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, Год журнала: 2024, Номер 82, С. 102732 - 102732

Опубликована: Июль 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.

Язык: Английский

---

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

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Май 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.

Язык: Английский

---

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

Environmental and Sustainability Indicators, Год журнала: 2024, Номер unknown, С. 100485 - 100485

Опубликована: Сен. 1, 2024

Язык: Английский

---

Analysing LULC transformations using remote sensing data: insights from a multilayer perceptron neural network approach

Annals of GIS, Год журнала: 2024, Номер unknown, С. 1 - 28

Опубликована: Май 4, 2024

The study examines the complex dynamics of changes in LULC over three decades, focused on years 1992, 2002, 2012, and 2022. research highlights significance comprehending these alterations within framework environmental socio-economic consequences. land use cover (LULC) have significant far-reaching effects ecosystems, biodiversity, human livelihoods. This offers useful information for politicians, conservationists, urban planners by examining historical patterns forecasting future changes. utilized a Multilayer Perceptron Neural Network (MLP-NN), well-known machine learning technique that excels at collecting intricate patterns. model's design had layers: input, hidden, output. model underwent 10,000 iterations during its training process, thorough statistical analysis was conducted to assess impact each driving component. MLP-NN demonstrated impressive performance, with skill measure 0.8724 an accuracy rate 89.08%. estimates 2022 verified comparing them observed data, ensuring reliability. Moreover, presence evidence likely found be factor substantial model. effectiveness accurately predicting LULC. exceptional proficiency make it powerful tool forecasts. Identifying primary causes performance understanding their implications may help enhance management strategies, encourage spatial planning, guide accurate decision-making, facilitate development policies align sustainable growth development.

Язык: Английский

---

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

Trees Forests and People, Год журнала: 2024, Номер 18, С. 100657 - 100657

Опубликована: Авг. 20, 2024

Язык: Английский

---

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

Earth Science Informatics, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 1, 2025

Язык: Английский

---

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

Ecology and Evolution, Год журнала: 2025, Номер 15(2)

Опубликована: Фев. 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.

Язык: Английский

---

Comparing machine learning algorithms to predict vegetation fire detections in Pakistan

Fire Ecology, Год журнала: 2024, Номер 20(1)

Опубликована: Июнь 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.

Язык: Английский

---

Assessing the Productivity of the Matang Mangrove Forest Reserve: Review of One of the Best-Managed Mangrove Forests

Forests, Год журнала: 2024, Номер 15(5), С. 747 - 747

Опубликована: Апрель 25, 2024

Mangrove ecosystems are crucial for biodiversity and coastal protection but face threats from climate change human activities. This review assesses the productivity of Matang Forest Reserve (MMFR) in Malaysia, which is recognised as one best-managed mangrove forests, while also addressing challenges such deforestation change-induced factors. explores concept highlighting their role carbon sequestration discussing litterfall measurements fundamental metrics assessing primary productivity. An analysis historical changes MMFR’s biomass revealed fluctuations influenced by logging, reforestation, climatic conditions. Trends MMFR indicate a concerning decline attributed to anthropogenic activities aquaculture industrial projects. A regression conducted on Rhizophora apiculata data with age predictor AGB response variable indicated positive trend (slope = 3.61, R-squared 0.686), suggesting quantitative increase age. Further significant negative overall over years (coefficient −3.974, p < 0.05) strong inverse relationship (rho −0.818, 0.05), indicating declining trends. Despite these challenges, this underscores significance sustainable management practices, effective conservation efforts, community engagement maintaining ecosystem health In conclusion, sharing lessons can contribute global forest fostering resilience vital ecosystems.

Язык: Английский

---

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

Heliyon, Год журнала: 2024, Номер 10(14), С. e34710 - e34710

Опубликована: Июль 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

Язык: Английский

---