Estimating forest aboveground carbon sink based on landsat time series and its response to climate change
Abstract Accurately estimating forest carbon sink and exploring their climate-driven mechanisms are critical to achieving carbon neutrality and sustainable development. Fewer studies have used machine learning-based dynamic models to estimate forest carbon sink. The climate-driven mechanisms in Shan...
Saved in:
| Main Authors: | Kun Yang, Kai Luo, Jialong Zhang, Bo Qiu, Feiping Wang, Qinglin Xiao, Jun Cao, Yunrun He, Jian Yang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-84258-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A New Spatiotemporal Filtering Method to Reconstruct Landsat Time-Series for Improving Estimation Accuracy of Forest Aboveground Carbon Stock
by: Kai Huang, et al.
Published: (2025-01-01) -
Forest aboveground biomass retrieval integrating ICESat-2, Landsat-8, and environmental factors
by: Sunjie Ma, et al.
Published: (2025-11-01) -
Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation
by: Min Peng, et al.
Published: (2025-05-01) -
Aboveground carbon stocks for different forest types in eastern Amazonia
by: Emily Ane Dionizio, et al.
Published: (2025-01-01) -
Harmonizing remote sensing and ground data for forest aboveground biomass estimation
by: Ying Su, et al.
Published: (2025-05-01)