气候变化领域集成服务门户(CCPortal): Improving Pinus densata Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables

CCPortal

DOI	10.3390/f15020394
	Improving Pinus densata Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables
	Han, Dongyang; Zhang, Jialong; Xu, Dongfan; Liao, Yi; Bao, Rui; Wang, Shuxian; Chen, Shaozhi
发表日期	2024
EISSN	1999-4907
起始页码	15
结束页码	2
卷号	15 期号:2
英文摘要	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 the spatial heterogeneity of bottom-up methods. In this study, we developed a method for analyzing space-sensing data that estimates and predicts long time series of forest carbon stock changes in an alpine region by considering the sample's spatial characteristics. We employed a nonlinear mixed-effects model and improved the model's accuracy by considering both static and dynamic aspects. We utilized ground sample point data from the National Forest Inventory (NFI) taken every five years, including tree and soil information. Additionally, we extracted spectral and texture information from Landsat and combined it with DEM data to obtain topographic information for the sample plots. Using static data and change data at various annual intervals, we built estimation models. We tested three non-parametric models (Random Forest, Gradient-Boosted Regression Tree, and K-Nearest Neighbor) and two parametric models (linear mixed-effects and non-linear mixed-effects) and selected the most accurate model to estimate Pinus densata's above-ground carbon stock. The results showed the following: (1) The texture information had a significant correlation with static and dynamic above-ground carbon stock changes. The highest correlation was for large-window mean, entropy, and variance. (2) The dynamic above-ground carbon stock model outperformed the static model. Additionally, the dynamic non-parametric models and parametric models experienced improvements in prediction accuracy. (3) In the multilevel nonlinear mixed-effects models, the highest accuracy was achieved with fixed effects for aspect and two-level nested random effects for the soil and elevation categories. (4) This study found that Pinus densata's above-ground carbon stock in Shangri-La followed a decreasing, and then, increasing trend from 1987 to 2017. The mean carbon density increased overall, from 19.575 t center dot hm-2 to 25.313 t center dot hm-2. We concluded that a dynamic model based on variability accurately reflects Pinus densata's above-ground carbon stock changes over time. Our approach can enhance time-series estimates of above-ground carbon stocks, particularly in complex topographies, by incorporating topographic factors and soil thickness into mixed-effects models.
英文关键词	Landsat; Pinus densata; topographic information; soil thickness; multilevel nonlinear mixed-effects model
语种	英语
WOS研究方向	Forestry
WOS类目	Forestry
WOS记录号	WOS:001172790000001
来源期刊	FORESTS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/306273
作者单位	Chinese Academy of Forestry; Research Institute of Forestry Policy & Information, CAF; Southwest Forestry University - China; Fudan University; Northwest A&F University - China; Chinese Academy of Forestry
推荐引用方式 GB/T 7714	Han, Dongyang,Zhang, Jialong,Xu, Dongfan,et al. Improving Pinus densata Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables[J],2024,15(2).
APA	Han, Dongyang.,Zhang, Jialong.,Xu, Dongfan.,Liao, Yi.,Bao, Rui.,...&Chen, Shaozhi.(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).
MLA	Han, Dongyang,et al."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(2024).