干旱区生态环境知识资源中心(Arid): Multi-Temporal LiDAR and Hyperspectral Data Fusion for Classification of Semi-Arid Woody Cover Species

Arid

DOI	10.3390/rs14122896
	Multi-Temporal LiDAR and Hyperspectral Data Fusion for Classification of Semi-Arid Woody Cover Species
	Norton, Cynthia L.; Hartfield, Kyle; Collins, Chandra D. Holifield; van Leeuwen, Willem J. D.; Metz, Loretta J.
通讯作者	Norton, CL
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:12
英文摘要	Mapping the spatial distribution of woody vegetation is important for monitoring, managing, and studying woody encroachment in grasslands. However, in semi-arid regions, remotely sensed discrimination of tree species is difficult primarily due to the tree similarities, small and sparse canopy cover, but may also be due to overlapping woody canopies as well as seasonal leaf retention (deciduous versus evergreen) characteristics. Similar studies in different biomes have achieved low accuracies using coarse spatial resolution image data. The objective of this study was to investigate the use of multi-temporal, airborne hyperspectral imagery and light detection and ranging (LiDAR) derived data for tree species classification in a semi-arid desert region. This study produces highly accurate classifications by combining multi-temporal fine spatial resolution hyperspectral and LiDAR data (similar to 1 m) through a reproducible scripting and machine learning approach that can be applied to larger areas and similar datasets. Combining multi-temporal vegetation indices and canopy height models led to an overall accuracy of 95.28% and kappa of 94.17%. Five woody species were discriminated resulting in producer accuracies ranging from 86.12% to 98.38%. The influence of fusing spectral and structural information in a random forest classifier for tree identification is evident. Additionally, a multi-temporal dataset slightly increases classification accuracies over a single data collection. Our results show a promising methodology for tree species classification in a semi-arid region using multi-temporal hyperspectral and LiDAR remote sensing data.
英文关键词	hyperspectral LiDAR species classification semi-arid
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:000815949800001
WOS关键词	RANDOM FOREST CLASSIFIER ; WATER-STRESS DETECTION ; IMAGING SPECTROSCOPY ; SHRUB ENCROACHMENT ; CARBON SINK ; VEGETATION ; IMAGERY ; INDEXES ; REFLECTANCE ; DESERT
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394152
推荐引用方式 GB/T 7714	Norton, Cynthia L.,Hartfield, Kyle,Collins, Chandra D. Holifield,et al. Multi-Temporal LiDAR and Hyperspectral Data Fusion for Classification of Semi-Arid Woody Cover Species[J],2022,14(12).
APA	Norton, Cynthia L.,Hartfield, Kyle,Collins, Chandra D. Holifield,van Leeuwen, Willem J. D.,&Metz, Loretta J..(2022).Multi-Temporal LiDAR and Hyperspectral Data Fusion for Classification of Semi-Arid Woody Cover Species.REMOTE SENSING,14(12).
MLA	Norton, Cynthia L.,et al."Multi-Temporal LiDAR and Hyperspectral Data Fusion for Classification of Semi-Arid Woody Cover Species".REMOTE SENSING 14.12(2022).