干旱区生态环境知识资源中心(Arid): Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach

Arid

DOI	10.3390/rs11182141
	Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach
	Dashti, Hamid 1; Poley, Andrew 2; Glenn, Nancy E.1; Ilangakoon, Nayani 1; Spaete, Lucas 3; Roberts, Dar 4; Enterkine, Josh 1; Flores, Alejandro N.1; Ustin, Susan L.5; Mitchell, Jessica J.6
通讯作者	Dashti, Hamid
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2019
卷号	11 期号:18
英文摘要	The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM's sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems.
英文关键词	drylands classification SAM MESMA LiDAR
类型	Article
语种	英语
国家	USA
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000489101500071
WOS关键词	SPECTRAL MIXTURE ANALYSIS ; INCORPORATING ENDMEMBER VARIABILITY ; PLANT-SPECIES RICHNESS ; REMOTE-SENSING DATA ; IMAGING SPECTROSCOPY ; SEMIARID ECOSYSTEMS ; FUSION ; COVER ; LANDSCAPE ; DISCRIMINATION
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
来源机构	University of California, Davis
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/218415
作者单位	1.Boise State Univ, Dept Geosci, 1910 W Univ Dr, Boise, ID 83725 USA; 2.Michigan Tech Res Inst, 3600 Green Court,Suite 100, Ann Arbor, MI 48105 USA; 3.Minnesota Dept Nat Resources, 1201 US 2, Grand Rapids, MN 55744 USA; 4.Univ Calif Santa Barbara, Dept Geog, Santa Barbara, CA 93106 USA; 5.Univ Calif Davis, Dept Land Air & Water Resources, Davis, CA 93106 USA; 6.Univ Montana, Montana Nat Heritage Program, Missoula, MT 59812 USA
推荐引用方式 GB/T 7714	Dashti, Hamid,Poley, Andrew,Glenn, Nancy E.,et al. Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach[J]. University of California, Davis,2019,11(18).
APA	Dashti, Hamid.,Poley, Andrew.,Glenn, Nancy E..,Ilangakoon, Nayani.,Spaete, Lucas.,...&Mitchell, Jessica J..(2019).Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach.REMOTE SENSING,11(18).
MLA	Dashti, Hamid,et al."Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach".REMOTE SENSING 11.18(2019).