Landslide Identification From Irs-P6 Liss-Iv Temporal Data-a Comparative Study Using Fuzzy Based Classifiers

Abstract

<p><strong>Abstract.</strong> While extracting land cover from remote sensing images, each pixel in the image is allocated to one of the possible class. In reality different land covers within a pixel can be found due to continuum of variation in landscape and intrinsic mixed nature of most classes. Mixed pixels may not be appropriately processed by traditional image classifiers, which assume that pixels are pure. The existence of mixed pixels led to the development of several approaches for soft (often termed fuzzy in the remote sensing literature) classification in which each pixel is allocated to all classes in varying proportions. However, while the proportions of each land cover within each pixel may be predicted, the spatial location of each land cover within each pixel is not. Thus, it is important to develop and implement a classifier that can work as soft classifiers for landslide identification. This work is an attempt to document and identify landslide areas by five spectral indices using temporal multi-spectral images from IRS-P6 LISS-IV images. To improve the spectral properties of spectral indices for specific class identification (in this case landslide) a Class Based Sensor Independent (CBSI) technique proposed. The result indicates that CBSI based Transformed Normalized Difference Vegetation Index (TNDVI) temporal indices data gives better results for landslide identification with minimum entropy and membership range.</p>

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

“…The vegetation indices are useful for discriminating landslide-induced barren lands, debris flows, and failure slopes from surrounding undisturbed-forest areas [10,16,18,20]. The improvement of landslide detection from different optical sensors by the incorporating vegetation indices like NDVI derived from ETM + [14,15], SPOT-5 [17], and GF-1 [10]; GNDVI derived from SPOT-5 [17] and Resourcesat-2 LISS-IV [20]; and TNDVI derived from IRS-P6 LISS-IV [22], was reported in different Although the topographic features derived from the DEM, such as TRI [41] and slope [9,10,13], were the top variables for mapping old landslides (Figures 6f and 7a,c), the textural features derived from the DEM, such as GLCM-Angle 2nd moment and GLCM-Entropy [27], were the important variables for mapping new landslides in the non-protected forests (Figure 5d). The integration of textural-derived features from the DEM, such as GLCM-features [24,26], was recommended to increase the accuracy of landslide mapping in the earlier studies [26].…”

“…The vegetation indices are useful for discriminating landslide-induced barren lands, debris flows, and failure slopes from surrounding undisturbed-forest areas [10,16,18,20]. The improvement of landslide detection from different optical sensors by the incorporating vegetation indices like NDVI derived from ETM + [14,15] , SPOT-5 [17], and GF-1 [10]; GNDVI derived from SPOT-5 [17] and Resourcesat-2 LISS-IV [20]; and TNDVI derived from IRS-P6 LISS-IV [22], was reported in different research. However, our results emphasize on the application of some Sentinel-2-based soil and water indices for mapping landslides as well.…”

“…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]…”