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Time-series analysis of satellite imagery for detecting vegetation cover changes in Indonesia

Indonesia has one of the world’s largest tropical forests; thus, its deforestation and environmental degradation are a global concern. This study is the first to perform comprehensive big data analyses with coherent vegetation criteria to measure the vegetation change at a high temporal resolution (...

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Autores principales: Furusawa, Takuro, Koera, Takuya, Siburian, Rikson, Wicaksono, Agung, Matsudaira, Kazunari, Ishioka, Yoshinori
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10212945/
https://www.ncbi.nlm.nih.gov/pubmed/37231076
http://dx.doi.org/10.1038/s41598-023-35330-1
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author Furusawa, Takuro
Koera, Takuya
Siburian, Rikson
Wicaksono, Agung
Matsudaira, Kazunari
Ishioka, Yoshinori
author_facet Furusawa, Takuro
Koera, Takuya
Siburian, Rikson
Wicaksono, Agung
Matsudaira, Kazunari
Ishioka, Yoshinori
author_sort Furusawa, Takuro
collection PubMed
description Indonesia has one of the world’s largest tropical forests; thus, its deforestation and environmental degradation are a global concern. This study is the first to perform comprehensive big data analyses with coherent vegetation criteria to measure the vegetation change at a high temporal resolution (every 16-day period) for 20 years and the high administrative resolution (regency or city) all over Indonesia. The normalized difference vegetation index (NDVI) of the Moderate Resolution Imaging Spectroradiometer is analyzed through state space modeling. The findings reveal that the NDVI increases in almost all regencies, except in urban areas. A high correlation between the NDVI change and the time is observed in Sumatra, Papua, and Kalimantan. The gain of the NDVI values is obvious in the Central and Eastern Java Island. Human activities, such as the expansion of agriculture and forestry and forest conservation policies, are the key factors for the observed pattern.
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spelling pubmed-102129452023-05-27 Time-series analysis of satellite imagery for detecting vegetation cover changes in Indonesia Furusawa, Takuro Koera, Takuya Siburian, Rikson Wicaksono, Agung Matsudaira, Kazunari Ishioka, Yoshinori Sci Rep Article Indonesia has one of the world’s largest tropical forests; thus, its deforestation and environmental degradation are a global concern. This study is the first to perform comprehensive big data analyses with coherent vegetation criteria to measure the vegetation change at a high temporal resolution (every 16-day period) for 20 years and the high administrative resolution (regency or city) all over Indonesia. The normalized difference vegetation index (NDVI) of the Moderate Resolution Imaging Spectroradiometer is analyzed through state space modeling. The findings reveal that the NDVI increases in almost all regencies, except in urban areas. A high correlation between the NDVI change and the time is observed in Sumatra, Papua, and Kalimantan. The gain of the NDVI values is obvious in the Central and Eastern Java Island. Human activities, such as the expansion of agriculture and forestry and forest conservation policies, are the key factors for the observed pattern. Nature Publishing Group UK 2023-05-25 /pmc/articles/PMC10212945/ /pubmed/37231076 http://dx.doi.org/10.1038/s41598-023-35330-1 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 Article
Furusawa, Takuro
Koera, Takuya
Siburian, Rikson
Wicaksono, Agung
Matsudaira, Kazunari
Ishioka, Yoshinori
Time-series analysis of satellite imagery for detecting vegetation cover changes in Indonesia
title Time-series analysis of satellite imagery for detecting vegetation cover changes in Indonesia
title_full Time-series analysis of satellite imagery for detecting vegetation cover changes in Indonesia
title_fullStr Time-series analysis of satellite imagery for detecting vegetation cover changes in Indonesia
title_full_unstemmed Time-series analysis of satellite imagery for detecting vegetation cover changes in Indonesia
title_short Time-series analysis of satellite imagery for detecting vegetation cover changes in Indonesia
title_sort time-series analysis of satellite imagery for detecting vegetation cover changes in indonesia
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10212945/
https://www.ncbi.nlm.nih.gov/pubmed/37231076
http://dx.doi.org/10.1038/s41598-023-35330-1
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