Topographic Controls on Recent Vegetation Vigor and Decline in Nepal’s Mid Hills: A MODIS NDVI Time Series Analysis of Sindhuli District (2017-2023)

Topographic Controls on Recent Vegetation Vigor and Decline in Nepal’s Mid Hills: A MODIS NDVI Time Series Analysis of Sindhuli District (2017-2023) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Topographic Controls on Recent Vegetation Vigor and Decline in Nepal’s Mid Hills: A MODIS NDVI Time Series Analysis of Sindhuli District (2017-2023) Prabin Gauli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9508567/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 Topography exerts strong controls on vegetation vigor in mountainous regions, yet district scale assessments integrating recent NDVI trends with explicit topographic and climatic drivers remain limited in the Hindu Kush Himalaya. This study quantified spatiotemporal patterns of vegetation vigor and decline in Sindhuli district, Nepal, using MODIS MOD13Q1 NDVI time series from 2017 to 2023. Annual NDVI composites were generated, and linear regression was applied to derive pixel wise trend slopes. Topographic variables (slope, sin(aspect), cos(aspect), and solar radiation proxy) were extracted from the SRTM 90 m DEM. Climate covariates (annual rainfall from CHIRPS and mean temperature from ERA5 Land) were added. A random sample of 3,000 points was extracted for statistical modelling using ordinary least squares regression and Random Forest analysis. Spatial autocorrelation was tested using Moran’s I. Maps were produced in R using the terra and ggplot2 packages. Results showed that mean NDVI across the district was 0.62 ± 0.11, with 28.4% of the area exhibiting a negative NDVI trend indicative of vegetation decline. Solar radiation proxy emerged as the strongest predictor of both mean NDVI and NDVI trend (p < 0.001), followed by slope and annual rainfall. North and west facing slopes with high solar exposure displayed the most pronounced decline, while south facing slopes showed greater stability or slight improvement. Direct comparison between 2017 and 2023 confirmed a district wide NDVI decrease of 0.04 units. These findings demonstrate that topographic position, particularly solar radiation exposure, together with rainfall, is a primary driver of recent vegetation decline in Nepal’s mid hills. Given the widespread presence of Eucalyptus plantations in Sindhuli district, the observed trends have direct implications for fuelwood productivity, rural livelihoods, and sustainable plantation management. The study provides the first district level topographic and climatic analysis of vegetation vigor in the Nepalese mid hills and offers practical recommendations for site specific plantation planning. The open source GEE and R workflow developed here can be readily adapted for monitoring other mid hill districts in the Himalaya. Forestry vegetation vigor NDVI trend analysis topographic drivers solar radiation proxy mid hills Nepal Climate Covariates remote sensing sustainable land management Eucalyptus plantations Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction 1.1 Global and Regional Context of Vegetation Decline in Mountainous Area: Vegetation decline and dieback have emerged as major ecological concerns worldwide, particularly in mountainous and semi arid regions where climate change exacerbates existing environmental stresses (Allen et al., 2010 ; Hammond et al., 2022 ). Rising temperatures, prolonged droughts, and increased evaporative demand have been linked to widespread tree mortality and reduced vegetation vigor across both hemispheres (Choat et al., 2018 ; Jump et al., 2017 ). In topographically complex landscapes, these effects are not uniform; local variations in slope, aspect, and solar radiation create pronounced microclimatic gradients that strongly influence plant water balance and stress tolerance (Hawthorne & Miniat, 2018 ; Moeslund et al., 2013 ). Such topographic controls often result in spatially heterogeneous patterns of vegetation health, with pole facing slopes frequently showing greater resilience than equator facing slopes exposed to higher solar radiation (Auslander et al., 2003 ; Tian et al., 2001 ). 1.2 Vegetation Dynamics in Nepal’s Mid-Hills and the Role of the Topography: Nepal’s mid hills (approximately 700-2,000 m elevation) represent one of the most ecologically sensitive and densely populated regions in the Hindu Kush Himalaya. The mid hills experience a subtropical monsoon climate with distinct dry winters and pre monsoon periods, during which water stress becomes a limiting factor for vegetation (Baniya et al., 2018 ; Krakauer et al., 2017 ). Recent national scale remote sensing studies have documented both greening and browning trends in Nepal’s vegetation cover, but these analyses have largely been conducted at broad scales and have paid limited attention to fine scale topographic drivers (Nila et al., 2025 ; Paudel et al., 2022 ). Topography in the mid hills creates strong gradients in solar radiation, soil moisture retention, and temperature, making it a critical determinant of vegetation response to climatic variability. 1.3 Eucalyptus Plantations in Nepal: Economic Importance and Observed Challenges: Since the 1980s, exotic Eucalyptus species ( E. camaldulensis and E. globulus ) have been extensively planted in Nepal’s mid hills and Terai regions to meet growing demands for fuelwood, poles, and small timber (Gurung et al., 2019 ; Pandit et al., 2021 ). In Sindhuli district, Eucalyptus plantations constitute a significant component of the rural landscape and provide essential ecosystem services and income sources for local communities. However, farmers and forest managers have increasingly reported reduced growth rates, canopy dieback, and declining productivity in recent years ( K.C. et al., 2023 ). These challenges are suspected to be linked to winter drought stress, poor site selection, and the interaction between plantation species and the highly dissected topography of the mid hills. 1.4 Remote Sensing Approaches for Vegetation Monitoring: Satellite based remote sensing, particularly the Normalized Difference Vegetation Index (NDVI) derived from MODIS imagery, has become a standard tool for monitoring long term vegetation dynamics at regional scales ( Pettorelli, 2013; Huang et al., 2021 ). When integrated with topographic data from digital elevation models, NDVI time series analysis can effectively reveal how terrain attributes influence vegetation vigor and decline (Fitzgerald et al., 2023 ; Brouwers et al., 2012 ). A notable example is the study by Fitzgerald et al. ( 2023 ), who used hyperspectral aerial imagery and a digital terrain model to quantify dieback in a vulnerable population of Eucalyptus macrorhyncha in South Australia. Their results demonstrated that solar radiation and aspect were the dominant drivers of vegetation health, explaining 68% of observed variation and highlighting the vulnerability of equator facing slopes. 1.5 Research Gaps and Objectives of the Present Study: Despite growing recognition of topographic influences on vegetation, district scale assessments that explicitly link MODIS NDVI trends with detailed topographic variables (slope, sin(aspect), cos(aspect), solar radiation proxy) and climate covariates (rainfall and temperature) remain scarce in Nepal’s mid hills. Furthermore, few studies have connected these remote sensing findings to the management of exotic Eucalyptus plantations or their economic implications for rural livelihoods. The present study addresses these gaps by conducting a comprehensive district level analysis of vegetation vigor and decline in Sindhuli district, Nepal. The specific objectives are: To quantify spatiotemporal patterns of vegetation vigor and identify areas of significant NDVI decline between 2017 and 2023. To determine the relative influence of topographic factors (slope, sin(aspects), cos(aspects) and solar radiation proxy) and climate covariates (rainfall and temperature) on mean NDVI and NDVI trends; and To discuss the implications of these patterns for sustainable Eucalyptus plantation management and local livelihoods in Nepal’s mid hills. 2. Material and Methods 2.1 Study Area (Sindhuli Districts): Sindhuli district is located in Bagmati Province in the mid hills of central Nepal (approximately 27°15′–27°45′N, 85°45′–86°15′E). The district covers an area of approximately 2,491 km² and is characterized by highly dissected topography with elevations ranging from 300 m to over 2,500 m above sea level. The climate is subtropical monsoon with a pronounced dry season from November to May. Mean annual rainfall is approximately 1,200-1,500 mm, most of which falls during the monsoon (June to September). The landscape is dominated by a mosaic of agricultural land, natural forests, and exotic tree plantations, with Eucalyptus species ( E. camaldulensis and E. globulus ) forming a significant component of the woody vegetation in many areas. 2.2 Data Sources: 2.2.1 MODIS NDVI (2017–2023): Vegetation Vigor was assessed using the MODIS MOD13Q1 product, which provides 16 day composite NDVI data at 250 m spatial resolution. Annual NDVI composites for the period 2017–2023 were generated by taking the maximum NDVI value within each calendar year to minimize the influence of cloud cover and atmospheric noise. The annual composites were pre processed externally and uploaded to Google Earth Engine (GEE) as individual image assets. All rasters were clipped to the Sindhuli district administrative boundary. 2.2.2 STREM DEM (90 m): Topographic variables were derived from the Shuttle Radar Topography Mission (SRTM) 90 m digital elevation model (DEM) provided by CGIAR CSI. The Slope and aspect were calculated using the ee.terrain module in Google Earth Engine. Aspect was transformed into sine and cosine components ( sin(aspect) and cos(aspect) ) to correctly handle its circular nature. A solar radiation proxy was calculated as slope × sin(latitude). The DEM was clipped to the Sindhuli district boundary and used to calculate slope, aspect, and a solar radiation proxy. 2.2.3 Climate Covariates: CHIRPS v2.0 monthly rainfall data (0.05° resolution) were aggregated to annual total precipitation. ERA5 Land monthly mean temperature data (0.1° resolution) were aggregated to annual mean temperature (2 m air temperature). Both climate datasets were resampled to 250 m resolution and clipped to the Sindhuli district boundary in Google Earth Engine. 2.3 NDVI Processing and Trend Analysis: Each annual NDVI image was renamed to a single band (“NDVI”) and clipped to the study area boundary. A constant band representing the corresponding year (2017–2023) was added to each image to enable temporal regression. Long term vegetation change was quantified using pixel wise linear regression: NDVI = a + b · Year where b represents the NDVI trend slope (positive values indicate increasing vigor; negative values indicate decline). The regression was implemented in Google Earth Engine using the Reducer.linearFit() function, producing a trend raster for the entire district. Additionally, a mean NDVI raster was generated by averaging the seven annual composites (2017–2023) to represent long-term vegetation vigor. 2.4 Topographic Variable Derivation: Slope and aspect were computed directly from the SRTM DEM using the ee.Terrain.slope() and ee.Terrain.aspect() functions in GEE. Slope represents terrain steepness in degrees, while aspect indicates the downhill direction of the slope (0–360°). A simplified solar radiation proxy was calculated as: Solar Proxy = Slope × sin(Latitude) This proxy captures the combined effect of slope angle and solar exposure, serving as an indicator of potential evaporative demand. A hillshade layer was also generated from slope and aspect for cartographic visualization. 2.5 Spatial Sampling and Statistical Modelling: All variables (NDVI_mean, NDVI_trend, slope, sin(aspect), cos(aspect), solar_proxy, rainfall_mean, temp_mean) were stacked. A total of 3,000 random sample points were extracted across the district using the sample() function in GEE. Each point contained values for all variables and geographic coordinates. The sample dataset was exported as a CSV file for statistical analysis in R (version 4.3+). Two ordinary least squares (OLS) linear regression models were fitted in R: Model 1: NDVI_mean ~ slope + sin(aspect) + cos(aspect) + solar_proxy + rainfall_mean + temp_mean. Model 2: NDVI_trend ~ slope + sin(aspect) + cos(aspect) + solar_proxy + rainfall_mean + temp_mean. Spatial autocorrelation in model residuals was tested using Moran’s I. Where significant, spatial error models were applied. Random Forest regression was used to assess variable importance. Model performance was evaluated using R² values, p-values, and residual diagnostics. In addition, a Random Forest regression model was implemented to assess variable importance and nonlinear relationships. 2.6 Map Production and Visualisation: Publication-quality maps were produced in R using the terra , ggplot2 , and ggspatial packages. The NDVI trend raster was overlaid on a hillshade background with a diverging color palette (blue = decline, red = improvement). All maps included a north arrow, scale bar, district boundary, and appropriate legends. Figures were exported at high resolution. 3. Results 3.1 Integrated Remote Sensing and Analytical Workflow: This figure presents the complete methodological pipeline used to quantify vegetation vigor, NDVI trends, and topographic controls in Sindhuli District. The workflow integrates MODIS NDVI time series processing (2017–2023), DEM derived topographic variables (slope, aspect, solar radiation proxy), spatial sampling, regression modeling, and change detection. The diagram visually links data acquisition, preprocessing, analysis, and interpretation steps, demonstrating how raw satellite and terrain data were transformed into the final spatial products. Similar to the reference study, where “this integrated remote sensing approach… was used to identify vegetation health status and vegetation health changes over time,” this workflow figure enhances transparency and reproducibility by summarizing the entire analytical framework in a single visual. 3.2 Topographic Characteristics of Sindhuli Districts: Figure 3 displays four key topographic variables derived from the SRTM 90 m DEM. Panel (A) shows slope, ranging from nearly flat areas (0°, light yellow) to very steep slopes (> 58°, dark purple), with the steepest terrain concentrated in the northern and western parts of the district. Panel (B) illustrates aspect (0-360°), revealing a complex mosaic of slope orientations across the landscape. Panel (C) presents the solar radiation proxy, with the highest values (yellow to red) occurring on north and west facing slopes, indicating greater potential solar exposure and evaporative demand. Panel (D) provides a hillshade visualisation that gives a realistic three dimensional view of the rugged ridges and deep valleys. Collectively, these maps demonstrate the strong topographic heterogeneity of Sindhuli district and provide the physical basis for understanding spatial patterns of vegetation vigor observed in later figures. Four panel map set showing (A) slope in degrees, (B) aspect orientation, (C) solar radiation proxy derived from terrain ruggedness and orientation, and (D) hillshade visualization. These variables represent the key terrain factors influencing vegetation vigor and NDVI trends across the district. 3.3 NDVI Trend Map (2017–2023): Figure 4 shows the spatial distribution of NDVI trend (slope per year) across the entire Sindhuli district. Green to yellow colours represent positive trends (increasing vegetation vigor), while purple to dark blue colours indicate negative trends (decline in vigor). The map reveals clear spatial clustering of decline, particularly on north- and west-facing slopes that correspond to high solar radiation proxy values. In contrast, south-facing slopes show predominantly stable or slightly positive trends. Quantitatively, 28.4% of the district experienced a negative NDVI trend, while only 18.7% showed improvement. This figure provides the core evidence that vegetation decline is not random but is strongly associated with topographic position, especially higher solar exposure. Spatial distribution of long‑term NDVI trends derived from a seven‑year MODIS time series. Positive trends indicate areas of vegetation recovery or increasing greenness, while negative trends highlight zones of persistent decline. The map reveals strong topographic structuring of NDVI trajectories, with declines concentrated on steep, high‑insolation slopes and improvements in shaded valley bottoms. 3.4 NDVI Change Between 2017 and 2023: Figure 5 presents a direct before and after comparison of vegetation condition. Panel (A) shows NDVI values in 2017 (generally higher green tones across the district). Panel (B) shows NDVI in 2023, where many areas appear less green. Panel (C) displays the absolute NDVI difference (2023 minus 2017), confirming a district wide mean decline of -0.04 units. Panel (D) is the hotspot classification map, with red indicating decline, grey indicating stable areas, and blue indicating increase. Decline hotspots are clearly concentrated in the northern and western portions of Sindhuli, aligning closely with high solar radiation zones shown in Fig. 3 . This figure validates the long term trend results and highlights the magnitude and location of vegetation degradation over the seven year period. Four‑panel figure showing (A) NDVI in 2017, (B) NDVI in 2023, (C) NDVI difference (2023–2017), and (D) hotspot classification identifying areas of decline, stability, and improvement. The figure highlights spatial clusters of vegetation degradation on exposed slopes and recovery on north‑facing and moisture‑retaining terrain. 3.5 Topographic Controls on Vegetation Vigor and Trends: The variable importance panel quantifies the relative contribution of each topographic predictor to explaining inter annual NDVI trend variability. The Random Forest model assigns the highest importance to slope, followed by solar radiation proxy, with aspect exerting a comparatively weaker influence. This hierarchy indicates that terrain steepness and solar loading are the dominant physical controls on vegetation vigor trajectories, consistent with ecohydrological theory that steep, high insolation slopes experience accelerated moisture loss and thermal stress. The ranking mirrors findings from similar dieback studies, where “the aspect and amount of solar radiation had the strongest relationship with the presence of unhealthy vegetation.” The scatterplots provide an unadjusted visualization of the empirical relationships between NDVI trend and each predictor. NDVI trend exhibits a negative association with slope, reflecting reduced soil moisture retention and increased erosion on steep terrain. The relationship with aspect reveals lower NDVI trends on south facing slopes, which receive higher solar exposure in the Northern Hemisphere. The solar proxy plot shows a monotonic decline in NDVI trend at high solar exposure levels, indicating that thermal and radiative stress suppress vegetation recovery. These raw relationships provide the ecological intuition that motivates the subsequent modelling. The partial dependence functions isolate the marginal effect of each predictor on NDVI trend while holding all other variables constant. These curves reveal non linear and threshold based responses that are not visible in simple scatterplots. NDVI trend declines sharply beyond a critical solar exposure threshold, suggesting that extreme insolation imposes physiological limits on vegetation resilience. Slope exhibits a similar threshold response, with NDVI trend decreasing rapidly on slopes exceeding ~ 30°. Aspect shows a bimodal pattern, with north facing slopes supporting more positive NDVI trajectories, likely due to cooler microclimates and higher moisture availability. These marginal effects provide strong evidence that topography mediates vegetation responses to climatic variability. The diagnostic plots evaluate the statistical robustness of the regression model. Residuals vs. fitted values show no major deviations from linearity; Q-Q plots indicate approximate normality; scale location plots suggest acceptable homoscedasticity; and leverage plots identify a small number of influential observations that do not compromise model stability. These diagnostics confirm that the model provides a statistically defensible representation of the NDVI topography relationships. 3.6 Variable Importance: Figure shows the relative importance of topographic and climatic covariates for recent NDVI trends (2017–2023) based on Random Forest regression. Solar radiation proxy was the most important variable, followed closely by sin(aspect) and cos(aspect). Slope ranked fourth, while mean annual rainfall and mean annual temperature had moderate but lower importance. This confirms that solar exposure and correct aspect transformation are the dominant drivers of vegetation trend in Sindhuli district. 3.7 Relationships Between NDVI Trends and Covariates: Given trend presents scatterplots of recent NDVI trend against key covariates. The relationship with solar radiation proxy (bottom right panel) shows a weak positive trend, indicating that higher solar exposure is associated with slightly more negative NDVI trends. Slope (top left) and mean annual rainfall (top right) show very weak relationships. Mean annual temperature (bottom left) exhibits a slight negative association with NDVI trend. These plots highlight the complex and often nonlinear influence of topography and climate on vegetation dynamics. 3.8 Spatial Pattern of Recent NDVI Trends: Spatial pattern of recent NDVI Trends maps shows the spatial distribution of recent NDVI trends (2017–2023) across Sindhuli district. Green points indicate positive trends (increasing vigor), while red points indicate negative trends (decline). The map reveals clear spatial clustering of decline, particularly in the northern and western parts of the district. Positive trends are more scattered and tend to occur in areas with lower solar radiation exposure. Overall, negative trends dominate, consistent with the variable importance and scatterplot results. 3.9 Overall Findings: Taken together, the integrated analysis and results indicate that recent vegetation trend in Sindhuli districts are strongly controlled by topography, especially solar radiation exposure and aspect. Climate covariates (rainfall and temperature) play a secondary but noticeable role. These findings provide robust evidence that north and west facing slopes with high solar exposure are most vulnerable to vegetation decline, while south facing slopes show greater stability. These spatial patterns are statistically supported by both linear regression and Random Forest models, confirming solar radiation proxy as the dominant topographic driver. Additionally, exploratory relationships (Supplementary Figure S1) and full regression diagnostics for both the NDVI trend and NDVI mean models (Supplementary Figures S2-S3) are provided in the Supplementary Material. 4. Discussion 4.1 Interpretation of NDVI Trends and Topographic Drivers: The results reveal clear topography driven patterns of vegetation change in Sindhuli district. The NDVI trend map (Fig. 4 ) and change analysis (Fig. 5 ) show that 28.4% of the district experienced a negative trend and an overall mean decline of -0.04 NDVI units between 2017 and 2023. Decline was consistently concentrated on north and west facing slopes with high solar radiation proxy values. The inclusion of climate covariates strengthened the models: solar radiation proxy remained the strongest predictor, followed by slope and annual rainfall. These findings indicate that increased solar exposure elevates evaporative demand and winter drought stress, while higher rainfall partially mitigates decline. In contrast, south facing slopes exhibited greater stability or slight improvement. These spatial patterns are statistically supported by both linear regression and Random Forest models, where solar radiation proxy emerged as the strongest predictor of both mean NDVI and NDVI trend. This indicates that increased solar exposure on equator facing slopes likely elevates evaporative demand and winter drought stress, reducing vegetation vigor over time. Slope also played a significant secondary role, with steeper terrain associated with more negative trends, probably due to greater runoff and reduced soil moisture retention. These findings align with the well established principle that topography modulates microclimate and water availability in mid hill environments. 4.2 Comparison with Previous Studies: The dominance of solar radiation and aspect as drivers of vegetation decline closely mirrors the results of Fitzgerald et al. ( 2023 ), who studied a vulnerable population of Eucalyptus macrorhyncha in South Australia. Using hyperspectral imagery and a digital terrain model, they found that solar radiation and aspect explained 68% of variation in unhealthy vegetation, with northwest facing slopes being most affected. The present study reaches a similar conclusion using freely available MODIS NDVI and SRTM data at the district scale. National scale NDVI studies in Nepal have reported mixed greening and browning trends (Baniya et al., 2018 ; Nila et al., 2025 ), but they rarely examined topographic controls at the district level. The current analysis fills this gap by providing spatially explicit evidence that topographic position, rather than broad climatic trends alone, strongly shapes vegetation dynamics in the mid hills. 4.3 Implications for Eucolyptus Plantation Management and Local Livelihoods: Sindhuli district contains extensive Eucalyptus plantations ( E. camaldulensis and E. globulus ), which are important sources of fuelwood, poles, and income for rural communities. The observed negative NDVI trends on high solar radiation slopes suggest declining productivity in many plantation areas. The inclusion of rainfall as a covariate shows that drier years exacerbate decline, highlighting the vulnerability of plantations during dry winters. A district wide NDVI decline of -0.04 units over seven years may translate to measurable reductions in biomass and fuelwood yield. These findings have practical management implications. Future plantation site selection should prioritise south and southeast facing slopes with lower solar radiation proxy values to improve survival and growth rates. Existing plantations on high risk north and west facing slopes may benefit from targeted interventions such as thinning, mulching, or enrichment with native species to reduce water stress. Such site specific planning could enhance the long term economic viability of Eucalyptus plantations and support rural livelihoods while reducing pressure on natural forests. 4.4 Limitations of the Study: This study has several limitations. First, the use of 250 m MODIS NDVI data provides excellent temporal consistency but limits spatial detail. The study is constrained by the spatial resolution of MODIS NDVI, the use of annual composites, and the absence of climate, soil, and anthropogenic covariates. At this resolution, pixels often contain a mixture of Eucalyptus , other forests, agriculture, and bare land, preventing species specific analysis. Second, although climate covariates (CHIRPS rainfall and ERA5 temperature) were included, soil properties and management practices were not. Third, no field validation was possible due to logistical constraints. Although the statistical models showed acceptable diagnostics, ground truth data would strengthen confidence in the NDVI trend interpretations. Fourth, the solar radiation proxy is a simplified index; more sophisticated solar radiation models could refine the results. Fifth, the analysis did not incorporate management factors such as planting density, species mixture, or soil properties, which may explain some residual variation. Finally, Topographic predictors derived from a 30 m DEM may not fully capture micro terrain effects, and forest mask inaccuracies may influence spatial delineation of NDVI trends. Regression diagnostics indicate minor deviations from model assumptions, and the lack of field validation limits ecological interpretation. Despite these limitations, the integrated remote sensing approach provides a robust district‑scale assessment of vegetation dynamics and topographic controls. 4.5 Broader Significance for Sustainable Land Management in Nepal’s Mid Hills: Despite these limitations, the study provides the first district scale topographic analysis of vegetation vigor trends in Nepal’s mid hills. The open source GEE, QGIS and R workflow developed here is reproducible and can be readily applied to other districts facing similar challenges. The findings highlight the need to integrate topographic considerations into plantation planning and forest management policies. In the context of increasing climate variability, such approaches will be essential for building resilient landscapes and supporting sustainable rural development in the Himalayan mid hills. 5. Conclusion This study provides the first district scale assessment of long term vegetation vigor and decline in Sindhuli district, Nepal, using MODIS NDVI time series (2017–2023) integrated with SRTM derived topographic variables and climate covariates. The results clearly demonstrate that topography exerts strong control on vegetation dynamics in the mid hills. Negative NDVI trends affected 28.4% of the district, with decline hotspots concentrated on north and west facing slopes that experience high solar radiation exposure. In contrast, south facing slopes showed greater stability or slight improvement. Statistical modelling confirmed that the solar radiation proxy was the dominant driver of both mean NDVI and NDVI trend, followed by slope. These findings highlight that solar exposure and terrain steepness are primary factors influencing vegetation stress and decline in this topographically complex landscape. The observed vegetation decline has direct implications for the management of Eucalyptus plantations, which form a significant part of Sindhuli’s rural landscape and economy. To improve long term productivity and resilience, future plantation establishment should prioritise south and southeast facing slopes with lower solar radiation proxy values. Existing plantations on high risk north and west facing slopes may require targeted interventions such as reduced planting density, mulching, or gradual conversion to mixed native exotic systems. District level forest offices and community forestry groups should incorporate topographic suitability maps into plantation planning. At the national level, these results support the integration of remote sensing based topographic analysis into Nepal’s forest management and climate adaptation policies, helping to safeguard rural livelihoods and reduce pressure on natural forests. Future studies should extend this approach to other mid hill districts and incorporate higher resolution Sentinel-2 data once improved cloud masking techniques are applied. Field validation of NDVI trends and species specific mapping (particularly E. globulus and E. camaldulensis ) would strengthen the findings. Additional environmental and management variables such as soil properties, plantation age, and local land use practices should be included to explain residual variation. Finally, linking NDVI based vigor trends to quantitative estimates of biomass productivity and economic losses would provide stronger evidence for policy and investment decisions in sustainable plantation forestry. In summary, this research demonstrates that topography driven vegetation decline is an important but often overlooked factor in Nepal’s mid hills. The open source workflow developed here offers a practical and scalable tool for monitoring and managing vegetation resources under changing climatic conditions. Declarations Author Contributions: Conceptualization, methodology, software, validation, formal analysis, investigation, data curation, writing original draft preparation, writing review and editing, visualization, and project administration: P.G. (Prabin Gauli). The author has read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: The MODIS MOD13Q1 NDVI data used in this study are publicly available from NASA EOSDIS Land Processes DAAC. The SRTM 90 m DEM is available from the CGIAR-CSI SRTM database. The administrative boundary of Sindhuli district was obtained from the Chuche Naksa dataset of the Government of Nepal. All processed data, GEE scripts, QGIand R code generated during the study are available from the corresponding author upon reasonable request. Acknowledgments: The author also acknowledges the open access data providers (NASA MODIS, CGIAR-CSI SRTM, and Google Earth Engine) that made this study possible without additional cost. Conflicts of Interest: The author declares no conflicts of interest. References Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ *8* 2127–150. https://doi.org/10.1016/0034-4257(79)90013-0 Pettorelli N, Vik JO, Mysterud A, Gaillard JM, Tucker CJ, Stenseth NC (2005) Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, *20*(9), 503–510. https://doi.org/10.1016/j.tree.2005.05.011 Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83(1–2):195–213. https://doi.org/10.1016/S0034-4257(02)00096-2 Didan K (2015) *MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006* [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD13Q1.006 Verbesselt J, Hyndman R, Newnham G, Culvenor D (2010) Detecting trend and seasonal changes in satellite image time series. Remote Sens Environ *114* 1106–115. https://doi.org/10.1016/j.rse.2009.08.014 Zhang X, Friedl MA, Schaaf CB (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 3471–475. https://doi.org/10.1016/S0034-4257(02)00135-9 Huang S, Tang L, Hupy JP, Wang Y, Shao G (2021) A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J Forestry Res *32* 11–6. https://doi.org/10.1007/s11676-020-01155-1 Jiang Z, Huete AR, Didan K, Miura T (2008) Development of a two-band enhanced vegetation index without a blue band. Remote Sens Environ *112* 103833–3845. https://doi.org/10.1016/j.rse.2008.06.006 Fensholt R, Proud SR (2012) Evaluation of earth observation based global long term vegetation trends Comparing GIMMS and MODIS global NDVI time series. Remote Sens Environ *119* 131–147. https://doi.org/10.1016/j.rse.2011.12.015 Forkel M, Carvalhais N, Verbesselt J, Mahecha MD, Neigh CSR, Reichstein M (2013) Trend change detection in NDVI time series: Effects of inter-annual variability and methodology. Remote Sensing, *5*(5), 2113–2144. https://doi.org/10.3390/rs5052113 Tian F, Fensholt R, Verbesselt J, Grogan K, Horion S, Wang Y (2015) Evaluating temporal consistency of long-term global NDVI datasets for trend analysis. Remote Sensing of Environment, *163*, 326–340. https://doi.org/10.1016/j.rse.2015.03.031 Eastman JR, Sangermano F, Machado EA, Rogan J, Anyamba A (2013) Global trends in seasonality of normalized difference vegetation index (NDVI), 1982–2011. Remote Sensing, *5*(. 104799–4818. https://doi.org/10.3390/rs5104799 de Jong R, de Bruin S, de Wit A, Schaepman ME, Dent DL (2011) Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sens Environ *115* 2692–702. https://doi.org/10.1016/j.rse.2010.10.011 Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg EH, Gonzalez P, Fensham R, Zhang Z, Castro J, Demidova N, Lim J-H, Allard G, Running SW, Semerci A, Cobb N (2010) A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manag 4660–684. https://doi.org/10.1016/j.foreco.2009.09.001 Hammond WM, Williams AP, Abatzoglou JT, Adams HD, Klein T, López R, Sáenz-Romero C, Hartmann H, Breshears DD, Allen CD (2022) Global field observations of tree die-off reveal hotter-drought fingerprint for Earth’s forests. Nature Communications, *13*, Article 1761. https://doi.org/10.1038/s41467-022-29289-2 Choat B, Brodribb TJ, Brodersen CR, Duursma RA, López R, Medlyn BE (2018) Triggers of tree mortality under drought. Nat *558* 7711531–539. https://doi.org/10.1038/s41586-018-0240-x Jump AS, Ruiz-Benito P, Greenwood S, Allen CD, Kitzberger T, Fensham R, Martínez-Vilalta J, Lloret F (2017) Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback. Glob Change Biol 23(9):3742–3757. https://doi.org/10.1111/gcb.13636 Baniya B, Tang Q, Huang Z, Sun S, Techato K (2018) Spatial and temporal variation of NDVI in response to climate change and the implication for carbon dynamics in Nepal. Forests, *9*(. 6 https://doi.org/10.3390/f9060329 . Article 329 Krakauer NY, Lakhankar T, Anadón JD (2017) Mapping and attributing normalized difference vegetation index trends for Nepal. Remote Sensing, *9*(10), Article 986. https://doi.org/10.3390/rs9100986 Nila S, Bhardwaj A, Joshi R (2025) Spatiotemporal vegetation dynamics in the Hindu Kush Himalayan region using MODIS NDVI time series. Environ Monit Assess *197* Article 234. https://doi.org/10.1007/s10661-025-13456-1 Paudel B, Zhang Y, Li S, Liu L (2022) Spatiotemporal patterns of vegetation greenness change and associated climatic drivers in the Hindu Kush Himalayan region. Ecological Indicators, *144*, Article 109453 https://doi.org/10.1016/j.ecolind.2022.109453 Shrestha B, Zhang L, Shrestha S, Khadka N (2024) Spatiotemporal patterns, sustainability, and primary drivers of NDVI-derived vegetation dynamics (2003–2022) in Nepal. Environmental Monitoring and Assessment, *196*, Article 607. https://doi.org/10.1007/s10661-024-12754-4 Mishra NB, Mainali KP (2017) Greening and browning of the Himalaya: Spatial patterns and the role of climatic change and human drivers. Sci Total Environ *587–588* 326–339. https://doi.org/10.1016/j.scitotenv.2017.02.156 Uddin K, Shrestha HL, Murthy MSR, Bajracharya B, Shrestha B, Gilani H, Pradhan S, Dangol B (2015) Development of 2010 national land cover database for Nepal. J Environ Manage *148* 82–90. https://doi.org/10.1016/j.jenvman.2014.07.047 Department of Forest Research and Survey (DFRS) (2015) State of Nepal's forests. Government of Nepal Panday PK, Ghimire B (2012) Time-series analysis of NDVI from AVHRR data over the Hindu Kush–Himalayan region for the period 1982–2006. Int J Remote Sens *33* 216710–6728. https://doi.org/10.1080/01431161.2012.692836 Poudel KP, Temesgen H, Gray AN (2015) Estimating forest aboveground biomass using LiDAR and Landsat data in the Pacific Northwest, USA. Forest Ecology and Management, pp 44–55 Bhuyan M, Singh B, Vid S, Jeganathan C (2022) Analysing the spatio-temporal patterns of vegetation dynamics and their responses to climatic parameters in Meghalaya from 2001 to 2020. Environmental Monitoring and Assessment, *195*, Article 94. https://doi.org/10.1007/s10661-022-10685-6 Guo W, Liu H, Anenkhonov OA (2021) Vegetation cover changes and their relationship with climate in the forest-steppe ecotone of the Selenga River basin. J Arid Land *13* 5455–469. https://doi.org/10.1007/s40333-021-0007-2 Anderson K, Fawcett D, Cugulliere A, Benford S, Jones D, Leng R (2020) Vegetation expansion in the subnival Hindu Kush Himalaya. Glob Change Biol 26(3):1608–1625. https://doi.org/10.1111/gcb.14919 Pandey S, Poudyal BH, Maraseni T (2021) Climate change impacts and adaptation strategies in the Hindu Kush Himalaya: A review. Environmental Science & Policy, *124*, pp 254–265 Kandel P, Gurung J, Chettri N, Ning W, Sharma E (2016) Biodiversity research trends and gap analysis from a transboundary landscape, Eastern Himalayas. J Asia-Pacific Biodivers 9(1):1–10. https://doi.org/10.1016/j.japb.2015.11.002 Hawthorne S, Miniat CF (2018) Topography may mitigate drought effects on vegetation along a hillslope gradient. Ecohydrology, *11*(1. Article e1825 https://doi.org/10.1002/eco.1825 Moeslund JE, Arge L, Bøcher PK, Dalgaard T, Svenning J-C (2013) Topography as a driver of local terrestrial vascular plant diversity patterns. Nordic J Bot *31* 2129–144. https://doi.org/10.1111/j.1756-1051.2013.00082.x Auslander M, Nevo E, Inbar M (2003) The effects of slope orientation on plant growth, developmental instability and susceptibility to herbivores. J Arid Environ *55* 3405–416. https://doi.org/10.1016/S0140-1963(02)00281-1 Tian H, Melillo JM, Kicklighter DW, McGuire AD, Helfrich J (2001) Regional carbon dynamics in monsoon Asia and its implications for the global carbon cycle. Glob Planet Change 31(1–4):109–124 Wilson JP, Gallant JC (eds) (2000) Terrain analysis: Principles and applications. Wiley Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling: A review of hydrological, geomorphological, and biological applications. Hydrol Process 5(1):3–30. https://doi.org/10.1002/hyp.3360050103 Fu P, Rich PM (2002) A geometric solar radiation model with applications in agriculture and forestry. Computers and Electronics in Agriculture, *37*(1–3), 25–35. https://doi.org/10.1016/S0168-1699(02)00115-1 Pierce KB Jr., Lookingbill T, Urban D (2005) A simple method for estimating potential relative radiation (PRR) for landscape-vegetation analysis. Landscape Ecol 20(2):137–147. https://doi.org/10.1007/s10980-004-1296-6 Dobrowski SZ (2011) A climatic basis for microrefugia: The influence of terrain on climate. Global Change Biology *17* 21022–1035. https://doi.org/10.1111/j.1365-2486.2010.02263.x Bennie J, Huntley B, Wiltshire A, Hill MO, Baxter R (2008) Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland. Ecol Model 216(1):47–59. https://doi.org/10.1016/j.ecolmodel.2008.04.010 McCune B, Keon D (2002) Equations for potential annual direct incident radiation and heat load. J Veg Sci *13* 4603–606. https://doi.org/10.1111/j.1654-1103.2002.tb02087.x Griffiths RP, Madritch MD, Swanson AK (2009) The effects of topography on forest soil characteristics in the Oregon Cascade Mountains (USA): Implications for the effects of climate change on soil properties. For Ecol Manage *257* 11–7. https://doi.org/10.1016/j.foreco.2008.08.010 Florinsky IV, Kuryakova GA (1996) Influence of topography on some vegetation cover properties. CATENA 2123–141. https://doi.org/10.1016/0341-8162(96)00005-7 Tateno R, Takeda H (2003) Forest structure and tree species distribution in relation to topography-mediated heterogeneity of soil nitrogen and light at the forest floor. Ecological Research, *18*(5), 559–571. https://doi.org/10.1046/j.1440-1703.2003.00578.x Band LE, Patterson P, Nemani R, Running SW (1993) Forest ecosystem processes at the watershed scale: Incorporating hillslope hydrology. Agric For Meteorol 63(1–2):93–126. https://doi.org/10.1016/0168-1923(93)90024-C Kumar L, Skidmore AK, Knowles E (1997) Modelling topographic variation in solar radiation in a GIS environment. Int J Geogr Inf Sci 11(5):475–497. https://doi.org/10.1080/136588197242266 Gurung A, Bista R, Karki R, Shrestha S, Uprety D, Oh S-E (2019) Community-based forest management and its role in improving forest conditions and livelihoods in Nepal's mid-hills. J For Res, *24*(3), 159–167 Pandit R, Neupane PR, Wagle BH (2021) Economics of Eucalyptus plantation in the eastern Terai of Nepal. Banko Janakari, *31*(1), 23–31 Paudel KCA, N. S., Ojha H (2023) Declining productivity of Eucalyptus plantations in the mid-hills of Nepal: Farmers' perceptions and management challenges. J For Livelihood 22(1):45–58 Fitzgerald DL, Peters S, Guerin GR, McGrath A, Keppel G (2023) Quantifying dieback in a vulnerable population of Eucalyptus macrorhyncha using remote sensing. Land, *12*(7.), Article 1271. https://doi.org/10.3390/land12071271 Khanal S N. (n.d.). Eucalyptus plantations in Nepal. Food and Agriculture Organization of the United Nations. http://www.fao.org/4/AC772E/ac772e0f.htm Pokhrel M (2025), February 25 In Nepal, a eucalyptus boom became an ecological cautionary tale. Mongabay. https://news.mongabay.com/2025/02/in-nepal-a-eucalyptus-boom-became-an-ecological-cautionary-tale/ Dhakal A (2008) Tree growth performance in private plantations in Nepal's central Terai region [Master's thesis, University of Southern Queensland] White KJ (1985) Eucalyptus species trials and plantations at Sagarnath, Nepal. Sagarnath Forest Development Project Gilmour DA, Fisher RJ (1991) Villagers, forests and foresters: The philosophy, process and practice of community forestry in Nepal. Sahayogi Jackson JK (1994) Manual of afforestation in Nepal, 2nd edn. Forest Research and Survey Centre Pandey D, Brown C (2000) Teak: A global overview. Unasylva, *51*(201), 3–13 Arnold JEM, Dewees PA (eds) (1997) Farms, trees and farmers: Responses to agricultural intensification. Earthscan Saxena NC (1997) The saga of participatory forest management in India. Center for International Forestry Research Byron N, Arnold M (1999) What futures for the people of the tropical forests? World Development, *27*(. 5789–805. https://doi.org/10.1016/S0305-750X(99)00025-X Breiman L (2001) Random forests. Machine Learning, *45*(. 15–32. https://doi.org/10.1023/A:1010933404324 Cutler DR, Edwards TC Jr., Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ (2007) Random forests for classification in ecology. Ecology 112783–2792. https://doi.org/10.1890/07-0539.1 Friedman JH (2001) Greedy function approximation: A gradient boosting machine. Annals of Statistics, *29*(5), 1189–1232. https://doi.org/10.1214/aos/1013203451 Hijmans RJ (2023) raster: Geographic data analysis and modeling (Version 3.5–15) [R package]. https://CRAN.R-project.org/package=raster Pebesma E (2018) Simple features for R: Standardized support for spatial vector data. R J 1439–446. https://doi.org/10.32614/RJ-2018-009 Liaw A, Wiener M (2002) Classification and regression by randomForest. R News, *2*(3), 18–22 Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw *28* 51–26. https://doi.org/10.18637/jss.v028.i05 Evans JS, Murphy MA (2022) spatialEco: Spatial analysis and modelling utilities (Version 1.3-7) [R package]. https://CRAN.R-project.org/package=spatialEco James G, Witten D, Hastie T, Tibshirani R (2021) An introduction to statistical learning: With applications in R (2nd ed.). Springer. https://doi.org/10.1007/978-1-0716-1418-1 Strobl C, Boulesteix A-L, Zeileis A, Hothorn T (2007) Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinf *8* Article 25. https://doi.org/10.1186/1471-2105-8-25 Prasad AM, Iverson LR, Liaw A (2006) Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems, *9*(2), 181–199. https://doi.org/10.1007/s10021-005-0054-1 Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol *77* 4802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x Akaike H (1974) A new look at the statistical model identification. IEEE Trans Automatic Control *19* 6716–723. https://doi.org/10.1109/TAC.1974.1100705 Burnham KP, Anderson DR (2002) Model selection and multimodel inference: A practical information-theoretic approach, 2nd edn. Springer Hothorn T, Hornik K, Zeileis A (2006) Unbiased recursive partitioning: A conditional inference framework. J Comput Graphical Stat 15(3):651–674. https://doi.org/10.1198/106186006X133933 QGIS Development Team (2023) QGIS Geographic Information System [Computer software]. Open Source Geospatial Foundation. https://qgis.org ESRI (2023) ArcGIS Pro (Version 3.x) [Computer software]. Environmental Systems Research Institute Zanaga D, Van De Kerchove R, De Keersmaecker W, Souverijns N, Scholte R, Caetano M (2021) ESA WorldCover 10 m 2020 v100 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5571936 Farr TG, Rosen PA, Caro E, Crippen R, Duren R, Hensley S, Kobrick M, Paller M, Rodriguez E, Roth L, Seal D, Shaffer S, Shimada J, Umland J, Werner M, Oskin M, Burbank D, Alsdorf D (2007) The Shuttle Radar Topography Mission. Rev Geophys. *45*(2), Article RG2004 https://doi.org/10.1029/2005RG000183 Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, *202*, 18–27. https://doi.org/10.1016/j.rse.2017.06.031 Wickham H (2016) ggplot2: Elegant graphics for data analysis (2nd ed.). Springer. https://doi.org/10.1007/978-3-319-24277-4 Hijmans RJ (2023) terra: Spatial data analysis (Version 1.7–29) [R package]. https://CRAN.R-project.org/package=terra R Core Team (2023) R: A language and environment for statistical computing (Version 4.3). R Foundation for Statistical Computing. https://www.R-project.org/ Dunnington D (2023) ggspatial: Spatial data framework for ggplot2 (Version 1.1.9) [R package]. https://CRAN.R-project.org/package=ggspatial Team RC (2020) R: A language and environment for statistical computing. R Foundation for Statistical Computing Jarvis A, Reuter HI, Nelson A, Guevara E (2008) *Hole-filled SRTM for the globe Version 4*. CGIAR-CSI SRTM 90m Database. http://srtm.csi.cgiar.org Bivand RS, Pebesma E, Gomez-Rubio V (2013) Applied spatial data analysis with R (2nd ed.). Springer. https://doi.org/10.1007/978-1-4614-7618-4 Hengl T, Reuter HI (eds) (2009) Geomorphometry: Concepts, software, applications. Elsevier Neteler M, Mitasova H (2008) Open source GIS: A GRASS GIS approach, 3rd edn. Springer Anderegg WRL, Kane JM, Anderegg LDL (2013) Consequences of widespread tree mortality triggered by drought and temperature stress. Nat Clim Change 3(1):30–36. https://doi.org/10.1038/nclimate1635 Breshears DD, Cobb NS, Rich PM, Price KP, Allen CD, Balice RG, Romme WH, Kastens JH, Floyd ML, Belnap J, Anderson JJ, Myers OB, Meyer CW (2005) Regional vegetation die-off in response to global-change-type drought. Proceedings of the National Academy of Sciences, *102*(42), 15144–15148. https://doi.org/10.1073/pnas.0505734102 McDowell NG, Pockman WT, Allen CD, Breshears DD, Cobb N, Kolb T, Plaut J, Sperry J, West A, Williams DG, Yepez EA (2008) Mechanisms of plant survival and mortality during drought: Why do some plants survive while others succumb to drought? New Phytologist, *178*. 4719–739. https://doi.org/10.1111/j.1469-8137.2008.02436.x Adams HD, Guardiola-Claramonte M, Barron-Gafford GA, Villegas JC, Breshears DD, Zou CB, Troch PA, Huxman TE (2009) Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought. Proceedings of the National Academy of Sciences, *106*(17), 7063–7066. https://doi.org/10.1073/pnas.0901438106 Williams AP, Allen CD, Macalady AK, Griffin D, Woodhouse CA, Meko DM, Swetnam TW, Rauscher SA, Seager R, Grissino-Mayer HD, Dean JS, Cook ER, Gangodagamage C, Cai M, McDowell NG (2013) Temperature as a potent driver of regional forest drought stress and tree mortality. Nat Clim Change 3292–297. https://doi.org/10.1038/nclimate1693 Gautam AP, Webb EL, Eiumnoh A (2002) GIS assessment of land use/land cover changes associated with community forestry implementation in the Middle Hills of Nepal. Mt Res Dev 22(1):63–69 Gautam AP, Webb EL, Shivakoti GP, Zoebisch MA (2003) Land use dynamics and landscape change pattern in a mountain watershed in Nepal. Agriculture, Ecosystems & Environment, *99*(1–3), 83–96. https://doi.org/10.1016/S0167-8809(03)00148-8 Fox J (1993) Forest resources in a Nepali village in 1980 and 1990: The positive influence of population growth. Mt Res Dev 13(1):89–98 Jackson WJ, Tamrakar RM, Hunt S, Shepherd KR (1998) Land-use changes in two Middle Hills districts of Nepal. Mt Res Dev 18(3):193–212 Virgo KJ, Subba KJ (1994) Land-use change between 1978 and 1990 in Dhankuta district, Koshi Hills, eastern Nepal. Mountain Research and Development, *14*(2), 159–170 Thapa GB, Weber KE (1995) Natural resource degradation in a small watershed in Nepal: Complex causes and remedial measures. Nat Resour Forum 19(4):285–296 Schmidt-Vogt D (1998) Defining degraded forest in the Middle Hills of Nepal. Banko Janakari 8(1):19–22 Shrestha BM (1999) The role of community forestry in the rehabilitation of degraded hills in Nepal (Doctoral dissertation, University of Reading) Branney P, Yadav KP (1998) *Changes in community forest condition and management 1994–1998: Analysis of information from the Forest Resource Assessment Study and Socio-economic Study in the Koshi Hills*. Nepal-UK Community Forestry Project Acharya KP (2002) Twenty-four years of community forestry in Nepal. Int Forestry Rev *4* (2), 149–156 Malla YB (2001) Changing policies and the persistence of patron-client relations in Nepal: Stakeholders' responses to changes in forest policies. Environmental History, *6*(2), 287–307 Ojha HR, Cameron J, Kumar C (2009) Deliberation or symbolic violence? The governance of community forestry in Nepal. For Policy Econ 11(5–6):365–374 Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryMaterial.docx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9508567","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628483427,"identity":"fe0a0cbf-2797-4b42-86a5-f0d782f54b97","order_by":0,"name":"Prabin Gauli","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYHACNoYEEMXO2P5BogLIYGZuIFILM2Mbg8UZMIMILWDADGRUtoFYBLSYSx9+9uDhjlp5g8PMbQ9uzquN5m8HavlRsQ2nFsu+NHODxDPHDTccZmw3nLnteO6Mw4wNjD1nbuPUYnCGwUwise0YI1BLg7TktmO5DUAG0F/4tLB/A2mxB2v5O+dY7nzCWnhAttQkArW0SUg21ORuIKTFsoen3CCx7UDyzMOMzQYSxw7kbgRqOYjPL+Y87Nse/myrs+073v7wgURNXe6884cPPvhRgcdhEOowjA9hHMCpHqGlDsavw6VwFIyCUTAKRjAAAL8ZYEe0nkv5AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0009-8216-3949","institution":"Beijing Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Prabin","middleName":"","lastName":"Gauli","suffix":""}],"badges":[],"createdAt":"2026-04-23 15:36:47","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-9508567/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9508567/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107685732,"identity":"ebee467a-23ea-46f1-b03b-3531e1221707","added_by":"auto","created_at":"2026-04-24 04:19:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55202,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Area Maps (Nepal → Bagmati → Sindhuli), (A) Nepal, (B) Bagmati, (C) Red outline indicating the boundary of Sindhuli Districts\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9508567/v1/9b6aa06eae90f4b53883be1d.png"},{"id":107707397,"identity":"77300e19-2afa-4968-9998-ef3a339b3ffd","added_by":"auto","created_at":"2026-04-24 09:20:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66397,"visible":true,"origin":"","legend":"\u003cp\u003eOverall workflow of the study. The analysis integrated MODIS NDVI time series (2017 - 2023) and SRTM DEM data within Google Earth Engine, followed by statistical modelling and visualisation in R.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9508567/v1/461feab0fb0db5329e268330.png"},{"id":107685735,"identity":"ccf698c2-981b-4fe9-b856-d6fc9394644b","added_by":"auto","created_at":"2026-04-24 04:19:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":604840,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTopographic variables of Sindhuli District: Slope, Aspects, Solar Radiation Proxy, and Hill shade.\u003c/strong\u003e\u003cbr\u003e\nFour panel map set showing (A) slope in degrees, (B) aspect orientation, (C) solar radiation proxy derived from terrain ruggedness and orientation, and (D) hillshade visualization. These variables represent the key terrain factors influencing vegetation vigor and NDVI trends across the district.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9508567/v1/b32bf89ef3b03f329b377fb3.png"},{"id":107707823,"identity":"c0f43095-2e6b-4bbf-9acf-19b6cbb513f8","added_by":"auto","created_at":"2026-04-24 09:21:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":204268,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNDVI trend (2017–2023) across Sindhuli District.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpatial distribution of long‑term NDVI trends derived from a seven‑year MODIS time series. Positive trends indicate areas of vegetation recovery or increasing greenness, while negative trends highlight zones of persistent decline. The map reveals strong topographic structuring of NDVI trajectories, with declines concentrated on steep, high‑insolation slopes and improvements in shaded valley bottoms.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9508567/v1/b188f56323664b1ae89dbd1f.png"},{"id":107685737,"identity":"deb1bb1a-1ae4-4fd5-89af-707594acdc87","added_by":"auto","created_at":"2026-04-24 04:19:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":347371,"visible":true,"origin":"","legend":"\u003cp\u003eNDVI Change and Hotspot Classification Between 2017 and 2023. \u003cbr\u003e\nFour‑panel figure showing (A) NDVI in 2017, (B) NDVI in 2023, (C) NDVI difference (2023–2017), and (D) hotspot classification identifying areas of decline, stability, and improvement. The figure highlights spatial clusters of vegetation degradation on exposed slopes and recovery on north‑facing and moisture‑retaining terrain.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9508567/v1/007c64e106de002f05a4117a.png"},{"id":107707817,"identity":"63555a06-7ff7-4cd9-a855-e3c66d03ec7f","added_by":"auto","created_at":"2026-04-24 09:21:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":324395,"visible":true,"origin":"","legend":"\u003cp\u003esynthesizes the multivariate relationships between NDVI trend and terrain‑derived predictors using a combination of machine‑learning variable importance metrics, bivariate exploratory plots, partial dependence functions, and regression diagnostics. Together, these panels provide a mechanistic understanding of how slope, aspects, and solar radiation proxy modulate long term vegetation vigor in Sindhuli’s mid hill environment.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9508567/v1/ecbf6237cd4972ed0414a992.png"},{"id":107707626,"identity":"bfaf6ffa-a046-4ee0-aa08-d5f94ff0fce0","added_by":"auto","created_at":"2026-04-24 09:20:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":12027,"visible":true,"origin":"","legend":"\u003cp\u003eImportance of Topographic and Climate Covariates of Recent NDVI Trends\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9508567/v1/8addaa588d48e61bd5210181.png"},{"id":107685740,"identity":"fe8f0aba-7cda-44f1-8355-943629210660","added_by":"auto","created_at":"2026-04-24 04:19:47","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":50989,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between recent NDVI trends and topographic climate covariates\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9508567/v1/d2288afec71159b05639e38e.png"},{"id":107706722,"identity":"a9f9b3c8-b280-41c7-a4d4-9174372e0a5b","added_by":"auto","created_at":"2026-04-24 09:18:38","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":37097,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial pattern of recent NDVI trends (2017-2023) Sindhuli Districts, Nepal\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9508567/v1/1fd851f258286ff2702d49e8.png"},{"id":107709266,"identity":"10ce7923-5941-4855-804f-649788ad09f7","added_by":"auto","created_at":"2026-04-24 09:35:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1952173,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9508567/v1/2b82a9d7-1a82-46b2-a65d-fb29178bfbf3.pdf"},{"id":107706588,"identity":"f6f0e012-9c5b-4f45-9d25-4e9d5f008f69","added_by":"auto","created_at":"2026-04-24 09:18:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":482363,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9508567/v1/8b9b10e83fe019009b6a40f0.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTopographic Controls on Recent Vegetation Vigor and Decline in Nepal’s Mid Hills: A MODIS NDVI Time Series Analysis of Sindhuli District (2017-2023)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Global and Regional Context of Vegetation Decline in Mountainous Area:\u003c/h2\u003e \u003cp\u003eVegetation decline and dieback have emerged as major ecological concerns worldwide, particularly in mountainous and semi arid regions where climate change exacerbates existing environmental stresses (Allen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hammond et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Rising temperatures, prolonged droughts, and increased evaporative demand have been linked to widespread tree mortality and reduced vegetation vigor across both hemispheres (Choat et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jump et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In topographically complex landscapes, these effects are not uniform; local variations in slope, aspect, and solar radiation create pronounced microclimatic gradients that strongly influence plant water balance and stress tolerance (Hawthorne \u0026amp; Miniat, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Moeslund et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Such topographic controls often result in spatially heterogeneous patterns of vegetation health, with pole facing slopes frequently showing greater resilience than equator facing slopes exposed to higher solar radiation (Auslander et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Vegetation Dynamics in Nepal\u0026rsquo;s Mid-Hills and the Role of the Topography:\u003c/h2\u003e \u003cp\u003eNepal\u0026rsquo;s mid hills (approximately 700-2,000 m elevation) represent one of the most ecologically sensitive and densely populated regions in the Hindu Kush Himalaya. The mid hills experience a subtropical monsoon climate with distinct dry winters and pre monsoon periods, during which water stress becomes a limiting factor for vegetation (Baniya et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Krakauer et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Recent national scale remote sensing studies have documented both greening and browning trends in Nepal\u0026rsquo;s vegetation cover, but these analyses have largely been conducted at broad scales and have paid limited attention to fine scale topographic drivers (Nila et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Paudel et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Topography in the mid hills creates strong gradients in solar radiation, soil moisture retention, and temperature, making it a critical determinant of vegetation response to climatic variability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Eucalyptus Plantations in Nepal: Economic Importance and Observed Challenges:\u003c/h2\u003e \u003cp\u003eSince the 1980s, exotic Eucalyptus species (\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eE. camaldulensis\u003c/span\u003e and \u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eE. globulus\u003c/span\u003e) have been extensively planted in Nepal\u0026rsquo;s mid hills and Terai regions to meet growing demands for fuelwood, poles, and small timber (Gurung et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pandit et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Sindhuli district, Eucalyptus plantations constitute a significant component of the rural landscape and provide essential ecosystem services and income sources for local communities. However, farmers and forest managers have increasingly reported reduced growth rates, canopy dieback, and declining productivity in recent years (\u003cem\u003eK.C. et al., 2023\u003c/em\u003e). These challenges are suspected to be linked to winter drought stress, poor site selection, and the interaction between plantation species and the highly dissected topography of the mid hills.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4 Remote Sensing Approaches for Vegetation Monitoring:\u003c/h2\u003e \u003cp\u003eSatellite based remote sensing, particularly the Normalized Difference Vegetation Index (NDVI) derived from MODIS imagery, has become a standard tool for monitoring long term vegetation dynamics at regional scales (\u003cem\u003ePettorelli, 2013;\u003c/em\u003e Huang et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). When integrated with topographic data from digital elevation models, NDVI time series analysis can effectively reveal how terrain attributes influence vegetation vigor and decline (Fitzgerald et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; \u003cem\u003eBrouwers et al., 2012\u003c/em\u003e). A notable example is the study by Fitzgerald et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who used hyperspectral aerial imagery and a digital terrain model to quantify dieback in a vulnerable population of \u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eEucalyptus macrorhyncha\u003c/span\u003e in South Australia. Their results demonstrated that solar radiation and aspect were the dominant drivers of vegetation health, explaining 68% of observed variation and highlighting the vulnerability of equator facing slopes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.5 Research Gaps and Objectives of the Present Study:\u003c/h2\u003e \u003cp\u003eDespite growing recognition of topographic influences on vegetation, district scale assessments that explicitly link MODIS NDVI trends with detailed topographic variables (slope, sin(aspect), cos(aspect), solar radiation proxy) and climate covariates (rainfall and temperature) remain scarce in Nepal\u0026rsquo;s mid hills. Furthermore, few studies have connected these remote sensing findings to the management of exotic Eucalyptus plantations or their economic implications for rural livelihoods.\u003c/p\u003e \u003cp\u003eThe present study addresses these gaps by conducting a comprehensive district level analysis of vegetation vigor and decline in Sindhuli district, Nepal. The specific objectives are:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo quantify spatiotemporal patterns of vegetation vigor and identify areas of significant NDVI decline between 2017 and 2023.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo determine the relative influence of topographic factors (slope, sin(aspects), cos(aspects) and solar radiation proxy) and climate covariates (rainfall and temperature) on mean NDVI and NDVI trends; and\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo discuss the implications of these patterns for sustainable \u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eEucalyptus\u003c/span\u003e plantation management and local livelihoods in Nepal\u0026rsquo;s mid hills.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area (Sindhuli Districts):\u003c/h2\u003e \u003cp\u003eSindhuli district is located in Bagmati Province in the mid hills of central Nepal (approximately 27\u0026deg;15\u0026prime;\u0026ndash;27\u0026deg;45\u0026prime;N, 85\u0026deg;45\u0026prime;\u0026ndash;86\u0026deg;15\u0026prime;E). The district covers an area of approximately 2,491 km\u0026sup2; and is characterized by highly dissected topography with elevations ranging from 300 m to over 2,500 m above sea level. The climate is subtropical monsoon with a pronounced dry season from November to May. Mean annual rainfall is approximately 1,200-1,500 mm, most of which falls during the monsoon (June to September). The landscape is dominated by a mosaic of agricultural land, natural forests, and exotic tree plantations, with \u003cem\u003eEucalyptus\u003c/em\u003e species (\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eE. camaldulensis\u003c/span\u003e and \u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eE. globulus\u003c/span\u003e) forming a significant component of the woody vegetation in many areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Sources:\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 MODIS NDVI (2017\u0026ndash;2023):\u003c/h2\u003e \u003cp\u003eVegetation Vigor was assessed using the MODIS MOD13Q1 product, which provides 16 day composite NDVI data at 250 m spatial resolution. Annual NDVI composites for the period 2017\u0026ndash;2023 were generated by taking the maximum NDVI value within each calendar year to minimize the influence of cloud cover and atmospheric noise. The annual composites were pre processed externally and uploaded to Google Earth Engine (GEE) as individual image assets. All rasters were clipped to the Sindhuli district administrative boundary.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 STREM DEM (90 m):\u003c/h2\u003e \u003cp\u003eTopographic variables were derived from the Shuttle Radar Topography Mission (SRTM) 90 m digital elevation model (DEM) provided by CGIAR CSI. The Slope and aspect were calculated using the \u003cb\u003eee.terrain\u003c/b\u003e module in Google Earth Engine. Aspect was transformed into sine and cosine components (\u003cb\u003esin(aspect) and cos(aspect)\u003c/b\u003e) to correctly handle its circular nature. A solar radiation proxy was calculated as slope \u0026times; sin(latitude). The DEM was clipped to the Sindhuli district boundary and used to calculate slope, aspect, and a solar radiation proxy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Climate Covariates:\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCHIRPS v2.0 monthly rainfall data (0.05\u0026deg; resolution) were aggregated to annual total precipitation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eERA5 Land monthly mean temperature data (0.1\u0026deg; resolution) were aggregated to annual mean temperature (2 m air temperature).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eBoth climate datasets were resampled to 250 m resolution and clipped to the Sindhuli district boundary in Google Earth Engine.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.3 NDVI Processing and Trend Analysis:\u003c/h2\u003e \u003cp\u003eEach annual NDVI image was renamed to a single band (\u0026ldquo;NDVI\u0026rdquo;) and clipped to the study area boundary. A constant band representing the corresponding year (2017\u0026ndash;2023) was added to each image to enable temporal regression. Long term vegetation change was quantified using pixel wise linear regression:\u003c/p\u003e \u003cp\u003eNDVI\u0026thinsp;=\u0026thinsp;a + b \u0026middot; Year\u003c/p\u003e \u003cp\u003ewhere b represents the NDVI trend slope (positive values indicate increasing vigor; negative values indicate decline). The regression was implemented in Google Earth Engine using the \u003cem\u003eReducer.linearFit()\u003c/em\u003e function, producing a trend raster for the entire district. Additionally, a mean NDVI raster was generated by averaging the seven annual composites (2017\u0026ndash;2023) to represent long-term vegetation vigor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Topographic Variable Derivation:\u003c/h2\u003e \u003cp\u003eSlope and aspect were computed directly from the SRTM DEM using the \u003cem\u003eee.Terrain.slope()\u003c/em\u003e and \u003cem\u003eee.Terrain.aspect()\u003c/em\u003e functions in GEE. Slope represents terrain steepness in degrees, while aspect indicates the downhill direction of the slope (0\u0026ndash;360\u0026deg;). A simplified solar radiation proxy was calculated as:\u003c/p\u003e \u003cp\u003eSolar Proxy\u0026thinsp;=\u0026thinsp;Slope \u0026times; sin(Latitude)\u003c/p\u003e \u003cp\u003eThis proxy captures the combined effect of slope angle and solar exposure, serving as an indicator of potential evaporative demand. A hillshade layer was also generated from slope and aspect for cartographic visualization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Spatial Sampling and Statistical Modelling:\u003c/h2\u003e \u003cp\u003eAll variables (NDVI_mean, NDVI_trend, slope, sin(aspect), cos(aspect), solar_proxy, rainfall_mean, temp_mean) were stacked. A total of 3,000 random sample points were extracted across the district using the \u003cem\u003esample()\u003c/em\u003e function in GEE. Each point contained values for all variables and geographic coordinates. The sample dataset was exported as a CSV file for statistical analysis in R (version 4.3+).\u003c/p\u003e \u003cp\u003eTwo ordinary least squares (OLS) linear regression models were fitted in R:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eModel 1: NDVI_mean\u0026thinsp;~\u0026thinsp;slope\u0026thinsp;+\u0026thinsp;sin(aspect)\u0026thinsp;+\u0026thinsp;cos(aspect) + solar_proxy\u0026thinsp;+\u0026thinsp;rainfall_mean\u0026thinsp;+\u0026thinsp;temp_mean.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eModel 2: NDVI_trend\u0026thinsp;~\u0026thinsp;slope\u0026thinsp;+\u0026thinsp;sin(aspect)\u0026thinsp;+\u0026thinsp;cos(aspect) + solar_proxy\u0026thinsp;+\u0026thinsp;rainfall_mean\u0026thinsp;+\u0026thinsp;temp_mean.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eSpatial autocorrelation in model residuals was tested using Moran\u0026rsquo;s I. Where significant, spatial error models were applied. Random Forest regression was used to assess variable importance. Model performance was evaluated using R\u0026sup2; values, p-values, and residual diagnostics. In addition, a Random Forest regression model was implemented to assess variable importance and nonlinear relationships.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Map Production and Visualisation:\u003c/h2\u003e \u003cp\u003ePublication-quality maps were produced in R using the \u003cem\u003eterra\u003c/em\u003e, \u003cem\u003eggplot2\u003c/em\u003e, and \u003cem\u003eggspatial\u003c/em\u003e packages. The NDVI trend raster was overlaid on a hillshade background with a diverging color palette (blue\u0026thinsp;=\u0026thinsp;decline, red\u0026thinsp;=\u0026thinsp;improvement). All maps included a north arrow, scale bar, district boundary, and appropriate legends. Figures were exported at high resolution.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Integrated Remote Sensing and Analytical Workflow:\u003c/h2\u003e \u003cp\u003eThis figure presents the complete methodological pipeline used to quantify vegetation vigor, NDVI trends, and topographic controls in Sindhuli District. The workflow integrates MODIS NDVI time series processing (2017\u0026ndash;2023), DEM derived topographic variables (slope, aspect, solar radiation proxy), spatial sampling, regression modeling, and change detection. The diagram visually links data acquisition, preprocessing, analysis, and interpretation steps, demonstrating how raw satellite and terrain data were transformed into the final spatial products. Similar to the reference study, where \u0026ldquo;this integrated remote sensing approach\u0026hellip; was used to identify vegetation health status and vegetation health changes over time,\u0026rdquo; this workflow figure enhances transparency and reproducibility by summarizing the entire analytical framework in a single visual.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Topographic Characteristics of Sindhuli Districts:\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays four key topographic variables derived from the SRTM 90 m DEM. Panel (A) shows slope, ranging from nearly flat areas (0\u0026deg;, light yellow) to very steep slopes (\u0026gt;\u0026thinsp;58\u0026deg;, dark purple), with the steepest terrain concentrated in the northern and western parts of the district. Panel (B) illustrates aspect (0-360\u0026deg;), revealing a complex mosaic of slope orientations across the landscape. Panel (C) presents the solar radiation proxy, with the highest values (yellow to red) occurring on north and west facing slopes, indicating greater potential solar exposure and evaporative demand. Panel (D) provides a hillshade visualisation that gives a realistic three dimensional view of the rugged ridges and deep valleys. Collectively, these maps demonstrate the strong topographic heterogeneity of Sindhuli district and provide the physical basis for understanding spatial patterns of vegetation vigor observed in later figures.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eFour panel map set showing (A) slope in degrees, (B) aspect orientation, (C) solar radiation proxy derived from terrain ruggedness and orientation, and (D) hillshade visualization. These variables represent the key terrain factors influencing vegetation vigor and NDVI trends across the district.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3 NDVI Trend Map (2017\u0026ndash;2023):\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the spatial distribution of NDVI trend (slope per year) across the entire Sindhuli district. Green to yellow colours represent positive trends (increasing vegetation vigor), while purple to dark blue colours indicate negative trends (decline in vigor). The map reveals clear spatial clustering of decline, particularly on north- and west-facing slopes that correspond to high solar radiation proxy values. In contrast, south-facing slopes show predominantly stable or slightly positive trends. Quantitatively, 28.4% of the district experienced a negative NDVI trend, while only 18.7% showed improvement. This figure provides the core evidence that vegetation decline is not random but is strongly associated with topographic position, especially higher solar exposure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSpatial distribution of long‑term NDVI trends derived from a seven‑year MODIS time series. Positive trends indicate areas of vegetation recovery or increasing greenness, while negative trends highlight zones of persistent decline. The map reveals strong topographic structuring of NDVI trajectories, with declines concentrated on steep, high‑insolation slopes and improvements in shaded valley bottoms.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4 NDVI Change Between 2017 and 2023:\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents a direct before and after comparison of vegetation condition. Panel (A) shows NDVI values in 2017 (generally higher green tones across the district). Panel (B) shows NDVI in 2023, where many areas appear less green. Panel (C) displays the absolute NDVI difference (2023 minus 2017), confirming a district wide mean decline of -0.04 units. Panel (D) is the hotspot classification map, with red indicating decline, grey indicating stable areas, and blue indicating increase. Decline hotspots are clearly concentrated in the northern and western portions of Sindhuli, aligning closely with high solar radiation zones shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This figure validates the long term trend results and highlights the magnitude and location of vegetation degradation over the seven year period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eFour‑panel figure showing (A) NDVI in 2017, (B) NDVI in 2023, (C) NDVI difference (2023\u0026ndash;2017), and (D) hotspot classification identifying areas of decline, stability, and improvement. The figure highlights spatial clusters of vegetation degradation on exposed slopes and recovery on north‑facing and moisture‑retaining terrain.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Topographic Controls on Vegetation Vigor and Trends:\u003c/h2\u003e \u003cp\u003eThe variable importance panel quantifies the relative contribution of each topographic predictor to explaining inter annual NDVI trend variability. The Random Forest model assigns the highest importance to slope, followed by solar radiation proxy, with aspect exerting a comparatively weaker influence. This hierarchy indicates that terrain steepness and solar loading are the dominant physical controls on vegetation vigor trajectories, consistent with ecohydrological theory that steep, high insolation slopes experience accelerated moisture loss and thermal stress. The ranking mirrors findings from similar dieback studies, where \u0026ldquo;the aspect and amount of solar radiation had the strongest relationship with the presence of unhealthy vegetation.\u0026rdquo; The scatterplots provide an unadjusted visualization of the empirical relationships between NDVI trend and each predictor. NDVI trend exhibits a negative association with slope, reflecting reduced soil moisture retention and increased erosion on steep terrain. The relationship with aspect reveals lower NDVI trends on south facing slopes, which receive higher solar exposure in the Northern Hemisphere. The solar proxy plot shows a monotonic decline in NDVI trend at high solar exposure levels, indicating that thermal and radiative stress suppress vegetation recovery. These raw relationships provide the ecological intuition that motivates the subsequent modelling. The partial dependence functions isolate the marginal effect of each predictor on NDVI trend while holding all other variables constant. These curves reveal non linear and threshold based responses that are not visible in simple scatterplots. NDVI trend declines sharply beyond a critical solar exposure threshold, suggesting that extreme insolation imposes physiological limits on vegetation resilience. Slope exhibits a similar threshold response, with NDVI trend decreasing rapidly on slopes exceeding\u0026thinsp;~\u0026thinsp;30\u0026deg;. Aspect shows a bimodal pattern, with north facing slopes supporting more positive NDVI trajectories, likely due to cooler microclimates and higher moisture availability. These marginal effects provide strong evidence that topography mediates vegetation responses to climatic variability. The diagnostic plots evaluate the statistical robustness of the regression model. Residuals vs. fitted values show no major deviations from linearity; Q-Q plots indicate approximate normality; scale location plots suggest acceptable homoscedasticity; and leverage plots identify a small number of influential observations that do not compromise model stability. These diagnostics confirm that the model provides a statistically defensible representation of the NDVI topography relationships.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Variable Importance:\u003c/h2\u003e \u003cp\u003eFigure shows the relative importance of topographic and climatic covariates for recent NDVI trends (2017\u0026ndash;2023) based on Random Forest regression. Solar radiation proxy was the most important variable, followed closely by sin(aspect) and cos(aspect). Slope ranked fourth, while mean annual rainfall and mean annual temperature had moderate but lower importance. This confirms that solar exposure and correct aspect transformation are the dominant drivers of vegetation trend in Sindhuli district.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Relationships Between NDVI Trends and Covariates:\u003c/h2\u003e \u003cp\u003eGiven trend presents scatterplots of recent NDVI trend against key covariates. The relationship with solar radiation proxy (bottom right panel) shows a weak positive trend, indicating that higher solar exposure is associated with slightly more negative NDVI trends. Slope (top left) and mean annual rainfall (top right) show very weak relationships. Mean annual temperature (bottom left) exhibits a slight negative association with NDVI trend. These plots highlight the complex and often nonlinear influence of topography and climate on vegetation dynamics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Spatial Pattern of Recent NDVI Trends:\u003c/h2\u003e \u003cp\u003eSpatial pattern of recent NDVI Trends maps shows the spatial distribution of recent NDVI trends (2017\u0026ndash;2023) across Sindhuli district. Green points indicate positive trends (increasing vigor), while red points indicate negative trends (decline). The map reveals clear spatial clustering of decline, particularly in the northern and western parts of the district. Positive trends are more scattered and tend to occur in areas with lower solar radiation exposure. Overall, negative trends dominate, consistent with the variable importance and scatterplot results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Overall Findings:\u003c/h2\u003e \u003cp\u003eTaken together, the integrated analysis and results indicate that recent vegetation trend in Sindhuli districts are strongly controlled by topography, especially solar radiation exposure and aspect. Climate covariates (rainfall and temperature) play a secondary but noticeable role. These findings provide robust evidence that north and west facing slopes with high solar exposure are most vulnerable to vegetation decline, while south facing slopes show greater stability. These spatial patterns are statistically supported by both linear regression and Random Forest models, confirming solar radiation proxy as the dominant topographic driver. Additionally, exploratory relationships (Supplementary Figure S1) and full regression diagnostics for both the NDVI trend and NDVI mean models (Supplementary Figures S2-S3) are provided in the Supplementary Material.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Interpretation of NDVI Trends and Topographic Drivers:\u003c/h2\u003e \u003cp\u003eThe results reveal clear topography driven patterns of vegetation change in Sindhuli district. The NDVI trend map (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and change analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) show that 28.4% of the district experienced a negative trend and an overall mean decline of -0.04 NDVI units between 2017 and 2023. Decline was consistently concentrated on north and west facing slopes with high solar radiation proxy values. The inclusion of climate covariates strengthened the models: solar radiation proxy remained the strongest predictor, followed by slope and annual rainfall. These findings indicate that increased solar exposure elevates evaporative demand and winter drought stress, while higher rainfall partially mitigates decline. In contrast, south facing slopes exhibited greater stability or slight improvement. These spatial patterns are statistically supported by both linear regression and Random Forest models, where solar radiation proxy emerged as the strongest predictor of both mean NDVI and NDVI trend. This indicates that increased solar exposure on equator facing slopes likely elevates evaporative demand and winter drought stress, reducing vegetation vigor over time. Slope also played a significant secondary role, with steeper terrain associated with more negative trends, probably due to greater runoff and reduced soil moisture retention. These findings align with the well established principle that topography modulates microclimate and water availability in mid hill environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Comparison with Previous Studies:\u003c/h2\u003e \u003cp\u003eThe dominance of solar radiation and aspect as drivers of vegetation decline closely mirrors the results of Fitzgerald et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who studied a vulnerable population of \u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eEucalyptus macrorhyncha\u003c/span\u003e in South Australia. Using hyperspectral imagery and a digital terrain model, they found that solar radiation and aspect explained 68% of variation in unhealthy vegetation, with northwest facing slopes being most affected. The present study reaches a similar conclusion using freely available MODIS NDVI and SRTM data at the district scale. National scale NDVI studies in Nepal have reported mixed greening and browning trends (Baniya et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nila et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), but they rarely examined topographic controls at the district level. The current analysis fills this gap by providing spatially explicit evidence that topographic position, rather than broad climatic trends alone, strongly shapes vegetation dynamics in the mid hills.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Implications for Eucolyptus Plantation Management and Local Livelihoods:\u003c/h2\u003e \u003cp\u003eSindhuli district contains extensive \u003cem\u003eEucalyptus\u003c/em\u003e plantations (\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eE. camaldulensis\u003c/span\u003e and \u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eE. globulus\u003c/span\u003e), which are important sources of fuelwood, poles, and income for rural communities. The observed negative NDVI trends on high solar radiation slopes suggest declining productivity in many plantation areas. The inclusion of rainfall as a covariate shows that drier years exacerbate decline, highlighting the vulnerability of plantations during dry winters. A district wide NDVI decline of -0.04 units over seven years may translate to measurable reductions in biomass and fuelwood yield. These findings have practical management implications. Future plantation site selection should prioritise south and southeast facing slopes with lower solar radiation proxy values to improve survival and growth rates. Existing plantations on high risk north and west facing slopes may benefit from targeted interventions such as thinning, mulching, or enrichment with native species to reduce water stress. Such site specific planning could enhance the long term economic viability of Eucalyptus plantations and support rural livelihoods while reducing pressure on natural forests.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Limitations of the Study:\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, the use of 250 m MODIS NDVI data provides excellent temporal consistency but limits spatial detail. The study is constrained by the spatial resolution of MODIS NDVI, the use of annual composites, and the absence of climate, soil, and anthropogenic covariates. At this resolution, pixels often contain a mixture of \u003cem\u003eEucalyptus\u003c/em\u003e, other forests, agriculture, and bare land, preventing species specific analysis. Second, although climate covariates (CHIRPS rainfall and ERA5 temperature) were included, soil properties and management practices were not. Third, no field validation was possible due to logistical constraints. Although the statistical models showed acceptable diagnostics, ground truth data would strengthen confidence in the NDVI trend interpretations. Fourth, the solar radiation proxy is a simplified index; more sophisticated solar radiation models could refine the results. Fifth, the analysis did not incorporate management factors such as planting density, species mixture, or soil properties, which may explain some residual variation. Finally, Topographic predictors derived from a 30 m DEM may not fully capture micro terrain effects, and forest mask inaccuracies may influence spatial delineation of NDVI trends. Regression diagnostics indicate minor deviations from model assumptions, and the lack of field validation limits ecological interpretation. Despite these limitations, the integrated remote sensing approach provides a robust district‑scale assessment of vegetation dynamics and topographic controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Broader Significance for Sustainable Land Management in Nepal\u0026rsquo;s Mid Hills:\u003c/h2\u003e \u003cp\u003eDespite these limitations, the study provides the first district scale topographic analysis of vegetation vigor trends in Nepal\u0026rsquo;s mid hills. The open source GEE, QGIS and R workflow developed here is reproducible and can be readily applied to other districts facing similar challenges. The findings highlight the need to integrate topographic considerations into plantation planning and forest management policies. In the context of increasing climate variability, such approaches will be essential for building resilient landscapes and supporting sustainable rural development in the Himalayan mid hills.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides the first district scale assessment of long term vegetation vigor and decline in Sindhuli district, Nepal, using MODIS NDVI time series (2017\u0026ndash;2023) integrated with SRTM derived topographic variables and climate covariates. The results clearly demonstrate that topography exerts strong control on vegetation dynamics in the mid hills. Negative NDVI trends affected 28.4% of the district, with decline hotspots concentrated on north and west facing slopes that experience high solar radiation exposure. In contrast, south facing slopes showed greater stability or slight improvement. Statistical modelling confirmed that the solar radiation proxy was the dominant driver of both mean NDVI and NDVI trend, followed by slope. These findings highlight that solar exposure and terrain steepness are primary factors influencing vegetation stress and decline in this topographically complex landscape.\u003c/p\u003e \u003cp\u003eThe observed vegetation decline has direct implications for the management of \u003cem\u003eEucalyptus\u003c/em\u003e plantations, which form a significant part of Sindhuli\u0026rsquo;s rural landscape and economy. To improve long term productivity and resilience, future plantation establishment should prioritise south and southeast facing slopes with lower solar radiation proxy values. Existing plantations on high risk north and west facing slopes may require targeted interventions such as reduced planting density, mulching, or gradual conversion to mixed native exotic systems. District level forest offices and community forestry groups should incorporate topographic suitability maps into plantation planning. At the national level, these results support the integration of remote sensing based topographic analysis into Nepal\u0026rsquo;s forest management and climate adaptation policies, helping to safeguard rural livelihoods and reduce pressure on natural forests.\u003c/p\u003e \u003cp\u003eFuture studies should extend this approach to other mid hill districts and incorporate higher resolution Sentinel-2 data once improved cloud masking techniques are applied. Field validation of NDVI trends and species specific mapping (particularly \u003cem\u003eE. globulus\u003c/em\u003e and \u003cem\u003eE. camaldulensis\u003c/em\u003e) would strengthen the findings. Additional environmental and management variables such as soil properties, plantation age, and local land use practices should be included to explain residual variation. Finally, linking NDVI based vigor trends to quantitative estimates of biomass productivity and economic losses would provide stronger evidence for policy and investment decisions in sustainable plantation forestry.\u003c/p\u003e \u003cp\u003eIn summary, this research demonstrates that topography driven vegetation decline is an important but often overlooked factor in Nepal\u0026rsquo;s mid hills. The open source workflow developed here offers a practical and scalable tool for monitoring and managing vegetation resources under changing climatic conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, methodology, software, validation, formal analysis, investigation, data curation, writing original draft preparation, writing review and editing, visualization, and project administration: P.G. (Prabin Gauli). The author has read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MODIS MOD13Q1 NDVI data used in this study are publicly available from NASA EOSDIS Land Processes DAAC. The SRTM 90 m DEM is available from the CGIAR-CSI SRTM database. The administrative boundary of Sindhuli district was obtained from the Chuche Naksa dataset of the Government of Nepal. All processed data, GEE scripts, QGIand R code generated during the study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author also acknowledges the open access data providers (NASA MODIS, CGIAR-CSI SRTM, and Google Earth Engine) that made this study possible without additional cost.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ *8* 2127\u0026ndash;150. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0034-4257(79)90013-0\u003c/span\u003e\u003cspan address=\"10.1016/0034-4257(79)90013-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePettorelli N, Vik JO, Mysterud A, Gaillard JM, Tucker CJ, Stenseth NC (2005) Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology \u0026amp; Evolution, *20*(9), 503\u0026ndash;510. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tree.2005.05.011\u003c/span\u003e\u003cspan address=\"10.1016/j.tree.2005.05.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83(1\u0026ndash;2):195\u0026ndash;213. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0034-4257(02)00096-2\u003c/span\u003e\u003cspan address=\"10.1016/S0034-4257(02)00096-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDidan K (2015) *MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006* [Data set]. NASA EOSDIS Land Processes DAAC. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5067/MODIS/MOD13Q1.006\u003c/span\u003e\u003cspan address=\"10.5067/MODIS/MOD13Q1.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerbesselt J, Hyndman R, Newnham G, Culvenor D (2010) Detecting trend and seasonal changes in satellite image time series. Remote Sens Environ *114* 1106\u0026ndash;115. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2009.08.014\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2009.08.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Friedl MA, Schaaf CB (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 3471\u0026ndash;475. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0034-4257(02)00135-9\u003c/span\u003e\u003cspan address=\"10.1016/S0034-4257(02)00135-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang S, Tang L, Hupy JP, Wang Y, Shao G (2021) A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J Forestry Res *32* 11\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11676-020-01155-1\u003c/span\u003e\u003cspan address=\"10.1007/s11676-020-01155-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang Z, Huete AR, Didan K, Miura T (2008) Development of a two-band enhanced vegetation index without a blue band. Remote Sens Environ *112* 103833\u0026ndash;3845. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2008.06.006\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2008.06.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFensholt R, Proud SR (2012) Evaluation of earth observation based global long term vegetation trends Comparing GIMMS and MODIS global NDVI time series. Remote Sens Environ *119* 131\u0026ndash;147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2011.12.015\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2011.12.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForkel M, Carvalhais N, Verbesselt J, Mahecha MD, Neigh CSR, Reichstein M (2013) Trend change detection in NDVI time series: Effects of inter-annual variability and methodology. Remote Sensing, *5*(5), 2113\u0026ndash;2144. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs5052113\u003c/span\u003e\u003cspan address=\"10.3390/rs5052113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian F, Fensholt R, Verbesselt J, Grogan K, Horion S, Wang Y (2015) Evaluating temporal consistency of long-term global NDVI datasets for trend analysis. Remote Sensing of Environment, *163*, 326\u0026ndash;340. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2015.03.031\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2015.03.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEastman JR, Sangermano F, Machado EA, Rogan J, Anyamba A (2013) Global trends in seasonality of normalized difference vegetation index (NDVI), 1982\u0026ndash;2011. Remote Sensing, *5*(. 104799\u0026ndash;4818. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs5104799\u003c/span\u003e\u003cspan address=\"10.3390/rs5104799\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Jong R, de Bruin S, de Wit A, Schaepman ME, Dent DL (2011) Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sens Environ *115* 2692\u0026ndash;702. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2010.10.011\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2010.10.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg EH, Gonzalez P, Fensham R, Zhang Z, Castro J, Demidova N, Lim J-H, Allard G, Running SW, Semerci A, Cobb N (2010) A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manag 4660\u0026ndash;684. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foreco.2009.09.001\u003c/span\u003e\u003cspan address=\"10.1016/j.foreco.2009.09.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHammond WM, Williams AP, Abatzoglou JT, Adams HD, Klein T, L\u0026oacute;pez R, S\u0026aacute;enz-Romero C, Hartmann H, Breshears DD, Allen CD (2022) Global field observations of tree die-off reveal hotter-drought fingerprint for Earth\u0026rsquo;s forests. Nature Communications, *13*, Article 1761. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-022-29289-2\u003c/span\u003e\u003cspan address=\"10.1038/s41467-022-29289-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoat B, Brodribb TJ, Brodersen CR, Duursma RA, L\u0026oacute;pez R, Medlyn BE (2018) Triggers of tree mortality under drought. Nat *558* 7711531\u0026ndash;539. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-018-0240-x\u003c/span\u003e\u003cspan address=\"10.1038/s41586-018-0240-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJump AS, Ruiz-Benito P, Greenwood S, Allen CD, Kitzberger T, Fensham R, Mart\u0026iacute;nez-Vilalta J, Lloret F (2017) Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback. Glob Change Biol 23(9):3742\u0026ndash;3757. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/gcb.13636\u003c/span\u003e\u003cspan address=\"10.1111/gcb.13636\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaniya B, Tang Q, Huang Z, Sun S, Techato K (2018) Spatial and temporal variation of NDVI in response to climate change and the implication for carbon dynamics in Nepal. Forests, *9*(. 6\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/f9060329\u003c/span\u003e\u003cspan address=\"10.3390/f9060329\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Article 329\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrakauer NY, Lakhankar T, Anad\u0026oacute;n JD (2017) Mapping and attributing normalized difference vegetation index trends for Nepal. Remote Sensing, *9*(10), Article 986. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs9100986\u003c/span\u003e\u003cspan address=\"10.3390/rs9100986\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNila S, Bhardwaj A, Joshi R (2025) Spatiotemporal vegetation dynamics in the Hindu Kush Himalayan region using MODIS NDVI time series. Environ Monit Assess *197* Article 234. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10661-025-13456-1\u003c/span\u003e\u003cspan address=\"10.1007/s10661-025-13456-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaudel B, Zhang Y, Li S, Liu L (2022) Spatiotemporal patterns of vegetation greenness change and associated climatic drivers in the Hindu Kush Himalayan region. Ecological Indicators, *144*, Article 109453 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolind.2022.109453\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2022.109453\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShrestha B, Zhang L, Shrestha S, Khadka N (2024) Spatiotemporal patterns, sustainability, and primary drivers of NDVI-derived vegetation dynamics (2003\u0026ndash;2022) in Nepal. Environmental Monitoring and Assessment, *196*, Article 607. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10661-024-12754-4\u003c/span\u003e\u003cspan address=\"10.1007/s10661-024-12754-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMishra NB, Mainali KP (2017) Greening and browning of the Himalaya: Spatial patterns and the role of climatic change and human drivers. Sci Total Environ *587\u0026ndash;588* 326\u0026ndash;339. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2017.02.156\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2017.02.156\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUddin K, Shrestha HL, Murthy MSR, Bajracharya B, Shrestha B, Gilani H, Pradhan S, Dangol B (2015) Development of 2010 national land cover database for Nepal. J Environ Manage *148* 82\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jenvman.2014.07.047\u003c/span\u003e\u003cspan address=\"10.1016/j.jenvman.2014.07.047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDepartment of Forest Research and Survey (DFRS) (2015) State of Nepal's forests. Government of Nepal\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanday PK, Ghimire B (2012) Time-series analysis of NDVI from AVHRR data over the Hindu Kush\u0026ndash;Himalayan region for the period 1982\u0026ndash;2006. Int J Remote Sens *33* 216710\u0026ndash;6728. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/01431161.2012.692836\u003c/span\u003e\u003cspan address=\"10.1080/01431161.2012.692836\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoudel KP, Temesgen H, Gray AN (2015) Estimating forest aboveground biomass using LiDAR and Landsat data in the Pacific Northwest, USA. Forest Ecology and Management, pp 44\u0026ndash;55\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhuyan M, Singh B, Vid S, Jeganathan C (2022) Analysing the spatio-temporal patterns of vegetation dynamics and their responses to climatic parameters in Meghalaya from 2001 to 2020. Environmental Monitoring and Assessment, *195*, Article 94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10661-022-10685-6\u003c/span\u003e\u003cspan address=\"10.1007/s10661-022-10685-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo W, Liu H, Anenkhonov OA (2021) Vegetation cover changes and their relationship with climate in the forest-steppe ecotone of the Selenga River basin. J Arid Land *13* 5455\u0026ndash;469. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40333-021-0007-2\u003c/span\u003e\u003cspan address=\"10.1007/s40333-021-0007-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson K, Fawcett D, Cugulliere A, Benford S, Jones D, Leng R (2020) Vegetation expansion in the subnival Hindu Kush Himalaya. Glob Change Biol 26(3):1608\u0026ndash;1625. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/gcb.14919\u003c/span\u003e\u003cspan address=\"10.1111/gcb.14919\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandey S, Poudyal BH, Maraseni T (2021) Climate change impacts and adaptation strategies in the Hindu Kush Himalaya: A review. Environmental Science \u0026amp; Policy, *124*, pp 254\u0026ndash;265\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKandel P, Gurung J, Chettri N, Ning W, Sharma E (2016) Biodiversity research trends and gap analysis from a transboundary landscape, Eastern Himalayas. J Asia-Pacific Biodivers 9(1):1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.japb.2015.11.002\u003c/span\u003e\u003cspan address=\"10.1016/j.japb.2015.11.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHawthorne S, Miniat CF (2018) Topography may mitigate drought effects on vegetation along a hillslope gradient. Ecohydrology, *11*(1. Article e1825 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/eco.1825\u003c/span\u003e\u003cspan address=\"10.1002/eco.1825\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoeslund JE, Arge L, B\u0026oslash;cher PK, Dalgaard T, Svenning J-C (2013) Topography as a driver of local terrestrial vascular plant diversity patterns. Nordic J Bot *31* 2129\u0026ndash;144. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1756-1051.2013.00082.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1756-1051.2013.00082.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAuslander M, Nevo E, Inbar M (2003) The effects of slope orientation on plant growth, developmental instability and susceptibility to herbivores. J Arid Environ *55* 3405\u0026ndash;416. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-1963(02)00281-1\u003c/span\u003e\u003cspan address=\"10.1016/S0140-1963(02)00281-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian H, Melillo JM, Kicklighter DW, McGuire AD, Helfrich J (2001) Regional carbon dynamics in monsoon Asia and its implications for the global carbon cycle. Glob Planet Change 31(1\u0026ndash;4):109\u0026ndash;124\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson JP, Gallant JC (eds) (2000) Terrain analysis: Principles and applications. Wiley\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling: A review of hydrological, geomorphological, and biological applications. Hydrol Process 5(1):3\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hyp.3360050103\u003c/span\u003e\u003cspan address=\"10.1002/hyp.3360050103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu P, Rich PM (2002) A geometric solar radiation model with applications in agriculture and forestry. Computers and Electronics in Agriculture, *37*(1\u0026ndash;3), 25\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0168-1699(02)00115-1\u003c/span\u003e\u003cspan address=\"10.1016/S0168-1699(02)00115-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePierce KB Jr., Lookingbill T, Urban D (2005) A simple method for estimating potential relative radiation (PRR) for landscape-vegetation analysis. Landscape Ecol 20(2):137\u0026ndash;147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10980-004-1296-6\u003c/span\u003e\u003cspan address=\"10.1007/s10980-004-1296-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDobrowski SZ (2011) A climatic basis for microrefugia: The influence of terrain on climate. Global Change Biology *17* 21022\u0026ndash;1035. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2486.2010.02263.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2486.2010.02263.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBennie J, Huntley B, Wiltshire A, Hill MO, Baxter R (2008) Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland. Ecol Model 216(1):47\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolmodel.2008.04.010\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolmodel.2008.04.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCune B, Keon D (2002) Equations for potential annual direct incident radiation and heat load. J Veg Sci *13* 4603\u0026ndash;606. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1654-1103.2002.tb02087.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1654-1103.2002.tb02087.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriffiths RP, Madritch MD, Swanson AK (2009) The effects of topography on forest soil characteristics in the Oregon Cascade Mountains (USA): Implications for the effects of climate change on soil properties. For Ecol Manage *257* 11\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foreco.2008.08.010\u003c/span\u003e\u003cspan address=\"10.1016/j.foreco.2008.08.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlorinsky IV, Kuryakova GA (1996) Influence of topography on some vegetation cover properties. CATENA 2123\u0026ndash;141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0341-8162(96)00005-7\u003c/span\u003e\u003cspan address=\"10.1016/0341-8162(96)00005-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTateno R, Takeda H (2003) Forest structure and tree species distribution in relation to topography-mediated heterogeneity of soil nitrogen and light at the forest floor. Ecological Research, *18*(5), 559\u0026ndash;571. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1440-1703.2003.00578.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1440-1703.2003.00578.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBand LE, Patterson P, Nemani R, Running SW (1993) Forest ecosystem processes at the watershed scale: Incorporating hillslope hydrology. Agric For Meteorol 63(1\u0026ndash;2):93\u0026ndash;126. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0168-1923(93)90024-C\u003c/span\u003e\u003cspan address=\"10.1016/0168-1923(93)90024-C\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar L, Skidmore AK, Knowles E (1997) Modelling topographic variation in solar radiation in a GIS environment. Int J Geogr Inf Sci 11(5):475\u0026ndash;497. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/136588197242266\u003c/span\u003e\u003cspan address=\"10.1080/136588197242266\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGurung A, Bista R, Karki R, Shrestha S, Uprety D, Oh S-E (2019) Community-based forest management and its role in improving forest conditions and livelihoods in Nepal's mid-hills. J For Res, *24*(3), 159\u0026ndash;167\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandit R, Neupane PR, Wagle BH (2021) Economics of Eucalyptus plantation in the eastern Terai of Nepal. Banko Janakari, *31*(1), 23\u0026ndash;31\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaudel KCA, N. S., Ojha H (2023) Declining productivity of Eucalyptus plantations in the mid-hills of Nepal: Farmers' perceptions and management challenges. J For Livelihood 22(1):45\u0026ndash;58\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFitzgerald DL, Peters S, Guerin GR, McGrath A, Keppel G (2023) Quantifying dieback in a vulnerable population of Eucalyptus macrorhyncha using remote sensing. Land, *12*(7.), Article 1271. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/land12071271\u003c/span\u003e\u003cspan address=\"10.3390/land12071271\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhanal S N. (n.d.). Eucalyptus plantations in Nepal. Food and Agriculture Organization of the United Nations. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fao.org/4/AC772E/ac772e0f.htm\u003c/span\u003e\u003cspan address=\"http://www.fao.org/4/AC772E/ac772e0f.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePokhrel M (2025), February 25 In Nepal, a eucalyptus boom became an ecological cautionary tale. Mongabay. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://news.mongabay.com/2025/02/in-nepal-a-eucalyptus-boom-became-an-ecological-cautionary-tale/\u003c/span\u003e\u003cspan address=\"https://news.mongabay.com/2025/02/in-nepal-a-eucalyptus-boom-became-an-ecological-cautionary-tale/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhakal A (2008) Tree growth performance in private plantations in Nepal's central Terai region [Master's thesis, University of Southern Queensland]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite KJ (1985) Eucalyptus species trials and plantations at Sagarnath, Nepal. Sagarnath Forest Development Project\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilmour DA, Fisher RJ (1991) Villagers, forests and foresters: The philosophy, process and practice of community forestry in Nepal. Sahayogi\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJackson JK (1994) Manual of afforestation in Nepal, 2nd edn. Forest Research and Survey Centre\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandey D, Brown C (2000) Teak: A global overview. Unasylva, *51*(201), 3\u0026ndash;13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArnold JEM, Dewees PA (eds) (1997) Farms, trees and farmers: Responses to agricultural intensification. Earthscan\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaxena NC (1997) The saga of participatory forest management in India. Center for International Forestry Research\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eByron N, Arnold M (1999) What futures for the people of the tropical forests? World Development, *27*(. 5789\u0026ndash;805. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0305-750X(99)00025-X\u003c/span\u003e\u003cspan address=\"10.1016/S0305-750X(99)00025-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBreiman L (2001) Random forests. Machine Learning, *45*(. 15\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1023/A:1010933404324\u003c/span\u003e\u003cspan address=\"10.1023/A:1010933404324\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCutler DR, Edwards TC Jr., Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ (2007) Random forests for classification in ecology. Ecology 112783\u0026ndash;2792. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/07-0539.1\u003c/span\u003e\u003cspan address=\"10.1890/07-0539.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedman JH (2001) Greedy function approximation: A gradient boosting machine. Annals of Statistics, *29*(5), 1189\u0026ndash;1232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1214/aos/1013203451\u003c/span\u003e\u003cspan address=\"10.1214/aos/1013203451\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHijmans RJ (2023) raster: Geographic data analysis and modeling (Version 3.5\u0026ndash;15) [R package]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=raster\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=raster\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePebesma E (2018) Simple features for R: Standardized support for spatial vector data. R J 1439\u0026ndash;446. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.32614/RJ-2018-009\u003c/span\u003e\u003cspan address=\"10.32614/RJ-2018-009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiaw A, Wiener M (2002) Classification and regression by randomForest. R News, *2*(3), 18\u0026ndash;22\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuhn M (2008) Building predictive models in R using the caret package. J Stat Softw *28* 51\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18637/jss.v028.i05\u003c/span\u003e\u003cspan address=\"10.18637/jss.v028.i05\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvans JS, Murphy MA (2022) spatialEco: Spatial analysis and modelling utilities (Version 1.3-7) [R package]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=spatialEco\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=spatialEco\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJames G, Witten D, Hastie T, Tibshirani R (2021) An introduction to statistical learning: With applications in R (2nd ed.). Springer. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-1-0716-1418-1\u003c/span\u003e\u003cspan address=\"10.1007/978-1-0716-1418-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrobl C, Boulesteix A-L, Zeileis A, Hothorn T (2007) Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinf *8* Article 25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2105-8-25\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-8-25\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrasad AM, Iverson LR, Liaw A (2006) Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems, *9*(2), 181\u0026ndash;199. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10021-005-0054-1\u003c/span\u003e\u003cspan address=\"10.1007/s10021-005-0054-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol *77* 4802\u0026ndash;813. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2656.2008.01390.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2656.2008.01390.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkaike H (1974) A new look at the statistical model identification. IEEE Trans Automatic Control *19* 6716\u0026ndash;723. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/TAC.1974.1100705\u003c/span\u003e\u003cspan address=\"10.1109/TAC.1974.1100705\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurnham KP, Anderson DR (2002) Model selection and multimodel inference: A practical information-theoretic approach, 2nd edn. Springer\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHothorn T, Hornik K, Zeileis A (2006) Unbiased recursive partitioning: A conditional inference framework. J Comput Graphical Stat 15(3):651\u0026ndash;674. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1198/106186006X133933\u003c/span\u003e\u003cspan address=\"10.1198/106186006X133933\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQGIS Development Team (2023) QGIS Geographic Information System [Computer software]. Open Source Geospatial Foundation. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://qgis.org\u003c/span\u003e\u003cspan address=\"https://qgis.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eESRI (2023) ArcGIS Pro (Version 3.x) [Computer software]. Environmental Systems Research Institute\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZanaga D, Van De Kerchove R, De Keersmaecker W, Souverijns N, Scholte R, Caetano M (2021) ESA WorldCover 10 m 2020 v100 [Data set]. Zenodo. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.5571936\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.5571936\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarr TG, Rosen PA, Caro E, Crippen R, Duren R, Hensley S, Kobrick M, Paller M, Rodriguez E, Roth L, Seal D, Shaffer S, Shimada J, Umland J, Werner M, Oskin M, Burbank D, Alsdorf D (2007) The Shuttle Radar Topography Mission. Rev Geophys. *45*(2), Article RG2004 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2005RG000183\u003c/span\u003e\u003cspan address=\"10.1029/2005RG000183\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, *202*, 18\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2017.06.031\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2017.06.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWickham H (2016) ggplot2: Elegant graphics for data analysis (2nd ed.). Springer. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-24277-4\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-24277-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHijmans RJ (2023) terra: Spatial data analysis (Version 1.7\u0026ndash;29) [R package]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=terra\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=terra\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team (2023) R: A language and environment for statistical computing (Version 4.3). R Foundation for Statistical Computing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunnington D (2023) ggspatial: Spatial data framework for ggplot2 (Version 1.1.9) [R package]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=ggspatial\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=ggspatial\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeam RC (2020) R: A language and environment for statistical computing. R Foundation for Statistical Computing\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJarvis A, Reuter HI, Nelson A, Guevara E (2008) *Hole-filled SRTM for the globe Version 4*. CGIAR-CSI SRTM 90m Database. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://srtm.csi.cgiar.org\u003c/span\u003e\u003cspan address=\"http://srtm.csi.cgiar.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBivand RS, Pebesma E, Gomez-Rubio V (2013) Applied spatial data analysis with R (2nd ed.). Springer. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-1-4614-7618-4\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4614-7618-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHengl T, Reuter HI (eds) (2009) Geomorphometry: Concepts, software, applications. Elsevier\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeteler M, Mitasova H (2008) Open source GIS: A GRASS GIS approach, 3rd edn. Springer\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderegg WRL, Kane JM, Anderegg LDL (2013) Consequences of widespread tree mortality triggered by drought and temperature stress. Nat Clim Change 3(1):30\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nclimate1635\u003c/span\u003e\u003cspan address=\"10.1038/nclimate1635\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBreshears DD, Cobb NS, Rich PM, Price KP, Allen CD, Balice RG, Romme WH, Kastens JH, Floyd ML, Belnap J, Anderson JJ, Myers OB, Meyer CW (2005) Regional vegetation die-off in response to global-change-type drought. Proceedings of the National Academy of Sciences, *102*(42), 15144\u0026ndash;15148. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.0505734102\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0505734102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcDowell NG, Pockman WT, Allen CD, Breshears DD, Cobb N, Kolb T, Plaut J, Sperry J, West A, Williams DG, Yepez EA (2008) Mechanisms of plant survival and mortality during drought: Why do some plants survive while others succumb to drought? New Phytologist, *178*. 4719\u0026ndash;739. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1469-8137.2008.02436.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1469-8137.2008.02436.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdams HD, Guardiola-Claramonte M, Barron-Gafford GA, Villegas JC, Breshears DD, Zou CB, Troch PA, Huxman TE (2009) Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought. Proceedings of the National Academy of Sciences, *106*(17), 7063\u0026ndash;7066. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.0901438106\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0901438106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams AP, Allen CD, Macalady AK, Griffin D, Woodhouse CA, Meko DM, Swetnam TW, Rauscher SA, Seager R, Grissino-Mayer HD, Dean JS, Cook ER, Gangodagamage C, Cai M, McDowell NG (2013) Temperature as a potent driver of regional forest drought stress and tree mortality. Nat Clim Change 3292\u0026ndash;297. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nclimate1693\u003c/span\u003e\u003cspan address=\"10.1038/nclimate1693\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGautam AP, Webb EL, Eiumnoh A (2002) GIS assessment of land use/land cover changes associated with community forestry implementation in the Middle Hills of Nepal. Mt Res Dev 22(1):63\u0026ndash;69\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGautam AP, Webb EL, Shivakoti GP, Zoebisch MA (2003) Land use dynamics and landscape change pattern in a mountain watershed in Nepal. Agriculture, Ecosystems \u0026amp; Environment, *99*(1\u0026ndash;3), 83\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0167-8809(03)00148-8\u003c/span\u003e\u003cspan address=\"10.1016/S0167-8809(03)00148-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox J (1993) Forest resources in a Nepali village in 1980 and 1990: The positive influence of population growth. Mt Res Dev 13(1):89\u0026ndash;98\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJackson WJ, Tamrakar RM, Hunt S, Shepherd KR (1998) Land-use changes in two Middle Hills districts of Nepal. Mt Res Dev 18(3):193\u0026ndash;212\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVirgo KJ, Subba KJ (1994) Land-use change between 1978 and 1990 in Dhankuta district, Koshi Hills, eastern Nepal. Mountain Research and Development, *14*(2), 159\u0026ndash;170\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThapa GB, Weber KE (1995) Natural resource degradation in a small watershed in Nepal: Complex causes and remedial measures. Nat Resour Forum 19(4):285\u0026ndash;296\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidt-Vogt D (1998) Defining degraded forest in the Middle Hills of Nepal. Banko Janakari 8(1):19\u0026ndash;22\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShrestha BM (1999) The role of community forestry in the rehabilitation of degraded hills in Nepal (Doctoral dissertation, University of Reading)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBranney P, Yadav KP (1998) *Changes in community forest condition and management 1994\u0026ndash;1998: Analysis of information from the Forest Resource Assessment Study and Socio-economic Study in the Koshi Hills*. Nepal-UK Community Forestry Project\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcharya KP (2002) Twenty-four years of community forestry in Nepal. Int Forestry Rev *4* (2), 149\u0026ndash;156\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalla YB (2001) Changing policies and the persistence of patron-client relations in Nepal: Stakeholders' responses to changes in forest policies. Environmental History, *6*(2), 287\u0026ndash;307\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOjha HR, Cameron J, Kumar C (2009) Deliberation or symbolic violence? The governance of community forestry in Nepal. For Policy Econ 11(5\u0026ndash;6):365\u0026ndash;374\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Beijing Forestry University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"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":"vegetation vigor, NDVI trend analysis, topographic drivers, solar radiation proxy, mid hills, Nepal, Climate Covariates, remote sensing, sustainable land management, Eucalyptus plantations","lastPublishedDoi":"10.21203/rs.3.rs-9508567/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9508567/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTopography exerts strong controls on vegetation vigor in mountainous regions, yet district scale assessments integrating recent NDVI trends with explicit topographic and climatic drivers remain limited in the Hindu Kush Himalaya. This study quantified spatiotemporal patterns of vegetation vigor and decline in Sindhuli district, Nepal, using MODIS MOD13Q1 NDVI time series from 2017 to 2023. Annual NDVI composites were generated, and linear regression was applied to derive pixel wise trend slopes. Topographic variables (slope, sin(aspect), cos(aspect), and solar radiation proxy) were extracted from the SRTM 90 m DEM. Climate covariates (annual rainfall from CHIRPS and mean temperature from ERA5 Land) were added. A random sample of 3,000 points was extracted for statistical modelling using ordinary least squares regression and Random Forest analysis. Spatial autocorrelation was tested using Moran\u0026rsquo;s I. Maps were produced in R using the terra and ggplot2 packages. Results showed that mean NDVI across the district was 0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11, with 28.4% of the area exhibiting a negative NDVI trend indicative of vegetation decline. Solar radiation proxy emerged as the strongest predictor of both mean NDVI and NDVI trend (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by slope and annual rainfall. North and west facing slopes with high solar exposure displayed the most pronounced decline, while south facing slopes showed greater stability or slight improvement. Direct comparison between 2017 and 2023 confirmed a district wide NDVI decrease of 0.04 units. These findings demonstrate that topographic position, particularly solar radiation exposure, together with rainfall, is a primary driver of recent vegetation decline in Nepal\u0026rsquo;s mid hills. Given the widespread presence of Eucalyptus plantations in Sindhuli district, the observed trends have direct implications for fuelwood productivity, rural livelihoods, and sustainable plantation management. The study provides the first district level topographic and climatic analysis of vegetation vigor in the Nepalese mid hills and offers practical recommendations for site specific plantation planning. The open source GEE and R workflow developed here can be readily adapted for monitoring other mid hill districts in the Himalaya.\u003c/p\u003e","manuscriptTitle":"Topographic Controls on Recent Vegetation Vigor and Decline in Nepal’s Mid Hills: A MODIS NDVI Time Series Analysis of Sindhuli District (2017-2023)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-24 04:19:41","doi":"10.21203/rs.3.rs-9508567/v1","editorialEvents":[{"type":"communityComments","content":0}],"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":"f3ff90d6-e805-43b3-b1c3-5e1e97e5bf8b","owner":[],"postedDate":"April 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66894886,"name":"Forestry"}],"tags":[],"updatedAt":"2026-04-24T04:19:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-24 04:19:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9508567","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9508567","identity":"rs-9508567","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}