干旱区生态环境知识资源中心(Arid): Estimation of the Restored Forest Spatial Structure in Semi-Arid Mine Dumps Using Worldview-2 Imagery

Arid

DOI	10.3390/f11060695
	Estimation of the Restored Forest Spatial Structure in Semi-Arid Mine Dumps Using Worldview-2 Imagery
	Zhu, Xiaoxiao; Zhou, Yongli; Yang, Yongjun; Hou, Huping; Zhang, Shaoliang; Liu, Run
通讯作者	Hou, HP
来源期刊	FORESTS
EISSN	1999-4907
出版年	2020
卷号	11 期号:6
英文摘要	Forest monitoring is critical to the management and successful evaluation of ecological restoration in mined areas. However, in the past, available monitoring has mainly focused on traditional parameters and lacked estimation of the spatial structural parameters (SSPs) of forests. The SSPs are important indicators of forest health and resilience. The purpose of this study was to assess the feasibility of estimating the SSPs of restored forest in semi-arid mine dumps using Worldview-2 imagery. We used the random forest to extract the dominant feature factor subset; then, a regression model and mind evolutionary algorithm-back propagation (MEA-BP) neural network model were established to estimate the forest SSP. The results show that the textural features found using 3 x 3 window have a relatively high importance score in the random forest model. This indicates that the 3 x 3 texture factors have a relatively strong ability to explain the restored forest SSPs when compared with spectral factors. The optimal regression model has anR(2)of 0.6174 and an MSRE of 0.1001. The optimal MEA-BP neural network model has anR(2)of 0.6975 and an MSRE of 0.0906, which shows that the MEA-BP neural network has greater accuracy than the regression model. The estimation shows that the tree-shrub-grass mode with an average of 0.7351 has the highest SSP, irrespective of the restoration age. In addition, the SSP of each forest configuration type increases with the increase in restoration age except for the single grass configuration. The increase range of SSP across all modes was 0.0047-0.1471 after more than ten years of restoration. In conclusion, the spatial structure of a mixed forest mode is relatively complex. Application cases show that Worldview-2 imagery and the MEA-BP neural network method can support the effective evaluation of the spatial structure of restored forest in semi-arid mine dumps.
英文关键词	forest spatial structure Worldview-2 MEA-BP neural network semi-arid mine dumps ecological restoration
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000551909100001
WOS关键词	SPECIES COMPOSITION ; BIOMASS ESTIMATION ; GAUSSIAN-PROCESSES ; VEGETATION ; LIDAR ; AREA ; PARAMETERS ; DYNAMICS ; COVER ; TEMPERATURE
WOS类目	Forestry
WOS研究方向	Forestry
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/325176
作者单位	[Zhu, Xiaoxiao; Yang, Yongjun; Hou, Huping; Zhang, Shaoliang; Liu, Run] China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221008, Jiangsu, Peoples R China; [Zhou, Yongli] Shenhua Zhungeer Energy Co Ltd, Ordos 010399, Peoples R China; [Yang, Yongjun; Hou, Huping; Zhang, Shaoliang] Minist Educ, Engn Res Ctr Mine Ecol Restorat, Xuzhou 221008, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Xiaoxiao,Zhou, Yongli,Yang, Yongjun,et al. Estimation of the Restored Forest Spatial Structure in Semi-Arid Mine Dumps Using Worldview-2 Imagery[J],2020,11(6).
APA	Zhu, Xiaoxiao,Zhou, Yongli,Yang, Yongjun,Hou, Huping,Zhang, Shaoliang,&Liu, Run.(2020).Estimation of the Restored Forest Spatial Structure in Semi-Arid Mine Dumps Using Worldview-2 Imagery.FORESTS,11(6).
MLA	Zhu, Xiaoxiao,et al."Estimation of the Restored Forest Spatial Structure in Semi-Arid Mine Dumps Using Worldview-2 Imagery".FORESTS 11.6(2020).