Semi-automatic Landslide Detection Using Google Earth Engine, a Case Study in Poi Village, Central Sulawesi
Fast and accurate landslide detection is important for landslide early warning systems. However, data available from local authorities and news reports vary in accuracy (time and location). In this work, we present a new method for identifying landslides, based on Google Earth Engine (GEE) and time-...
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| Format: | Conference or Workshop Item |
| Language: | English |
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2022
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| Online Access: | https://repository.ugm.ac.id/282133/1/Subiyantoro%20et%20al%20-%202022%20-%20Semi-automatic_Landslide_Detection_Using_Google_Earth_Engine_a_Case_Study_in_Poi_Village_Central_Sulawesi.pdf |
| _version_ | 1797037540509745152 |
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| author | Subiyantoro, Andy Van Westen, Cees J. den Bout, Bastian V. Yuniawan, Ragil Andika Mulyana, Arif Rahmat |
| author_facet | Subiyantoro, Andy Van Westen, Cees J. den Bout, Bastian V. Yuniawan, Ragil Andika Mulyana, Arif Rahmat |
| author_sort | Subiyantoro, Andy |
| collection | UGM |
| description | Fast and accurate landslide detection is important for landslide early warning systems. However, data available from local authorities and news reports vary in accuracy (time and location). In this work, we present a new method for identifying landslides, based on Google Earth Engine (GEE) and time-series analysis of Sentinel-2 optical satellite images. The method uses vegetation loss as a proxy for disturbance caused by earthquake-related landslides, and applies a change detection algorithm to compute the Normalized Different Vegetation Index (NDVI) and Relative Different NDVI (rdNDVI). As a test case, we applied this approach to the area of Palu, Central Sulawesi, which was hit by a major earthquake on September 28, 2018. Using time series data from 2015 to 2020, we were able to accurately capture the massive landslide in Poi Village caused by this earthquake. Using GEE had many advantages: the process is semi-automatic, fast and versatile, and the boundaries of the landslide zones can be auto-generated. In addition, the analysis does not require expensive high-resolution data. Our results demonstrate the potential of this new method to produce landslide inventories in a fast, accurate and low-cost manner. © 2022 IEEE. |
| first_indexed | 2024-03-14T00:04:51Z |
| format | Conference or Workshop Item |
| id | oai:generic.eprints.org:282133 |
| institution | Universiti Gadjah Mada |
| language | English |
| last_indexed | 2024-03-14T00:04:51Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | oai:generic.eprints.org:2821332023-11-29T08:35:36Z https://repository.ugm.ac.id/282133/ Semi-automatic Landslide Detection Using Google Earth Engine, a Case Study in Poi Village, Central Sulawesi Subiyantoro, Andy Van Westen, Cees J. den Bout, Bastian V. Yuniawan, Ragil Andika Mulyana, Arif Rahmat Civil Engineering not elsewhere classified Fast and accurate landslide detection is important for landslide early warning systems. However, data available from local authorities and news reports vary in accuracy (time and location). In this work, we present a new method for identifying landslides, based on Google Earth Engine (GEE) and time-series analysis of Sentinel-2 optical satellite images. The method uses vegetation loss as a proxy for disturbance caused by earthquake-related landslides, and applies a change detection algorithm to compute the Normalized Different Vegetation Index (NDVI) and Relative Different NDVI (rdNDVI). As a test case, we applied this approach to the area of Palu, Central Sulawesi, which was hit by a major earthquake on September 28, 2018. Using time series data from 2015 to 2020, we were able to accurately capture the massive landslide in Poi Village caused by this earthquake. Using GEE had many advantages: the process is semi-automatic, fast and versatile, and the boundaries of the landslide zones can be auto-generated. In addition, the analysis does not require expensive high-resolution data. Our results demonstrate the potential of this new method to produce landslide inventories in a fast, accurate and low-cost manner. © 2022 IEEE. 2022 Conference or Workshop Item PeerReviewed application/pdf en https://repository.ugm.ac.id/282133/1/Subiyantoro%20et%20al%20-%202022%20-%20Semi-automatic_Landslide_Detection_Using_Google_Earth_Engine_a_Case_Study_in_Poi_Village_Central_Sulawesi.pdf Subiyantoro, Andy and Van Westen, Cees J. and den Bout, Bastian V. and Yuniawan, Ragil Andika and Mulyana, Arif Rahmat (2022) Semi-automatic Landslide Detection Using Google Earth Engine, a Case Study in Poi Village, Central Sulawesi. In: 2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES). https://ieeexplore.ieee.org/document/9993507 |
| spellingShingle | Civil Engineering not elsewhere classified Subiyantoro, Andy Van Westen, Cees J. den Bout, Bastian V. Yuniawan, Ragil Andika Mulyana, Arif Rahmat Semi-automatic Landslide Detection Using Google Earth Engine, a Case Study in Poi Village, Central Sulawesi |
| title | Semi-automatic Landslide Detection Using Google Earth Engine, a Case Study in Poi Village, Central Sulawesi |
| title_full | Semi-automatic Landslide Detection Using Google Earth Engine, a Case Study in Poi Village, Central Sulawesi |
| title_fullStr | Semi-automatic Landslide Detection Using Google Earth Engine, a Case Study in Poi Village, Central Sulawesi |
| title_full_unstemmed | Semi-automatic Landslide Detection Using Google Earth Engine, a Case Study in Poi Village, Central Sulawesi |
| title_short | Semi-automatic Landslide Detection Using Google Earth Engine, a Case Study in Poi Village, Central Sulawesi |
| title_sort | semi automatic landslide detection using google earth engine a case study in poi village central sulawesi |
| topic | Civil Engineering not elsewhere classified |
| url | https://repository.ugm.ac.id/282133/1/Subiyantoro%20et%20al%20-%202022%20-%20Semi-automatic_Landslide_Detection_Using_Google_Earth_Engine_a_Case_Study_in_Poi_Village_Central_Sulawesi.pdf |
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