Spatio-Temporal Variation Characteristics of Aboveground Biomass in the Headwater of the Yellow River Based on Machine Learning
Accurate estimation of the aboveground biomass (AGB) of grassland is a key link in understanding the regional carbon cycle. We used 501 aboveground measurements, 29 environmental variables, and machine learning algorithms to construct and verify a custom model of grassland biomass in the Headwater o...
Main Authors: | Rong Tang, Yuting Zhao, Huilong Lin |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-08-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/13/17/3404 |
Similar Items
-
Estimating the grassland aboveground biomass in the Three-River Headwater Region of China using machine learning and Bayesian model averaging
by: Na Zeng, et al.
Published: (2021-01-01) -
Ecological–Economic Assessment and Managerial Significance of Water Conservation in the Headwaters of the Yellow River
by: Danni Wang, et al.
Published: (2022-08-01) -
Spatio‐Temporal Dynamics of Aboveground Biomass in China's Oasis Grasslands Between 1989 and 2021
by: Peng Chen, et al.
Published: (2024-03-01) -
Satellite-Derived Estimation of Grassland Aboveground Biomass in the Three-River Headwaters Region of China during 1982–2018
by: Ruiyang Yu, et al.
Published: (2021-07-01) -
SWAT-Based Runoff Simulation and Runoff Responses to Climate Change in the Headwaters of the Yellow River, China
by: Jingwen Wu, et al.
Published: (2019-08-01)