{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T08:57:22Z","timestamp":1776329842944,"version":"3.50.1"},"reference-count":82,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"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":["NNX17AE69G"],"award-info":[{"award-number":["NNX17AE69G"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NSF 2021086"],"award-info":[{"award-number":["NSF 2021086"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The majority of the aboveground biomass on the Earth\u2019s land surface is stored in forests. Thus, forest biomass plays a critical role in the global carbon cycle. Yet accurate estimate of forest aboveground biomass (FAGB) remains elusive. This study proposed a new conceptual model to map FAGB using remotely sensed data from multiple sensors. The conceptual model, which provides guidance for selecting remotely sensed data, is based on the principle of estimating FAGB on the ground using allometry, which needs species, diameter at breast height (DBH), and tree height as inputs. Based on the conceptual model, we used multiseasonal Landsat images to provide information about species composition for the forests in the study area, LiDAR data for canopy height, and the image texture and image texture ratio at two spatial resolutions for tree crown size, which is related to DBH. Moreover, we added RaDAR data to provide canopy volume information to the model. All the data layers were fed to a Random Forest (RF) regression model. The study was carried out in eastern North Carolina. We used biomass from the USFS Forest Inventory and Analysis plots to train and test the model performance. The best model achieved an R2 of 0.625 with a root mean squared error (RMSE) of 18.8 Mg\/ha (47.6%) with the \u201cout-of-bag\u201d samples at 30 \u00d7 30 m spatial resolution. The top five most important variables include the 95th, 85th, 75th, and 50th percentile heights of the LiDAR points and their standard deviations of 85th heights. Numerous features from multiseasonal Sentinel-1 C-Band SAR, multiseasonal Landsat 8 imagery along with image texture features from very high-resolution imagery were selected. But the importance of the height metrics dwarfed all other variables. More tests of the conceptual model in places with a broader range of biomass and more diverse species composition are needed to evaluate the importance of other input variables.<\/jats:p>","DOI":"10.3390\/rs14051115","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T21:11:07Z","timestamp":1645737067000},"page":"1115","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Mapping Forest Aboveground Biomass Using Multisource Remotely Sensed Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Dekker","family":"Ehlers","sequence":"first","affiliation":[{"name":"Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5170-1710","authenticated-orcid":false,"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Earth, Marine and Environment Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA"}]},{"given":"John","family":"Coulston","sequence":"additional","affiliation":[{"name":"Eastern Forest Environmental Threat Assessment Center, USDA Forest Service, Raleigh, NC 27709, USA"}]},{"given":"Yulong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute for a Secure and Sustainable Environment, University of Tennessee at Knoxville, Knoxville, TN 37996, USA"}]},{"given":"Tamlin","family":"Pavelsky","sequence":"additional","affiliation":[{"name":"Department of Earth, Marine and Environment Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA"}]},{"given":"Elizabeth","family":"Frankenberg","sequence":"additional","affiliation":[{"name":"Carolina Population Center, University of North Carlina at Chapel Hill, Chapel Hill, NC 27599, USA"},{"name":"Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA"}]},{"given":"Curtis","family":"Woodcock","sequence":"additional","affiliation":[{"name":"Department of Earth and Environment, Boston University, Boston, MA 02215, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4099-4906","authenticated-orcid":false,"given":"Conghe","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA"},{"name":"Carolina Population Center, University of North Carlina at Chapel Hill, Chapel Hill, NC 27599, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"ref_1","unstructured":"MassonDelmotte, V., Zhai, P., Pirani, A., Connors, S.L., P\u00e9an, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., and Gomis, M.I. 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