Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation DOI Creative Commons
Min Peng,

Mingrui Xu,

Jialong Zhang

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 19, 2025

Accurately estimating forest aboveground carbon stock (ACS) is essential for achieving neutrality. At present, most non-parametric models still have errors in regions. Given the autocorrelation inherent spatial interpolation, combining with interpolation offers significant potential. In this study, we combined random (RF) ordinary kriging and co-kriging of mean annual temperature, precipitation, slope, elevation to establish residual (RFRK) model. Meanwhile, also developed multiple linear regression (MLRRK) model Finally, selected optimal estimation mapping ACS. The results indicate that: (1) achieves an R2 0.871, P 90.4%, RMSE 3.948 t/hm2; (2) RFCK precipitation (RFCKpre) outperforms one temperature (RFCKtem), while RFOK exhibits lowest accuracy; (3) RFCKpre exponential has highest accuracy, 0.63 RI (0.23), 9.3 SSR (41,612). These findings suggest that RFRKpre improved accuracy ACS regional forests.

Language: 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

et al.

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

Citations

47
A comparative analysis of machine learning techniques for aboveground biomass estimation: A case study of the Western Ghats, India DOI Creative Commons
Kurian Ayushi, Kanda Naveen Babu, Narayanan Ayyappan

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 80, P. 102479 - 102479

Published: Jan. 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.

Language: Английский

Citations

17
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

et al.

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

Published: Jan. 1, 2025

Language: Английский

Citations

2
Combining GEDI and sentinel data to estimate forest canopy mean height and aboveground biomass DOI

Qiyu Guo,

Shouhang Du, Jinbao Jiang

et al.

Ecological Informatics, Journal Year: 2023, Volume and Issue: 78, P. 102348 - 102348

Published: Oct. 24, 2023

Language: Английский

Citations

30
Modelling aboveground biomass of a multistage managed forest through synergistic use of Landsat-OLI, ALOS-2 L-band SAR and GEDI metrics DOI
Hitendra Padalia,

Ankit Prakash,

Taibanganba Watham

et al.

Ecological Informatics, Journal Year: 2023, Volume and Issue: 77, P. 102234 - 102234

Published: July 26, 2023

Language: Английский

Citations

23
Aboveground biomass retrieval of wetland vegetation at the species level using UAV hyperspectral imagery and machine learning DOI Creative Commons

Wei Zhuo,

Wu Nan, Runhe Shi

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 166, P. 112365 - 112365

Published: July 13, 2024

Language: Английский

Citations

10
Remote sensing-based mangrove blue carbon assessment in the Asia-Pacific: A systematic review DOI

Abhilash Dutta Roy,

Pavithra S. Pitumpe Arachchige, Michael S. Watt

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 938, P. 173270 - 173270

Published: May 19, 2024

Language: Английский

Citations

9
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

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 102997 - 102997

Published: Jan. 1, 2025

Language: Английский

Citations

1
Optimising carbon fixation through agroforestry: Estimation of aboveground biomass using multi-sensor data synergy and machine learning DOI Creative Commons
Raj Kumar Singh, Çhandrashekhar Biradar, Mukunda Dev Behera

et al.

Ecological Informatics, Journal Year: 2023, Volume and Issue: 79, P. 102408 - 102408

Published: Dec. 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.

Language: Английский

Citations

21
Modeling forest above-ground biomass using freely available satellite and multisource datasets DOI

Ai Hojo,

Ram Avtar, Tatsuro Nakaji

et al.

Ecological Informatics, Journal Year: 2023, Volume and Issue: 74, P. 101973 - 101973

Published: Jan. 5, 2023

Language: Английский

Citations

20