{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T12:25:30Z","timestamp":1764332730681,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,3,7]],"date-time":"2019-03-07T00:00:00Z","timestamp":1551916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006196","name":"Jet Propulsion Laboratory","doi-asserted-by":"publisher","award":["281945.02.61.04.10-NISAR SDT"],"award-info":[{"award-number":["281945.02.61.04.10-NISAR SDT"]}],"id":[{"id":"10.13039\/100006196","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We present a flexible methodology to identify forest loss in synthetic aperture radar (SAR) L-band ALOS\/PALSAR images. Instead of single pixel analysis, we generate spatial segments (i.e., superpixels) based on local image statistics to track homogeneous patches of forest across a time-series of ALOS\/PALSAR images. Forest loss detection is performed using an ensemble of Support Vector Machines (SVMs) trained on local radar backscatter features derived from superpixels. This method is applied to time-series of ALOS-1 and ALOS-2 radar images over a boreal forest within the Laurentides Wildlife Reserve in Qu\u00e9bec, Canada. We evaluate four spatial arrangements including (1) single pixels, (2) square grid cells, (3) superpixels based on segmentation of the radar images, and (4) superpixels derived from ancillary optical Landsat imagery. Detection of forest loss using superpixels outperforms single pixel and regular square grid cell approaches, especially when superpixels are generated from ancillary optical imagery. Results are validated with official Qu\u00e9bec forestry data and Hansen et al. forest loss products. Our results indicate that this approach can be applied to monitor forest loss across large study areas using L-band radar instruments such as ALOS\/PALSAR, particularly when combined with superpixels generated from ancillary optical data.<\/jats:p>","DOI":"10.3390\/rs11050556","type":"journal-article","created":{"date-parts":[[2019,3,8]],"date-time":"2019-03-08T04:58:35Z","timestamp":1552021115000},"page":"556","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Monitoring Forest Loss in ALOS\/PALSAR Time-Series with Superpixels"],"prefix":"10.3390","volume":"11","author":[{"given":"Charlie","family":"Marshak","sequence":"first","affiliation":[{"name":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"}]},{"given":"Marc","family":"Simard","sequence":"additional","affiliation":[{"name":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"}]},{"given":"Michael","family":"Denbina","sequence":"additional","affiliation":[{"name":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2014.04.014","article-title":"New global Forest\/Non-Forest Maps from ALOS PALSAR Data (2007\u20132010)","volume":"155","author":"Shimada","year":"2014","journal-title":"Remote Sens. 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