{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T09:49:06Z","timestamp":1768902546334,"version":"3.49.0"},"reference-count":77,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T00:00:00Z","timestamp":1710115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41825002"],"award-info":[{"award-number":["41825002"]}]},{"name":"National Natural Science Foundation of China","award":["42201499"],"award-info":[{"award-number":["42201499"]}]},{"name":"National Natural Science Foundation of China","award":["CBAS2022ORP03"],"award-info":[{"award-number":["CBAS2022ORP03"]}]},{"name":"Open Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["41825002"],"award-info":[{"award-number":["41825002"]}]},{"name":"Open Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["42201499"],"award-info":[{"award-number":["42201499"]}]},{"name":"Open Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["CBAS2022ORP03"],"award-info":[{"award-number":["CBAS2022ORP03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Previous studies on global carbon emissions from forest loss have been marked by great discrepancies due to uncertainties regarding the lost area and the densities of different carbon pools. In this study, we employed a new global 30 m land cover dynamic dataset (GLC_FCS30D) to improve the assessment of forest loss areas; then, we combined multi-sourced carbon stock products to enhance the information on carbon density. Afterwards, we estimated the global carbon emissions from forest loss over the period of 1985\u20132020 based on the method recommended by the Intergovernmental Panel on Climate Change Guidelines (IPCC). The results indicate that global forest loss continued to accelerate over the past 35 years, totaling about 582.17 Mha and leading to total committed carbon emissions of 35.22 \u00b1 9.38 PgC. Tropical zones dominated global carbon emissions (~2\/3) due to their higher carbon density and greater forest loss. Furthermore, global emissions more than doubled in the period of 2015\u20132020 (1.77 \u00b1 0.44 PgC\/yr) compared to those in 1985\u20132000 (0.69 \u00b1 0.21 PgC\/yr). Notably, the forest loss at high altitudes (i.e., above 1000 m) more than tripled in mountainous regions, resulting in more pronounced carbon emissions in these areas. Therefore, the accelerating trend of global carbon emissions from forest loss indicates that great challenges still remain for achieving the COP 26 Declaration to halt forest loss by 2030.<\/jats:p>","DOI":"10.3390\/rs16060978","type":"journal-article","created":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T08:56:41Z","timestamp":1710147401000},"page":"978","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Characterizing the Accelerated Global Carbon Emissions from Forest Loss during 1985\u20132020 Using Fine-Resolution Remote Sensing Datasets"],"prefix":"10.3390","volume":"16","author":[{"given":"Wendi","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}]},{"given":"Hong","family":"Xu","sequence":"additional","affiliation":[{"name":"The High-Tech Research & Development Center (HTRDC) of the Ministry of Science & Technology, Beijing 100044, China"}]},{"given":"Tingting","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"College of Geomatics, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"given":"Jinqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Zhehua","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7987-037X","authenticated-orcid":false,"given":"Liangyun","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,11]]},"reference":[{"key":"ref_1","unstructured":"FAO (2020). 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