The study examined the landslide distribution, processes, and susceptibility of the Lalbakaiya watershed using GIS and remote sensing technology. Inventory of landslides was done using high-resolution satellite imager...The study examined the landslide distribution, processes, and susceptibility of the Lalbakaiya watershed using GIS and remote sensing technology. Inventory of landslides was done using high-resolution satellite imagery available on Google Earth and was verified and further investigated during the field visit. Geomorphic as well as statistical approaches were applied to assess landslides susceptibility and the significance of their outputs was discussed. Map layers representing conditioning and triggering factors of landslide occurrence were produced from various spatial data sources. The study found that the landslide of the Lalbakaiya watershed is primarily controlled by geology representing young, weak, fragile, and weathered sedimentary rocks. Besides, the role of topography such as steep slope, high relative relief, and land use and land cover played an important role in determining the landslide susceptibility. These processes are triggered by monsoon precipitation, seismicity, and land use change in addition to other factors. The geomorphic approach produces a reliable landslide susceptible map as evidenced by past and present (active) failures on a landscape unit, but this map has low predictability of the landslides occurrence. In contrast, the landslide susceptibility map derived from the landslide index method fairly conforms with that derived from the geomorphic approach. Susceptibility calculated by landslide index map is represented by a pixel value that indicates a probability of landslides occurrence, and is amenable to group into various susceptible classes. The model can predict areas of landslides based on quantitative relation between landslides and geo-ecological factors. The limitation of this approach is that these susceptible areas do not represent clearly defined landscape units, and can also overlook highly erodible areas where landslides are not apparent despite severe erosion and numerous minor failures. The study confirms that both geomorphic and statistical approaches can be complementarily integrated to produce predictable, reliable, and applicable landslide susceptibility maps that can make a plausible planning tool for conservation, development, and disaster risk reduction in the populated slopes of the Himalayas and like.展开更多
文摘The study examined the landslide distribution, processes, and susceptibility of the Lalbakaiya watershed using GIS and remote sensing technology. Inventory of landslides was done using high-resolution satellite imagery available on Google Earth and was verified and further investigated during the field visit. Geomorphic as well as statistical approaches were applied to assess landslides susceptibility and the significance of their outputs was discussed. Map layers representing conditioning and triggering factors of landslide occurrence were produced from various spatial data sources. The study found that the landslide of the Lalbakaiya watershed is primarily controlled by geology representing young, weak, fragile, and weathered sedimentary rocks. Besides, the role of topography such as steep slope, high relative relief, and land use and land cover played an important role in determining the landslide susceptibility. These processes are triggered by monsoon precipitation, seismicity, and land use change in addition to other factors. The geomorphic approach produces a reliable landslide susceptible map as evidenced by past and present (active) failures on a landscape unit, but this map has low predictability of the landslides occurrence. In contrast, the landslide susceptibility map derived from the landslide index method fairly conforms with that derived from the geomorphic approach. Susceptibility calculated by landslide index map is represented by a pixel value that indicates a probability of landslides occurrence, and is amenable to group into various susceptible classes. The model can predict areas of landslides based on quantitative relation between landslides and geo-ecological factors. The limitation of this approach is that these susceptible areas do not represent clearly defined landscape units, and can also overlook highly erodible areas where landslides are not apparent despite severe erosion and numerous minor failures. The study confirms that both geomorphic and statistical approaches can be complementarily integrated to produce predictable, reliable, and applicable landslide susceptibility maps that can make a plausible planning tool for conservation, development, and disaster risk reduction in the populated slopes of the Himalayas and like.
