Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102732 - 102732

Published: July 22, 2024

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

Language: Английский

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Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: May 23, 2024

Abstract This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables CHIRPS ERA5 datasets, our approach leverages Google Earth Engine (GEE) efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, cross wavelet transform analyses. The findings highlight significant spatial temporal NDVI, an annual increase averaging 0.00197 per year (p < 0.0001). positive is coupled precipitation by 0.4801 mm/year = 0.0016). In contrast, analysis recorded a slight decrease temperature (− 0.01011 °C/year, p 0.05) reduction solar radiation 0.27526 W/m 2 /year, 0.05). Notably, cross-wavelet underscored coherence NDVI factors, revealing periods of synchronized fluctuations distinct lagged relationships. particularly highlighted as primary driver growth, illustrating its crucial impact across various Pakistani regions. Moreover, revealed seasonal patterns, indicating that health most responsive during monsoon season, correlating strongly peaks precipitation. Our investigation has Pakistan's complex association which varies different Through analysis, we have identified phase critical influence drivers on patterns. These insights are developing regional adaptation strategies informing sustainable agricultural environmental management practices face ongoing changes.

Language: Английский

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Unveiling Fractional Vegetation Cover Dynamics: A Spatiotemporal Analysis Using MODIS NDVI and Machine Learning

Environmental and Sustainability Indicators, Journal Year: 2024, Volume and Issue: unknown, P. 100485 - 100485

Published: Sept. 1, 2024

Language: Английский

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Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions

Trees Forests and People, Journal Year: 2024, Volume and Issue: 18, P. 100657 - 100657

Published: Aug. 20, 2024

Language: Английский

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Spatiotemporal dynamics of vegetation cover: integrative machine learning analysis of multispectral imagery and environmental predictors

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 1, 2025

Language: Английский

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Machine Learning and Spatio Temporal Analysis for Assessing Ecological Impacts of the Billion Tree Afforestation Project

Ecology and Evolution, Journal Year: 2025, Volume and Issue: 15(2)

Published: Feb. 1, 2025

ABSTRACT This study evaluates the Billion Tree Afforestation Project (BTAP) in Pakistan's Khyber Pakhtunkhwa (KPK) province using remote sensing and machine learning. Applying Random Forest (RF) classification to Sentinel‐2 imagery, we observed an increase tree cover from 25.02% 2015 29.99% 2023 a decrease barren land 20.64% 16.81%, with accuracy above 85%. Hotspot spatial clustering analyses revealed significant vegetation recovery, high‐confidence hotspots rising 36.76% 42.56%. A predictive model for Normalized Difference Vegetation Index (NDVI), supported by SHAP analysis, identified soil moisture precipitation as primary drivers of growth, ANN achieving R 2 0.8556 RMSE 0.0607 on testing dataset. These results demonstrate effectiveness integrating learning framework support data‐driven afforestation efforts inform sustainable environmental management practices.

Language: Английский

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Derivation of Allometric Equations and Carbon Content Estimation in Mangrove Forests of Malaysia

Environmental and Sustainability Indicators, Journal Year: 2025, Volume and Issue: unknown, P. 100618 - 100618

Published: Jan. 1, 2025

Language: Английский

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Comparing machine learning algorithms to predict vegetation fire detections in Pakistan

Fire Ecology, Journal Year: 2024, Volume and Issue: 20(1)

Published: June 24, 2024

Abstract Vegetation fires have major impacts on the ecosystem and present a significant threat to human life. consists of forest fires, cropland other vegetation in this study. Currently, there is limited amount research long-term prediction Pakistan. The exact effect every factor frequency remains unclear when using standard analysis. This utilized high proficiency machine learning algorithms combine data from several sources, including MODIS Global Fire Atlas dataset, topographic, climatic conditions, different types acquired between 2001 2022. We tested many ultimately chose four models for formal processing. Their selection was based their performance metrics, such as accuracy, computational efficiency, preliminary test results. model’s logistic regression, random forest, support vector machine, an eXtreme Gradient Boosting were used identify select nine key factors and, case vegetation, seven that cause fire findings indicated achieved accuracies ranging 78.7 87.5% 70.4 84.0% 66.6 83.1% vegetation. Additionally, area under curve (AUC) values ranged 83.6 93.4% 72.6 90.6% 74.2 90.7% model had highest accuracy rate also AUC value proving be most optimal model. provided predictive insights into specific conditions regional susceptibilities occurrences, adding beyond initial detection data. maps generated analyze Pakistan’s risk showed geographical distribution areas with high, moderate, low risks, highlighting assessments rather than historical detections.

Language: Английский

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Assessing Forest Fragmentation due to Land use Changes from 1992 to 2023: A Spatio-Temporal Analysis Using Remote Sensing Data

Heliyon, Journal Year: 2024, Volume and Issue: 10(14), P. e34710 - e34710

Published: July 1, 2024

The increasing pressures of urban development and agricultural expansion have significant implications for land use cover (LULC) dynamics, particularly in ecologically sensitive regions like the Murree Kotli Sattian tehsils Rawalpindi district Pakistan. This study's primary objective is to assess spatial variations within each LULC category over three decades (1992-2023) using cross-tabulation ArcGIS identify changes investigates into forest fragmentation analysis Landscape Fragmentation Tool (LFTv2.0) classify several classes such as patch, edge, perforated, small core, medium large core. Utilizing remote sensing data from Landsat 5 9 satellites, research focuses on temporal dynamics various including Coniferous Forest (CF), Evergreen (EF), Arable Land (AR), Buildup Area (BU), Barren (BA), Water (WA), Grassland (GL). Support Vector Machine (SVM) classifier software were employed image processing classification, ensuring accuracy categorizing different types. Our results indicate a notable reduction forested areas, with (CF) decreasing 363.9 km

Language: Английский

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Comparative analysis of sensors and classification algorithms for land cover classification in Islamabad, Pakistan

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: Jan. 29, 2025

Language: Английский

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