PIXEL BASED LANDSLIDE IDENTIFICATION USING LANDSAT 8 AND GEE
Landslide is one of the most common natural disasters triggered mainly due to heavy rainfall, cloud burst, earthquake, volcanic eruptions, unorganized constructions of roads, and deforestation. In India, field surveying is the most common method used to identify potential landslide regions and updat...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2021-06-01
|
| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2021/721/2021/isprs-archives-XLIII-B3-2021-721-2021.pdf |
| _version_ | 1819021006229995520 |
|---|---|
| author | P. Singh V. Maurya R. Dwivedi |
| author_facet | P. Singh V. Maurya R. Dwivedi |
| author_sort | P. Singh |
| collection | DOAJ |
| description | Landslide is one of the most common natural disasters triggered mainly due to heavy rainfall, cloud burst, earthquake, volcanic eruptions, unorganized constructions of roads, and deforestation. In India, field surveying is the most common method used to identify potential landslide regions and update the landslide inventories maintained by the Geological Survey of India, but it is very time-consuming, costly, and inefficient. Alternatively, advanced remote sensing technologies in landslide analysis allow rapid and easy data acquisitions and help to improve the traditional method of landslide detection capabilities. Supervised Machine learning algorithms, for example, Support Vector Machine (SVM), are challenging to conventional techniques by predicting disasters with astounding accuracy. In this research work, we have utilized open-source datasets (Landsat 8 multi-band images and JAXA ALOS DSM) and Google Earth Engine (GEE) to identify landslides in Rudraprayag using machine learning techniques. Rudraprayag is a district of Uttarakhand state in India, which has always been the center of attention of geological studies due to its higher density of landslide-prone zones. For the training and validation purpose, labeled landslide locations obtained from landslide inventory (prepared by the Geological Survey of India) and layers such as NDVI, NDWI, and slope (generated from JAXA ALOS DSM and Landsat 8 satellite multi-band imagery) were used. The landslide identification has been performed using SVM, Classification and Regression Trees (CART), Minimum Distance, Random forest (RF), and Naïve Bayes techniques, in which SVM and RF outperformed all other techniques by achieving an 87.5% true positive rate (TPR). |
| first_indexed | 2024-12-21T04:00:14Z |
| format | Article |
| id | doaj.art-37396856eace47b29a71a5f7aa3bf48c |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-21T04:00:14Z |
| publishDate | 2021-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-37396856eace47b29a71a5f7aa3bf48c2022-12-21T19:16:44ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342021-06-01XLIII-B3-202172172610.5194/isprs-archives-XLIII-B3-2021-721-2021PIXEL BASED LANDSLIDE IDENTIFICATION USING LANDSAT 8 AND GEEP. Singh0V. Maurya1R. Dwivedi2GIS Cell, MNNIT Allahabad, Prayagraj, IndiaGIS Cell, MNNIT Allahabad, Prayagraj, IndiaGIS Cell, MNNIT Allahabad, Prayagraj, IndiaLandslide is one of the most common natural disasters triggered mainly due to heavy rainfall, cloud burst, earthquake, volcanic eruptions, unorganized constructions of roads, and deforestation. In India, field surveying is the most common method used to identify potential landslide regions and update the landslide inventories maintained by the Geological Survey of India, but it is very time-consuming, costly, and inefficient. Alternatively, advanced remote sensing technologies in landslide analysis allow rapid and easy data acquisitions and help to improve the traditional method of landslide detection capabilities. Supervised Machine learning algorithms, for example, Support Vector Machine (SVM), are challenging to conventional techniques by predicting disasters with astounding accuracy. In this research work, we have utilized open-source datasets (Landsat 8 multi-band images and JAXA ALOS DSM) and Google Earth Engine (GEE) to identify landslides in Rudraprayag using machine learning techniques. Rudraprayag is a district of Uttarakhand state in India, which has always been the center of attention of geological studies due to its higher density of landslide-prone zones. For the training and validation purpose, labeled landslide locations obtained from landslide inventory (prepared by the Geological Survey of India) and layers such as NDVI, NDWI, and slope (generated from JAXA ALOS DSM and Landsat 8 satellite multi-band imagery) were used. The landslide identification has been performed using SVM, Classification and Regression Trees (CART), Minimum Distance, Random forest (RF), and Naïve Bayes techniques, in which SVM and RF outperformed all other techniques by achieving an 87.5% true positive rate (TPR).https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2021/721/2021/isprs-archives-XLIII-B3-2021-721-2021.pdf |
| spellingShingle | P. Singh V. Maurya R. Dwivedi PIXEL BASED LANDSLIDE IDENTIFICATION USING LANDSAT 8 AND GEE The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | PIXEL BASED LANDSLIDE IDENTIFICATION USING LANDSAT 8 AND GEE |
| title_full | PIXEL BASED LANDSLIDE IDENTIFICATION USING LANDSAT 8 AND GEE |
| title_fullStr | PIXEL BASED LANDSLIDE IDENTIFICATION USING LANDSAT 8 AND GEE |
| title_full_unstemmed | PIXEL BASED LANDSLIDE IDENTIFICATION USING LANDSAT 8 AND GEE |
| title_short | PIXEL BASED LANDSLIDE IDENTIFICATION USING LANDSAT 8 AND GEE |
| title_sort | pixel based landslide identification using landsat 8 and gee |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2021/721/2021/isprs-archives-XLIII-B3-2021-721-2021.pdf |
| work_keys_str_mv | AT psingh pixelbasedlandslideidentificationusinglandsat8andgee AT vmaurya pixelbasedlandslideidentificationusinglandsat8andgee AT rdwivedi pixelbasedlandslideidentificationusinglandsat8andgee |