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

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 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.

Язык: Английский

Aboveground biomass inversion of forestland in a Jinsha River dry-hot valley by integrating high and medium spatial resolution optical images: A case study on Yuanmou County of Southwest China DOI Creative Commons
Zihao Liu,

Tian‐Bao Huang,

Yong Wu

и другие.

Ecological Informatics, Год журнала: 2024, Номер 83, С. 102796 - 102796

Опубликована: Авг. 25, 2024

It is crucial to develop a comprehensive method for estimating the aboveground biomass (AGB) of trees, shrubs, grasslands, and sparse tree areas in ecologically fragile dry, hot valley regions with vertical zonation. Multi-source remote-sensing data can fulfill this requirement, providing help monitoring health ecosystems basis regional biodiversity conservation restoration. Sentinel-2A satellite imagery was used classify forests, grasslands Yuanmou County, Chuxiong Yi Autonomous Prefecture, Yunnan Province, China. The Gaofen-2 (GF-2) extract canopy width calculate valley-type savanna region. These were combined factors measured survey data, random forest (RF) extreme gradient boosting (XGBoost) models estimate biomass. Using GF-2 images segment effectively reduced overestimation low-resolution images, enabling AGB trees be accurately estimated. estimations based on attained coefficient determination (R2) values 0.45 0.47 forest, 0.55 0.61 0.32 0.37 using RF XGBoost models, respectively, demonstrating variable effectiveness across vegetation types. In addition, model more robust than all three Our methodology provides scientific support sustainable development valleys areas.

Язык: Английский

Процитировано

4
Linking meteorological and phenological observations in forests DOI
Amar Prakash, Mukunda Dev Behera,

M. Mukhopadhyay

и другие.

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 183 - 200

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

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

и другие.

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103160 - 103160

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

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

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 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.

Язык: Английский

Процитировано

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

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 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.

Язык: Английский

Процитировано

0