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

A Synergistic Approach Using Machine Learning and Deep Learning for Forest Fire Susceptibility in Himalayan Forests DOI

Parthiva Shome,

A. Jaya Prakash,

Mukunda Dev Behera

et al.

Journal of the Indian Society of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 27, 2025

Language: Английский

Citations

0
Remote sensing and integration of machine learning algorithms for above-ground biomass estimation in Larix principis-rupprechtii Mayr plantations: a case study using Sentinel-2 and Landsat-9 data in northern China DOI Creative Commons

Jamshid Ali,

Haoran Wang, Kaleem Mehmood

et al.

Frontiers in Environmental Science, Journal Year: 2025, Volume and Issue: 13

Published: April 2, 2025

Estimating above-ground biomass (AGB) is important for ecological assessment, carbon stock evaluation, and forest management. This research assesses the performance of machine learning algorithms XGBoost, SVM, RF using data from Sentinel-2 Landsat-9 satellites. The study influence significant spectral bands vegetation indices on accuracy AGB estimate. results presented in paper indicate that were more effective than data. mainly because it had higher spatial resolution, which enabled model gradients structural attributes accurately. XGBoost performed best with an R 2 0.82 RMSE 0.73 Mg/ha 0.80 0.71 Landsat-9. In current study, SVM also showed a substantial 0.79 0.76 For Sentinel-2, random achieved 0.74 0.93 Mg/ha, Landsat 9 yielded 0.72 0.88 Mg/ha. Thus, variable importance analysis, have predicting AGB. As expected their application research, these predictors consistently emerged as highly across models datasets. demonstrates potential integrating remote sensing to achieve accurate efficient assessment.

Language: Английский

Citations

0
Estimation of mangrove heights and aboveground biomass using UAV-LiDAR, Sentinel-1 and ZY-3 stereo images DOI Creative Commons
Bolin Fu, Yingying Wei,

Linhang Jiang

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103160 - 103160

Published: April 1, 2025

Language: Английский

Citations

0
Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation DOI
Min Peng,

Mingrui Xu,

Jialong Zhang

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 24, 2025

Abstract 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 Forest (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, R²of 0.63 RI (0.23), 9.3and SSR (41612). These findings suggest that RFRKpre improved accuracy ACS regional forests.

Language: Английский

Citations

0
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

Citations

0