Articles | Volume 16, issue 1
https://doi.org/10.5194/essd-16-321-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-321-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
15 Jan 2024
Data description article |
| 15 Jan 2024
| 15 Jan 2024
Annual maps of forest cover in the Brazilian Amazon from analyses of PALSAR and MODIS images
Yuanwei Qin
School of Biological Sciences, University of Oklahoma, Norman, OK 73019, USA
Xiangming Xiao
CORRESPONDING AUTHOR
School of Biological Sciences, University of Oklahoma, Norman, OK 73019, USA
Department of Geography, National University of Singapore, 1 Arts Link, Kent Ridge, 117570 Singapore
Ralph Dubayah
Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA
Russell Doughty
College of Atmospheric and Geographic Sciences, University of Oklahoma, Norman, OK 73019, USA
Diyou Liu
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Fang Liu
School of Biological Sciences, University of Oklahoma, Norman, OK 73019, USA
Yosio Shimabukuro
Brazilian National Institute for Space Research, INPE, São José dos Campos, SP 12227, Brazil
Egidio Arai
Brazilian National Institute for Space Research, INPE, São José dos Campos, SP 12227, Brazil
Xinxin Wang
Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Institute of Biodiversity Science and Institute of Eco-Chongming, School of Life Sciences, Fudan University, Shanghai 200438, China
Berrien Moore III
College of Atmospheric and Geographic Sciences, University of Oklahoma, Norman, OK 73019, USA
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Cited
11 citations as recorded by crossref.
- Hierarchy Clustering for Cloud Detection Assisted by Spectral Features of Ground Covers W. Song et al. https://doi.org/10.3390/rs18050698
- A hierarchical-modular ecosystem mapping framework for high-intensity human activity zones J. Xin et al. https://doi.org/10.1080/17538947.2025.2564468
- Mapping interannual changes of evergreen forests in Beijing, China at 30-m spatial resolution during 1990–2023 Z. Li et al. https://doi.org/10.1080/10106049.2026.2685918
- Annual maps of forest and evergreen forest in the contiguous United States during 2015–2017 from analyses of PALSAR-2 and Landsat images J. Wang et al. https://doi.org/10.5194/essd-16-4619-2024
- Automatic Mapping of 10 m Tropical Evergreen Forest Cover in Central African Republic with Sentinel-2 Dynamic World Dataset W. Zhao et al. https://doi.org/10.3390/rs17040722
- Mapping annual 10-meter evergreen and non-evergreen forests in China from 2017 to 2023 with multi-source remote sensing data W. Zhao et al. https://doi.org/10.1080/15481603.2026.2702254
- Photon-Counting Lidar Remote Sensing: Current progress and future trends L. Wu et al. https://doi.org/10.1109/MGRS.2026.3660928
- An empirical study of the impact of environmental regulation on the eco-efficiency of digital agriculture: a quasi-natural experiment based on China’s carbon emissions trading pilot policy Z. Lu et al. https://doi.org/10.1186/s13021-026-00449-x
- Monitoring vegetation degradation using remote sensing and machine learning over India – a multi-sensor, multi-temporal and multi-scale approach K. Sur et al. https://doi.org/10.3389/ffgc.2024.1382557
- EMLARDE tree: ensemble machine learning based random de-correlated extra decision tree for the forest cover type prediction T. Guhan & N. Revathy https://doi.org/10.1007/s11760-024-03470-0
- Time-series reconstruction and mapping of forest aboveground biomass in the Great Xing’an Mountains of China using GEDI, MODIS, and machine learning C. Yang et al. https://doi.org/10.1016/j.ecolind.2025.114375
11 citations as recorded by crossref.
- Hierarchy Clustering for Cloud Detection Assisted by Spectral Features of Ground Covers W. Song et al. https://doi.org/10.3390/rs18050698
- A hierarchical-modular ecosystem mapping framework for high-intensity human activity zones J. Xin et al. https://doi.org/10.1080/17538947.2025.2564468
- Mapping interannual changes of evergreen forests in Beijing, China at 30-m spatial resolution during 1990–2023 Z. Li et al. https://doi.org/10.1080/10106049.2026.2685918
- Annual maps of forest and evergreen forest in the contiguous United States during 2015–2017 from analyses of PALSAR-2 and Landsat images J. Wang et al. https://doi.org/10.5194/essd-16-4619-2024
- Automatic Mapping of 10 m Tropical Evergreen Forest Cover in Central African Republic with Sentinel-2 Dynamic World Dataset W. Zhao et al. https://doi.org/10.3390/rs17040722
- Mapping annual 10-meter evergreen and non-evergreen forests in China from 2017 to 2023 with multi-source remote sensing data W. Zhao et al. https://doi.org/10.1080/15481603.2026.2702254
- Photon-Counting Lidar Remote Sensing: Current progress and future trends L. Wu et al. https://doi.org/10.1109/MGRS.2026.3660928
- An empirical study of the impact of environmental regulation on the eco-efficiency of digital agriculture: a quasi-natural experiment based on China’s carbon emissions trading pilot policy Z. Lu et al. https://doi.org/10.1186/s13021-026-00449-x
- Monitoring vegetation degradation using remote sensing and machine learning over India – a multi-sensor, multi-temporal and multi-scale approach K. Sur et al. https://doi.org/10.3389/ffgc.2024.1382557
- EMLARDE tree: ensemble machine learning based random de-correlated extra decision tree for the forest cover type prediction T. Guhan & N. Revathy https://doi.org/10.1007/s11760-024-03470-0
- Time-series reconstruction and mapping of forest aboveground biomass in the Great Xing’an Mountains of China using GEDI, MODIS, and machine learning C. Yang et al. https://doi.org/10.1016/j.ecolind.2025.114375
Saved (final revised paper)
Latest update: 04 Sep 2026
Short summary
Forest definition has two major biophysical parameters, i.e., canopy height and canopy coverage. However, few studies have assessed forest cover maps in terms of these two parameters at a large scale. Here, we assessed the annual forest cover maps in the Brazilian Amazon using 1.1 million footprints of canopy height and canopy coverage. Over 93 % of our forest cover maps are consistent with the FAO forest definition, showing the high accuracy of these forest cover maps in the Brazilian Amazon.
Forest definition has two major biophysical parameters, i.e., canopy height and canopy coverage....
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