Landslide disaster management using satellite imagery

Mass movements are one of the most severe and frequent natural disasters that pose severe hazards to human life, property as well as environmental resources. Effective disaster management can also help reduce the impacts. In this paper we introduced to managing the landslide disaster from satellite imagery all at once system. The methodology presented combines advanced remote sensing, machine learning and geographic information system (GIS) methods for better landslide prediction, monitoring and response. Remote sensing satellite data are used to determine landslide susceptible areas using (i) terrain analysis, (ii) vegetation cover, and (iii) soil moisture determination. Such models based on machine learning classifier, have been developed using historical landslide information and satellite-derived variables to predict the susceptibility with great accuracy. In addition, live satellite data allows the active landslide zones to be monitored on a 24/7 basis and can thus be utilized in early warning and evacuation processes. After the disaster, satellite images can be used to assess the damage and plan recovery based on detailed spatial information. prone area, demonstrating its potential for disaster risk reduction. The study demonstrates the vital importance of satellite imagery for landslide disaster management, providing a scalable and low-cost solution for worldwide susceptible communities.