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

Predictive modeling of regional carbon storage dynamics in response to land use/land cover changes: An InVEST-based analysis DOI Creative Commons
Zeeshan Zafar, Muhammad Zubair, Yuanyuan Zha

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102701 - 102701

Published: June 21, 2024

Assessment of carbon stock (CS) in various land use/land cover (LULC) types is essential for environmental policies focused on reducing CO2 emissions and mitigating climate change. This study utilized the CA-Markov model to simulate future LULC scenarios InVEST evaluate CS changes Pakistan from 2001 2030. The employed two decades yearly composite data MODIS, achieving high accuracy with a kappa value 0.856. results indicate that an increase 38.1 × 103 km2 cultivated could lead increment 13.5 Tg Pakistan's total CS. In comparison, forest area can be reason raising above-ground (AGC) by 16.8 Tg. These findings enhance understanding long-term Pakistan. provides valuable insights governments refine use strategies, adjust emission reduction policies, design better regulations based study's findings. Key recommendations include promoting vertical urban development preserve sequestration areas, implementing strict agricultural zoning laws, expanding afforestation initiatives like Billion Tree Tsunami Green Pakistan, establishing national monitoring program. Integrating sources will create comprehensive database inform policy decisions management practices, contributing global change mitigation efforts.

Language: Английский

Citations

30
Modelling the impact of ecosystem fragmentation on ecosystem services in the degraded Ethiopian highlands DOI Creative Commons
Tegegne Molla Sitotaw, Louise Willemen, Derege Tsegaye Meshesha

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103100 - 103100

Published: March 1, 2025

Language: Английский

Citations

1
Estimating Spatiotemporal Dynamics of Carbon Storage in Roinia pseudoacacia Plantations in the Caijiachuan Watershed Using Sample Plots and Uncrewed Aerial Vehicle-Borne Laser Scanning Data DOI Creative Commons

Yawei Hu,

Ruoxiu Sun,

Miaomiao He

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(8), P. 1365 - 1365

Published: April 11, 2025

Forest ecosystems play a pivotal role in the global carbon cycle and climate change mitigation. aboveground biomass (AGB), critical indicator of storage sequestration capacity, has garnered significant attention ecological research. Recently, uncrewed aerial vehicle-borne laser scanning (ULS) technology emerged as promising tool for rapidly acquiring three-dimensional spatial information on AGB vegetation storage. This study evaluates applicability accuracy UAV-LiDAR estimating spatiotemporal dynamics Robinia pseudoacacia (R. pseudoacacia) plantations gully regions Loess Plateau, China. At sample plot scale, optimal parameters individual tree segmentation (ITS) based canopy height model (CHM) were determined, was validated. The results showed root mean square error (RMSE) values 13.17 trees (25.16%) count, 0.40 m (3.57%) average (AH), 320.88 kg (16.94%) AGB. regression model, which links with AH generated estimates that closely matched observed values. watershed ULS data used to estimate R. Caijiachuan watershed. analysis revealed total 68,992 trees, 2890.34 Mg density 62.46 ha−1. Low-density forest areas (<1500 ha−1) dominated landscape, accounting 94.38% 82.62% area, 92.46% Analysis tree-ring variation onset growth decline across different classes aged 0–30 years, higher-density stands exhibiting delayed compared lower-density stands. Compared traditional methods diameter at breast (DBH), assessments demonstrated superior scientific validity. underscores feasibility potential estimation complex terrain, such Plateau. It highlights importance topographic factors enhance accuracy. findings provide valuable support management high-quality development present an efficient approach precise sink accounting.

Language: Английский

Citations

0
Spatiotemporal Dynamics of Forest Carbon Sinks in China’s Qinba Mountains: Insights from Sun-Induced Chlorophyll Fluorescence Remote Sensing DOI Creative Commons

Yuhang Lian,

Yi He, Li Wang

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(8), P. 1418 - 1418

Published: April 16, 2025

Forest carbon sinks are crucial in mitigating climate change as integral components of the global cycle. Accurately estimating forest using traditional remote sensing indices, such Normalized Difference Vegetation Index(NDVI), presents significant challenges, particularly complex terrains and regions with variable climates. These limitations hinder effective capture photosynthetic dynamics. To address this gap, study leverages Sun-Induced Chlorophyll Fluorescence (SIF) sensing, highlighting its superiority over indices capturing processes offering a more precise approach to climate-sensitive mountainous areas. Using SIF data from GOSIF, alongside models for light-use efficiency ecosystem respiration, estimates Qinba Mountains China during growing season (June September) 2011 2018. The results further validated analyzed terms age type. Key findings include: (1) average annual was approximately 24.51 TgC; (2) Spatially, higher values (average 36.79 gC·m⁻2·month⁻1) were concentrated western central areas, while southeastern central-northern exhibited lower 7.75 gC·m⁻2·month⁻1); (3) Temporally, minimal interannual variation observed northwest, whereas southeast showed fluctuating trends, an initial decline followed by increase; (4) significantly influenced age, type, altitude. Our demonstrate that plantation forests aged 10 30 years exhibit superior sequestration capacity compared natural forests, 70 90 also show potential. underscore influence characteristics on By examining these spatiotemporal patterns Mountains, our offers valuable insights advancing China’s ‘dual carbon’ goals, emphasizing importance strategic management change.

Language: Английский

Citations

0
Evaluation of Land Use and Land Cover Change on Ecosystem Carbon Storage Using Mlpnn-Markov and Invest Model Using Field Data in Kedarnath Wildlife Sanctuary, Temperate Forests, India from 2010 to 2035 DOI

pawan negi,

Rajiv Pandey

Published: Jan. 1, 2025

Language: Английский

Citations

0
Refining landsat-based annual NDVImax estimation using shape model fitting and phenological metrics DOI Creative Commons
Lihao Zhang, Miaogen Shen, Licong Liu

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103107 - 103107

Published: March 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