Landslide Detection Analysis in North Vietnam Base on Satellite Images and Digital Geographical Information - Landsat 8 Satellite and Historical Data Approaches -

Abstract

Mountainous area in North Vietnam is considered as one of the most prone region to landslide in Vietnam. Landslides in this area often occurs under the influences of heavy rainfall or tropical storms, steep slopes on mountainous sides and human activities such as road or house constructions. This paper applied Landsat satellite images and calculated Normalized Difference Indexes (NDIs) to evaluate the condition of vegetation, soil and water and detected 43 landslide points in Bac Kan, Ha Giang, Thai Nguyen and Tuyen Quang provinces in North Vietnam in 2015. In this paper, historical landslide data in 2013 was also used to analyse causative factors. Historical landslides often occur in a slope range from 10 to 40 on the elevation from 100 m -300 m (48.23%) and on forests and crop sites. With regard to triggering factor and slope condition, three typical types of landslides are classified as i) landslide caused by rainfall on construction sites, ii) landslide caused by rainfall on natural slopes, and iii) landslide caused by rainfall and water drawdown on banks of reservoirs or streams. Considering the Landsat result type 2 and type 3 consist larger proportions (58.14 % and 25.58 %) compared to those in historical data (11.7 % and 1.53 %). This means that many landslide would occur in mountainous areas that far from residential or road areas (type 1). This paper thus would provide initial assessment for further studies on landslide problems on the bank of water sites such as reservoirs or streams.

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.…”

“…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]…”

Correlación de variables morfométricas para deslizamientos en la cuenca del río Combeima, Colombia

“…En la identificación visual de los deslizamientos según [36], el apoyo prestado por las composiciones de color verdadero, falso color y NDVI en la imagen RapidEye jugó un papel importante, lo cual es consecuente con las conclusiones brindadas por otros autores [41], [42], [43], [44], al igual que la resolución espacial de las imágenes, hecho coherente con las afirmaciones realizadas por [45], [46], [47]. Cabe señalar que, si bien la resolución espacial de las imágenes es un factor altamente influyente para la delimitación de los deslizamientos, una menor resolución espacial puede ser compensada con una mayor resolución espectral, que brinda la posibilidad de emplear otras técnicas como el NDVI y las composiciones de bandas para complementar su análisis.…”