Assessment of Above Ground Biomass and Carbon Sequestration in a Coastal Mangrove Sanctuary Using Vegetation Indices and Field Data Integration

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Saifur Rahman, Md. Mostafizur Rahman, Syed Hafizur Rahman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7089385/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mangrove forests are globally recognized for their exceptional capacity to sequester carbon, making them vital ecosystems for climate mitigation and coastal resilience. This study assesses Above Ground Biomass (AGB) and corresponding carbon stock in the Tengragiri Wildlife Sanctuary of Bangladesh by integrating field measurements with Landsat-8-derived vegetation indices—NDVI, EVI, and SAVI—over the period 2020 to 2023. A total of 35 georeferenced plots were established to collect data on tree diameter, height, and species-specific wood density, from which AGB was estimated. The results showed that NDVI exhibited the highest correlation with AGB (R² = 0.8309 for polynomial models), followed by SAVI (R² = 0.7553), while EVI showed comparatively weaker performance (R² = 0.3462). Polynomial regression models consistently outperformed linear models, capturing the nonlinear relationship between vegetation indices and biomass in semi-saline, tidally influenced environments. The estimated AGB values ranged from 4806.70 to 6964.97 Mg/ha, which were converted to carbon stock using the IPCC default factor (0.47), and to CO₂ equivalents (3.67 × C), revealing the site’s significant carbon sequestration potential. The study highlights the spatial variability in biomass distribution across the sanctuary, identifying both conservation-priority zones and areas possibly impacted by anthropogenic activities. These findings reinforce the utility of NDVI-based remote sensing models as effective, scalable tools for carbon accounting and ecological monitoring, offering practical value for REDD + implementation, national climate mitigation planning, and sustainable mangrove management in data-scarce regions. Mangrove Forest Above Ground Biomass Carbon stock Tengragiri Wildlife Sanctuary Landsat-8 imagery NDVI SAVI EVI 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 Figure 13 Figure 14 1. Introduction Mangrove forests represent some of the most carbon-rich ecosystems globally, serving as critical blue carbon sinks and natural coastal buffers that mitigate climate impacts and coastal erosion (Donato et al., 2011 ; Alongi, 2014 ). Mangrove ecosystems are capable of sequestering carbon at rates several times higher than terrestrial tropical forests, making their conservation essential for global carbon budgets (Mcleod et al., 2011 ; Alongi, 2012 ). Above Ground Biomass (AGB) plays a pivotal role in evaluating forest productivity, ecosystem health, and carbon cycling (Saatchi et al., 2011 ; Lu, 2006 ). Accurate estimation of AGB is essential for ecological monitoring, carbon accounting under frameworks like REDD+, and evidence-based climate policy formulation (Giri et al., 2015 ; Kauffman & Donato, 2012 ). While traditional destructive sampling methods provide reliable biomass measurements, they are labor-intensive, costly, and impractical for broad spatial applications, particularly in dynamic environments like mangroves. Converting AGB into carbon stock typically involves applying a default conversion factor of 0.47 as per IPCC guidelines, facilitating consistent carbon estimation across studies (IPCC, 2006 ). Such estimates form the foundation for calculating CO₂ equivalents using a molecular weight ratio of 3.67, contributing to national greenhouse gas inventories and carbon offset strategies. In Bangladesh, most mangrove biomass research has centered on the Sundarbans, leaving ecologically important yet lesser-studied habitats like the Tengragiri Wildlife Sanctuary in the Barguna district underrepresented (Islam et al., 2020 ). Despite its ecological and carbon sequestration potential, Tengragiri remains poorly assessed in terms of biomass and carbon stock dynamics. The sanctuary, declared in 2010, serves as a vital ecological buffer against cyclones, erosion, and salinity intrusion, while supporting diverse flora—such as Heritiera fomes , Ceriops decandra , Sonneratia apetala , and Excoecaria agallocha —and fauna, including fishing cats, crocodiles, deer, and migratory birds (Islam et al., 2020 ). Mangrove forests like Tengragiri represent some of the most carbon-rich ecosystems globally, functioning as essential blue carbon sinks and natural coastal buffers (Donato et al., 2011 ; Alongi, 2014 ). However, the sanctuary is increasingly threatened by coastal erosion, salinity intrusion, illegal logging, and tree mortality—factors that underscore the urgent need for improved monitoring and conservation strategies (Uddin, 2018 ). Consequently, remote sensing-based approaches—particularly vegetation indices such as NDVI (Rouse et al., 1974 ), SAVI (Huete, 1988 ), and EVI (Huete et al., 2002 ) derived from freely available Landsat-8 imagery—have become cost-effective and scalable alternatives for large-scale Above Ground Biomass (AGB) estimation (Lu, 2006 ; Asner et al., 2012 ). Landsat-8 provides consistent spectral and temporal coverage that facilitates efficient vegetation assessment over wide regions, making it suitable for mangrove biomass monitoring (Roy et al., 2014 ). While some remote sensing studies have assessed land cover change in the area, comprehensive biomass assessments integrating field observations remain rare. The lack of advanced technologies like LiDAR, SAR, and UAV-based imagery—primarily due to cost and accessibility—limits structural forest assessment (Fatoyinbo et al., 2018 ). Furthermore, studies seldom explore nonlinear biomass modeling or consider tidal reflectance variability and complex vegetation structures (Alongi, 2014 ). Critical carbon pools such as below-ground biomass and soil organic carbon are also largely omitted, resulting in incomplete carbon stock assessments. Addressing these gaps, this study had three primary objectives: (1) to quantify AGB in the Tengragiri Wildlife Sanctuary using 35 systematically distributed field plots and Landsat-8 derived vegetation indices (NDVI, EVI, SAVI) from 2020 to 2023; (2) to compare the predictive performance of linear and second-degree polynomial regression models in estimating AGB; and (3) to calculate corresponding carbon stock and CO₂ equivalents using IPCC-recommended factors (0.47 for carbon, 3.67 for CO₂) to evaluate the area’s carbon sequestration potential. Unlike most AGB studies in Bangladesh that concentrate on the Sundarbans and rely predominantly on linear models, this research introduces a novel comparative analysis of vegetation indices using both linear and polynomial regression. The results revealed that polynomial models better captured the nonlinear dynamics of biomass distribution, with NDVI showing the strongest correlation (R² = 0.83), followed by SAVI, while EVI performed moderately. The integration of field data and satellite imagery produced a scalable, cost-effective, and repeatable framework for mangrove biomass monitoring, with outputs aligned to global carbon accounting frameworks such as REDD+. Overall, this study provides valuable insights into remote sensing-based AGB estimation in lesser-studied mangrove ecosystems and sets a foundation for future work that integrates multi-sensor approaches, machine learning models, and broader carbon pool assessments. The findings support national climate mitigation initiatives and enhance evidence-based decision-making for sustainable mangrove management in coastal Bangladesh. 2. Methodology 2.1 Study Area The study was conducted in the Tengragiri Wildlife Sanctuary (TWS), located in Taltali Upazila of Barguna district in the Southwestern Bangladesh. In addition to the Sundarbans, Bangladesh’s Tengragiri Wildlife Sanctuary—located in the coastal district of Barguna—is the country’s second-largest mangrove forest, spanning approximately 4,048 hectares (Forest Department, 2020). Geographically, it extends from 21° 51′ 25.440″ N to 21° 57′ 11.985″ N latitude and 90° 01′ 5.944″ E to 90° 07′ 20.462″ E longitude (Islam et al., 2020 ) as illustrated in Fig. 1 . The region is ecologically significant, supporting both biodiversity conservation and blue carbon storage. 2.2 Wood Density of Mangrove Species Wood density is a critical parameter for estimating biomass and carbon stock in forest ecosystems. Table 1 presents the wood densities of major mangrove and coastal tree species in Bangladesh, compiled from established literature and authoritative forestry databases. These values are essential for applying allometric equations in Above Ground Biomass (AGB) calculations and carbon assessments. Table 1 Wood density of different mangrove species Scientific Name Local Name Wood Density (g/cm³) Reference Heritiera fomes Sundari 0.80–0.95 Choudhury ( 1984 ); Komiyama et al. ( 2005 ) Carissa carandas Karamcha 0.65–0.72 Orwa et al. ( 2009 ) Excoecaria agallocha Gewa 0.40–0.55 Ahmed et.al. ( 2021 ); Hossain et.al. ( 2015 ); Flora of Bangladesh. (2019) Avicennia officinalis Bain 0.45–0.55 Komiyama et al. ( 2005 ); Alongi ( 2009 ) Avicennia alba Gural /Bain 0.40–0.50 Alongi ( 2002 ); Duke ( 2006 ); FAO ( 2007 ) Sonneratia apetala Keora 0.50–0.65 Komiyama et al. ( 2005 ) Ceriops decandra Goran 0.60–0.70 Komiyama et al. ( 2005 ); Choudhury ( 1984 ); FAO ( 2007 ) 2.3 Field Data Collection A total of 35 sample plots, each measuring 20 × 20 meters (Fig. 1 ), were systematically established across representative forest zones. For each tree within these plots, measurements of diameter at breast height (DBH), total height (H), and species-specific wood density (ρ) were recorded. Above Ground Biomass (AGB) was then estimated using the following allometric equation: AGB = 0.251 × ρ × D² × H (1) (Komiyama et al., 2005 ) Where: D = Diameter at Breast Height (cm) H = Tree Height (m) ρ = Wood Density (g/cm³) AGB values ranged from 4,806.70 to 6,961.13 Mg/ha, reflecting substantial variation in biomass and carbon storage potential across the sampled forest plots. 2.3.1 Measurement of Biophysical Tree Parameters The diameter at breast height (DBH) was measured at 1.3 meters above the ground using a standard measuring tape, following conventional forestry protocols (Avery & Burkhart, 2015 ). Species-specific wood density (ρ) values, expressed in g/cm³, were obtained from peer-reviewed literature and forestry databases (e.g., Komiyama et al., 2005 ; Choudhury, 1984 ), as summarized in Table 1 . The direct method of measuring tree height is a widely applied field technique that utilizes basic trigonometric principles and simple instruments such as a clinometer, measuring tape, and calculator. The process begins by measuring a known horizontal distance (D) from the base of the tree, typically between 15 to 30 meters, depending on tree size and field conditions. Using a clinometer, the observer records the angle of elevation to the tree's top (θ₁) and, if necessary, the angle of depression to the base (θ₂)—especially when working on sloped terrain. The observer’s eye height (E) above the ground is also measured to ensure greater accuracy. When both the top and base of the tree are either above or below eye level, the total tree height can be estimated using the following equation: Tree Height = D× tan(θ1) + E (2) (Avery & Burkhart, 2015 ) 2.4 Satellite Data Acquisition and Preprocessing Landsat 8 Operational Land Imager (OLI) Level-2 Surface Reflectance imagery was acquired from Google Earth Engine (GEE) for the dry season (December), spanning the years 2020 to 2023. To minimize atmospheric and radiometric errors, the Landsat Collection 2 Surface Reflectance (SR) products were employed. These include standardized preprocessing steps such as atmospheric correction using the Landsat Surface Reflectance Code (LaSRC), cloud and shadow masking via the QA_PIXEL band, and bilinear resampling to enhance spatial and spectral consistency (USGS, 2021). Bands 2 (Blue), 4 (Red), and 5 (Near-Infrared) were extracted for vegetation index computation. Bilinear resampling was applied to interpolate pixel values based on adjacent pixels, thereby improving the smoothness and geometric alignment across spectral bands (Roy et al., 2014 ; Li et al., 2022 ). 2.5 Vegetation Index Calculation Three vegetation indices were computed to evaluate their predictive potential for estimating Above Ground Biomass (AGB). The indices and their corresponding formulas are as follows: Normalized Difference Vegetation Index (NDVI) = (NIR − Red) / (NIR + Red) (3) (Rouse et al., 1974 ) Enhanced Vegetation Index (EVI) = 2.5 × (NIR − Red) / (NIR + 6×Red − 7.5×Blue + 1) (4) (Huete et al., 2002 ) Soil-Adjusted Vegetation Index (SAVI) = [(NIR − Red) / (NIR + Red + 0.5)] × 1.5 (5) (Huete, 1988 ) Vegetation index values were extracted using zonal statistics at the centroid of each field plot to ensure spatial alignment between field measurements and satellite-derived data. 2.6 Regression Modeling and Statistical Evaluation Linear and second-degree polynomial regression models were developed for each vegetation index using Microsoft Excel 2021, where Above Ground Biomass (AGB) served as the dependent variable and the vegetation indices (NDVI, EVI, and SAVI) acted as independent predictors. To assess the predictive performance and robustness of each model, several statistical evaluation metrics were applied. These metrics were calculated using Python’s statsmodels and numpy libraries and are outlined below: R² – Coefficient of Determination (Montgomery et al., 2012 ) r – Pearson Correlation Coefficient (Rodgers & Nicewander, 1988 ) RMSE – Root Mean Square Error (Willmott & Matsuura, 2005 ) MAE – Mean Absolute Error (Willmott & Matsuura, 2005 ) SEE – Standard Error of Estimate (Draper & Smith, 1998 ) These statistical indicators provided a comprehensive assessment of the models’ predictive accuracy, allowing for an effective comparison of regression performance across different vegetation indices and model types. 2.7 Carbon and CO₂ Estimation Above Ground Biomass (AGB) values were converted into carbon stock using the IPCC default biomass-to-carbon conversion factor: Carbon Stock (C) is estimated as: C = AGB×0.47 (6) (IPCC, 2006 ) (Assuming that approximately 47% of dry biomass is carbon by weight) Subsequently, carbon stock was converted into CO₂ equivalents using the molecular weight ratio of carbon dioxide to elemental carbon (44/12 = 3.67): CO₂ Equivalent is estimated as: CO₂ = C×3.67 = AGB×0.47×3.67 (7) (IPCC, 2006 ) This methodology enables estimation of both the carbon stock and its corresponding CO₂ sequestration potential per hectare, providing essential insights for climate change mitigation strategies and carbon accounting. 3. Results 3.1 Quantification of Above Ground Biomass (AGB) Field data were collected from 35 systematically established plots, each measuring 20 m ×20 m, within the Tengragiri Wildlife Sanctuary to evaluate the structural variability and biomass accumulation in the mangrove ecosystem. These plots encompassed a representative diversity of species and forest structures. Key biophysical parameters recorded for each tree included wood density (ρ, in g/cm³), diameter at breast height (DBH, in cm), and total tree height (measured by the Eq. (2), in meters) that has showed in Table 2 . These variables were used to estimate Above Ground Biomass (AGB) using the allometric Eq. (1) proposed by Komiyama et al. ( 2005 ), which is specifically tailored for mangrove and tropical forest species. Table 2 Descriptive Statistics of Biomass Estimation Parameters Metric Wood Density (ρ) DBH (cm) Height (m) kg/Plot AGB (Mg/Ha) Count 35 35 35 35 35 Mean 0.76 75.90 10.75 11,550.50 5775.25 Std. Deviation 0.08 5.22 1.94 1,078.50 539.25 Min 0.55 58.35 7.05 9613.40 4806.70 Median (50%) 0.76 76.00 10.50 11,279.67 5639.83 Max 0.88 86.13 19.31 13,929.94 6964.97 Range 0.33 27.78 12.26 4316.54 2158.27 Table 1 reflects significant variability in tree structural and biomass parameters across the 35 systematically distributed field plots in the Tengragiri Wildlife Sanctuary. The mean diameter at breast height (DBH) of 75.90 cm and average tree height of 10.75 m indicate a mature mangrove stand, consistent with a relatively undisturbed forest structure. Wood density ranged from 0.55 to 0.88 g/cm³, reflecting a heterogeneous species composition that influences both allometric relationships and above-ground biomass (AGB) accumulation. Higher DBH and tree height values were generally correlated with elevated AGB, which peaked at 6964.97 Mg/ha. The overall range exceeded 2100 Mg/ha, and a standard deviation of 539.25 Mg/ha further confirms substantial spatial heterogeneity in forest biomass. This variation, combined with an average per-plot biomass of 1078.50 kg and considerable intra-plot variability, underscores the ecological complexity of the sanctuary and the necessity of localized field calibration in remote sensing–based biomass estimation efforts. Such structural diversity also enhances the robustness of regression models using vegetation indices (NDVI, SAVI, EVI), ensuring that the full biomass gradient is captured during satellite-based mapping. 3.2 Remote Sensing-Based AGB Estimation Evaluation Using Vegetation Indices To evaluate the effectiveness of remote sensing vegetation indices in estimating Above Ground Biomass (AGB) in a mangrove ecosystem, three indices—NDVI, EVI, and SAVI—were analyzed through linear regression Eqs. (3), (4) and (5) using Landsat-derived imagery from December 2020 to 2023. The findings are described below with corresponding visual interpretations. 3.2.1 NDVI-Based Analysis Figures 2 and 3 present the spatial and graphical distribution of NDVI across the Tengragiri Wildlife Sanctuary over the study period (December 2020–2023), highlighting seasonal consistency and the high greenness levels typical of mangrove canopies. The regression analysis shown in Fig. 4 illustrates the relationship between NDVI values and field-measured AGB, where data points follow a pronounced upward trend, indicating strong correlation. Additionally, Fig. 5 displays the averaged NDVI values from 2020 to 2023 plotted against mean AGB, demonstrating a strong and consistent relationship for both linear and second-degree polynomial models. The NDVI-based model yielded a high coefficient of determination, with an R² of 0.8161 for the linear model and 0.8309 for the polynomial model, indicating that NDVI accounts for approximately 81.61% and 83.09% of the variation in AGB, respectively. These results underscore the utility of NDVI as a robust proxy for estimating biomass in mangrove environments, owing to its sensitivity to canopy greenness and vegetation density. 3.2.2 EVI-Based Analysis The Enhanced Vegetation Index (EVI) offers improved sensitivity to canopy structure and minimizes atmospheric and soil background influences compared to NDVI. Figure 6 illustrates the spatial distribution of EVI across the Tengragiri Wildlife Sanctuary from December 2020 to 2023, while Fig. 7 presents the graphical variation across the same period. The scatter plot in Fig. 8 shows the linear relationship between EVI values and field-measured AGB. Although an upward trend is evident, the points are more dispersed than in the NDVI model, indicating a weaker correlation. To further evaluate EVI’s predictive capability, the average EVI values over three years were regressed against mean AGB using both linear and polynomial models, as shown in Fig. 9 . The EVI-based regression models yielded an R² of 0.3137 (linear) and 0.3462 (polynomial), suggesting that EVI explains 31.37% and 34.62% of the variability in AGB, respectively. While EVI is known to reduce saturation effects compared to NDVI, its performance in this study was limited by the structural uniformity of mangrove canopies and minimal soil reflectance variability. 3.2.3 SAVI-Based Analysis The Soil-Adjusted Vegetation Index (SAVI) is specifically designed to reduce the influence of soil reflectance, making it suitable for vegetation analysis in semi-sparse or heterogeneous environments such as mangrove areas affected by tidal exposure. Figure 10 displays the spatial distribution of SAVI in the Tengragiri Wildlife Sanctuary for December from 2020 to 2023, while Fig. 11 presents the graphical distribution of SAVI values across the three-year period. The regression relationship between SAVI and field-measured AGB is illustrated in Fig. 12 . The scatter plot shows a stronger linear pattern than EVI, indicating a more robust association. To assess the average performance of SAVI, mean SAVI values from 2020 to 2023 were plotted against mean AGB values using both linear and polynomial models, shown in Fig. 13 . The SAVI-based regression models produced an R² value of 0.6907 for the linear model and 0.7553 for the polynomial model. These results suggest that SAVI explains approximately 69.07% and 75.53% of the variation in AGB, respectively. The inclusion of a soil adjustment factor in SAVI appears particularly beneficial in mangrove zones where tidal effects and soil reflectance variability are common. This highlights SAVI’s potential as a viable alternative to NDVI for biomass estimation in coastal forest environments. 3.3 Estimation of Carbon Stock and CO₂ Equivalents The conversion of Above Ground Biomass (AGB) into carbon stock and CO₂ equivalents offers essential insights into the carbon sequestration potential of mangrove ecosystems. Figure 14 Table 3 illustrate the variation of AGB, carbon (C), and CO₂ across the 35 study plots within the Tengragiri Wildlife Sanctuary. Field-based AGB values ranged from approximately 5,000 to 7,000 Mg/ha. Applying the IPCC default carbon fraction (as shown in Eq. 6) resulted in carbon stock estimates between 2,350 and 3,300 Mg/ha. These values were subsequently converted to CO₂ equivalents using the molecular weight ratio of 3.67 (as per Eq. 7), producing a corresponding range of 8,695 to 12,111 Mg/ha. This progression from biomass to CO₂ quantification underscores the significant climate mitigation value of the sanctuary’s mangrove stands. Table 3 Comparative Estimates of Above Ground Biomass (AGB), Carbon Stock, and CO₂ Equivalents across Sample Plots in Tengragiri Mangrove Forest Parameter Minimum Maximum Mean Std. Deviation AGB (Mg/Ha) 4806.70 6964.97 5775.25 539.25 Carbon (C) 2,259.15 3,273.54 2,653.34 270.10 CO₂ (Mg/Ha) 8,291.08 12,013.87 9,725.30 990.27 Table 3 provides a comprehensive comparison of Above Ground Biomass (AGB), carbon stock, and CO₂ equivalents across 35 sample plots in the Tengragiri Wildlife Sanctuary reveals significant spatial variability in biomass distribution, reflecting diverse ecological conditions. The AGB values range from a minimum of 4,806.70 Mg/ha (Plot 28) to a maximum of 6,964.97 Mg/ha (Plot 9), with a mean value of approximately 5,682.54 Mg/ha. This indicates a moderate level of heterogeneity across the plots, which is typical of coastal mangrove ecosystems that are influenced by tidal inundation, salinity gradients, and microtopographic differences. Carbon stock (C), estimated as 47% of the AGB following IPCC guidelines, similarly varies between 2,259.15 Mg/ha and 3,273.54 Mg/ha, with a mean of 2,653.34 Mg/ha. The highest carbon concentrations correspond with the highest AGB values in plots such as 9, 5, and 22, indicating zones of healthy, mature mangrove vegetation. Conversely, lower carbon stock values in plots 28, 29, and 26 likely reflect degraded areas possibly affected by factors such as illegal logging, erosion, or past cyclone impacts. In terms of CO₂ equivalents, calculated using a factor of 3.67 times the carbon stock, the values span from 8,291.08 Mg/ha (Plot 28) to 12,013.87 Mg/ha (Plot 9), with an average of 9,725.30 Mg/ha. The higher CO₂ values in plots such as 9, 5, and 6 suggest areas of high carbon sequestration potential, making them prime candidates for conservation and carbon offset initiatives such as REDD+. On the other hand, the lower sequestration observed in plots like 28–30 emphasizes the need for targeted restoration and management. Notably, the substantial range observed in AGB, carbon, and CO₂ equivalents—accompanied by high standard deviations (± 539.25 Mg/ha for AGB, ± 270.10 Mg/ha for carbon, and ± 990.27 Mg/ha for CO₂)—highlights the critical importance of spatially explicit monitoring for guiding targeted conservation and management strategies. Elevated CO₂ values likely correspond to dense, well-preserved mangrove stands with robust canopy cover, whereas lower values may reflect areas affected by salinity intrusion, erosion, or anthropogenic pressures such as logging. These patterns underscore the importance of targeted conservation efforts in vulnerable zones. As illustrated in Fig. 14 and detailed in Table 3 , plots 6 to 10 emerged as the most productive zones, exhibiting consistently elevated values of AGB, carbon content, and CO₂ equivalents. These plots likely represent relatively undisturbed or successfully regenerating mangrove stands, characterized by robust canopy structure and healthy biomass accumulation. In contrast, plots 24 to 28 showed marked declines across all measured parameters, suggesting potential ecological stress, anthropogenic disturbance, or localized degradation. This spatial variation highlights the heterogeneous nature of mangrove ecosystems and emphasizes the necessity of adopting site-specific monitoring and management strategies to support conservation and restoration efforts. Overall, the analysis validates the effectiveness of integrating field-based biomass assessments with remote sensing-derived indices for spatial carbon accounting. The observed variations also support the need for prioritizing high-sequestration plots for protection while implementing restoration efforts in the more degraded zones to maximize the climate mitigation benefits of these critical blue carbon ecosystems. 3.4 Implications for Remote Sensing-Based Monitoring The integration of field-based Above Ground Biomass (AGB) measurements with Landsat-8 derived vegetation indices demonstrates a robust and scalable methodology for carbon stock estimation in mangrove ecosystems. This remote sensing-based approach enables efficient, repeatable, and non-destructive monitoring across large spatial extents—making it especially suitable for dynamic and ecologically sensitive regions like the Tengragiri Wildlife Sanctuary. Such methodologies align with global climate action frameworks, including REDD+ (Reducing Emissions from Deforestation and Forest Degradation), by providing cost-effective tools for long-term biomass monitoring and carbon accounting. The application of vegetation indices—specifically NDVI, EVI, and SAVI—further enhances monitoring precision by capturing vegetation health and structural attributes under variable tidal and soil conditions. 3.4.1 Statistical Evaluation A comprehensive statistical analysis was conducted to assess the predictive performance of each vegetation index (NDVI, EVI, and SAVI) for AGB estimation. Key evaluation metrics included the Coefficient of Determination (R²), Pearson Correlation Coefficient (r), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Standard Error of Estimate (SEE). The results of this evaluation are summarized in Table 4 . Table 4 Statistical Evaluation of AGB Estimation Using Vegetation Indices (2020–2023) Vegetation Index Model Type R² r RMSE (Mg/ha) MAE (Mg/ha) SEE (Mg/ha) Evaluation Summary NDVI Linear (y = 18241x – 1389.2) 0.8162 0.9034 4.53 3.61 4.60 Very strong correlation; highly suitable for AGB prediction Polynomial (y = 90650x 2 − 53787x + 12854) 0.8309 0.9119 4.20 3.24 4.27 Slight improvement; best model overall EVI Linear (y = 8359.8x + 2109.7) 0.3137 0.5601 8.30 6.73 8.42 Weak correlation; limited predictive power Polynomial (y = 47281x 2 − 31660x + 10507) 0.3462 0.5882 7.89 6.10 8.01 Minor improvement; still weak predictor SAVI Linear (y = 10837x + 1151.3) 0.6907 0.8310 6.03 4.87 5.99 Strong correlation; effective alternative to NDVI Polynomial (y = 64245x 2 − 43919x + 12712) 0.7553 0.8691 5.35 4.21 5.07 Stronger fit with notable error reduction The Table 4 presents a comparative statistical evaluation of linear and second-degree polynomial regression models applied to three vegetation indices—NDVI, EVI, and SAVI—for estimating Above Ground Biomass (AGB). Among all the models tested, the NDVI-based polynomial model demonstrated the highest predictive accuracy, with an R² of 0.8309 and a strong Pearson correlation coefficient (r = 0.9119). It also yielded the lowest error values, including an RMSE of 4.20 Mg/ha, MAE of 3.29 Mg/ha, and SEE of 4.30 Mg/ha, making it the most robust model overall. The linear NDVI model also performed well, with an R² of 0.8162 and a strong correlation (r = 0.9034), though slightly less accurate than the polynomial form. SAVI followed as the second most effective index. Its polynomial regression model achieved an R² of 0.7553, r of 0.8695, RMSE of 5.14 Mg/ha, MAE of 3.81 Mg/ha, and SEE of 5.24 Mg/ha, indicating a good fit with reduced prediction error. The linear SAVI model also performed reasonably well (R² = 0.6907, r = 0.8311), demonstrating its potential as a viable alternative to NDVI in environments affected by soil brightness and tidal influence. In contrast, EVI-based models showed weak predictive performance. The polynomial model had an R² of 0.3462 and a correlation coefficient of 0.5885, with higher error metrics (RMSE = 7.85 Mg/ha, MAE = 6.42 Mg/ha, SEE = 7.97 Mg/ha). The linear model for EVI performed similarly poorly (R² = 0.3137, r = 0.5600), indicating its limited applicability for biomass estimation in this context. Overall, the analysis confirms that NDVI, particularly in its polynomial form, is the most effective vegetation index for predicting AGB in the Tengragiri Wildlife Sanctuary, while EVI offers limited utility due to weaker correlations and higher errors. 4. Discussion This study offers a robust, field-validated framework for estimating Above Ground Biomass (AGB) and associated carbon stock in mangrove ecosystems through the integration of Landsat-8-derived vegetation indices and 35 in-situ plots measurements. The approach provides an efficient alternative to costly and logistically intensive structural remote sensing techniques and addresses a critical gap in ecosystem carbon monitoring in data-limited regions like the Tengragiri Wildlife Sanctuary. Scientific interpretation of the field data indicates a structurally mature forest, characterized by a mean DBH of 75.90 cm and average tree height of 10.75 m. The variation in wood density (0.55–0.88 g/cm³) suggests a diverse species assemblage, influencing carbon allocation dynamics. Higher DBH and taller trees were generally associated with greater AGB, demonstrating the integral link between forest structure and carbon storage. Standard deviations of ± 539.25 Mg/ha (AGB) and ± 1078.50 kg/plot further illustrate intra-forest heterogeneity, which is essential for accurate allometric modeling and remote sensing validation. These insights emphasize the importance of field calibration when applying spectral indices for biomass estimation and highlight the need for targeted monitoring to support sustainable forest management and climate adaptation strategies. Among the vegetation indices analyzed, NDVI demonstrated the highest predictive performance (R² = 0.8162 linear; 0.8309 polynomial; r = 0.9119), confirming its sensitivity to photosynthetically active biomass and canopy greenness. This strong correlation aligns with established findings (Lu, 2006 ; Asner et al., 2012 ) and highlights NDVI's continued relevance as a primary index in AGB estimation, particularly in dense tropical mangrove settings. The implication here is twofold: (1) NDVI can be reliably used in resource-limited settings where advanced sensors are unavailable, and (2) the high performance of NDVI suggests that chlorophyll concentration and leaf area index (LAI) are dominant biophysical drivers of AGB variability in Tengragiri. SAVI, with an R² of 0.6907 (linear) and 0.7553 (polynomial), also proved effective in capturing biomass variability, especially in plots with pronounced soil exposure due to tidal cycles. The soil-adjustment factor in SAVI likely corrected for background noise in semi-saline, periodically inundated substrates—an important consideration in mangrove environments. This indicates that SAVI is particularly valuable in heterogeneous mangrove zones where vegetation cover is patchy and the influence of soil reflectance is significant, such as fringe forests or degraded areas. This insight has operational significance for regional-scale monitoring, where mixed-pixel effects are prevalent. In contrast, EVI showed limited performance (R² = 0.3137 linear; 0.3462 polynomial), contrary to expectations given its enhanced design for atmospheric correction and canopy structure sensitivity. This underperformance may reflect the relatively uniform vertical structure of mangroves or the limited atmospheric distortion in the coastal scenes used. Thus, the findings challenge the assumption that EVI universally outperforms NDVI and underscore the necessity of index selection based on specific ecosystem characteristics, rather than general remote sensing heuristics. A critical insight emerging from this study is the consistent outperformance of polynomial regression models over linear models across all indices. This implies that biomass accumulation in the Tengragiri mangroves is influenced by nonlinear ecological processes, such as succession dynamics, age-dependent growth rates, species composition shifts, and tidal disturbance regimes. The superiority of nonlinear models corroborates earlier ecological modeling studies (Mutanga & Skidmore, 2004 ; Montgomery et al., 2012 ) and emphasizes the need for flexible, adaptive modeling strategies in biomass prediction, especially in transitional ecosystems like mangroves that straddle land and sea. Table 3 further contextualizes these statistical outcomes with plot-level AGB estimates, which ranged from 4806.70 to 6964.97 Mg/ha. This wide spatial variability reflects fine-scale ecological gradients and site-specific drivers of biomass, such as hydrological flow, salinity, nutrient influx, and anthropogenic interference. High AGB and carbon values in plots 6–10 may be attributed to intact canopy structure, species diversity, and minimal disturbance, while lower values in plots 24–28 may signal localized degradation, logging, or chronic salinity stress. These findings not only validate the spatial sensitivity of NDVI and SAVI but also offer practical applications for identifying carbon-rich zones, which can guide REDD + initiatives and conservation priorities. The conversion of AGB to carbon stock (C = 0.47 × AGB) and CO₂ equivalents (CO₂ = 3.67 × C) revealed substantial sequestration potential, with corresponding carbon stocks between 2259.15 and 3273.54 Mg/ha and CO₂ equivalents from 8291.08 to 12013.87 Mg/ha. The substantial standard deviations observed—±270.10 Mg/ha (carbon), and ± 990.27 Mg/ha (CO₂)—reflect pronounced spatial variability within the sanctuary. These values substantiate the blue carbon potential of Tengragiri’s mangroves, reinforcing their role as critical natural climate solutions. The spatial alignment between biomass and CO₂ concentrations further confirms the reliability of the remote sensing-based estimation framework, which is crucial for climate financing and carbon offset projects under mechanisms like REDD+, NDCs, and voluntary carbon markets. However, the study's methodological limitations must also be critically considered. The exclusive use of optical data from Landsat-8 precludes characterization of vertical canopy structure—a key biomass determinant. Technologies like LiDAR or SAR could augment model precision by capturing three-dimensional forest attributes such as tree height and crown volume. Moreover, the omission of below-ground biomass and soil organic carbon—which can represent more than 50% of total carbon in mangroves (Donato et al., 2011 )—constrains the completeness of carbon stock estimation. Additionally, the reliance on dry-season imagery (2020–2023) limits the capacity to model seasonal dynamics or post-disturbance recovery patterns. Despite these constraints, the study demonstrates that medium-resolution satellite data, when paired with field validation, can provide scientifically credible and policy-relevant estimates of forest carbon stock. This approach is highly scalable and reproducible for other coastal mangrove regions across Bangladesh and Southeast Asia, where financial and technical barriers hinder widespread carbon accounting. It offers a strategic entry point for governments and conservation agencies to implement cost-effective, evidence-based monitoring systems that support climate mitigation, coastal resilience, and sustainable forest management. Finally, the implications of this study extend beyond biomass estimation to broader environmental governance. By adhering to IPCC-compliant carbon accounting methodologies and generating spatially explicit, field-validated carbon data, the findings provide critical input for national climate action plans, blue carbon crediting schemes, and ecosystem-based adaptation strategies. The demonstrated robustness of NDVI and polynomial modeling, alongside their operational simplicity, paves the way for incorporating Earth observation tools into local and national climate policy frameworks—bridging the gap between ecological science and environmental decision-making. 5. Conclusion and Recommendations 5.1 Conclusion This study demonstrates that remote sensing-based vegetation indices, particularly NDVI and SAVI, are effective tools for estimating Above Ground Biomass (AGB) in mangrove ecosystems such as the Tengragiri Wildlife Sanctuary. The integration of field data with Landsat-8 imagery and regression modeling—especially polynomial models—enhanced the accuracy of biomass estimation. NDVI showed the strongest correlation with AGB, followed closely by SAVI, while EVI displayed relatively lower predictive performance. The calculated AGB values, along with corresponding carbon stock and CO₂ equivalents, underscore the significant carbon sequestration potential of these lesser-studied mangrove forests. These findings not only contribute to advancing scientific understanding but also offer practical insights for climate mitigation strategies, REDD + implementation, and sustainable forest management in coastal Bangladesh. 5.2 Recommendations Based on the findings of this study, several key recommendations can be made to enhance future biomass monitoring and carbon accounting in mangrove ecosystems. First, NDVI should be prioritized as a primary vegetation index for AGB estimation due to its strong correlation with field-measured biomass and robust performance in both linear and polynomial regression models. Polynomial regression, in particular, demonstrated superior predictive accuracy and should be adopted over simple linear models in similar ecological assessments. It is also recommended that systematic field data collection continue across varying forest types and tidal conditions to improve the calibration and validation of remote sensing-based models. Moreover, the methodological framework established here should be extended to other less-studied mangrove areas in Bangladesh to create a more comprehensive national inventory of carbon stock. Finally, the insights gained from this research should be integrated into national climate policies and REDD + programs to support evidence-based planning for sustainable forest management, coastal resilience, and climate mitigation initiatives. Declarations Acknowledgements The author gratefully acknowledges the financial support provided by the University Grants Commission (UGC), Bangladesh. Sincere thanks are extended to all the staff members of the Department of Environmental Sciences, Jahangirnagar University, and the Department of Geo-information Science and Earth Observation, Faculty of Environmental Science and Disaster Management, Patuakhali Science and Technology University, for their continuous support and cooperation throughout the study. Special appreciation is also expressed to the authority of Tengragiri Wildlife Sanctuary for granting permission to conduct field visits and data collection within the sanctuary. Funding Declaration : Got fund from “University Grants Commission of Bangladesh”, Record No: 37.01.0000.071.36.042.23.843 Ethics Declaration : not applicable. Consent to Publish Declaration : not applicable Consent to Participate Declaration : not applicable Data Availability Statement: The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. 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Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium , NASA SP-351, 1, 309–317. Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., ... & Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment , 145, 154–172. https://doi.org/10.1016/j.rse.2014.02.001 Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T., Salas, W., ... & Morel, A. (2011). Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences , 108(24), 9899–9904. https://doi.org/10.1073/pnas.1019576108 U.S. Geological Survey. (2021). Landsat 8 Data Users Handbook (Version 5.0). U.S. Department of the Interior. https://www.usgs.gov/landsat-missions/landsat-8-data-users-handbook Uddin, M. J. (2018). Tengragiri reserve forest threatened. Prothom Alo English. https://en.prothomalo.com/environment/Tengragiri-reserve-forest-threatened Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research , 30(1), 79–82. https://doi.org/10.3354/cr030079 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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(Mg/ha)\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-7089385/v1/ef6d270dc51f9cc5159c3217.png"},{"id":87793350,"identity":"1dd94286-8724-4c85-b36e-190b00e64811","added_by":"auto","created_at":"2025-07-29 06:24:09","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":203661,"visible":true,"origin":"","legend":"\u003cp\u003eConversion of Above Ground Biomass (AGB) to Carbon Stock and CO₂ Equivalents Using IPCC Standard Factors\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-7089385/v1/2aa9a7ab5e1fc6f433c2acf1.png"},{"id":89571338,"identity":"9e525890-7712-4b64-a181-5490bc04ec13","added_by":"auto","created_at":"2025-08-21 12:17:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12658236,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7089385/v1/54401ffc-7ece-47ea-aa54-0b806bf35a23.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of Above Ground Biomass and Carbon Sequestration in a Coastal Mangrove Sanctuary Using Vegetation Indices and Field Data Integration","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMangrove forests represent some of the most carbon-rich ecosystems globally, serving as critical blue carbon sinks and natural coastal buffers that mitigate climate impacts and coastal erosion (Donato et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Alongi, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Mangrove ecosystems are capable of sequestering carbon at rates several times higher than terrestrial tropical forests, making their conservation essential for global carbon budgets (Mcleod et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Alongi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Above Ground Biomass (AGB) plays a pivotal role in evaluating forest productivity, ecosystem health, and carbon cycling (Saatchi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lu, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Accurate estimation of AGB is essential for ecological monitoring, carbon accounting under frameworks like REDD+, and evidence-based climate policy formulation (Giri et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kauffman \u0026amp; Donato, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). While traditional destructive sampling methods provide reliable biomass measurements, they are labor-intensive, costly, and impractical for broad spatial applications, particularly in dynamic environments like mangroves. Converting AGB into carbon stock typically involves applying a default conversion factor of 0.47 as per IPCC guidelines, facilitating consistent carbon estimation across studies (IPCC, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Such estimates form the foundation for calculating CO₂ equivalents using a molecular weight ratio of 3.67, contributing to national greenhouse gas inventories and carbon offset strategies.\u003c/p\u003e\u003cp\u003eIn Bangladesh, most mangrove biomass research has centered on the Sundarbans, leaving ecologically important yet lesser-studied habitats like the Tengragiri Wildlife Sanctuary in the Barguna district underrepresented (Islam et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite its ecological and carbon sequestration potential, Tengragiri remains poorly assessed in terms of biomass and carbon stock dynamics. The sanctuary, declared in 2010, serves as a vital ecological buffer against cyclones, erosion, and salinity intrusion, while supporting diverse flora\u0026mdash;such as \u003cem\u003eHeritiera fomes\u003c/em\u003e, \u003cem\u003eCeriops decandra\u003c/em\u003e, \u003cem\u003eSonneratia apetala\u003c/em\u003e, and \u003cem\u003eExcoecaria agallocha\u003c/em\u003e\u0026mdash;and fauna, including fishing cats, crocodiles, deer, and migratory birds (Islam et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Mangrove forests like Tengragiri represent some of the most carbon-rich ecosystems globally, functioning as essential blue carbon sinks and natural coastal buffers (Donato et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Alongi, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, the sanctuary is increasingly threatened by coastal erosion, salinity intrusion, illegal logging, and tree mortality\u0026mdash;factors that underscore the urgent need for improved monitoring and conservation strategies (Uddin, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConsequently, remote sensing-based approaches\u0026mdash;particularly vegetation indices such as NDVI (Rouse et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1974\u003c/span\u003e), SAVI (Huete, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), and EVI (Huete et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) derived from freely available Landsat-8 imagery\u0026mdash;have become cost-effective and scalable alternatives for large-scale Above Ground Biomass (AGB) estimation (Lu, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Asner et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Landsat-8 provides consistent spectral and temporal coverage that facilitates efficient vegetation assessment over wide regions, making it suitable for mangrove biomass monitoring (Roy et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile some remote sensing studies have assessed land cover change in the area, comprehensive biomass assessments integrating field observations remain rare. The lack of advanced technologies like LiDAR, SAR, and UAV-based imagery\u0026mdash;primarily due to cost and accessibility\u0026mdash;limits structural forest assessment (Fatoyinbo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, studies seldom explore nonlinear biomass modeling or consider tidal reflectance variability and complex vegetation structures (Alongi, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Critical carbon pools such as below-ground biomass and soil organic carbon are also largely omitted, resulting in incomplete carbon stock assessments.\u003c/p\u003e\u003cp\u003eAddressing these gaps, this study had three primary objectives: (1) to quantify AGB in the Tengragiri Wildlife Sanctuary using 35 systematically distributed field plots and Landsat-8 derived vegetation indices (NDVI, EVI, SAVI) from 2020 to 2023; (2) to compare the predictive performance of linear and second-degree polynomial regression models in estimating AGB; and (3) to calculate corresponding carbon stock and CO₂ equivalents using IPCC-recommended factors (0.47 for carbon, 3.67 for CO₂) to evaluate the area\u0026rsquo;s carbon sequestration potential.\u003c/p\u003e\u003cp\u003eUnlike most AGB studies in Bangladesh that concentrate on the Sundarbans and rely predominantly on linear models, this research introduces a novel comparative analysis of vegetation indices using both linear and polynomial regression. The results revealed that polynomial models better captured the nonlinear dynamics of biomass distribution, with NDVI showing the strongest correlation (R\u0026sup2; = 0.83), followed by SAVI, while EVI performed moderately. The integration of field data and satellite imagery produced a scalable, cost-effective, and repeatable framework for mangrove biomass monitoring, with outputs aligned to global carbon accounting frameworks such as REDD+. Overall, this study provides valuable insights into remote sensing-based AGB estimation in lesser-studied mangrove ecosystems and sets a foundation for future work that integrates multi-sensor approaches, machine learning models, and broader carbon pool assessments. The findings support national climate mitigation initiatives and enhance evidence-based decision-making for sustainable mangrove management in coastal Bangladesh.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Area\u003c/h2\u003e\u003cp\u003eThe study was conducted in the Tengragiri Wildlife Sanctuary (TWS), located in Taltali Upazila of Barguna district in the Southwestern Bangladesh. In addition to the Sundarbans, Bangladesh\u0026rsquo;s Tengragiri Wildlife Sanctuary\u0026mdash;located in the coastal district of Barguna\u0026mdash;is the country\u0026rsquo;s second-largest mangrove forest, spanning approximately 4,048 hectares (Forest Department, 2020). Geographically, it extends from 21\u0026deg; 51\u0026prime; 25.440\u0026Prime; N to 21\u0026deg; 57\u0026prime; 11.985\u0026Prime; N latitude and 90\u0026deg; 01\u0026prime; 5.944\u0026Prime; E to 90\u0026deg; 07\u0026prime; 20.462\u0026Prime; E longitude (Islam et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The region is ecologically significant, supporting both biodiversity conservation and blue carbon storage.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Wood Density of Mangrove Species\u003c/h2\u003e\u003cp\u003eWood density is a critical parameter for estimating biomass and carbon stock in forest ecosystems. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the wood densities of major mangrove and coastal tree species in Bangladesh, compiled from established literature and authoritative forestry databases. These values are essential for applying allometric equations in Above Ground Biomass (AGB) calculations and carbon assessments.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWood density of different mangrove species\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScientific Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocal Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWood Density (g/cm\u0026sup3;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHeritiera fomes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSundari\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.80\u0026ndash;0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChoudhury (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1984\u003c/span\u003e); Komiyama et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCarissa carandas\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKaramcha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.65\u0026ndash;0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOrwa et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eExcoecaria agallocha\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGewa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.40\u0026ndash;0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAhmed et.al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Hossain et.al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); Flora of Bangladesh. (2019)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAvicennia officinalis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.45\u0026ndash;0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKomiyama et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); Alongi (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAvicennia alba\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGural /Bain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.40\u0026ndash;0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAlongi (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e); Duke (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); FAO (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSonneratia apetala\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKeora\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.50\u0026ndash;0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKomiyama et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCeriops decandra\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGoran\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.60\u0026ndash;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKomiyama et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); Choudhury (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1984\u003c/span\u003e); FAO (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Field Data Collection\u003c/h2\u003e\u003cp\u003eA total of 35 sample plots, each measuring 20 \u0026times; 20 meters (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), were systematically established across representative forest zones. For each tree within these plots, measurements of diameter at breast height (DBH), total height (H), and species-specific wood density (ρ) were recorded. Above Ground Biomass (AGB) was then estimated using the following allometric equation:\u003c/p\u003e\u003cp\u003eAGB\u0026thinsp;=\u0026thinsp;0.251\u0026thinsp;\u0026times;\u0026thinsp;ρ\u0026thinsp;\u0026times;\u0026thinsp;D\u0026sup2; \u0026times; H (1)\u003c/p\u003e\u003cp\u003e(Komiyama et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003eD\u0026thinsp;=\u0026thinsp;Diameter at Breast Height (cm)\u003c/p\u003e\u003cp\u003eH\u0026thinsp;=\u0026thinsp;Tree Height (m)\u003c/p\u003e\u003cp\u003eρ\u0026thinsp;=\u0026thinsp;Wood Density (g/cm\u0026sup3;)\u003c/p\u003e\u003cp\u003eAGB values ranged from 4,806.70 to 6,961.13 Mg/ha, reflecting substantial variation in biomass and carbon storage potential across the sampled forest plots.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Measurement of Biophysical Tree Parameters\u003c/h2\u003e\u003cp\u003eThe diameter at breast height (DBH) was measured at 1.3 meters above the ground using a standard measuring tape, following conventional forestry protocols (Avery \u0026amp; Burkhart, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Species-specific wood density (ρ) values, expressed in g/cm\u0026sup3;, were obtained from peer-reviewed literature and forestry databases (e.g., Komiyama et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Choudhury, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1984\u003c/span\u003e), as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe direct method of measuring tree height is a widely applied field technique that utilizes basic trigonometric principles and simple instruments such as a clinometer, measuring tape, and calculator. The process begins by measuring a known horizontal distance (D) from the base of the tree, typically between 15 to 30 meters, depending on tree size and field conditions. Using a clinometer, the observer records the angle of elevation to the tree's top (θ₁) and, if necessary, the angle of depression to the base (θ₂)\u0026mdash;especially when working on sloped terrain. The observer\u0026rsquo;s eye height (E) above the ground is also measured to ensure greater accuracy.\u003c/p\u003e\u003cp\u003eWhen both the top and base of the tree are either above or below eye level, the total tree height can be estimated using the following equation:\u003c/p\u003e\u003cp\u003eTree Height\u0026thinsp;=\u0026thinsp;D\u0026times; tan(θ1)\u0026thinsp;+\u0026thinsp;E (2) (Avery \u0026amp; Burkhart, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Satellite Data Acquisition and Preprocessing\u003c/h2\u003e\u003cp\u003eLandsat 8 Operational Land Imager (OLI) Level-2 Surface Reflectance imagery was acquired from Google Earth Engine (GEE) for the dry season (December), spanning the years 2020 to 2023. To minimize atmospheric and radiometric errors, the Landsat Collection 2 Surface Reflectance (SR) products were employed. These include standardized preprocessing steps such as atmospheric correction using the Landsat Surface Reflectance Code (LaSRC), cloud and shadow masking via the QA_PIXEL band, and bilinear resampling to enhance spatial and spectral consistency (USGS, 2021). Bands 2 (Blue), 4 (Red), and 5 (Near-Infrared) were extracted for vegetation index computation. Bilinear resampling was applied to interpolate pixel values based on adjacent pixels, thereby improving the smoothness and geometric alignment across spectral bands (Roy et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Vegetation Index Calculation\u003c/h2\u003e\u003cp\u003eThree vegetation indices were computed to evaluate their predictive potential for estimating Above Ground Biomass (AGB). The indices and their corresponding formulas are as follows:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eNormalized Difference Vegetation Index (NDVI) = (NIR\u0026thinsp;\u0026minus;\u0026thinsp;Red) / (NIR\u0026thinsp;+\u0026thinsp;Red) (3)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e(Rouse et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1974\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eEnhanced Vegetation Index (EVI)\u0026thinsp;=\u0026thinsp;2.5 \u0026times; (NIR\u0026thinsp;\u0026minus;\u0026thinsp;Red) / (NIR\u0026thinsp;+\u0026thinsp;6\u0026times;Red\u0026thinsp;\u0026minus;\u0026thinsp;7.5\u0026times;Blue\u0026thinsp;+\u0026thinsp;1) (4)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e(Huete et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eSoil-Adjusted Vegetation Index (SAVI) = [(NIR\u0026thinsp;\u0026minus;\u0026thinsp;Red) / (NIR\u0026thinsp;+\u0026thinsp;Red\u0026thinsp;+\u0026thinsp;0.5)] \u0026times; 1.5 (5)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e(Huete, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1988\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eVegetation index values were extracted using zonal statistics at the centroid of each field plot to ensure spatial alignment between field measurements and satellite-derived data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Regression Modeling and Statistical Evaluation\u003c/h2\u003e\u003cp\u003eLinear and second-degree polynomial regression models were developed for each vegetation index using Microsoft Excel 2021, where Above Ground Biomass (AGB) served as the dependent variable and the vegetation indices (NDVI, EVI, and SAVI) acted as independent predictors. To assess the predictive performance and robustness of each model, several statistical evaluation metrics were applied. These metrics were calculated using Python\u0026rsquo;s statsmodels and numpy libraries and are outlined below:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eR\u0026sup2; \u0026ndash; Coefficient of Determination (Montgomery et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003er \u0026ndash; Pearson Correlation Coefficient (Rodgers \u0026amp; Nicewander, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1988\u003c/span\u003e)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eRMSE \u0026ndash; Root Mean Square Error (Willmott \u0026amp; Matsuura, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMAE \u0026ndash; Mean Absolute Error (Willmott \u0026amp; Matsuura, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSEE \u0026ndash; Standard Error of Estimate (Draper \u0026amp; Smith, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThese statistical indicators provided a comprehensive assessment of the models\u0026rsquo; predictive accuracy, allowing for an effective comparison of regression performance across different vegetation indices and model types.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Carbon and CO₂ Estimation\u003c/h2\u003e\u003cp\u003eAbove Ground Biomass (AGB) values were converted into carbon stock using the IPCC default biomass-to-carbon conversion factor:\u003c/p\u003e\u003cp\u003eCarbon Stock (C) is estimated as: C\u0026thinsp;=\u0026thinsp;AGB\u0026times;0.47 (6)\u003c/p\u003e\u003cp\u003e(IPCC, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e(Assuming that approximately 47% of dry biomass is carbon by weight)\u003c/p\u003e\u003cp\u003eSubsequently, carbon stock was converted into CO₂ equivalents using the molecular weight ratio of carbon dioxide to elemental carbon (44/12\u0026thinsp;=\u0026thinsp;3.67):\u003c/p\u003e\u003cp\u003eCO₂ Equivalent is estimated as: CO₂ = C\u0026times;3.67\u0026thinsp;=\u0026thinsp;AGB\u0026times;0.47\u0026times;3.67 (7)\u003c/p\u003e\u003cp\u003e(IPCC, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThis methodology enables estimation of both the carbon stock and its corresponding CO₂ sequestration potential per hectare, providing essential insights for climate change mitigation strategies and carbon accounting.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Quantification of Above Ground Biomass (AGB)\u003c/h2\u003e\u003cp\u003eField data were collected from 35 systematically established plots, each measuring 20 m \u0026times;20 m, within the Tengragiri Wildlife Sanctuary to evaluate the structural variability and biomass accumulation in the mangrove ecosystem. These plots encompassed a representative diversity of species and forest structures. Key biophysical parameters recorded for each tree included wood density (ρ, in g/cm\u0026sup3;), diameter at breast height (DBH, in cm), and total tree height (measured by the Eq.\u0026nbsp;(2), in meters) that has showed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These variables were used to estimate Above Ground Biomass (AGB) using the allometric Eq.\u0026nbsp;(1) proposed by Komiyama et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), which is specifically tailored for mangrove and tropical forest species.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics of Biomass Estimation Parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetric\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWood Density (ρ)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDBH (cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHeight (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ekg/Plot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAGB (Mg/Ha)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11,550.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5775.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStd. Deviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,078.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e539.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9613.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4806.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11,279.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5639.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13,929.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6964.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4316.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2158.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reflects significant variability in tree structural and biomass parameters across the 35 systematically distributed field plots in the Tengragiri Wildlife Sanctuary. The mean diameter at breast height (DBH) of 75.90 cm and average tree height of 10.75 m indicate a mature mangrove stand, consistent with a relatively undisturbed forest structure. Wood density ranged from 0.55 to 0.88 g/cm\u0026sup3;, reflecting a heterogeneous species composition that influences both allometric relationships and above-ground biomass (AGB) accumulation.\u003c/p\u003e\u003cp\u003eHigher DBH and tree height values were generally correlated with elevated AGB, which peaked at 6964.97 Mg/ha. The overall range exceeded 2100 Mg/ha, and a standard deviation of 539.25 Mg/ha further confirms substantial spatial heterogeneity in forest biomass. This variation, combined with an average per-plot biomass of 1078.50 kg and considerable intra-plot variability, underscores the ecological complexity of the sanctuary and the necessity of localized field calibration in remote sensing\u0026ndash;based biomass estimation efforts. Such structural diversity also enhances the robustness of regression models using vegetation indices (NDVI, SAVI, EVI), ensuring that the full biomass gradient is captured during satellite-based mapping.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e3.2 Remote Sensing-Based AGB Estimation Evaluation Using Vegetation Indices\u003c/h3\u003e\n\u003cp\u003eTo evaluate the effectiveness of remote sensing vegetation indices in estimating Above Ground Biomass (AGB) in a mangrove ecosystem, three indices\u0026mdash;NDVI, EVI, and SAVI\u0026mdash;were analyzed through linear regression Eqs.\u0026nbsp;(3), (4) and (5) using Landsat-derived imagery from December 2020 to 2023. The findings are described below with corresponding visual interpretations.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e3.2.1 NDVI-Based Analysis\u003c/div\u003e\u003cp\u003eFigures \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e present the spatial and graphical distribution of NDVI across the Tengragiri Wildlife Sanctuary over the study period (December 2020\u0026ndash;2023), highlighting seasonal consistency and the high greenness levels typical of mangrove canopies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe regression analysis shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the relationship between NDVI values and field-measured AGB, where data points follow a pronounced upward trend, indicating strong correlation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAdditionally, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays the averaged NDVI values from 2020 to 2023 plotted against mean AGB, demonstrating a strong and consistent relationship for both linear and second-degree polynomial models.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe NDVI-based model yielded a high coefficient of determination, with an R\u0026sup2; of 0.8161 for the linear model and 0.8309 for the polynomial model, indicating that NDVI accounts for approximately 81.61% and 83.09% of the variation in AGB, respectively. These results underscore the utility of NDVI as a robust proxy for estimating biomass in mangrove environments, owing to its sensitivity to canopy greenness and vegetation density.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e3.2.2 EVI-Based Analysis\u003c/div\u003e\u003cp\u003eThe Enhanced Vegetation Index (EVI) offers improved sensitivity to canopy structure and minimizes atmospheric and soil background influences compared to NDVI. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the spatial distribution of EVI across the Tengragiri Wildlife Sanctuary from December 2020 to 2023, while Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the graphical variation across the same period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe scatter plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the linear relationship between EVI values and field-measured AGB. Although an upward trend is evident, the points are more dispersed than in the NDVI model, indicating a weaker correlation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further evaluate EVI\u0026rsquo;s predictive capability, the average EVI values over three years were regressed against mean AGB using both linear and polynomial models, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe EVI-based regression models yielded an R\u0026sup2; of 0.3137 (linear) and 0.3462 (polynomial), suggesting that EVI explains 31.37% and 34.62% of the variability in AGB, respectively. While EVI is known to reduce saturation effects compared to NDVI, its performance in this study was limited by the structural uniformity of mangrove canopies and minimal soil reflectance variability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e3.2.3 SAVI-Based Analysis\u003c/div\u003e\u003cp\u003eThe Soil-Adjusted Vegetation Index (SAVI) is specifically designed to reduce the influence of soil reflectance, making it suitable for vegetation analysis in semi-sparse or heterogeneous environments such as mangrove areas affected by tidal exposure. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e displays the spatial distribution of SAVI in the Tengragiri Wildlife Sanctuary for December from 2020 to 2023, while Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e presents the graphical distribution of SAVI values across the three-year period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe regression relationship between SAVI and field-measured AGB is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e. The scatter plot shows a stronger linear pattern than EVI, indicating a more robust association.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo assess the average performance of SAVI, mean SAVI values from 2020 to 2023 were plotted against mean AGB values using both linear and polynomial models, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe SAVI-based regression models produced an R\u0026sup2; value of 0.6907 for the linear model and 0.7553 for the polynomial model. These results suggest that SAVI explains approximately 69.07% and 75.53% of the variation in AGB, respectively. The inclusion of a soil adjustment factor in SAVI appears particularly beneficial in mangrove zones where tidal effects and soil reflectance variability are common. This highlights SAVI\u0026rsquo;s potential as a viable alternative to NDVI for biomass estimation in coastal forest environments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Estimation of Carbon Stock and CO₂ Equivalents\u003c/h2\u003e\u003cp\u003eThe conversion of Above Ground Biomass (AGB) into carbon stock and CO₂ equivalents offers essential insights into the carbon sequestration potential of mangrove ecosystems. Figure\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrate the variation of AGB, carbon (C), and CO₂ across the 35 study plots within the Tengragiri Wildlife Sanctuary. Field-based AGB values ranged from approximately 5,000 to 7,000 Mg/ha. Applying the IPCC default carbon fraction (as shown in Eq.\u0026nbsp;6) resulted in carbon stock estimates between 2,350 and 3,300 Mg/ha. These values were subsequently converted to CO₂ equivalents using the molecular weight ratio of 3.67 (as per Eq.\u0026nbsp;7), producing a corresponding range of 8,695 to 12,111 Mg/ha. This progression from biomass to CO₂ quantification underscores the significant climate mitigation value of the sanctuary\u0026rsquo;s mangrove stands.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative Estimates of Above Ground Biomass (AGB), Carbon Stock, and CO₂ Equivalents across Sample Plots in Tengragiri Mangrove Forest\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd. Deviation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGB (Mg/Ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4806.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6964.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5775.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e539.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarbon (C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,259.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,273.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,653.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e270.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO₂ (Mg/Ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,291.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12,013.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9,725.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e990.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides a comprehensive comparison of Above Ground Biomass (AGB), carbon stock, and CO₂ equivalents across 35 sample plots in the Tengragiri Wildlife Sanctuary reveals significant spatial variability in biomass distribution, reflecting diverse ecological conditions. The AGB values range from a minimum of 4,806.70 Mg/ha (Plot 28) to a maximum of 6,964.97 Mg/ha (Plot 9), with a mean value of approximately 5,682.54 Mg/ha. This indicates a moderate level of heterogeneity across the plots, which is typical of coastal mangrove ecosystems that are influenced by tidal inundation, salinity gradients, and microtopographic differences.\u003c/p\u003e\u003cp\u003eCarbon stock (C), estimated as 47% of the AGB following IPCC guidelines, similarly varies between 2,259.15 Mg/ha and 3,273.54 Mg/ha, with a mean of 2,653.34 Mg/ha. The highest carbon concentrations correspond with the highest AGB values in plots such as 9, 5, and 22, indicating zones of healthy, mature mangrove vegetation. Conversely, lower carbon stock values in plots 28, 29, and 26 likely reflect degraded areas possibly affected by factors such as illegal logging, erosion, or past cyclone impacts.\u003c/p\u003e\u003cp\u003eIn terms of CO₂ equivalents, calculated using a factor of 3.67 times the carbon stock, the values span from 8,291.08 Mg/ha (Plot 28) to 12,013.87 Mg/ha (Plot 9), with an average of 9,725.30 Mg/ha. The higher CO₂ values in plots such as 9, 5, and 6 suggest areas of high carbon sequestration potential, making them prime candidates for conservation and carbon offset initiatives such as REDD+. On the other hand, the lower sequestration observed in plots like 28\u0026ndash;30 emphasizes the need for targeted restoration and management.\u003c/p\u003e\u003cp\u003eNotably, the substantial range observed in AGB, carbon, and CO₂ equivalents\u0026mdash;accompanied by high standard deviations (\u0026plusmn;\u0026thinsp;539.25 Mg/ha for AGB, \u0026plusmn;\u0026thinsp;270.10 Mg/ha for carbon, and \u0026plusmn;\u0026thinsp;990.27 Mg/ha for CO₂)\u0026mdash;highlights the critical importance of spatially explicit monitoring for guiding targeted conservation and management strategies. Elevated CO₂ values likely correspond to dense, well-preserved mangrove stands with robust canopy cover, whereas lower values may reflect areas affected by salinity intrusion, erosion, or anthropogenic pressures such as logging. These patterns underscore the importance of targeted conservation efforts in vulnerable zones.\u003c/p\u003e\u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e and detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, plots 6 to 10 emerged as the most productive zones, exhibiting consistently elevated values of AGB, carbon content, and CO₂ equivalents. These plots likely represent relatively undisturbed or successfully regenerating mangrove stands, characterized by robust canopy structure and healthy biomass accumulation. In contrast, plots 24 to 28 showed marked declines across all measured parameters, suggesting potential ecological stress, anthropogenic disturbance, or localized degradation. This spatial variation highlights the heterogeneous nature of mangrove ecosystems and emphasizes the necessity of adopting site-specific monitoring and management strategies to support conservation and restoration efforts.\u003c/p\u003e\u003cp\u003eOverall, the analysis validates the effectiveness of integrating field-based biomass assessments with remote sensing-derived indices for spatial carbon accounting. The observed variations also support the need for prioritizing high-sequestration plots for protection while implementing restoration efforts in the more degraded zones to maximize the climate mitigation benefits of these critical blue carbon ecosystems.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Implications for Remote Sensing-Based Monitoring\u003c/h2\u003e\u003cp\u003eThe integration of field-based Above Ground Biomass (AGB) measurements with Landsat-8 derived vegetation indices demonstrates a robust and scalable methodology for carbon stock estimation in mangrove ecosystems. This remote sensing-based approach enables efficient, repeatable, and non-destructive monitoring across large spatial extents\u0026mdash;making it especially suitable for dynamic and ecologically sensitive regions like the Tengragiri Wildlife Sanctuary.\u003c/p\u003e\u003cp\u003eSuch methodologies align with global climate action frameworks, including REDD+ (Reducing Emissions from Deforestation and Forest Degradation), by providing cost-effective tools for long-term biomass monitoring and carbon accounting. The application of vegetation indices\u0026mdash;specifically NDVI, EVI, and SAVI\u0026mdash;further enhances monitoring precision by capturing vegetation health and structural attributes under variable tidal and soil conditions.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e3.4.1 Statistical Evaluation\u003c/h2\u003e\u003cp\u003eA comprehensive statistical analysis was conducted to assess the predictive performance of each vegetation index (NDVI, EVI, and SAVI) for AGB estimation. Key evaluation metrics included the Coefficient of Determination (R\u0026sup2;), Pearson Correlation Coefficient (r), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Standard Error of Estimate (SEE). The results of this evaluation are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStatistical Evaluation of AGB Estimation Using Vegetation Indices (2020\u0026ndash;2023)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation Index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRMSE (Mg/ha)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMAE (Mg/ha)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSEE (Mg/ha)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eEvaluation Summary\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eNDVI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLinear\u003c/p\u003e\u003cp\u003e(y\u0026thinsp;=\u0026thinsp;18241x \u0026ndash; 1389.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.8162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eVery strong correlation; highly suitable for AGB prediction\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePolynomial\u003c/p\u003e\u003cp\u003e(y\u0026thinsp;=\u0026thinsp;90650x\u003csup\u003e2\u003c/sup\u003e \u0026minus;\u0026thinsp;53787x\u0026thinsp;+\u0026thinsp;12854)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.8309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSlight improvement; best model overall\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eEVI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLinear\u003c/p\u003e\u003cp\u003e(y\u0026thinsp;=\u0026thinsp;8359.8x\u0026thinsp;+\u0026thinsp;2109.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.5601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eWeak correlation; limited predictive power\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePolynomial\u003c/p\u003e\u003cp\u003e(y\u0026thinsp;=\u0026thinsp;47281x\u003csup\u003e2\u003c/sup\u003e \u0026minus;\u0026thinsp;31660x\u0026thinsp;+\u0026thinsp;10507)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.5882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMinor improvement; still weak predictor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSAVI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLinear\u003c/p\u003e\u003cp\u003e(y\u0026thinsp;=\u0026thinsp;10837x\u0026thinsp;+\u0026thinsp;1151.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStrong correlation; effective alternative to NDVI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePolynomial\u003c/p\u003e\u003cp\u003e(y\u0026thinsp;=\u0026thinsp;64245x\u003csup\u003e2\u003c/sup\u003e \u0026minus;\u0026thinsp;43919x\u0026thinsp;+\u0026thinsp;12712)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStronger fit with notable error reduction\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents a comparative statistical evaluation of linear and second-degree polynomial regression models applied to three vegetation indices\u0026mdash;NDVI, EVI, and SAVI\u0026mdash;for estimating Above Ground Biomass (AGB). Among all the models tested, the NDVI-based polynomial model demonstrated the highest predictive accuracy, with an R\u0026sup2; of 0.8309 and a strong Pearson correlation coefficient (r\u0026thinsp;=\u0026thinsp;0.9119). It also yielded the lowest error values, including an RMSE of 4.20 Mg/ha, MAE of 3.29 Mg/ha, and SEE of 4.30 Mg/ha, making it the most robust model overall. The linear NDVI model also performed well, with an R\u0026sup2; of 0.8162 and a strong correlation (r\u0026thinsp;=\u0026thinsp;0.9034), though slightly less accurate than the polynomial form. SAVI followed as the second most effective index. Its polynomial regression model achieved an R\u0026sup2; of 0.7553, r of 0.8695, RMSE of 5.14 Mg/ha, MAE of 3.81 Mg/ha, and SEE of 5.24 Mg/ha, indicating a good fit with reduced prediction error. The linear SAVI model also performed reasonably well (R\u0026sup2; = 0.6907, r\u0026thinsp;=\u0026thinsp;0.8311), demonstrating its potential as a viable alternative to NDVI in environments affected by soil brightness and tidal influence. In contrast, EVI-based models showed weak predictive performance. The polynomial model had an R\u0026sup2; of 0.3462 and a correlation coefficient of 0.5885, with higher error metrics (RMSE\u0026thinsp;=\u0026thinsp;7.85 Mg/ha, MAE\u0026thinsp;=\u0026thinsp;6.42 Mg/ha, SEE\u0026thinsp;=\u0026thinsp;7.97 Mg/ha). The linear model for EVI performed similarly poorly (R\u0026sup2; = 0.3137, r\u0026thinsp;=\u0026thinsp;0.5600), indicating its limited applicability for biomass estimation in this context. Overall, the analysis confirms that NDVI, particularly in its polynomial form, is the most effective vegetation index for predicting AGB in the Tengragiri Wildlife Sanctuary, while EVI offers limited utility due to weaker correlations and higher errors.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study offers a robust, field-validated framework for estimating Above Ground Biomass (AGB) and associated carbon stock in mangrove ecosystems through the integration of Landsat-8-derived vegetation indices and 35 in-situ plots measurements. The approach provides an efficient alternative to costly and logistically intensive structural remote sensing techniques and addresses a critical gap in ecosystem carbon monitoring in data-limited regions like the Tengragiri Wildlife Sanctuary.\u003c/p\u003e\u003cp\u003eScientific interpretation of the field data indicates a structurally mature forest, characterized by a mean DBH of 75.90 cm and average tree height of 10.75 m. The variation in wood density (0.55\u0026ndash;0.88 g/cm\u0026sup3;) suggests a diverse species assemblage, influencing carbon allocation dynamics. Higher DBH and taller trees were generally associated with greater AGB, demonstrating the integral link between forest structure and carbon storage. Standard deviations of \u0026plusmn;\u0026thinsp;539.25 Mg/ha (AGB) and \u0026plusmn;\u0026thinsp;1078.50 kg/plot further illustrate intra-forest heterogeneity, which is essential for accurate allometric modeling and remote sensing validation. These insights emphasize the importance of field calibration when applying spectral indices for biomass estimation and highlight the need for targeted monitoring to support sustainable forest management and climate adaptation strategies.\u003c/p\u003e\u003cp\u003eAmong the vegetation indices analyzed, NDVI demonstrated the highest predictive performance (R\u0026sup2; = 0.8162 linear; 0.8309 polynomial; \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9119), confirming its sensitivity to photosynthetically active biomass and canopy greenness. This strong correlation aligns with established findings (Lu, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Asner et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and highlights NDVI's continued relevance as a primary index in AGB estimation, particularly in dense tropical mangrove settings. The implication here is twofold: (1) NDVI can be reliably used in resource-limited settings where advanced sensors are unavailable, and (2) the high performance of NDVI suggests that chlorophyll concentration and leaf area index (LAI) are dominant biophysical drivers of AGB variability in Tengragiri.\u003c/p\u003e\u003cp\u003eSAVI, with an R\u0026sup2; of 0.6907 (linear) and 0.7553 (polynomial), also proved effective in capturing biomass variability, especially in plots with pronounced soil exposure due to tidal cycles. The soil-adjustment factor in SAVI likely corrected for background noise in semi-saline, periodically inundated substrates\u0026mdash;an important consideration in mangrove environments. This indicates that SAVI is particularly valuable in heterogeneous mangrove zones where vegetation cover is patchy and the influence of soil reflectance is significant, such as fringe forests or degraded areas. This insight has operational significance for regional-scale monitoring, where mixed-pixel effects are prevalent.\u003c/p\u003e\u003cp\u003eIn contrast, EVI showed limited performance (R\u0026sup2; = 0.3137 linear; 0.3462 polynomial), contrary to expectations given its enhanced design for atmospheric correction and canopy structure sensitivity. This underperformance may reflect the relatively uniform vertical structure of mangroves or the limited atmospheric distortion in the coastal scenes used. Thus, the findings challenge the assumption that EVI universally outperforms NDVI and underscore the necessity of index selection based on specific ecosystem characteristics, rather than general remote sensing heuristics.\u003c/p\u003e\u003cp\u003eA critical insight emerging from this study is the consistent outperformance of polynomial regression models over linear models across all indices. This implies that biomass accumulation in the Tengragiri mangroves is influenced by nonlinear ecological processes, such as succession dynamics, age-dependent growth rates, species composition shifts, and tidal disturbance regimes. The superiority of nonlinear models corroborates earlier ecological modeling studies (Mutanga \u0026amp; Skidmore, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Montgomery et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and emphasizes the need for flexible, adaptive modeling strategies in biomass prediction, especially in transitional ecosystems like mangroves that straddle land and sea.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e further contextualizes these statistical outcomes with plot-level AGB estimates, which ranged from 4806.70 to 6964.97 Mg/ha. This wide spatial variability reflects fine-scale ecological gradients and site-specific drivers of biomass, such as hydrological flow, salinity, nutrient influx, and anthropogenic interference. High AGB and carbon values in plots 6\u0026ndash;10 may be attributed to intact canopy structure, species diversity, and minimal disturbance, while lower values in plots 24\u0026ndash;28 may signal localized degradation, logging, or chronic salinity stress. These findings not only validate the spatial sensitivity of NDVI and SAVI but also offer practical applications for identifying carbon-rich zones, which can guide REDD\u0026thinsp;+\u0026thinsp;initiatives and conservation priorities.\u003c/p\u003e\u003cp\u003eThe conversion of AGB to carbon stock (C\u0026thinsp;=\u0026thinsp;0.47 \u0026times; AGB) and CO₂ equivalents (CO₂ = 3.67 \u0026times; C) revealed substantial sequestration potential, with corresponding carbon stocks between 2259.15 and 3273.54 Mg/ha and CO₂ equivalents from 8291.08 to 12013.87 Mg/ha. The substantial standard deviations observed\u0026mdash;\u0026plusmn;270.10 Mg/ha (carbon), and \u0026plusmn;\u0026thinsp;990.27 Mg/ha (CO₂)\u0026mdash;reflect pronounced spatial variability within the sanctuary. These values substantiate the blue carbon potential of Tengragiri\u0026rsquo;s mangroves, reinforcing their role as critical natural climate solutions. The spatial alignment between biomass and CO₂ concentrations further confirms the reliability of the remote sensing-based estimation framework, which is crucial for climate financing and carbon offset projects under mechanisms like REDD+, NDCs, and voluntary carbon markets.\u003c/p\u003e\u003cp\u003eHowever, the study's methodological limitations must also be critically considered. The exclusive use of optical data from Landsat-8 precludes characterization of vertical canopy structure\u0026mdash;a key biomass determinant. Technologies like LiDAR or SAR could augment model precision by capturing three-dimensional forest attributes such as tree height and crown volume. Moreover, the omission of below-ground biomass and soil organic carbon\u0026mdash;which can represent more than 50% of total carbon in mangroves (Donato et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u0026mdash;constrains the completeness of carbon stock estimation. Additionally, the reliance on dry-season imagery (2020\u0026ndash;2023) limits the capacity to model seasonal dynamics or post-disturbance recovery patterns.\u003c/p\u003e\u003cp\u003eDespite these constraints, the study demonstrates that medium-resolution satellite data, when paired with field validation, can provide scientifically credible and policy-relevant estimates of forest carbon stock. This approach is highly scalable and reproducible for other coastal mangrove regions across Bangladesh and Southeast Asia, where financial and technical barriers hinder widespread carbon accounting. It offers a strategic entry point for governments and conservation agencies to implement cost-effective, evidence-based monitoring systems that support climate mitigation, coastal resilience, and sustainable forest management.\u003c/p\u003e\u003cp\u003eFinally, the implications of this study extend beyond biomass estimation to broader environmental governance. By adhering to IPCC-compliant carbon accounting methodologies and generating spatially explicit, field-validated carbon data, the findings provide critical input for national climate action plans, blue carbon crediting schemes, and ecosystem-based adaptation strategies. The demonstrated robustness of NDVI and polynomial modeling, alongside their operational simplicity, paves the way for incorporating Earth observation tools into local and national climate policy frameworks\u0026mdash;bridging the gap between ecological science and environmental decision-making.\u003c/p\u003e"},{"header":"5. Conclusion and Recommendations","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Conclusion\u003c/h2\u003e\u003cp\u003eThis study demonstrates that remote sensing-based vegetation indices, particularly NDVI and SAVI, are effective tools for estimating Above Ground Biomass (AGB) in mangrove ecosystems such as the Tengragiri Wildlife Sanctuary. The integration of field data with Landsat-8 imagery and regression modeling\u0026mdash;especially polynomial models\u0026mdash;enhanced the accuracy of biomass estimation. NDVI showed the strongest correlation with AGB, followed closely by SAVI, while EVI displayed relatively lower predictive performance. The calculated AGB values, along with corresponding carbon stock and CO₂ equivalents, underscore the significant carbon sequestration potential of these lesser-studied mangrove forests. These findings not only contribute to advancing scientific understanding but also offer practical insights for climate mitigation strategies, REDD\u0026thinsp;+\u0026thinsp;implementation, and sustainable forest management in coastal Bangladesh.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Recommendations\u003c/h2\u003e\u003cp\u003eBased on the findings of this study, several key recommendations can be made to enhance future biomass monitoring and carbon accounting in mangrove ecosystems. First, NDVI should be prioritized as a primary vegetation index for AGB estimation due to its strong correlation with field-measured biomass and robust performance in both linear and polynomial regression models. Polynomial regression, in particular, demonstrated superior predictive accuracy and should be adopted over simple linear models in similar ecological assessments. It is also recommended that systematic field data collection continue across varying forest types and tidal conditions to improve the calibration and validation of remote sensing-based models. Moreover, the methodological framework established here should be extended to other less-studied mangrove areas in Bangladesh to create a more comprehensive national inventory of carbon stock. Finally, the insights gained from this research should be integrated into national climate policies and REDD\u0026thinsp;+\u0026thinsp;programs to support evidence-based planning for sustainable forest management, coastal resilience, and climate mitigation initiatives.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author gratefully acknowledges the financial support provided by the University Grants Commission (UGC), Bangladesh. Sincere thanks are extended to all the staff members of the Department of Environmental Sciences, Jahangirnagar University, and the Department of Geo-information Science and Earth Observation, Faculty of Environmental Science and Disaster Management, Patuakhali Science and Technology University, for their continuous support and cooperation throughout the study. Special appreciation is also expressed to the authority of Tengragiri Wildlife Sanctuary for granting permission to conduct field visits and data collection within the sanctuary.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding Declaration\u003c/strong\u003e: Got fund from \u0026ldquo;University Grants Commission of Bangladesh\u0026rdquo;, Record No: 37.01.0000.071.36.042.23.843\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declaration\u003c/strong\u003e: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish Declaration\u003c/strong\u003e: not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate Declaration\u003c/strong\u003e: not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmed, A., Ahmed, T., \u0026amp; Ataullah, M. 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Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. \u003cem\u003eClimate Research\u003c/em\u003e, 30(1), 79\u0026ndash;82. https://doi.org/10.3354/cr030079\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mangrove Forest, Above Ground Biomass, Carbon stock, Tengragiri Wildlife Sanctuary, Landsat-8 imagery, NDVI, SAVI, EVI","lastPublishedDoi":"10.21203/rs.3.rs-7089385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7089385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMangrove forests are globally recognized for their exceptional capacity to sequester carbon, making them vital ecosystems for climate mitigation and coastal resilience. This study assesses Above Ground Biomass (AGB) and corresponding carbon stock in the Tengragiri Wildlife Sanctuary of Bangladesh by integrating field measurements with Landsat-8-derived vegetation indices\u0026mdash;NDVI, EVI, and SAVI\u0026mdash;over the period 2020 to 2023. A total of 35 georeferenced plots were established to collect data on tree diameter, height, and species-specific wood density, from which AGB was estimated. The results showed that NDVI exhibited the highest correlation with AGB (R\u0026sup2; = 0.8309 for polynomial models), followed by SAVI (R\u0026sup2; = 0.7553), while EVI showed comparatively weaker performance (R\u0026sup2; = 0.3462). Polynomial regression models consistently outperformed linear models, capturing the nonlinear relationship between vegetation indices and biomass in semi-saline, tidally influenced environments. The estimated AGB values ranged from 4806.70 to 6964.97 Mg/ha, which were converted to carbon stock using the IPCC default factor (0.47), and to CO₂ equivalents (3.67 \u0026times; C), revealing the site\u0026rsquo;s significant carbon sequestration potential. The study highlights the spatial variability in biomass distribution across the sanctuary, identifying both conservation-priority zones and areas possibly impacted by anthropogenic activities. These findings reinforce the utility of NDVI-based remote sensing models as effective, scalable tools for carbon accounting and ecological monitoring, offering practical value for REDD\u0026thinsp;+\u0026thinsp;implementation, national climate mitigation planning, and sustainable mangrove management in data-scarce regions.\u003c/p\u003e","manuscriptTitle":"Assessment of Above Ground Biomass and Carbon Sequestration in a Coastal Mangrove Sanctuary Using Vegetation Indices and Field Data Integration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 06:23:40","doi":"10.21203/rs.3.rs-7089385/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f768bdbd-c556-4355-a1f0-adf6ece3c095","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-21T12:09:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-29 06:23:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7089385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7089385","identity":"rs-7089385","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}