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

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A comparative analysis of machine learning techniques for aboveground biomass estimation: A case study of the Western Ghats, India

Ecological Informatics, Год журнала: 2024, Номер 80, С. 102479 - 102479

Опубликована: Янв. 20, 2024

Accurate assessment of aboveground biomass (AGB) in tropical forests, particularly within a biodiversity hotspot, is vital for sustainable resource management and the preservation ecosystems. However, estimating AGB forests complex due to diverse intricate nature vegetation, necessitating integration data from multiple sources. To tackle this challenge, our study utilized seven machine learning algorithms analyze various combination multisource datasets. We developed models/scenarios that incorporated Sentinel-1, Sentinel-2 as well environmental factors such topography, soil climate identify key variables accurate estimation AGB. For optimal performance, hyperparameters were fine-tuned through 10-fold cross-validation their accuracy assessed using testing dataset. found integrated model satellite datasets, climate, exhibited highest accuracy, where ensemble stacking, combined MLAs, proved be reliable best suited predicting (mean absolute error-3.97 Mg 0.1 ha−1, root mean square error-5.67 coefficient determination - 0.82). Notably, top predictor included bands (near infrared green), properties (pH organic carbon), topography (elevation). The emphasizes significance incorporating (specifically properties) along with Sentinel datasets improve estimation. This approach has potential broader applications, specifically regions vegetation productivity governed by conditions.

Язык: Английский

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Correcting forest aboveground biomass biases by incorporating independent canopy height retrieval with conventional machine learning models using GEDI and ICESat-2 data

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103045 - 103045

Опубликована: Янв. 1, 2025

Язык: Английский

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Combining GEDI and sentinel data to estimate forest canopy mean height and aboveground biomass

Ecological Informatics, Год журнала: 2023, Номер 78, С. 102348 - 102348

Опубликована: Окт. 24, 2023

Язык: Английский

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Modelling aboveground biomass of a multistage managed forest through synergistic use of Landsat-OLI, ALOS-2 L-band SAR and GEDI metrics

Ecological Informatics, Год журнала: 2023, Номер 77, С. 102234 - 102234

Опубликована: Июль 26, 2023

Язык: Английский

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Aboveground biomass retrieval of wetland vegetation at the species level using UAV hyperspectral imagery and machine learning

Ecological Indicators, Год журнала: 2024, Номер 166, С. 112365 - 112365

Опубликована: Июль 13, 2024

Язык: Английский

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Remote sensing-based mangrove blue carbon assessment in the Asia-Pacific: A systematic review

The Science of The Total Environment, Год журнала: 2024, Номер 938, С. 173270 - 173270

Опубликована: Май 19, 2024

Язык: Английский

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Enhancing carbon stock estimation in forests: Integrating multi-data predictors with random forest method

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 102997 - 102997

Опубликована: Янв. 1, 2025

Язык: Английский

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Optimising carbon fixation through agroforestry: Estimation of aboveground biomass using multi-sensor data synergy and machine learning

Ecological Informatics, Год журнала: 2023, Номер 79, С. 102408 - 102408

Опубликована: Дек. 3, 2023

As agricultural land expansion is the primary driver of deforestation, agroforestry could be an optimal use strategy for climate change mitigation and reducing pressure on forests. Agroforestry a promising method carbon sequestration. With recent advancements in geospatial data science technology, ability to predict aboveground biomass (AGB) assess ecosystem services rapidly expanding. This study was conducted Belpada Block Balangir, Odisha, forest-dominated region eastern India. We recorded species occurrence measured plant parameters, including Circumference at Breast Height (CBH), height, geolocation, 196 plots (0.09 ha) intervention sites noted tree species. used Sentinel-1 Sentinel-2 multi sensor achieve synergy AGB estimation. Three machine learning models were used: Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN). The RF model exhibited highest level prediction accuracy (R2 = 0.69 RMSE 17.07 Mg/ha), followed by ANN 0.63 19.35 SVM 0.54, 21.97 Mg/ha. spectral vegetation indices that are (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted (SAVI), Enhanced (EVI), Modified Simple Ratio (MSR), (MSAVI), (DVI), SAR backscatter values, found important variables prediction. findings revealed interventions plantations resulted average stock increase 15 Mg/ha over five years area. Plant Value (PVI), which indicates importance local economy storage, showed Tectona grandis dominant with PVI value (88.35), Eucalyptus globulus (56.87), Mangifera indica (53.75), Azadirachta (15.45). approach enables monitoring efforts systems, thereby promoting effective management strategies.

Язык: Английский

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Modeling forest above-ground biomass using freely available satellite and multisource datasets

Ecological Informatics, Год журнала: 2023, Номер 74, С. 101973 - 101973

Опубликована: Янв. 5, 2023

Язык: Английский

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