Google earth engine for spatio-temporal drought monitoring in Bangkalan, Indonesia
Drought occurrence in a certain area can be monitored by remote sensing through cloud-based platform of Google Earth Engine (GEE). The objective this study was to analyze spatially and temporally distribution of drought in Bangkalan Regency between 2017 to 2022 with GEE. This study employed CHIRPS a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2024/18/bioconf_icafes2024_05006.pdf |
| _version_ | 1797221541478400000 |
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| author | Rahman Fahmi Arief Suryawati Sinar Supriyadi Slamet Basuki |
| author_facet | Rahman Fahmi Arief Suryawati Sinar Supriyadi Slamet Basuki |
| author_sort | Rahman Fahmi Arief |
| collection | DOAJ |
| description | Drought occurrence in a certain area can be monitored by remote sensing through cloud-based platform of Google Earth Engine (GEE). The objective this study was to analyze spatially and temporally distribution of drought in Bangkalan Regency between 2017 to 2022 with GEE. This study employed CHIRPS and satellite images of Landsat 8 at Level 2 covering Bangkalan area from 2017 to 2022. Masking and Cloud masking had been carried out before analyzed the satellite images. Data was processed using Java scrip API algorithm in GEE to obtain rainfall, LST, NDVI, NDWI and NDDI data. Result of rainfall analysis from CHIRPS data showed that dry months from 2017 to 2022 occurred from June to October. The value of LST was between 24.75 38.87°C. Drought events in the study area from 2017 to 2022 were dominated by severe and extreme drought. The severe drought covers the area of 83.17% (2017), 57.34% (2018), 67.13% (2019), 84% (2020), 80.93% (2021), and 89.89% (2022). Meanwhile, the extreme drought wraps the area of 14.05% (2017), 40.05% (2018), 30.17% (2019), 13.15% (2020), 16.27% (2021), and 7.03% (2022). The area with severe drought was always over the one with extreme drought, and when the severe drought area increased; the extreme drought area decreased Result of this study could be employed in planning of drought mitigation and adaptation, the use of water and land resources, and public information on risks and actions for drought-affected communities. |
| first_indexed | 2024-04-24T13:07:04Z |
| format | Article |
| id | doaj.art-2cb6eb39a83c4201a8c2d7c33c1b3b70 |
| institution | Directory Open Access Journal |
| issn | 2117-4458 |
| language | English |
| last_indexed | 2024-04-24T13:07:04Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj.art-2cb6eb39a83c4201a8c2d7c33c1b3b702024-04-05T07:29:40ZengEDP SciencesBIO Web of Conferences2117-44582024-01-01990500610.1051/bioconf/20249905006bioconf_icafes2024_05006Google earth engine for spatio-temporal drought monitoring in Bangkalan, IndonesiaRahman Fahmi Arief0Suryawati Sinar1Supriyadi Slamet2Basuki3University of Trunojoyo Madura, Agroecotechnology, Agriculture FacultyUniversity of Trunojoyo Madura, Agroecotechnology, Agriculture FacultyUniversity of Trunojoyo Madura, Agroecotechnology, Agriculture FacultyUniversity of Jember, Soil Science, Agriculture FacultyDrought occurrence in a certain area can be monitored by remote sensing through cloud-based platform of Google Earth Engine (GEE). The objective this study was to analyze spatially and temporally distribution of drought in Bangkalan Regency between 2017 to 2022 with GEE. This study employed CHIRPS and satellite images of Landsat 8 at Level 2 covering Bangkalan area from 2017 to 2022. Masking and Cloud masking had been carried out before analyzed the satellite images. Data was processed using Java scrip API algorithm in GEE to obtain rainfall, LST, NDVI, NDWI and NDDI data. Result of rainfall analysis from CHIRPS data showed that dry months from 2017 to 2022 occurred from June to October. The value of LST was between 24.75 38.87°C. Drought events in the study area from 2017 to 2022 were dominated by severe and extreme drought. The severe drought covers the area of 83.17% (2017), 57.34% (2018), 67.13% (2019), 84% (2020), 80.93% (2021), and 89.89% (2022). Meanwhile, the extreme drought wraps the area of 14.05% (2017), 40.05% (2018), 30.17% (2019), 13.15% (2020), 16.27% (2021), and 7.03% (2022). The area with severe drought was always over the one with extreme drought, and when the severe drought area increased; the extreme drought area decreased Result of this study could be employed in planning of drought mitigation and adaptation, the use of water and land resources, and public information on risks and actions for drought-affected communities.https://www.bio-conferences.org/articles/bioconf/pdf/2024/18/bioconf_icafes2024_05006.pdf |
| spellingShingle | Rahman Fahmi Arief Suryawati Sinar Supriyadi Slamet Basuki Google earth engine for spatio-temporal drought monitoring in Bangkalan, Indonesia BIO Web of Conferences |
| title | Google earth engine for spatio-temporal drought monitoring in Bangkalan, Indonesia |
| title_full | Google earth engine for spatio-temporal drought monitoring in Bangkalan, Indonesia |
| title_fullStr | Google earth engine for spatio-temporal drought monitoring in Bangkalan, Indonesia |
| title_full_unstemmed | Google earth engine for spatio-temporal drought monitoring in Bangkalan, Indonesia |
| title_short | Google earth engine for spatio-temporal drought monitoring in Bangkalan, Indonesia |
| title_sort | google earth engine for spatio temporal drought monitoring in bangkalan indonesia |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2024/18/bioconf_icafes2024_05006.pdf |
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