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Accurately estimating aboveground biomass (AGB) in forest ecosystems facilitates efficient resource management, carbon accounting, and conservation efforts. This study examines the relationship between predictors from Landsat-9 remote sensing data and several topographical features. While Landsat-9 provides reliable data crucial for long-term monitoring, it is part of a broader suite of available remote sensing technologies. We employ machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Random Forest (RF), alongside linear regression techniques like Multiple Linear Regression (MLR). The primary objectives of this study encompass two key aspects. Firstly, the research methodically selects optimal predictor combinations from four distinct variable groups: Landsat-9 (L1) data, a fusion of Landsat-9 data and Vegetation-based indices (L2), and the integration of Landsat-9 data with the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) (L3) and the combination of best predictors (L4) derived from L1, L2, and L3. Secondly, the research systematically assesses the effectiveness of different algorithms to identify the most precise method for establishing any potential relationship between field-measured AGB and predictor variables. Our study revealed that the Random Forest (RF) model was the most efficient method utilizing Landsat-9 OLI and SRTM DEM (L3) predictors, achieving remarkable accuracy. This conclusion was reached by assessing its outstanding performance when compared to an independent validation dataset. The RF model exhibited remarkable accuracy, presenting relative mean absolute error (RMAE), relative root mean square error (RRMSE), and R2 values of 14.33%, 22.23%, and 0.81, respectively. The XGBoost model is the subsequent choice with RMAE, RRMSE, and R2 values of 15.54%, 23.85%, and 0.77, respectively. The study further highlights the significance of specific spectral bands, notably B4 and B5 from Landsat 9 OLI data, in capturing spatial AGB distribution patterns. Integration of vegetation-based indices, including TNDVI, NDVI, RVI, and GNDVI, further refines AGB mapping precision. Elevation, slope, and the Topographic Wetness Index (TWI) are crucial proxies for representing biophysical and biological mechanisms impacting AGB. Through the utilization of openly accessible fine-resolution data and employing the RF algorithm, the research demonstrated promising outcomes in the identification of optimal predictor-algorithm combinations for forest AGB mapping. This comprehensive approach offers a valuable avenue for informed decision-making in forest management, carbon assessment, and ecological monitoring initiatives. • RF model with Landsat-9 & SRTM DEM excels in forest AGB estimation. • Key spectral bands B4, B5 crucial for AGB distribution patterns. • Topography (elevation, slope, TWI) significantly influences AGB. • Study provides essential insights for precise forest management strategies. [ABSTRACT FROM AUTHOR]

Understanding the intricate relationship between climate variables and the Normalized Difference Vegetation Index (NDVI) is essential for effective ecosystem management. This study focuses on the spatiotemporal dynamics of NDVI and its interaction with climate variables in the ecologically diverse Khyber Pakhtunkhwa (KPK) Province, Pakistan, from 2000 to 2022. The research methodology involves analyzing satellite images and meteorological datasets to examine NDVI and surface latent heat flux (SHF), total precipitation (TPP), temperature (T), soil temperature (ST), and total pressure (TP). KPK Province's ecological significance and complex climate-vegetation interactions drive the selection of this study area. The study uses multiple linear regression analysis to investigate how T, TPP, SHF, and TP influence NDVI. The Mann-Kendall test detects trends, with Sen's slope estimator quantifying trend magnitudes. Additionally, correlation coefficients provide insights into long-term changes and association strengths. The findings highlight a consistent upward trend in mean NDVI over the 23 years, revealing an overall increase in NDVI, particularly in vegetation-dense areas where it rose from 0.27 to 0.32. The research showed an annual growth rate of 0.84% in the entire area, with specific vegetated zones exhibiting a slightly lower rate of 0.80%. However, the average yearly increase in NDVI is higher in vegetation-specific zones (0.00237) compared to the whole area (0.00151). This increase in NDVI occurs alongside a statistically significant decrease in SHF and PPT, suggesting a complex adaptation of vegetation to changing climate conditions in the KPK Province. In contrast, SHF exhibits a statistically significant negative slope of −5.952e-06 (p < 0.05), indicating a pronounced downward trend. Similarly, Sen's slope estimate for precipitation demonstrates a significant negative trend of −0.0001 (p < 0.05), showing diminishing precipitation. The study uncovers intricate linkages between climate variables and vegetation dynamics within KPK Province. These insights have far-reaching implications, guiding decision-making in land management, conservation efforts, and global climate resilience strategies. Ultimately, the research underscores the critical role of data-driven approaches in shaping a greener and more sustainable future. • Upward NDVI trend shows dense area growth over 23 years. • Zones show 0.80% annual growth, revealing nuanced vegetation dynamics in Province. • Clear SHF decrease & diminishing precipitation show complex vegetation adaptation. • Complex climate-NDVI dynamics revealed, highlighting changing conditions' impact. • Data insights guide global land, conservation, and climate strategies. [ABSTRACT FROM AUTHOR]