Landslide mapping using object-based image analysis and open source tools
| dc.contributor.author | Amatya, Pukar | |
| dc.contributor.author | Kirschbaum, Dalia | |
| dc.contributor.author | Stanley, Thomas | |
| dc.contributor.author | Tanyas, Hakan | |
| dc.date.accessioned | 2022-10-03T21:25:24Z | |
| dc.date.available | 2022-10-03T21:25:24Z | |
| dc.date.issued | 2021-01-18 | |
| dc.description.abstract | Availability of high-resolution optical imagery and advances in image processing technologies have significantly improved our ability to map landslides. In recent years object-based image analysis (OBIA) has been gaining in popularity for landslide mapping due to its ability to incorporate spectral, textural, morphological and topographical properties. Many studies have been conducted based on commercial software. In this study, we create an open source Semi-Automatic Landslide Detection (SALaD) system utilizing OBIA and machine learning. Configured to run in Linux environment, it uses various open source Python packages and modules. This system was tested in 575 km2 area along the Pasang Lhamu Highway, Nepal where large numbers of landslides were triggered by the 2015 Gorkha earthquake. Comparison with a manual inventory highlighted that this system was able to detect 70% of the landslide area. The speed and efficiency with which this system was able to detect landslides makes it a viable alternative to manual techniques for landslide mapping over large areas, when establishing approximate landslide locations is of prime importance. | en_US |
| dc.description.sponsorship | This research was funded by the NASA Understanding Changes in High Mountain Asia Program (NNH15ZDA001N-HMA), the NASA Disaster Risk Reduction and Response Program (NNH18ZDA001N-DISASTERS) and the NASA Commercial Smallsat Data Acquisition Program. We would like to thank Mark Carroll, Jian Li and Glenn Tamkin from NASA Center for Climate Simulation (NCCS) for their help in optimizing the code. We would also like to thank authors of the Python packages used in this research. | en_US |
| dc.description.uri | https://www.sciencedirect.com/science/article/pii/S0013795221000119?via%3Dihub | en_US |
| dc.format.extent | 10 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.identifier | doi:10.13016/m2b44e-o20k | |
| dc.identifier.citation | Amatya, P., D. Kirschbaum, T. Stanley, and H. Tanyas. 2021. "Landslide mapping using object-based image analysis and open source tools." Engineering Geology, 282: 106000. https://doi.org/10.1016/j.enggeo.2021.106000 | en_US |
| dc.identifier.uri | https://doi.org/10.1016/j.enggeo.2021.106000 | |
| dc.identifier.uri | http://hdl.handle.net/11603/26087 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Elsevier | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC GESTAR II Collection | |
| dc.rights | This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law. | en_US |
| dc.rights | Public Domain Mark 1.0 | * |
| dc.rights.uri | http://creativecommons.org/publicdomain/mark/1.0/ | * |
| dc.title | Landslide mapping using object-based image analysis and open source tools | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0003-2288-0363 | en_US |