干旱区生态环境知识资源中心(Arid): Fusion of airborne hyperspectral and LiDAR canopy-height data for estimating fractional cover of tall woody plants, herbaceous vegetation, and other soil cover types in a semi-arid savanna ecosystem

Arid

DOI	10.1080/01431161.2022.2105176
	Fusion of airborne hyperspectral and LiDAR canopy-height data for estimating fractional cover of tall woody plants, herbaceous vegetation, and other soil cover types in a semi-arid savanna ecosystem
	Pervin, Rubaya; Robeson, Scott M.; MacBean, Natasha
通讯作者	Pervin, R
来源期刊	INTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN	0143-1161
EISSN	1366-5901
出版年	2022
卷号	43 期号:10 页码:3890-3926
英文摘要	Vegetation in semi-arid regions is often a spatially heterogeneous mix of bare soil, herbaceous vegetation, and woody plants. Detecting the fractional cover of woody plants versus herbaceous vegetation, in addition to non-photosynthetically active cover types such as biological soil crusts, dry grasses, and bare ground, is crucial for understanding spatial patterns and temporal changes in semi-arid savanna ecosystems. With and without the inclusion of LiDAR-derived canopy height information, we tested different configurations of unsupervised linear unmixing classification of hyperspectral data to estimate fractional cover of tall woody plants, herbaceous vegetation, and other non-photosynthetically active cover types. To perform this analysis, we used 1 m resolution hyperspectral and LiDAR data collected by the NEON airborne observation platform at the Santa Rita Experimental Range in Arizona. Our results show that both hyperspectral and canopy height information are needed in the linear unmixing algorithm to correctly identify tall woody plants versus herbaceous vegetation. However, including height data is not always sufficient to be able to separate the two vegetation types: selecting the optimum number of endmembers is also crucial for separating these two types of photosynthetically active vegetation. In comparison to our reference dataset, fractional cover accuracy was highest for the woody plant class based on two accuracy assessment methods, although the fractional cover of dense woody plant is likely underestimated because linear unmixing also detects sub-canopy elements. The accuracy of the herbaceous vegetation class was lower than the other classes, likely because of the presence of both active and senesced grasses. Our study presents the first use of both hyperspectral and canopy height information to separately detect fractional cover of woody plants versus herbaceous vegetation in addition to other bare soil cover types that should be useful for fractional cover classification in other semi-arid savanna ecosystems where hyperspectral and LiDAR data are available.
英文关键词	Image classification remote sensing spectral unmixing semi-arid fractional cover multimodal data fusion
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000839379000001
WOS关键词	RANDOM FOREST ; LAND-COVER ; IMAGING SPECTROSCOPY ; CLASSIFICATION ; ENCROACHMENT ; STATES ; BIOMASS ; MODELS ; IMPACT
WOS类目	Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	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/2XILL650/393211
推荐引用方式 GB/T 7714	Pervin, Rubaya,Robeson, Scott M.,MacBean, Natasha. Fusion of airborne hyperspectral and LiDAR canopy-height data for estimating fractional cover of tall woody plants, herbaceous vegetation, and other soil cover types in a semi-arid savanna ecosystem[J],2022,43(10):3890-3926.
APA	Pervin, Rubaya,Robeson, Scott M.,&MacBean, Natasha.(2022).Fusion of airborne hyperspectral and LiDAR canopy-height data for estimating fractional cover of tall woody plants, herbaceous vegetation, and other soil cover types in a semi-arid savanna ecosystem.INTERNATIONAL JOURNAL OF REMOTE SENSING,43(10),3890-3926.
MLA	Pervin, Rubaya,et al."Fusion of airborne hyperspectral and LiDAR canopy-height data for estimating fractional cover of tall woody plants, herbaceous vegetation, and other soil cover types in a semi-arid savanna ecosystem".INTERNATIONAL JOURNAL OF REMOTE SENSING 43.10(2022):3890-3926.