensity of extreme heat events\u0026mdash;a benefit corroborated by studies on urban forestry in regions such as Canada (87). Such multifaceted climate mitigation functions underscore the role of temple forests as nature-based solutions that provide sustainable alternatives to energy-intensive cooling systems.\u003c/p\u003e\n\u003cp\u003eBuddhist temple forests play a vital role in biodiversity conservation and climate mitigation, yet they face vegetation health fluctuations due to urban pressures. The NDVI decline observed in FGSM and FGBM from 2016\u0026ndash;2019, followed by partial recovery (2019\u0026ndash;2022), reflects trends seen in Ethiopian church forests, where afforestation efforts counteract habitat fragmentation. Targeted afforestation programs using native high-carbon sequestration species should be prioritized to enhance long-term resilience, as demonstrated in Chinese Fengshui forests (88).\u003c/p\u003e\n\u003cp\u003eBeyond afforestation, carbon sequestration stagnation is a growing concern in old-growth temple forests, where biomass saturation limits further carbon absorption. Similar patterns in Chinese temple forests and Indigenous sacred groves suggest that sequestration equilibrium is inevitable without active management (60).\u003c/p\u003e\n\u003cp\u003eStrategies such as:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eSoil carbon stabilization techniques (biochar application, microbial soil enhancement) (3, 60).\u003c/li\u003e\n \u003cli\u003eImplementing selective thinning and light management enhances biomass renewal (36, 51, 89).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eDespite their high sequestration potential, Buddhist temple forests remain excluded from carbon markets, missing a significant financial opportunity. The carbon stock valuation of Fo Guang Shan increased from $16.74M (2016) to $19.26M (2022), yet lack of certification prevents market participation. Studies on Nepalese and Thai religious forests indicate that including faith-managed forests in voluntary carbon markets can generate conservation funding (7, 90). Aligning temple forests with Taiwan\u0026rsquo;s 2024 carbon pricing policy could enable them to participate in global offset schemes, ensuring financial sustainability (91).\u003c/p\u003e\n\u003cp\u003eEcotourism presents an alternative revenue model for conservation. Chinese sacred groves have successfully integrated faith-based conservation tourism, where visitors support heritage protection and biodiversity conservation (31, 32, 92). Buddhist temple forests could adopt similar models by integrating guided ecological tours, sustainable visitor facilities, and community engagement (93). However, careful zoning regulations and impact assessments are needed to prevent over-commercialization, as seen in overdeveloped sacred landscapes in China (32, 35, 37, 94).\u003c/p\u003e\n\u003cp\u003eA significant challenge in conserving Buddhist temple forests is their lack of formal policy recognition, restricting legal protection and funding access. Faith-managed forests in Ethiopia, China, and India face similar exclusions despite demonstrated ecological benefits. We recommend the following policy measures to address this issue :\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eLegal Protection as Conservation Reserves \u0026ndash; Temple forests should be designated as protected ecological reserves, ensuring long-term financial and legal safeguards (28, 44, 95).\u003c/li\u003e\n \u003cli\u003eInstitutional Collaboration \u0026ndash; Partnerships between monasteries, government agencies, and conservation organizations should establish co-managed forest governance, as seen in Chinese Fengshui forests (88).\u003c/li\u003e\n \u003cli\u003eIntegration into National Green Infrastructure \u0026ndash; Temple forests should be incorporated into urban resilience planning, mirroring China\u0026rsquo;s recognition of faith-managed landscapes as climate buffers (70).\u003c/li\u003e\n \u003cli\u003eCommunity-Led Conservation \u0026ndash; Local communities should actively engage in forest management, following successful Indigenous sacred forest models (28, 96).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFurther, fostering collaborative research partnerships between monastic institutions and environmental scientists can significantly enhance conservation efforts (93, 97). Integrating GIS-based biodiversity monitoring allows for precisely tracking vegetation changes and habitat health (96). At the same time, remote sensing analysis provides large-scale insights into forest cover dynamics and environmental stressors (70). Additionally, data-driven afforestation strategies can optimize reforestation by selecting suitable native species and ensuring long-term ecosystem stability (70, 96). By merging scientific methodologies with faith-based conservation practices, Buddhist temple forests can be more effectively protected and sustainably managed as resilient ecological sanctuaries in rapidly urbanizing landscapes (98, 99). This study comprehensively assesses the carbon sequestration capacity, biodiversity conservation potential, and environmental benefits of the FGSM and FGBM temple forest, highlighting the intersection of cultural heritage management and climate resilience. Through quantitative geospatial analysis and ecological modeling, the findings confirm that faith-managed landscapes serve as critical carbon sinks while enhancing biodiversity stability and urban sustainability (32, 37, 45).\u003c/p\u003e\n\u003cp\u003eBetween 2016 and 2022, carbon storage increased from 3,788.09 to 4,462.48 tons, with an annual sequestration rate of 52.26 \u0026plusmn; 2.55 tons of Carbon (191.63 \u0026plusmn; 9.34 tons CO₂ equivalent). However, a slight decline of 0.89% in sequestration between 2019 and 2022 suggests that biomass saturation, soil carbon flux limitations, and land-use modifications require strategic intervention. The i-Tree Canopy and IPCC models corroborate these estimates, reinforcing the temple forest\u0026apos;s function as a long-term carbon reservoir with an average carbon stock of 67.36 tons per hectare.\u003c/p\u003e\n\u003cp\u003eBeyond carbon sequestration, the FRAGSTATS landscape analysis revealed that temple forests contribute to habitat connectivity, with an Aggregation Index (AI) of 88.73%, facilitating species migration and ecological continuity. However, the declining Contagion Index (CONTAG) suggests increasing habitat fragmentation, which may disrupt ecological integrity. In contrast, Shannon\u0026rsquo;s Diversity Index (SHDI) and Simpson\u0026rsquo;s Diversity Index (SIDI) improvements indicate species diversification and enhanced ecological resilience, further emphasizing the ecological significance of Buddhist temple forests as biodiversity sanctuaries (37, 67).\u003c/p\u003e\n\u003cp\u003eFrom an economic perspective, the conservation initiatives at Fo Guang Shan align with Taiwan\u0026apos;s 2024 Carbon Fee Policy, with carbon market valuations increasing from $16.74 million (2016) to $19.26 million (2022). These findings highlight the potential for Buddhist temple forests to be formally integrated into global carbon trading markets, creating sustainable revenue streams for conservation efforts (27, 100). Comparative analysis of Ethiopian church forests and Indian sacred groves underscores Fo Guang Shan\u0026apos;s proactive conservation model, characterized by afforestation, sustainable landscape management, and carbon credit potential (91).\u003c/p\u003e\n\u003cp\u003eDespite these ecological and economic benefits, temple forests remain underrepresented in formal environmental and urban planning policies. Their exclusion from urban air quality management, climate mitigation policies, and carbon finance mechanisms limits their full impact. Addressing these gaps requires strategic conservation planning, policy integration, and scientific monitoring. Future efforts should focus on:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eExpanding afforestation and reforestation programs to enhance carbon sequestration and mitigate fragmentation (72, 91).\u003c/li\u003e\n \u003cli\u003eImplementing soil carbon stabilization mechanisms prevents sequestration stagnation and maximizes long-term carbon storage potential (70, 71).\u003c/li\u003e\n \u003cli\u003eStrengthening conservation finance models, integrating temple forests into carbon markets and green infrastructure frameworks (95, 100).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis study underscores the urgent need for policy frameworks incorporating religious landscapes into climate mitigation strategies. As cultural heritage sites increasingly contribute to biodiversity resilience and climate adaptation, targeted conservation strategies\u0026mdash;including afforestation, reforestation, and enhanced ecosystem monitoring\u0026mdash;are essential to ensure ecological and economic sustainability. By aligning faith-based conservation with global climate policy, Buddhist temple forests can continue functioning as vital ecological assets, reinforcing their role in urban sustainability, climate change mitigation, and long-term biodiversity conservation.\u003c/p\u003e\n\u003cp\u003eWhile this study enhances the understanding of religious landscapes as carbon sinks and biodiversity refuges, several limitations must be acknowledged. These constraints provide a foundation for future research to refine methodologies, expand comparative frameworks, and strengthen policy integration.\u003c/p\u003e\n\u003cp\u003eOne of the primary limitations lies in data resolution and temporal scope. Although high-resolution satellite imagery and geospatial models offer valuable insights, the absence of field-based validation techniques such as tree-core sampling, LiDAR mapping, and soil carbon analysis limits the precision of sequestration estimates. The six-year study period (2016\u0026ndash;2022) also provides useful short-term trends. However, longitudinal analyses spanning multiple decades would yield more substantial insights into the long-term effects of climate variability, biomass saturation, and ecosystem adaptation.\u003c/p\u003e\n\u003cp\u003eA further challenge stems from uncertainties in carbon estimation models. While i-Tree Canopy, InVEST, and IPCC models provide reliable sequestration estimates, regional variability in biomass expansion factors may introduce discrepancies. Developing a calibrated, site-specific biomass model tailored to Buddhist temple forests would improve estimation accuracy, particularly when considering species composition, soil carbon flux, and age-dependent sequestration variations.\u003c/p\u003e\n\u003cp\u003eAdditionally, biodiversity monitoring in this study relies primarily on landscape-level FRAGSTATS metrics, which capture habitat connectivity and fragmentation trends but lack direct species assessments. Future research should integrate faunal surveys, camera trapping, and soil microbial diversity analysis to corroborate species richness patterns and assess ecological resilience in Buddhist temple forests.\u003c/p\u003e\n\u003cp\u003eFrom an economic and policy perspective, while this study monetizes carbon sequestration values, the actual financial viability of temple forests in carbon markets depends on policy implementation, legal recognition, and market accessibility. Future research should explore government incentives, regulatory frameworks, and voluntary carbon credit mechanisms to facilitate the inclusion of religious conservation landscapes in global climate finance.\u003c/p\u003e\n\u003cp\u003eFinally, while this study compares Buddhist temple forests with Ethiopian church forests and Indian sacred groves, a broader comparative framework is needed to assess regional conservation strategies and policy adaptations. Expanding research to Buddhist temple forests in Japan, Thailand, and China would provide a more comprehensive understanding of faith-based conservation models across different cultural and ecological contexts.\u003c/p\u003e\n\u003cp\u003eAddressing these limitations through expanded temporal analyses, direct biodiversity monitoring, improved carbon modeling, and global comparative studies will further strengthen the role of religious landscapes in climate mitigation and ecological conservation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Fo Guang Shan Monastery (FGSM) and Fo Guang Shan Buddha Museum (FGBM), located in Dashu District, Kaohsiung, Taiwan, form an extensive religious, cultural, and ecological complex. Established in 1967 by Venerable Master Hsing Yun, FGSM serves as the spiritual and administrative headquarters of the Fo Guang Shan Buddhist Order. The FGBM, inaugurated in 2011, extends this mission by integrating Buddhist teachings, cultural heritage preservation, and religious tourism. These sites exemplify a holistic approach to monastic practice, heritage conservation, and sustainable landscape management\u0026nbsp;(11, 12).\u003c/p\u003e\n\u003cp\u003eFGSM and FGBM sit at 22.7276\u0026deg; N and 120.4042\u0026deg; E within a tropical monsoon climate zone, where hot, humid summers and mild winters prevail. The area receives an average annual rainfall of 2,000 mm between May and September. This climate fosters lush vegetation and diverse ecological landscapes, reinforcing the sacred atmosphere of the complex while supporting its environmental conservation initiatives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLand Use and Architectural Features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Fo Guang Shan Buddha Museum spans approximately 100 hectares, integrating traditional Buddhist architectural elements with contemporary cultural and educational functions. The Buddha Memorial Center (BMC), positioned at the heart of the museum, enshrines a sacred Buddha tooth relic, serving as a focal point for devotional practice and pilgrimage. Eight pagodas surround the central structure, each symbolizing an aspect of the Noble Eightfold Path, which reinforces the philosophical and didactic dimensions of the museum. The Great Buddha Hall, dominated by a 108-meter bronze Buddha statue, is a defining feature of the complex, reflecting architectural grandeur and religious symbolism (Fig. 2).\u003c/p\u003e\n\u003cp\u003eThe Fo Guang Shan Buddha Temple spans 50 hectares, incorporating monastic residences, administrative buildings, meditation halls, and sacred shrines. Although the combined area of the temple and its associated museum covers 150 hectares, official tourism maps indicate that only 55 hectares are designated for public access, tourism, and pilgrimage (Fig. 3). This distribution suggests that most of the site is allocated for monastic activities, ecological conservation, and restricted areas, preserving its spiritual and environmental integrity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEcological Significance and Green Space Distribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough precise woodland data is unavailable, qualitative insights from key informant interviews indicate that built-up structures occupy less than 20% of the total area. In contrast, greenspace, including landscaped gardens, forested areas, and vegetative land covers, comprises more than 50%. These green spaces contribute significantly to carbon sequestration, biodiversity conservation, and microclimate regulation, aligning with Buddhist ecological ethics emphasizing harmonious coexistence between human activities and nature\u0026nbsp;(1, 2, 6, 14, 29).\u003c/p\u003e\n\u003cp\u003eBeyond its monastic and architectural significance, FGSM and FGBM are interdisciplinary research hubs, fostering academic inquiries into heritage conservation, sustainable tourism, and environmental management (12). The museum\u0026apos;s integration of cultural heritage preservation with contemporary sustainability practices positions it as a model for heritage site conservation and religious landscape management. The surrounding forested and landscaped areas, designed by Buddhist ecological principles, play a crucial role in carbon sequestration, habitat preservation, and climate resilience.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeospatial Analysis and Research Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo provide spatial clarity, Figure 4 presents high-resolution satellite imagery of FGSM and FGBM, illustrating the distribution of built structures, green spaces, and ecological buffer zones. This geospatial analysis enables a quantitative evaluation of the site\u0026apos;s cultural and ecological functions, ensuring analytical coherence with broader conservation and sustainability research (30, 31).\u003c/p\u003e\n\u003cp\u003eIn this study, GIS-based classification and remote sensing techniques\u0026mdash;including NDVI (Normalized Difference Vegetation Index) and supervised land cover classification\u0026mdash;were applied to refine estimates of green space coverage and assess its ecological contributions (32-35). These analyses provide further insights into the role of religious landscapes in sustainable environmental stewardship and heritage conservation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRemote Sensing and Land Cover Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSatellite Data Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employs Pleiades-1A and Pleiades-1B high-resolution satellite imagery, operated by Airbus Defence and Space, due to their 0.5-meter spatial resolution and stereo imaging capabilities, which facilitate detailed land-use classification and vegetation analysis (36). Compared to alternative satellite sensors, such as Sentinel-2 (10m resolution) and Landsat-8 (30m resolution), Pleiades provides superior spatial precision, enabling the accurate detection of micro-scale land-use changes and vegetation dynamics. This higher resolution is particularly beneficial for assessing subtle variations in vegetation density, anthropogenic land modifications, and conservation effectiveness.\u003c/p\u003e\n\u003cp\u003eThree datasets\u0026mdash;2016, 2019, and 2022\u0026mdash;were selected to assess temporal changes in vegetation cover, land-use transitions, and conservation practices within the study site (Fig. 5, Table 11). The selection of 2016 as the baseline year was determined by data availability, as this was the earliest Pleiades satellite imagery accessible from the Center for Space and Remote Sensing Research, NCU, Taiwan. To ensure a comprehensive longitudinal assessment, subsequent datasets from 2019 and 2022 were incorporated, allowing for the analysis of long-term environmental trends in relation to heritage site management. This temporal framework enables a detailed evaluation of the impact of sustainable conservation strategies, afforestation efforts, and climate variability on landscape stability and ecological resilience (37). As the analysis was concluded in 2024, this dataset selection provides a methodologically robust and temporally consistent basis for monitoring heritage landscapes through remote sensing techniques.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 11\u0026nbsp;\u003c/strong\u003ePleiades satellite imagery datasets for remote sensing analysis\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eDate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eImage ID\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e2016.12.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eP1B_H1M_20161214_024525\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e2019.12.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eP1A_H1M_20191219_023401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e2022.12.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eP1A_H1M_20221201_024138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e(Source: CNES (2016, 2019, 2022), Distributed by Airbus DS)\u003c/p\u003e\n\u003cp\u003eThese datasets provide multi-temporal insights into vegetation health, urban expansion, and conservation effectiveness, supporting a comprehensive geospatial analysis of the FGSM and FGBM landscape evolution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVegetation Analysis: Normalized Difference Vegetation Index (NDVI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed the Normalized Difference Vegetation Index (NDVI) to evaluate vegetation health and detect land-cover changes (38). This widely used spectral vegetation index quantifies plant vigor by measuring the difference in reflectance between near-infrared (NIR) and red light (39). Healthy vegetation exhibits strong reflectance in the NIR spectrum (700\u0026ndash;1100 nm) while absorbing most red light (620\u0026ndash;750 nm) for photosynthesis (40). The NDVI calculation follows the standard formula (41, 42):\u003c/p\u003e\n\u003cp\u003eNDVI=(NIR+Red)(NIR\u0026minus;Red) \u0026nbsp; \u0026nbsp;(1)\u003c/p\u003e\n\u003cp\u003eThe index produces values ranging from -1 to 1, where higher values indicate greater vegetation density and vitality. NDVI values above 0.6 typically correspond to dense, healthy vegetation, while values between 0.2 and 0.6 indicate moderate vegetation cover. Values below 0.2 suggest sparse vegetation or non-vegetated surfaces. By offering a quantitative measure of vegetation health, NDVI is instrumental in assessing ecological stability, monitoring land degradation, and evaluating climate-induced shifts in vegetation patterns (43).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInVEST Carbon Storage and Sequestration Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCarbon Storage and Sequestration Modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Carbon Storage and Sequestration Model, developed by the Natural Capital Project, to assess carbon stock dynamics within the FGSM and FGBM. The model provides a spatially explicit assessment of carbon storage based on land cover classifications, integrating four major carbon pools: aboveground biomass, belowground biomass, soil organic carbon, and dead organic matter (31, 44, 45). The model quantifies carbon sequestration potential by analyzing temporal variations in land cover and vegetation, supporting heritage conservation strategies and climate adaptation policies (18, 46).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection and Processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLand Cover Classification and Input Preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized high-resolution Pleiades-1A and Pleiades-1B satellite imagery to generate land cover maps for 2016, 2019, and 2022, employing Support Vector Machine (SVM) classification within ArcGIS Pro (3.0) for data processing (32, 35). The classification system identified key vegetation types, including evergreen broad-leaved forests and grasslands, both of which function as primary carbon sinks (33, 34). To enhance spatial accuracy and boundary delineation, administrative boundary data and conservation zoning layers were integrated into the analysis of the FGSM-FGBM site.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCarbon Pool Parameterization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearchers assigned carbon stock values to each land cover class based on IPCC (2019) guidelines, supplementing them with regional ecological datasets for improved estimation accuracy. Each land cover type was attributed carbon pool coefficients, representing aboveground biomass, belowground biomass, soil organic carbon, and dead organic matter (47-51). These coefficients were applied uniformly across the study area, enabling the calculation of total carbon storage per unit area and facilitating spatial comparisons over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCarbon Storage Estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe total carbon storage (\u003cem\u003eC\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e) for each land parcel was computed as the sum of the four primary carbon pools\u0026nbsp;(52):\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eC\u003csub\u003ei\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e= \u003cem\u003eC\u003csub\u003ei\u003cu\u003e\u0026nbsp;\u003c/u\u003ea\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e+ \u003cem\u003eC\u003csub\u003ei\u003cu\u003e\u0026nbsp;\u003c/u\u003eb\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e+ \u003cem\u003eC\u003csub\u003ei\u003cu\u003e\u0026nbsp;\u003c/u\u003es\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e+ \u003cem\u003eC\u003csub\u003ei\u003cu\u003e\u0026nbsp;\u003c/u\u003ed\u003c/sub\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e(2)\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cem\u003eC\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e represents total Carbon stored in land parcel \u003cem\u003ei\u003c/em\u003e (t/ha),\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eC\u003csub\u003ei\u003cu\u003e\u0026nbsp;\u003c/u\u003ea\u003c/sub\u003e\u003c/em\u003e is aboveground biomass carbon (t/ha),\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eC\u003csub\u003ei\u003cu\u003e\u0026nbsp;\u003c/u\u003eb\u003c/sub\u003e\u003c/em\u003e is belowground biomass carbon (t/ha),\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eC\u003csub\u003ei\u003cu\u003e\u0026nbsp;\u003c/u\u003es\u003c/sub\u003e\u003c/em\u003e is soil organic carbon (t/ha), and\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eC\u003csub\u003ei\u003cu\u003e\u0026nbsp;\u003c/u\u003ed\u003c/sub\u003e\u003c/em\u003e is dead organic matter carbon (t/ha).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe cumulative carbon storage across the study area was obtained by aggregating individual land parcel values:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eC\u003csub\u003etotal \u0026nbsp; \u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= \u003cimg width=\"81\" height=\"19\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;(3)\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cem\u003eC\u003csub\u003etotal\u003c/sub\u003e\u003c/em\u003e: Total Carbon stored in the study area.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eS\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e: Area of each land cover type (ha).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eTable 12\u0026nbsp;\u003c/strong\u003eCarbon storage parameters by land cover type\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eLand cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eC\u003csub\u003ei\u003cu\u003e\u0026nbsp;\u003c/u\u003ea\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cem\u003eC\u003csub\u003ei\u003cu\u003e\u0026nbsp;\u003c/u\u003eb\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cem\u003eC\u003csub\u003ei\u003cu\u003e\u0026nbsp;\u003c/u\u003es\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cem\u003eC\u003csub\u003ei\u003cu\u003e\u0026nbsp;\u003c/u\u003ed\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eEvergreen broad-leaved forests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e67.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eGrassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e(Units: tons per hectare (t/ha))\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCarbon Sequestration Estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe calculated the net carbon sequestration (\u0026Delta;C) between the selected periods as follows:\u003c/p\u003e\n\u003cp\u003e\u0026Delta;C=C\u003csub\u003et2\u003c/sub\u003e\u0026minus;C\u003csub\u003et1\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eC\u003csub\u003et1\u003c/sub\u003e represents carbon storage in the initial year (2016),\u003c/li\u003e\n \u003cli\u003eC\u003csub\u003et2\u003c/sub\u003e represents carbon storage in the final year (2022), and\u003c/li\u003e\n \u003cli\u003e\u0026Delta;C quantifies the net change in carbon stocks (tons CO₂ per year).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis approach detects land-use-induced carbon gains or losses, providing insights into afforestation, deforestation, and conservation-driven carbon sequestration dynamics (49, 50). The InVEST carbon model was applied to estimate carbon storage and sequestration using IPCC Tier 1 default values and regional datasets. The resulting carbon stock estimates were analyzed using GIS-based spatial mapping techniques, producing high-resolution carbon distribution maps to visualize sequestration patterns across the FGSM-FGBM landscape.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ei-Tree Canopy Model for Vegetation and Carbon Sequestration Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection and Processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe i-Tree Canopy Model, developed by the USDA Forest Service, was used to estimate tree canopy coverage, impervious surface extent, and vegetation composition within FGSM and FGBM. The model employs randomized point sampling of high-resolution aerial imagery, coupled with manual classification and validation, to ensure higher accuracy in land cover estimation\u0026nbsp;(53).\u003c/p\u003e\n\u003cp\u003eGIS software delineated a geo-referenced boundary of the 57.85-hectare study area and imported it into i-Tree Canopy. We systematically generated 1,016 randomized sampling points within this boundary and used high-resolution aerial imagery from Google Earth for classification (54).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLand Cover Classification and Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe manually classified each sampling point into four primary land cover types: tree canopy, grassland/shrubland, impervious surfaces, and water bodies (53). Classification accuracy was ensured through systematic verification and cross-referencing with high-resolution satellite imagery and ancillary spatial data (9, 55). Any discrepancies were resolved using cross-validation techniques and expert judgment, ensuring consistency and reliability in land cover identification. The model automatically calculated the percent land cover for each category, estimating vegetation distribution and urbanization extent (53).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCarbon Sequestration Estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe calculated carbon sequestration potential using the i-Tree Canopy tool, incorporating region-specific parameters calibrated to reflect local tree species and environmental conditions in Taiwan (55, 56). To ensure consistency in carbon stock assessments, we compared sequestration estimates from i-Tree with those obtained from the InVEST Carbon Storage and Sequestration Model. Cross-validating these two models enhanced methodological rigor and accuracy in our carbon stock estimations (57).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIPCC Carbon Estimation Methodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiomass Carbon Stock Estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCarbon storage and sequestration were estimated using the Intergovernmental Panel on Climate Change (IPCC) Tier 1 methodology, a standardized framework for quantifying greenhouse gas (GHG) emissions and removals (58). This approach ensures comparability with global carbon assessments and aligns with climate policy reporting requirements.\u003c/p\u003e\n\u003cp\u003eWe calculated the total biomass carbon stock (C) using the equation recommended by the IPCC (59) :\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eC\u0026nbsp;\u003c/em\u003e= \u003cem\u003eVt \u0026times; BD \u0026times; BEF \u0026times;\u0026nbsp;\u003c/em\u003e(1 + \u003cem\u003eR\u003c/em\u003e) \u003cem\u003e\u0026times; CF \u0026nbsp;\u003c/em\u003e(4)\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cem\u003eC\u003c/em\u003e = Total Carbon stored in biomass (tC)\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eVt\u003c/em\u003e = Aboveground biomass volume (m\u0026sup3;/ha), derived from regional land cover classifications\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eBD\u003c/em\u003e = Biomass bulk density (t/m\u0026sup3;), obtained from regional ecological datasets\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eBEF\u003c/em\u003e = Biomass Expansion Factor, accounting for unmeasured biomass components such as branches and leaves\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eR\u003c/em\u003e = Root-to-shoot ratio, used to estimate belowground biomass\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eCF\u003c/em\u003e = Carbon fraction of biomass (0.47 as per IPCC default)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe used regional forest inventory data and land cover classifications to estimate \u003cem\u003eVt\u003c/em\u003e for different vegetation types, including broadleaf forests and grasslands (7). Biomass density values were obtained from Taiwan\u0026rsquo;s National Forest Carbon Database, ensuring the application of region-specific values in carbon stock calculations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEconomic Valuation of Carbon Sequestration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe financial value of carbon sequestration was estimated based on international carbon market prices, incorporating data from three primary carbon trading mechanisms:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eEuropean Union Emission Trading System (EU ETS): \u0026euro;63.50/tCO₂\u003c/li\u003e\n \u003cli\u003eCalifornia Cap-and-Trade Market (USA): $39.80/tCO₂\u003c/li\u003e\n \u003cli\u003eAustralian Carbon Credit Units (ACCU): $39.20/tCO₂\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe determined the total economic value (\u003cem\u003eV\u003c/em\u003e) of carbon sequestration as:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eV\u003c/em\u003e=\u003cem\u003eCsequestered\u003c/em\u003e \u0026times; \u003cem\u003ePcarbon\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cem\u003eV\u003c/em\u003e = Total economic value of carbon sequestration\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eCsequestered\u003c/em\u003e = Total Carbon sequestered (tCO₂/yr), derived from IPCC and InVEST model estimates\u003c/li\u003e\n \u003cli\u003e\u003cem\u003ePcarbon\u003c/em\u003e = Carbon price per metric ton, adjusted for currency conversion\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eEcosystem-Specific Carbon Valuation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCarbon sequestration rates were analyzed separately for forests, grasslands, and urban vegetation to ensure an ecosystem-based assessment, reflecting variation in sequestration potential across land cover types (47, 51, 60). Carbon sequestration values were aggregated within each land cover class to produce a spatially explicit valuation of carbon storage within the heritage landscape.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation and Accuracy Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained all carbon pricing data from verified carbon trading platforms and government agencies to ensure the accuracy and reliability of monetary valuation. To maintain currency precision, we retrieved exchange rates from the Bank of Taiwan (2025).\u003c/p\u003e\n\u003cp\u003eWe conducted a comparative analysis using carbon sequestration estimates derived from the InVEST model to ensure consistency with IPCC-derived values. To validate the final results, we cross-referenced them with Taiwan\u0026apos;s National Carbon Inventory, confirming methodological consistency and reinforcing the robustness of the economic valuation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLandscape Structure Analysis Using FRAGSTATS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Acquisition and Preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-resolution Pleiades satellite imagery (2016, 2019, and 2022) was acquired and preprocessed to analyze landscape structural changes and biodiversity implications within the FGSM and FGBM. We employed supervised classification techniques using a support vector machine (SVM) in ArcGIS Pro (3.0) to classify land cover, ensuring consistency in detecting vegetation cover changes, habitat connectivity, and fragmentation patterns (61). Classified images were converted into raster format, allowing for comparative analysis of spatial patterns across study years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLandscape Metrics Computation and Biodiversity Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe FRAGSTATS model was used to compute key landscape metrics associated with biodiversity conservation, fragmentation, and spatial organization (20). The analysis was conducted at three hierarchical levels (class, patch, and landscape) to assess habitat distribution and ecological integrity\u0026nbsp;(21):\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eClass-Level Metrics (assessing habitat extent and fragmentation):\u003cul type=\"circle\"\u003e\n \u003cli\u003eClass Area (CA, ha) and Percentage of Landscape (PLAND, %) quantified each land cover type\u0026apos;s total area and proportion, mainly focusing on tree cover and green spaces essential for biodiversity.\u003c/li\u003e\n \u003cli\u003eThe most extensive Patch Index (LPI, %) identified the dominance of the largest contiguous natural habitat, which is critical for species movement and ecosystem stability.\u003c/li\u003e\n \u003cli\u003eAggregation Index (AI, %) measured the degree of clustering within land cover classes, where lower values indicate higher fragmentation and habitat isolation.\u003c/li\u003e\n \u003cli\u003eNormalized Landscape Shape Index (NLSI) assesses habitat shape complexity, as irregular and fragmented patches can impact wildlife dispersal and biodiversity richness.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003ePatch-Level Metrics (evaluating habitat fragmentation and complexity):\u003cul type=\"circle\"\u003e\n \u003cli\u003ePatch Density (PD, patches/ha) measured the number of habitat patches, with higher values suggesting increased fragmentation and potential loss of contiguous habitat for species survival.\u003c/li\u003e\n \u003cli\u003eThe Landscape Shape Index (LSI) quantified the morphological complexity of habitat patches, where more irregular patches may indicate disturbance or land-use pressures on biodiversity.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003eLandscape-Level Metrics (assessing ecosystem diversity and connectivity):\u003cul type=\"circle\"\u003e\n \u003cli\u003eContagion Index (CONTAG) evaluated habitat continuity, where lower values indicate higher landscape fragmentation and loss of significant, connected habitats essential for biodiversity corridors.\u003c/li\u003e\n \u003cli\u003eShannon\u0026rsquo;s Diversity Index (SHDI) and Simpson\u0026rsquo;s Diversity Index (SIDI) measured overall habitat diversity, with higher values indicating a more heterogeneous and ecologically diverse landscape supporting multiple species.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLandscape metrics were computed for each study year (2016, 2019, and 2022) to identify temporal trends in habitat loss, biodiversity-supporting landscape stability, and land-use modifications\u0026nbsp;(62-65).\u003c/p\u003e\n\u003cp\u003eWe analyzed landscape composition and fragmentation using FRAGSTATS and summarized key metrics in Table 13.\u003c/p\u003e\n\u003cp\u003eTable 13 Summary of landscape metrics used for spatial and biodiversity analysis\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEcological Relevance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCA (ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal area of land cover class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndicates dominant habitat size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePLAND (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercentage of the total landscape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeasures spatial distribution of habitats\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLPI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLargest contiguous patch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReflects habitat dominance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAggregation Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigher values = better connectivity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNLSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNormalized Landscape Shape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComplexity of land cover boundaries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePD (patches/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePatch Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigher values = increased fragmentation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCONTAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eContagion Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeasures clustering of land cover types\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSHDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShannon\u0026rsquo;s Diversity Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRepresents habitat diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSIDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSimpson\u0026rsquo;s Diversity Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeasures species richness and evenness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eFragmentation and Habitat Connectivity Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further assess habitat fragmentation and connectivity, temporal trends in AI, PD, LSI, and CONTAG were analyzed. A decrease in AI and CONTAG, combined with an increase in PD and LSI, would indicate a shift toward higher fragmentation and reduced habitat connectivity, which can negatively impact biodiversity and species movement across the landscape (40, 66, 67).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegration of Landscape Metrics for Biodiversity and Conservation Planning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results from FRAGSTATS landscape metrics were integrated into biodiversity conservation planning to support the sustainable management of green spaces within the FGSM-FGBM site (23, 26). By quantifying changes in habitat structure, fragmentation, and spatial diversity, this study provides empirical evidence for land-use policies to preserve ecological integrity (19, 22, 23). The findings contribute to decision-making in heritage landscape conservation, ensuring that cultural and ecological values remain balanced over time (65).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files. Additional geospatial data (e.g., NDVI, LULC maps) and carbon modeling outputs are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to the Fo Guang Shan Monastery and Fo Guang Shan Buddha Museum for their collaboration and data access. Special thanks to the Center for Space and Remote Sensing Research (CSRSR), National Central University, for providing satellite imagery and technical support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChih-Lin Liu led the research and contributed most substantially to the work. He conceptualized the study, collected and analyzed the data, wrote the original draft, and completed all revisions in response to editorial and reviewer feedback.\u003c/p\u003e\n\u003cp\u003eWan-Yu Liu served as the supervising professor, providing academic guidance, critical feedback, and oversight of the writing process.\u003c/p\u003e\n\u003cp\u003eBoth authors reviewed and approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnderson DM, Salick J, Moseley RK, Ou XK. Conserving the sacred medicine mountains: A vegetation analysis of Tibetan sacred sites in Northwest Yunnan. Biodiversity and Conservation. 2005;14(13):3065-91.\u003c/li\u003e\n\u003cli\u003eTatay J, Merino A. What is sacred in sacred natural sites? 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Forests. 2021;12(1).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-heritage-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hsci","sideBox":"Learn more about [Heritage Science](http://heritagesciencejournal.springeropen.com)","snPcode":"40494","submissionUrl":"https://submission.nature.com/new-submission/40494/3","title":"npj Heritage Science","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sacred landscapes, Cultural ecosystem services, Carbon sequestration, Remote sensing, Sustainable heritage management","lastPublishedDoi":"10.21203/rs.3.rs-5513941/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5513941/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReligious landscapes provide underexplored contributions to carbon sequestration and biodiversity conservation. This study uses remote sensing and spatial analysis to evaluate the carbon storage potential and ecological resilience of the Fo Guang Shan Buddha Museum in Kaohsiung, Taiwan. Between 2016 and 2022, carbon storage increased by 17.8%, with tree canopy contributing over 1,300 tons of carbon. Despite a recent decline in sequestration rate, habitat connectivity remains high, while biodiversity indices indicate growing species richness and evenness. Economic valuation estimates the site\u0026rsquo;s carbon sequestration potential at USD 16\u0026ndash;19\u0026nbsp;million, aligned with Taiwan\u0026rsquo;s 2024 Carbon Fee Policy. Comparative landscape metrics suggest that temple-managed green spaces mitigate fragmentation more effectively than passive conservation models. These findings support the integration of faith-driven sacred landscapes into carbon offset programs and nature-based climate solutions. The study highlights the policy relevance of religious sites as multifunctional heritage spaces contributing to ecological sustainability and national climate strategies.\u003c/p\u003e","manuscriptTitle":"Sacred Heritage and Environmental Sustainability: Carbon and Biodiversity Insights from Taiwan’s Fo Guang Shan Monastery and Buddha Museum","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 18:21:37","doi":"10.21203/rs.3.rs-5513941/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-29T21:03:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-29T09:03:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213850469179864660002301480845585309034","date":"2025-04-08T03:15:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-06T08:05:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164468406134814684927769899449604793503","date":"2025-04-06T07:54:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-05T20:13:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-04T11:04:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Heritage Science","date":"2025-03-10T03:22:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-heritage-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hsci","sideBox":"Learn more about [Heritage Science](http://heritagesciencejournal.springeropen.com)","snPcode":"40494","submissionUrl":"https://submission.nature.com/new-submission/40494/3","title":"npj Heritage Science","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c81abf8-3474-4d81-b313-7d720dd30212","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:21:07+00:00","versionOfRecord":{"articleIdentity":"rs-5513941","link":"https://doi.org/10.1038/s40494-025-01836-2","journal":{"identity":"npj-heritage-science","isVorOnly":false,"title":"npj Heritage Science"},"publishedOn":"2025-07-03 15:58:46","publishedOnDateReadable":"July 3rd, 2025"},"versionCreatedAt":"2025-05-06 18:21:37","video":"","vorDoi":"10.1038/s40494-025-01836-2","vorDoiUrl":"https://doi.org/10.1038/s40494-025-01836-2","workflowStages":[]},"version":"v1","identity":"rs-5513941","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5513941","identity":"rs-5513941","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}