{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T16:12:52Z","timestamp":1740154372582,"version":"3.37.3"},"reference-count":55,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2016,1,14]],"date-time":"2016-01-14T00:00:00Z","timestamp":1452729600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Continuous monitoring of forest cover condition is key to understanding the carbon dynamics of forest ecosystems. This paper addresses how to integrate single-year airborne LiDAR and time-series Landsat imagery to derive forest cover change information. LiDAR data were used to extract forest cover at the sub-pixel level of Landsat for a single year, and the Landtrendr algorithm was applied to Landsat spectral data to explore the temporal information of forest cover change. Four different approaches were employed to model the relationship between forest cover and Landsat spectral data. The result shows incorporating the historic information using the temporal trajectory fitting process could infuse the model with better prediction power. Random forest modeling performs the best for quantitative forest cover estimation. Temporal trajectory fitting with random forest model shows the best agreement with validation data (R2 = 0.82 and RMSE = 5.19%). We applied our approach to Youyu county in Shanxi province of China, as part of the Three North Shelter Forest Program, to map multi-decadal forest cover dynamics. With the availability of global time-series Landsat imagery and affordable airborne LiDAR data, the approach we developed has the potential to derive large-scale forest cover dynamics.<\/jats:p>","DOI":"10.3390\/rs8010062","type":"journal-article","created":{"date-parts":[[2016,1,15]],"date-time":"2016-01-15T14:26:33Z","timestamp":1452867993000},"page":"62","source":"Crossref","is-referenced-by-count":16,"title":["Quantifying Multi-Decadal Change of Planted Forest Cover Using Airborne LiDAR and Landsat Imagery"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0251-5553","authenticated-orcid":false,"given":"Xiaoyi","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (CAS), Beijing 100101, China"},{"name":"Department of Environmental Sciences, Policy & Management, University of California, Berkeley, CA 94720, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6253-8437","authenticated-orcid":false,"given":"Huabing","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (CAS), Beijing 100101, China"},{"name":"Department of Environmental Sciences, Policy & Management, University of California, Berkeley, CA 94720, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1513-3765","authenticated-orcid":false,"given":"Peng","family":"Gong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (CAS), Beijing 100101, China"},{"name":"Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China"},{"name":"Joint Center for Global Change Studies, Beijing 100875, China"}]},{"given":"Gregory","family":"Biging","sequence":"additional","affiliation":[{"name":"Department of Environmental Sciences, Policy & Management, University of California, Berkeley, CA 94720, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1146-4874","authenticated-orcid":false,"given":"Qinchuan","family":"Xin","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]},{"given":"Yanlei","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Environmental Sciences, Policy & Management, University of California, Berkeley, CA 94720, USA"}]},{"given":"Jun","family":"Yang","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China"}]},{"given":"Caixia","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (CAS), Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1126\/science.1155121","article-title":"Forests and climate change: Forcings, feedbacks, and the climate benefits of forests","volume":"320","author":"Bonan","year":"2008","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1126\/science.1201609","article-title":"A large and persistent carbon sink in the world\u2019s forests","volume":"333","author":"Pan","year":"2011","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1038\/nclimate1908","article-title":"The role of satellite remote sensing in climate change studies","volume":"3","author":"Yang","year":"2013","journal-title":"Nat. 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