TOWARDS THE OPTIMAL PIXEL SIZE OF DEM FOR AUTOMATIC MAPPING OF LANDSLIDE AREAS

被引:14
作者
Pawluszek, K. [1 ]
Borkowski, A. [1 ]
Tarolli, P. [2 ]
机构
[1] Wroclaw Univ Environm & Life Sci, Inst Geodesy & Geoinformat, Wroclaw, Poland
[2] Univ Padua, Dept Land Environm Agr & Forestry, Padua, Italy
来源
ISPRS HANNOVER WORKSHOP: HRIGI 17 - CMRT 17 - ISA 17 - EUROCOW 17 | 2017年 / 42-1卷 / W1期
关键词
landslide; landslide mapping; DEM resolution; Neural Net classification; Maximum Likelihood classification; ROZNOW LAKE; RESOLUTION; IDENTIFICATION; SUSCEPTIBILITY;
D O I
10.5194/isprs-archives-XLII-1-W1-83-2017
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
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 1m, 2m, 5m and 10m 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 1m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5m DEM-resolution for FFNN and 1m DEM resolution for results. The best performance was found to be using 5m DEM-resolution for FFNN and 1m DEM resolution for ML classification.
引用
收藏
页码:83 / 90
页数:8
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