Forest aboveground biomass estimation based on spaceborne LiDAR combining machine learning model and geostatistical method DOI Creative Commons
Li Xu, Jinge Yu, Qingtai Shu

и другие.

Frontiers in Plant Science, Год журнала: 2024, Номер 15

Опубликована: Дек. 11, 2024

Estimation of forest biomass at regional scale based on GEDI spaceborne LiDAR data is great significance for quality assessment and carbon cycle. To solve the problem discontinuous footprints, this study mapped different echo indexes in footprints to surface by inverse distance weighted interpolation method, verified influence number results. Random algorithm was chosen estimate spruce-fir combined with parameters provided 138 sample plots Shangri-La. The results show that: (1) By extracting numbers visualize it, revealed that a higher correlates denser distribution more pronounced stripe phenomenon. (2) prediction accuracy improves as decreases. group highest R 2 , lowest RMSE MAE footprint extracted every 100 shots, 10 shots had worst effect. (3) inverted random ranged from 51.33 t/hm 179.83 an average 101.98 . total value 3035.29 × 4 This shows will have certain impact mapping information presents methodological reference selecting appropriate derive various vertical structure ecosystems.

Язык: Английский

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 DOI Creative Commons
Shoaib Ahmad Anees, Kaleem Mehmood, Waseem Razzaq Khan

и другие.

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

Процитировано

52
Enhancing carbon stock estimation in forests: Integrating multi-data predictors with random forest method DOI Creative Commons
Gabriel E. Suárez-Fernández, J. Martínez-Sánchez, Pedro Arias

и другие.

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

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

3
A multi-source approach combining GEDI LiDAR, satellite data, and machine learning algorithms for estimating forest aboveground biomass on Google Earth engine platform DOI Creative Commons
Hamdi A. Zurqani

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103052 - 103052

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

3
Effects of precipitation changes on fractional vegetation cover in the Jinghe River basin from 1998 to 2019 DOI Creative Commons
Yu Liu,

Tingting Huang,

Zhiyuan Qiu

и другие.

Ecological Informatics, Год журнала: 2024, Номер 80, С. 102505 - 102505

Опубликована: Янв. 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.

Язык: Английский

Процитировано

13
Estimation of above ground biomass in tropical heterogeneous forests in India using GEDI DOI Creative Commons

Indu Indirabai,

Mats Nilsson

Ecological Informatics, Год журнала: 2024, Номер 82, С. 102712 - 102712

Опубликована: Июнь 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.

Язык: Английский

Процитировано

12
Forest Aboveground Biomass Estimation and Inventory: Evaluating Remote Sensing-Based Approaches DOI Open Access
Muhammad Nouman Khan, Yumin Tan, Ahmad Ali Gul

и другие.

Forests, Год журнала: 2024, Номер 15(6), С. 1055 - 1055

Опубликована: Июнь 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.

Язык: Английский

Процитировано

10
Correcting forest aboveground biomass biases by incorporating independent canopy height retrieval with conventional machine learning models using GEDI and ICESat-2 data DOI Creative Commons
Biao Zhang, Zhichao Wang, Tiantian Ma

и другие.

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

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

2
Three decades of spatiotemporal dynamics in forest biomass density in the Qinba Mountains DOI Creative Commons

Jiahui Chang,

Chang Huang

Ecological Informatics, Год журнала: 2024, Номер 81, С. 102566 - 102566

Опубликована: Март 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.

Язык: Английский

Процитировано

8
Modelling above ground biomass for a mixed-tree urban arboretum forest based on a LiDAR-derived canopy height model and field-sampled data DOI Creative Commons
Jigme Thinley, Catherine Marina Pickering, Christopher E. Ndehedehe

и другие.

GEOMATICA, Год журнала: 2025, Номер unknown, С. 100047 - 100047

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
From Air to Space: A Comprehensive Approach to Optimizing Aboveground Biomass Estimation on UAV-Based Datasets DOI Open Access
Muhammad Nouman Khan, Yumin Tan, Lingfeng He

и другие.

Forests, Год журнала: 2025, Номер 16(2), С. 214 - 214

Опубликована: Янв. 23, 2025

Estimating aboveground biomass (AGB) is vital for sustainable forest management and helps to understand the contributions of forests carbon storage emission goals. In this study, effectiveness plot-level AGB estimation using height crown diameter derived from UAV-LiDAR, calibration GEDI-L4A GEDI-L2A rh98 heights, spectral variables UAV-multispectral RGB data were assessed. These calibrated values UAV-derived used fit estimations a random (RF) regression model in Fuling District, China. Using Pearson correlation analysis, we identified 10 most important predictor prediction model, including GEDI height, Visible Atmospherically Resistant Index green (VARIg), Red Blue Ratio (RBRI), Difference Vegetation (DVI), canopy cover (CC), (ARVI), Red-Edge Normalized (NDVIre), Color (CIVI), elevation, slope. The results showed that, general, second based on Sentinel-2 indices, slope datasets with evaluation metric (for training: R2 = 0.941 Mg/ha, RMSE 13.514 MAE 8.136 Mg/ha) performed better than first prediction. result was between 23.45 Mg/ha 301.81 standard error 0.14 10.18 Mg/ha. This hybrid approach significantly improves accuracy addresses uncertainties modeling. findings provide robust framework enhancing stock assessment contribute global-scale monitoring, advancing methodologies ecological research.

Язык: Английский

Процитировано

1