干旱区生态环境知识资源中心(Arid): Soil respiration estimation in desertified mining areas based on UAV remote sensing and machine learning

Arid

DOI	10.1007/s12145-023-01094-5
	Soil respiration estimation in desertified mining areas based on UAV remote sensing and machine learning
	Liu, Ying; Lin, Jiaquan; Yue, Hui
通讯作者	Yue, H
来源期刊	EARTH SCIENCE INFORMATICS
ISSN	1865-0473
EISSN	1865-0481
出版年	2023
卷号	16 期号:4 页码:3433-3448
英文摘要	Timely and accurate monitoring of soil respiration (Rs) in desertified mining areas is helpful to understand its spatial and temporal distribution and changes, which is crucial for assessing the carbon cycle of ecologically fragile open-pit mining ecosystems. In this study, we acquired multispectral and thermal infrared images of five experimental sites in Hongshaquan mining area including Dump reclamation area, Plantation forest, Tamarisk forest, Southern line, and Hongsha spring by unmanned aerial vehicle (UAV) and collected ground soil respiration data by gas chamber method. The spectral indices were constructed based on the UAV spectral information, and the relevant combinations of independent variables affecting soil respiration were identified by Pearson correlation analysis and Random Forest (RF) importance assessment. Multiple Linear Regression (MLR), Random Forest Regression (RFR), Support Vector Regression (SVR), Back Propagation Neural Network (BPNN), and Particle Swarm Optimization Support Vector Regression (PSO-SVR) were used to construct soil respiration remote sensing inversion model, and through determination coefficient (R2), root mean square error (RMSE) and Akaike's information criterion (AIC) to evaluate the accuracy. The results indicated that the accuracy of MLR was weaker than machine learning methods, and the highest accuracy model was the PSO-SVR soil respiration inversion model based on the combination of vegetation indices (VIS): Green band chlorophyll vegetation index (CIgreen), Red-edge band chlorophyll vegetation index (CIrededge) and Green wave atmospheric resistivity index (GARI), surface temperature (ST), and salinity index (SI) variables (R2 = 0.959, RMSE = 0.497, AIC = -0.561). High-resolution UAV multispectral and thermal infrared systems combined with machine learning methods can better estimate soil respiration in desertification mines and provide reference information and basic data for soil respiration and ecosystem carbon cycle monitoring in mining areas.
英文关键词	Soil respiration UAV remote sensing Desertification mining area Particle swarm optimization Support vector regression
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001062598600002
WOS关键词	PRECISION AGRICULTURE ; ECOSYSTEM RESPIRATION ; VEGETATION INDEXES ; ABIOTIC FACTORS ; DRIVERS ; CLIMATE ; WHEAT
WOS类目	Computer Science, Interdisciplinary Applications ; Geosciences, Multidisciplinary
WOS研究方向	Computer Science ; Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395872
推荐引用方式 GB/T 7714	Liu, Ying,Lin, Jiaquan,Yue, Hui. Soil respiration estimation in desertified mining areas based on UAV remote sensing and machine learning[J],2023,16(4):3433-3448.
APA	Liu, Ying,Lin, Jiaquan,&Yue, Hui.(2023).Soil respiration estimation in desertified mining areas based on UAV remote sensing and machine learning.EARTH SCIENCE INFORMATICS,16(4),3433-3448.
MLA	Liu, Ying,et al."Soil respiration estimation in desertified mining areas based on UAV remote sensing and machine learning".EARTH SCIENCE INFORMATICS 16.4(2023):3433-3448.