{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T08:02:30Z","timestamp":1771488150304,"version":"3.50.1"},"reference-count":89,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T00:00:00Z","timestamp":1639612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100011131","name":"Maine Agricultural and Forest Experiment Station","doi-asserted-by":"publisher","award":["TBD following review"],"award-info":[{"award-number":["TBD following review"]}],"id":[{"id":"10.13039\/100011131","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Maine Agricultural Forest Experiment Station","award":["ME0-41907"],"award-info":[{"award-number":["ME0-41907"]}]},{"name":"National Science Foundation RII Track-2 FEC","award":["Award #1920908"],"award-info":[{"award-number":["Award #1920908"]}]},{"name":"Maine Agricultural and Forest Experiment Publication","award":["Number 3866"],"award-info":[{"award-number":["Number 3866"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we present a model using a 3-D convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud, combined with coincident satellite data (spectral, phenology, and disturbance history). We compared this approach to traditional modeling used for making forest predictions from LiDAR data (height metrics and random forest) and found that the CNN had consistently lower uncertainty. We then applied the CNN to public data over six New England states in the USA, generating maps of 14 forest attributes at a 10 m resolution over 85% of the region. Aboveground biomass estimates produced a root mean square error of 36 Mg ha\u22121 (44%) and were within the 97.5% confidence of independent county-level estimates for 33 of 38 or 86.8% of the counties examined. CNN predictions for stem density and percentage of conifer attributes were moderately successful, while predictions for detailed species groupings were less successful. The approach shows promise for improving the prediction of forest attributes from regional LiDAR data and for combining disparate LiDAR datasets into a common framework for large-scale estimation.<\/jats:p>","DOI":"10.3390\/rs13245113","type":"journal-article","created":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T21:32:40Z","timestamp":1639690360000},"page":"5113","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9095-6687","authenticated-orcid":false,"given":"Elias","family":"Ayrey","sequence":"first","affiliation":[{"name":"Pachama Inc., 1435 48th Ave, San Francisco, CA 94122, USA"},{"name":"School of Forest Resources, University of Maine, 5755 Nutting Hall, Orono, ME 04469, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3011-7934","authenticated-orcid":false,"given":"Daniel J.","family":"Hayes","sequence":"additional","affiliation":[{"name":"School of Forest Resources, University of Maine, 5755 Nutting Hall, Orono, ME 04469, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9421-6480","authenticated-orcid":false,"given":"John B.","family":"Kilbride","sequence":"additional","affiliation":[{"name":"College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, 114 Wilkinson Hall, Corvallis, OR 97331, USA"}]},{"given":"Shawn","family":"Fraver","sequence":"additional","affiliation":[{"name":"School of Forest Resources, University of Maine, 5755 Nutting Hall, Orono, ME 04469, USA"}]},{"suffix":"Jr.","given":"John A.","family":"Kershaw","sequence":"additional","affiliation":[{"name":"Faculty of Forestry and Environmental Management, University of New Brunswick, P.O. Box 4400, 28 Dineen Drive, Fredericton, NB E3B5A3, Canada"}]},{"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, Biospheric Sciences Laboratory, Code 618, Greenbelt, MD 20771, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2534-4478","authenticated-orcid":false,"given":"Aaron R.","family":"Weiskittel","sequence":"additional","affiliation":[{"name":"Center for Research on Sustainable Forests, University of Maine, 5755 Nutting Hall, Orono, ME 04469, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ayrey, E., and Hayes, D.J. (2018). The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory. 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