{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:42:50Z","timestamp":1773967370475,"version":"3.50.1"},"reference-count":81,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Joint Venture Agreement between UW-PFC and USDA FS PNW Station","award":["16-JV-11261989-094"],"award-info":[{"award-number":["16-JV-11261989-094"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.<\/jats:p>","DOI":"10.3390\/rs13101863","type":"journal-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T02:14:05Z","timestamp":1620699245000},"page":"1863","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data"],"prefix":"10.3390","volume":"13","author":[{"given":"Caileigh","family":"Shoot","sequence":"first","affiliation":[{"name":"Remote Sensing and Geospatial Analysis Laboratory, University of Washington, Seattle, WA 98195, USA"}]},{"given":"Hans-Erik","family":"Andersen","sequence":"additional","affiliation":[{"name":"USDA Forest Service Pacific Northwest Research Station, University of Washington, Seattle, WA 98195, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1563-6506","authenticated-orcid":false,"given":"L. Monika","family":"Moskal","sequence":"additional","affiliation":[{"name":"Remote Sensing and Geospatial Analysis Laboratory, University of Washington, Seattle, WA 98195, USA"}]},{"given":"Chad","family":"Babcock","sequence":"additional","affiliation":[{"name":"Department of Forest Resources, University of Minnesota, Cleveland, MN 55108, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8528-000X","authenticated-orcid":false,"given":"Bruce D.","family":"Cook","sequence":"additional","affiliation":[{"name":"NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA"}]},{"given":"Douglas C.","family":"Morton","sequence":"additional","affiliation":[{"name":"NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"596","DOI":"10.5589\/m12-003","article-title":"Using multi-level remote sensing and ground data to estimate forest biomass resources in remote regions: A case study in the boreal forests of interior Alaska","volume":"37","author":"Andersen","year":"2012","journal-title":"Can. 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