TOWARDS THE OPTIMAL PIXEL SIZE OF DEM FOR AUTOMATIC MAPPING OF LANDSLIDE AREAS
Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-05-01
|
| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-1-W1/83/2017/isprs-archives-XLII-1-W1-83-2017.pdf |
| _version_ | 1811314700914262016 |
|---|---|
| author | K. Pawłuszek A. Borkowski P. Tarolli |
| author_facet | K. Pawłuszek A. Borkowski P. Tarolli |
| author_sort | K. Pawłuszek |
| collection | DOAJ |
| description | Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1 m, 2 m, 5 m and 10 m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1 m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5 m DEM-resolution for FFNN and 1 m DEM resolution for results. The best performance was found to be using 5 m DEM-resolution for FFNN and 1 m DEM resolution for ML classification. |
| first_indexed | 2024-04-13T11:17:07Z |
| format | Article |
| id | doaj.art-ead80bb547a6480497cb81f1c339c8cd |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-13T11:17:07Z |
| publishDate | 2017-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-ead80bb547a6480497cb81f1c339c8cd2022-12-22T02:48:57ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342017-05-01XLII-1-W1839010.5194/isprs-archives-XLII-1-W1-83-2017TOWARDS THE OPTIMAL PIXEL SIZE OF DEM FOR AUTOMATIC MAPPING OF LANDSLIDE AREASK. Pawłuszek0A. Borkowski1P. Tarolli2Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, PolandInstitute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, PolandDepartment of Land, Environment, Agriculture and Forestry, University of Padova, ItalyDetermining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1 m, 2 m, 5 m and 10 m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1 m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5 m DEM-resolution for FFNN and 1 m DEM resolution for results. The best performance was found to be using 5 m DEM-resolution for FFNN and 1 m DEM resolution for ML classification.http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-1-W1/83/2017/isprs-archives-XLII-1-W1-83-2017.pdf |
| spellingShingle | K. Pawłuszek A. Borkowski P. Tarolli TOWARDS THE OPTIMAL PIXEL SIZE OF DEM FOR AUTOMATIC MAPPING OF LANDSLIDE AREAS The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | TOWARDS THE OPTIMAL PIXEL SIZE OF DEM FOR AUTOMATIC MAPPING OF LANDSLIDE AREAS |
| title_full | TOWARDS THE OPTIMAL PIXEL SIZE OF DEM FOR AUTOMATIC MAPPING OF LANDSLIDE AREAS |
| title_fullStr | TOWARDS THE OPTIMAL PIXEL SIZE OF DEM FOR AUTOMATIC MAPPING OF LANDSLIDE AREAS |
| title_full_unstemmed | TOWARDS THE OPTIMAL PIXEL SIZE OF DEM FOR AUTOMATIC MAPPING OF LANDSLIDE AREAS |
| title_short | TOWARDS THE OPTIMAL PIXEL SIZE OF DEM FOR AUTOMATIC MAPPING OF LANDSLIDE AREAS |
| title_sort | towards the optimal pixel size of dem for automatic mapping of landslide areas |
| url | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-1-W1/83/2017/isprs-archives-XLII-1-W1-83-2017.pdf |
| work_keys_str_mv | AT kpawłuszek towardstheoptimalpixelsizeofdemforautomaticmappingoflandslideareas AT aborkowski towardstheoptimalpixelsizeofdemforautomaticmappingoflandslideareas AT ptarolli towardstheoptimalpixelsizeofdemforautomaticmappingoflandslideareas |