Mapping Annual Global Forest Gain From 1983 to 2021 With Landsat Imagery
The world's forests are experiencing rapid changes due to land use and climate change. However, a detailed map of global forest gain at fine spatial and temporal resolutions is still missing. To fill this gap, we developed an automatic framework for mapping annual forest gain globally usi...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10103610/ |
| _version_ | 1797830184800354304 |
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| author | Zhenrong Du Le Yu Jianyu Yang David Coomes Kasturi Kanniah Haohuan Fu Peng Gong |
| author_facet | Zhenrong Du Le Yu Jianyu Yang David Coomes Kasturi Kanniah Haohuan Fu Peng Gong |
| author_sort | Zhenrong Du |
| collection | DOAJ |
| description | The world's forests are experiencing rapid changes due to land use and climate change. However, a detailed map of global forest gain at fine spatial and temporal resolutions is still missing. To fill this gap, we developed an automatic framework for mapping annual forest gain globally using Landsat time series, the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm, and the Google Earth engine platform. First, stable forest samples collected based on the first all-season sample set and an automated sample migrate method were used to determine annual normalized burn ratio (NBR) thresholds for forest gain detection. Second, with the NBR time series from 1982 to 2021 and LandTrendr algorithm, we produced a dataset of global forest gain year from 1983 to 2021 based on a set of decision rules. Our results reveal that over 60% gains occurred in Russia, Canada, the United States, Indonesia, and China, and approximately half of global forest gain occurred between 2001 and 2010. The forest gain map developed in this study exhibited good consistency with statistical inventories and independent regional and global products. Our dataset can be useful for policy-relevant research on the global carbon cycle, and our method provides an efficient and transferable approach for monitoring other types of land cover dynamics. |
| first_indexed | 2024-04-09T13:32:07Z |
| format | Article |
| id | doaj.art-ade06b88618e43d3b8bc0d59290e6a4e |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-09T13:32:07Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-ade06b88618e43d3b8bc0d59290e6a4e2023-05-09T23:00:11ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-01164195420410.1109/JSTARS.2023.326779610103610Mapping Annual Global Forest Gain From 1983 to 2021 With Landsat ImageryZhenrong Du0https://orcid.org/0000-0003-4439-8543Le Yu1https://orcid.org/0000-0003-3115-2042Jianyu Yang2David Coomes3https://orcid.org/0000-0002-8261-2582Kasturi Kanniah4https://orcid.org/0000-0001-6736-4819Haohuan Fu5https://orcid.org/0000-0002-6982-2235Peng Gong6College of Land Science and Technology, China Agricultural University, Beijing, ChinaMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing, ChinaConservation Research Institute and the Department of Plant Sciences, University of Cambridge, Cambridge, U.K.Centre for Environmental Sustainability and Water Security (IPASA), Research Institute for Sustainable Environment and Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, ChinaMinistry of Education Ecological Field Station for East Asia Migratory Birds, Tsinghua University, Beijing, ChinaThe world's forests are experiencing rapid changes due to land use and climate change. However, a detailed map of global forest gain at fine spatial and temporal resolutions is still missing. To fill this gap, we developed an automatic framework for mapping annual forest gain globally using Landsat time series, the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm, and the Google Earth engine platform. First, stable forest samples collected based on the first all-season sample set and an automated sample migrate method were used to determine annual normalized burn ratio (NBR) thresholds for forest gain detection. Second, with the NBR time series from 1982 to 2021 and LandTrendr algorithm, we produced a dataset of global forest gain year from 1983 to 2021 based on a set of decision rules. Our results reveal that over 60% gains occurred in Russia, Canada, the United States, Indonesia, and China, and approximately half of global forest gain occurred between 2001 and 2010. The forest gain map developed in this study exhibited good consistency with statistical inventories and independent regional and global products. Our dataset can be useful for policy-relevant research on the global carbon cycle, and our method provides an efficient and transferable approach for monitoring other types of land cover dynamics.https://ieeexplore.ieee.org/document/10103610/Change detectionforest disturbanceland coverLandsat-based detection of trends in disturbance and recovery (LandTrendr) |
| spellingShingle | Zhenrong Du Le Yu Jianyu Yang David Coomes Kasturi Kanniah Haohuan Fu Peng Gong Mapping Annual Global Forest Gain From 1983 to 2021 With Landsat Imagery IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Change detection forest disturbance land cover Landsat-based detection of trends in disturbance and recovery (LandTrendr) |
| title | Mapping Annual Global Forest Gain From 1983 to 2021 With Landsat Imagery |
| title_full | Mapping Annual Global Forest Gain From 1983 to 2021 With Landsat Imagery |
| title_fullStr | Mapping Annual Global Forest Gain From 1983 to 2021 With Landsat Imagery |
| title_full_unstemmed | Mapping Annual Global Forest Gain From 1983 to 2021 With Landsat Imagery |
| title_short | Mapping Annual Global Forest Gain From 1983 to 2021 With Landsat Imagery |
| title_sort | mapping annual global forest gain from 1983 to 2021 with landsat imagery |
| topic | Change detection forest disturbance land cover Landsat-based detection of trends in disturbance and recovery (LandTrendr) |
| url | https://ieeexplore.ieee.org/document/10103610/ |
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