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Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach

Department of Geosciences, Boise State University, 1910 W University Dr, Boise, ID 83725, USA
Michigan Tech Research Institute, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USA
Minnesota Department of Natural Resources, 1201 US-2, Grand Rapids, MN 55744, USA
Department of Geography, University of California, Santa Barbara, CA 93106-4060, USA
Department of Land, Air, and Water Resources, University of California, Davis, CA 93106-4060, USA
Montana Natural Heritage Program, University of Montana, Missoula, MT 59812, USA
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(18), 2141;
Received: 20 August 2019 / Revised: 7 September 2019 / Accepted: 10 September 2019 / Published: 14 September 2019
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)
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. View Full-Text
Keywords: drylands; classification; SAM; MESMA; LiDAR drylands; classification; SAM; MESMA; LiDAR
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MDPI and ACS Style

Dashti, H.; Poley, A.; F. Glenn, N.; Ilangakoon, N.; Spaete, L.; Roberts, D.; Enterkine, J.; N. Flores, A.; L. Ustin, S.; J. Mitchell, J. Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach. Remote Sens. 2019, 11, 2141.

AMA Style

Dashti H, Poley A, F. Glenn N, Ilangakoon N, Spaete L, Roberts D, Enterkine J, N. Flores A, L. Ustin S, J. Mitchell J. Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach. Remote Sensing. 2019; 11(18):2141.

Chicago/Turabian Style

Dashti, Hamid, Andrew Poley, Nancy F. Glenn, Nayani Ilangakoon, Lucas Spaete, Dar Roberts, Josh Enterkine, Alejandro N. Flores, Susan L. Ustin, and Jessica J. Mitchell. 2019. "Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach" Remote Sensing 11, no. 18: 2141.

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