Spatial and Temporal Availability of Cloud-free Optical Observations in the Tropics to Monitor Deforestation
Abstract State-of-the-art methodologies to monitor deforestation rely mostly on optical satellite observations. High-density optical time series can enable the detection of deforestation almost as soon as it occurs. However, deforestation monitoring in the tropics can be hindered by high cloud cover...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-08-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-02439-x |
| _version_ | 1797453977937248256 |
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| author | Africa I. Flores-Anderson Jeffrey Cardille Khashayar Azad Emil Cherrington Yingtong Zhang Sylvia Wilson |
| author_facet | Africa I. Flores-Anderson Jeffrey Cardille Khashayar Azad Emil Cherrington Yingtong Zhang Sylvia Wilson |
| author_sort | Africa I. Flores-Anderson |
| collection | DOAJ |
| description | Abstract State-of-the-art methodologies to monitor deforestation rely mostly on optical satellite observations. High-density optical time series can enable the detection of deforestation almost as soon as it occurs. However, deforestation monitoring in the tropics can be hindered by high cloud coverage, and thus the responsiveness of managers, enforcement agencies, and scientists. To understand the implications of cloud contamination in freely available optical data we analyzed combined time series from Landsat 7, 8, and Sentinel-2 over the tropics from 2017–2021. Datasets derived for each 30 m × 30 m of the 59.4 M km2 domain include a) number of cloud-free observations per year, b) maximum consecutive days without clear imagery within a year, and c) final date of the longest waiting period. The datasets reflect where and when data gaps in optical time series exist due to cloud contamination. Scripts to access and extend the datasets are shared and documented. The datasets can be used to prioritize areas where complementary observations, such as radar imagery, are needed for implementing effective deforestation alert systems. |
| first_indexed | 2024-03-09T15:30:39Z |
| format | Article |
| id | doaj.art-de559ad39d664bf4b0f1ade4a6f46c29 |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-03-09T15:30:39Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-de559ad39d664bf4b0f1ade4a6f46c292023-11-26T12:17:56ZengNature PortfolioScientific Data2052-44632023-08-011011810.1038/s41597-023-02439-xSpatial and Temporal Availability of Cloud-free Optical Observations in the Tropics to Monitor DeforestationAfrica I. Flores-Anderson0Jeffrey Cardille1Khashayar Azad2Emil Cherrington3Yingtong Zhang4Sylvia Wilson5 Department of Natural Resource Sciences, McGill University Department of Natural Resource Sciences, McGill University Department of Computer Science and Software Engineering, Concordia UniversityEarth System Science Center, University of Alabama in HuntsvilleDepartment of Earth and Environment, Boston University National Land Imaging Program, United States Geological ServiceAbstract State-of-the-art methodologies to monitor deforestation rely mostly on optical satellite observations. High-density optical time series can enable the detection of deforestation almost as soon as it occurs. However, deforestation monitoring in the tropics can be hindered by high cloud coverage, and thus the responsiveness of managers, enforcement agencies, and scientists. To understand the implications of cloud contamination in freely available optical data we analyzed combined time series from Landsat 7, 8, and Sentinel-2 over the tropics from 2017–2021. Datasets derived for each 30 m × 30 m of the 59.4 M km2 domain include a) number of cloud-free observations per year, b) maximum consecutive days without clear imagery within a year, and c) final date of the longest waiting period. The datasets reflect where and when data gaps in optical time series exist due to cloud contamination. Scripts to access and extend the datasets are shared and documented. The datasets can be used to prioritize areas where complementary observations, such as radar imagery, are needed for implementing effective deforestation alert systems.https://doi.org/10.1038/s41597-023-02439-x |
| spellingShingle | Africa I. Flores-Anderson Jeffrey Cardille Khashayar Azad Emil Cherrington Yingtong Zhang Sylvia Wilson Spatial and Temporal Availability of Cloud-free Optical Observations in the Tropics to Monitor Deforestation Scientific Data |
| title | Spatial and Temporal Availability of Cloud-free Optical Observations in the Tropics to Monitor Deforestation |
| title_full | Spatial and Temporal Availability of Cloud-free Optical Observations in the Tropics to Monitor Deforestation |
| title_fullStr | Spatial and Temporal Availability of Cloud-free Optical Observations in the Tropics to Monitor Deforestation |
| title_full_unstemmed | Spatial and Temporal Availability of Cloud-free Optical Observations in the Tropics to Monitor Deforestation |
| title_short | Spatial and Temporal Availability of Cloud-free Optical Observations in the Tropics to Monitor Deforestation |
| title_sort | spatial and temporal availability of cloud free optical observations in the tropics to monitor deforestation |
| url | https://doi.org/10.1038/s41597-023-02439-x |
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