{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T06:08:03Z","timestamp":1773986883196,"version":"3.50.1"},"reference-count":91,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2019,9,14]],"date-time":"2019-09-14T00:00:00Z","timestamp":1568419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["NNX14AD81G"],"award-info":[{"award-number":["NNX14AD81G"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/rs11182141","type":"journal-article","created":{"date-parts":[[2019,9,16]],"date-time":"2019-09-16T03:17:57Z","timestamp":1568603877000},"page":"2141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach"],"prefix":"10.3390","volume":"11","author":[{"given":"Hamid","family":"Dashti","sequence":"first","affiliation":[{"name":"Department of Geosciences, Boise State University, 1910 W University Dr, Boise, ID 83725, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1937-9327","authenticated-orcid":false,"given":"Andrew","family":"Poley","sequence":"additional","affiliation":[{"name":"Michigan Tech Research Institute, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2124-7654","authenticated-orcid":false,"given":"Nancy","family":"F. Glenn","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Boise State University, 1910 W University Dr, Boise, ID 83725, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6607-0605","authenticated-orcid":false,"given":"Nayani","family":"Ilangakoon","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Boise State University, 1910 W University Dr, Boise, ID 83725, USA"}]},{"given":"Lucas","family":"Spaete","sequence":"additional","affiliation":[{"name":"Minnesota Department of Natural Resources, 1201 US-2, Grand Rapids, MN 55744, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3555-4842","authenticated-orcid":false,"given":"Dar","family":"Roberts","sequence":"additional","affiliation":[{"name":"Department of Geography, University of California, Santa Barbara, CA 93106-4060, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6956-3619","authenticated-orcid":false,"given":"Josh","family":"Enterkine","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Boise State University, 1910 W University Dr, Boise, ID 83725, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7240-9265","authenticated-orcid":false,"given":"Alejandro","family":"N. Flores","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Boise State University, 1910 W University Dr, Boise, ID 83725, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8551-0461","authenticated-orcid":false,"given":"Susan","family":"L. Ustin","sequence":"additional","affiliation":[{"name":"Department of Land, Air, and Water Resources, University of California, Davis, CA 93106-4060, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3126-010X","authenticated-orcid":false,"given":"Jessica","family":"J. Mitchell","sequence":"additional","affiliation":[{"name":"Montana Natural Heritage Program, University of Montana, Missoula, MT 59812, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1126\/science.aaa1668","article-title":"The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink","volume":"348","author":"Raupach","year":"2015","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1038\/nature13376","article-title":"Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle","volume":"509","author":"Poulter","year":"2014","journal-title":"Natur"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1111\/j.0906-7590.2006.04605.x","article-title":"Plant Species Richness and Environmental Heterogeneity in a Mountain Landscape: Effects of Variability and Spatial Configuration","volume":"29","author":"Dufour","year":"2006","journal-title":"Ecography"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1111\/j.2007.0906-7590.05246.x","article-title":"Effects of Topographic Variability on the Scaling of Plant Species Richness in Gradient Dominated Landscapes","volume":"31","author":"Hofer","year":"2008","journal-title":"Ecography"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s11273-009-9169-z","article-title":"Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review","volume":"18","author":"Adam","year":"2010","journal-title":"Wetl. 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