Landslides Extraction From Diverse Remote Sensing Data Sources Using Semantic Reasoning Scheme

Abstract

ABSTRACT:Using high resolution satellite imagery to detect, analyse and extract landslides automatically is an increasing strong support for rapid response after disaster. This requires the formulation of procedures and knowledge that encapsulate the content of disaster area in the images. Object-oriented approach has been proved useful in solving this issue by partitioning land-cover parcels into objects and classifies them on the basis of expert rules. Since the landslides information present in the images is often complex, the extraction procedure based on the object-oriented approach should consider primarily the semantic aspects of the data. In this paper, we propose a scheme for recognizing landslides by using an object-oriented analysis technique and a semantic reasoning model on high spatial resolution optical imagery. Three case regions with different data sources are presented to evaluate its practicality. The procedure is designed as follows: first, the Gray Level Co-occurrence Matrix (GLCM) is used to extract texture features after the image explanation. Spectral features, shape features and thematic features are derived for semiautomatic landslide recognition. A semantic reasoning model is used afterwards to refine the classification results, by representing expert knowledge as first-order logic (FOL) rules. The experimental results are essentially consistent with the experts' field interpretation, which demonstrate the feasibility and accuracy of the proposed approach. The results also show that the scheme has a good generality on diverse data sources.

A Synergetic Analysis of Sentinel-1 and -2 for Mapping Historical Landslides Using Object-Oriented Random Forest in the Hyrcanian Forests

“…However, our results emphasize on the application of some Sentinel-2-based soil and water indices for mapping landslides as well. While soil brightness indices were proposed [16] for detecting landslide-disturbed vegetation, and NDWI for detecting landslide-opened water bodies [6,19,21], the adjusted BI and NDWI (i.e., BI2 and NDWI2) derived from Sentinel-2A were superior for mapping landslides in this study. Moreover, this study suggests a high performance of some vegetation indices that are less sensitive to the atmospheric effects, such as the ARVI and PVI for landslide mapping in forest areas.…”

“…The other top variables were the contrast derived from the GLCM of NDWI2 (Figure 8b), the mean difference to neighbors of the blue band (Figure 8c), the dissimilarity derived from the GLCM of NDWI2 (Figure 8d), and the standard deviation values of the red-edge 3 band (Figure 8e). Nevertheless, some earlier researchers reported the superiority of other derived features from the GLCM such as homogeneity, density, mean, and the contrast of other satellite images for landslide mapping [6]. NDWI2 (Figure 8b), the mean difference to neighbors of the blue band (Figure 8c), the dissimilarity derived from the GLCM of NDWI2 (Figure 8d), and the standard deviation values of the red-edge 3 band (Figure 8e).…”

“…NDWI2 (Figure 8b), the mean difference to neighbors of the blue band (Figure 8c), the dissimilarity derived from the GLCM of NDWI2 (Figure 8d), and the standard deviation values of the red-edge 3 band (Figure 8e). Nevertheless, some earlier researchers reported the superiority of other derived features from the GLCM such as homogeneity, density, mean, and the contrast of other satellite images for landslide mapping [6].…”

“…Therefore, there is a need to compare influential Sentinel-derived features for mapping historical landslides in the forest regions. Landslides create instability through movements or failures in slopes, which are correlated with anomalies in vegetation and hydrological systems [5,6]. Different characteristics of satellite data can be applied to mapping, classifying, identifying influential triggers, and assessing susceptibility and risk of landslide hazards [7].…”

“…There is a high contrast in spectral, geometrical, textural, and contextual characteristics of landslide-caused forest-loss objects, and their surrounding undisturbed-forest objects, within satellite images [8][9][10]. Landslide mapping was conducted by incorporating the first-order statistics of satellite-derived features such as the spectral information of main bands [11][12][13], spectral indices [6,10,[14][15][16][17][18][19][20][21][22], or the second-order statistics of satellite-derived features, such as geometry [12,15,23], mean difference to neighbors [11,12,15,23], and textures derived from the gray-level co-occurrence matrix (GLCM) [6,9,10,12,[23][24][25][26][27][28][29][30] of images-ranging from optical [8,9,11,[31][32][33] to radar [18,[34][35][36]…”