Improving <i>Pinus densata</i> Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables

Improving <i>Pinus densata</i> Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables

Forest carbon sinks are vital in mitigating climate change, making it crucial to have highly accurate estimates of forest carbon stocks. A method that accounts for the spatial characteristics of inventory samples is necessary for the long-term estimation of above-ground forest carbon stocks due to t...

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Bibliographic Details
Main Authors: Dongyang Han, Jialong Zhang, Dongfan Xu, Yi Liao, Rui Bao, Shuxian Wang, Shaozhi Chen
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Forests
Subjects:
Landsat
<i>Pinus</i> <i>densata</i>
topographic information
soil thickness
multilevel nonlinear mixed-effects model
Online Access:https://www.mdpi.com/1999-4907/15/2/394

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https://www.mdpi.com/1999-4907/15/2/394

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