{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T14:35:16Z","timestamp":1744382116195,"version":"3.37.3"},"reference-count":69,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,4,22]],"date-time":"2017-04-22T00:00:00Z","timestamp":1492819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["PIOF-GA-2013-629376"],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000270","name":"Natural Environment Research Council","doi-asserted-by":"publisher","award":["National Centre for Earth Observation"],"id":[{"id":"10.13039\/501100000270","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006959","name":"USDA Forest Service","doi-asserted-by":"publisher","award":["10-IA-11130400-009."],"id":[{"id":"10.13039\/100006959","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University of California Davis","award":["10-IA-11130400-009."]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Accurate, spatially explicit information about forest canopy fuel properties is essential for ecosystem management strategies for reducing the severity of forest fires. Airborne LiDAR technology has demonstrated its ability to accurately map canopy fuels. However, its geographical and temporal coverage is limited, thus making it difficult to characterize fuel properties over large regions before catastrophic events occur. This study presents a two-step methodology for integrating post-fire airborne LiDAR and pre-fire Landsat OLI (Operational Land Imager) data to estimate important pre-fire canopy fuel properties for crown fire spread, namely canopy fuel load (CFL), canopy cover (CC), and canopy bulk density (CBD). This study focused on a fire prone area affected by the large 2013 Rim fire in the Sierra Nevada Mountains, California, USA. First, LiDAR data was used to estimate CFL, CC, and CBD across an unburned 2 km buffer with similar structural characteristics to the burned area. Second, the LiDAR-based canopy fuel properties were extrapolated over the whole area using Landsat OLI data, which yielded an R2 of 0.8, 0.79, and 0.64 and RMSE of 3.76 Mg\u00b7ha\u22121, 0.09, and 0.02 kg\u00b7m\u22123 for CFL, CC, and CBD, respectively. The uncertainty of the estimates was estimated for each pixel using a bootstrapping approach, and the 95% confidence intervals are reported. The proposed methodology provides a detailed spatial estimation of forest canopy fuel properties along with their uncertainty that can be readily integrated into fire behavior and fire effects models. The methodology could be also integrated into the LANDFIRE program to improve the information on canopy fuels.<\/jats:p>","DOI":"10.3390\/rs9040394","type":"journal-article","created":{"date-parts":[[2017,4,24]],"date-time":"2017-04-24T17:10:11Z","timestamp":1493053811000},"page":"394","source":"Crossref","is-referenced-by-count":45,"title":["Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data"],"prefix":"10.3390","volume":"9","author":[{"given":"Mariano","family":"Garc\u00eda","sequence":"first","affiliation":[{"name":"Centre for Landscape and Climate Research, Department of Geography, University of Leicester, Leicester LE1 7RH, UK"},{"name":"Jet Propulsion Laboratory (JPL), California Institute of Technology, Pasadena, CA 91109, USA"}]},{"given":"Sassan","family":"Saatchi","sequence":"additional","affiliation":[{"name":"Jet Propulsion Laboratory (JPL), California Institute of Technology, Pasadena, CA 91109, USA"}]},{"given":"Angeles","family":"Casas","sequence":"additional","affiliation":[{"name":"The Climate Corporation, 201 Third Street, Suite 1100, San Francisco, CA 94103, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9874-6043","authenticated-orcid":false,"given":"Alexander","family":"Koltunov","sequence":"additional","affiliation":[{"name":"Center for Spatial Technologies and Remote Sensing (CSTARS), University of California Davis, Davis, CA 95618, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8551-0461","authenticated-orcid":false,"given":"Susan","family":"Ustin","sequence":"additional","affiliation":[{"name":"Center for Spatial Technologies and Remote Sensing (CSTARS), University of California Davis, Davis, CA 95618, USA"}]},{"given":"Carlos","family":"Ramirez","sequence":"additional","affiliation":[{"name":"Region 5 Remote Sensing Lab, McClellan, USDA Forest Service, Vallejo, CA 95652, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9053-4684","authenticated-orcid":false,"given":"Heiko","family":"Balzter","sequence":"additional","affiliation":[{"name":"Centre for Landscape and Climate Research, Department of Geography, University of Leicester, Leicester LE1 7RH, UK"},{"name":"National Centre for Earth Observation, University of Leicester, Leicester LE1 7RH, UK"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1071\/WF01028","article-title":"Mapping wildland fuels for fire management across multiple scales: Integrating remote sensing, GIS, and biophysical modeling","volume":"10","author":"Keane","year":"2001","journal-title":"Int. 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