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Spatial and Temporal Availability of Cloud-free Optical Observations in the Tropics to Monitor Deforestation

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...

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Autores principales: Flores-Anderson, Africa I., Cardille, Jeffrey, Azad, Khashayar, Cherrington, Emil, Zhang, Yingtong, Wilson, Sylvia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10444863/
https://www.ncbi.nlm.nih.gov/pubmed/37607919
http://dx.doi.org/10.1038/s41597-023-02439-x
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author Flores-Anderson, Africa I.
Cardille, Jeffrey
Azad, Khashayar
Cherrington, Emil
Zhang, Yingtong
Wilson, Sylvia
author_facet Flores-Anderson, Africa I.
Cardille, Jeffrey
Azad, Khashayar
Cherrington, Emil
Zhang, Yingtong
Wilson, Sylvia
author_sort Flores-Anderson, Africa I.
collection PubMed
description 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.
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spelling pubmed-104448632023-08-24 Spatial and Temporal Availability of Cloud-free Optical Observations in the Tropics to Monitor Deforestation Flores-Anderson, Africa I. Cardille, Jeffrey Azad, Khashayar Cherrington, Emil Zhang, Yingtong Wilson, Sylvia Sci Data Data Descriptor 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. Nature Publishing Group UK 2023-08-22 /pmc/articles/PMC10444863/ /pubmed/37607919 http://dx.doi.org/10.1038/s41597-023-02439-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Flores-Anderson, Africa I.
Cardille, Jeffrey
Azad, Khashayar
Cherrington, Emil
Zhang, Yingtong
Wilson, Sylvia
Spatial and Temporal Availability of Cloud-free Optical Observations in the Tropics to Monitor Deforestation
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
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10444863/
https://www.ncbi.nlm.nih.gov/pubmed/37607919
http://dx.doi.org/10.1038/s41597-023-02439-x
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