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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A multi-source approach combining GEDI LiDAR, satellite data, and machine learning algorithms for estimating forest aboveground biomass on Google Earth engine platform

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103052 - 103052

Published: Jan. 1, 2025

Language: Английский

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Effects of precipitation changes on fractional vegetation cover in the Jinghe River basin from 1998 to 2019

Ecological Informatics, Journal Year: 2024, Volume and Issue: 80, P. 102505 - 102505

Published: Jan. 30, 2024

Studying the spatiotemporal evolutionary characteristics of vegetation and effect precipitation changes is necessary for understanding regional ecological environment. We used trend analysis, partial correlation significance tests, residual analysis to analyze evolution driving factors fractional cover (FVC) in Jinghe River Basin (JRB) from 1998 2019. The results showed that coverage JRB significantly improved FVC an increasing 90.64% areas JRB, overall annual change was extremely significant (p ≤ 0.01). However, insignificant trend; distribution developed a uniform direction centroid tended move backward. area with between concentration index accounted largest proportion (18.47%). Precipitation generally favored recovery; however, limited non-precipitation dominated FVC. Our study contributes more comprehensive effects patterns on facilitate protection.

Language: Английский

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Estimation of above ground biomass in tropical heterogeneous forests in India using GEDI

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102712 - 102712

Published: June 30, 2024

Quantifying above ground biomass (AGB) and its spatial distribution can significantly contribute to monitor carbon stocks as well the storage dynamics in forests. For effective forest monitoring management case of complex tropical Indian forests, there is a need obtain reliable estimates amount sequestration at regional national levels, but estimation quite challenging. The main objective study validate usefulness gridded density (AGBD) (ton/ha) spaceborne LiDAR Global Ecosystem Dynamics Investigation data (GEDI L4B, Version 2) across two heterogeneous forests India, Betul Mudumalai Methodology includes, for each area, linear regression model which predicts AGB from Sentinel-2 MSI was developed using reference comparing it with GEDI AGBD values. Central India had RMSE 13.9 ton/ha, relative = 8.7% R2 0.88, bias −0.28 comparison between modelled 1 km resolution show relatively strong correlation (0.66) no or little bias. It also found that footprint value underestimated compared according model. southern an 29.1 10.8%, 0.79 −0.022. 0.84, field values lies 42.2 ton/ha 238.8 75.9 353.6 ton/ha. results indicates underestimates AGB, used produce product needs be adjusted provide information on balance changes over time type exists test areas.

Language: Английский

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Forest Aboveground Biomass Estimation and Inventory: Evaluating Remote Sensing-Based Approaches

Forests, Journal Year: 2024, Volume and Issue: 15(6), P. 1055 - 1055

Published: June 18, 2024

Remote sensing datasets offer robust approaches for gaining reliable insights into forest ecosystems. Despite numerous studies reviewing aboveground biomass estimation using remote approaches, a comprehensive synthesis of synergetic integration methods to map and estimate AGB is still needed. This article reviews the integrated discusses significant advances in estimating from space- airborne sensors. review covers research articles published during 2015–2023 ascertain recent developments. A total 98 peer-reviewed journal were selected under Preferred Reporting Items Systematic Reviews Meta-Analysis (PRISMA) guidelines. Among scrutinized studies, 54 relevant spaceborne, 22 airborne, datasets. empirical models used, random regression model accounted most (32). The highest number utilizing dataset originated China (24), followed by USA (15). datasets, Sentinel-1 2, Landsat, GEDI, Airborne LiDAR widely employed with parameters that encompassed tree height, canopy cover, vegetation indices. results co-citation analysis also determined be objectives this review. focuses on provides accuracy reliability modeling.

Language: Английский

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Modelling above ground biomass for a mixed-tree urban arboretum forest based on a LiDAR-derived canopy height model and field-sampled data

GEOMATICA, Journal Year: 2025, Volume and Issue: unknown, P. 100047 - 100047

Published: Jan. 1, 2025

Language: 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, Journal Year: 2025, Volume and Issue: unknown, P. 103045 - 103045

Published: Jan. 1, 2025

Language: Английский

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Three decades of spatiotemporal dynamics in forest biomass density in the Qinba Mountains

Ecological Informatics, Journal Year: 2024, Volume and Issue: 81, P. 102566 - 102566

Published: March 20, 2024

The forest ecosystem plays a pivotal role in the global carbon cycle and is crucial for investigating atmospheric exchanges. Forest biomass, fundamental quantitative measure of ecosystem, serves as critical indicator stocks sequestration capacity. This study utilizes GIMMS NDVI3g dataset to downscale inventory data spanning from 1989 2018, creating 1 km resolution map biomass density Qinba Mountains. initially decreased but has been increasing since 2004. northern region Mountains exhibits high (>100 Mg/hm2), while southern relatively lower density. provides longest-term estimation date. It foundation regional-scale management carbonization decision-making. research significant importance enhancing understanding regional cycling supporting sustainable ecological development.

Language: Английский

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Mapping Forest Aboveground Biomass Using Multi-Source Remote Sensing Data Based on the XGBoost Algorithm

Forests, Journal Year: 2025, Volume and Issue: 16(2), P. 347 - 347

Published: Feb. 15, 2025

Aboveground biomass (AGB) serves as an important indicator for assessing the productivity of forest ecosystems and exploring global carbon cycle. However, accurate estimation AGB remains a significant challenge, especially when integrating multi-source remote sensing data, effects different feature combinations results are unclear. In this study, we proposed method estimating by combining Gao Fen 7 (GF-7) stereo imagery with data from Sentinel-1 (S1), Sentinel-2 (S2), Advanced Land Observing Satellite digital elevation model (ALOS DEM), field survey data. The continuous tree height (TH) was derived using GF-7 ALOS DEM. Spectral features were extracted S1 S2, topographic Using these features, 15 constructed. recursive elimination (RFE) used to optimize each combination, which then input into extreme gradient boosting (XGBoost) estimation. Different estimate compared. best selected mapping distribution at 30 m resolution. outcomes showed that composed 13 including TH, topographic, spectral S2 This achieved prediction performance, determination coefficient (R2) 0.71 root mean square error (RMSE) 18.11 Mg/ha. TH found be most predictive feature, followed optical radar features.

Language: Английский

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Upscaling UAV and Lidar-derived forest gap area and edge length extractions using radar and optical sentinel images

International Journal of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 31

Published: May 2, 2025

Language: Английский

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