{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T07:31:48Z","timestamp":1774942308709,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T00:00:00Z","timestamp":1658016000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"U.S. Forest Service, Pacific Northwest Research Station","award":["PNW 19-JV-11261959-064"],"award-info":[{"award-number":["PNW 19-JV-11261959-064"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We demonstrate the potential for pushbroom Digital Aerial Photogrammetry (DAP) to enhance forest modeling (and mapping) over large areas, especially when combined with multitemporal Landsat derivatives. As part of the National Agricultural Imagery Program (NAIP), high resolution (30\u201360 cm) photogrammetric forest structure measurements can be acquired at low cost (as low as $0.23\/km2 when acquired for entire states), repeatedly (2\u20133 years), over the entire conterminous USA. Our three objectives for this study are to: (1) characterize agreement between DAP measurements with Landsat and biophysical variables, (2) quantify the separate and combined explanatory power of the three auxiliary data sources for 19 separate forest attributes (e.g., age, biomass, trees per hectare, and down dead woody from 2015 USFS Forest Inventory and Analysis plot measurements in Washington state, USA) and (3) assess local biases in mapped predictions. DAP showed the greatest explanatory power for the widest range of forest attributes, but performance was appreciably improved with the addition of Landsat predictors. Biophysical variables contribute little explanatory power to our models with DAP or Landsat variables present. There is need for further investigation, however, as we observed spatial correlation in the coarse single-year grid (\u22481 plot\/25,000 ha), which suggests local biases at typical scales of mapped inferences (e.g., county, watershed or stand). DAP, in combination with Landsat, provides an unparalleled opportunity for high-to-medium resolution forest structure measurements and mapping, which makes this auxiliary data source immediately viable to enhance large-scale forest mapping projects.<\/jats:p>","DOI":"10.3390\/rs14143433","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T01:53:22Z","timestamp":1658109202000},"page":"3433","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Pushbroom Photogrammetric Heights Enhance State-Level Forest Attribute Mapping with Landsat and Environmental Gradients"],"prefix":"10.3390","volume":"14","author":[{"given":"Jacob L.","family":"Strunk","sequence":"first","affiliation":[{"name":"USDA Forest Service Pacific Northwest Research Station, 3625 93rd Ave SW, Olympia, WA 98512, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2673-5836","authenticated-orcid":false,"given":"David M.","family":"Bell","sequence":"additional","affiliation":[{"name":"USDA Forest Service Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2522-7872","authenticated-orcid":false,"given":"Matthew J.","family":"Gregory","sequence":"additional","affiliation":[{"name":"Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97331, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1139\/x02-011","article-title":"Predictive Mapping of Forest Composition and Structure with Direct Gradient Analysis and Nearest- Neighbor Imputation in Coastal Oregon, U.S.A","volume":"32","author":"Ohmann","year":"2002","journal-title":"Can. 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