Han, D.; Zhang, J.; Xu, D.; Liao, Y.; Bao, R.; Wang, S.; Chen, S.
Improving Pinus densata Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables. Forests 2024, 15, 394.
https://doi.org/10.3390/f15020394
AMA Style
Han D, Zhang J, Xu D, Liao Y, Bao R, Wang S, Chen S.
Improving Pinus densata Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables. Forests. 2024; 15(2):394.
https://doi.org/10.3390/f15020394
Chicago/Turabian Style
Han, Dongyang, Jialong Zhang, Dongfan Xu, Yi Liao, Rui Bao, Shuxian Wang, and Shaozhi Chen.
2024. "Improving Pinus densata Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables" Forests 15, no. 2: 394.
https://doi.org/10.3390/f15020394
APA Style
Han, D., Zhang, J., Xu, D., Liao, Y., Bao, R., Wang, S., & Chen, S.
(2024). Improving Pinus densata Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables. Forests, 15(2), 394.
https://doi.org/10.3390/f15020394
