The Landslide Susceptibility Assessment using Bi-variate Statistical Information Value Model of Chenab River Valley, Jammu and Kashmir (India)

Almost every year, the Himalayan region suffers from a landslide disaster that is directly associated with the prosperity and development of the area. The study of landslide disasters helps planners, decision-makers and local communities for the development of anthropogenic structures in order to enhance the safety of society. Therefore, the prime aim of this research is to produce the landslide susceptibility map for the Chenab river valley using the bi-variate statistical information value model to detect and demarcate the areas of potential landslide incidence. The object-based image analysis method identified about 84 potential sites of landslides as landslide inventory. The statistical information value model is derived from the landslide inventory and multiple causative factors. The outcome showed that 23% area of the Chenab river valley falls into the class of a very high landslide susceptibility zone. The ROC curve method is used to validate the model which denoted the acceptable result for the landslide susceptibility zonation with 0.826 AUC value for the Chenab river valley.

Landslide Susceptibility Assessment based on Information Value Model in Amzaz Watershed in Northern Morocco

This paper aims to identify potential areas of landslides in the Amzaz watershed in northern Morocco with its precarious environmental balance using the Information Value (IV) Model. Van Westen (1994) defines bivariate methods as a modified form of the quantitative map combination with the exception that weightings are assigned based upon the statistical relationship between past landslides and various factor maps, individual factor maps (independent variable). A set of factor maps were overlaid with a landslide map (dependent variable) to create cross-tabulations for each one and class. The landslide inventory is used to result in the susceptibility maps for better mitigation of the risks and losses related to this phenomenon. The results demonstrated that the percentage of rotational landslides varies between 8.79 and 30.08%, and between 9.79 and 23.36% for translational slides susceptibility in the Amzaz watershed.

Intangible economic benefit of remote sensing data in Indonesia

Indonesian National Institute of Aeronautics and Space (LAPAN) as a space ecosystem runner government institution of the space agency in Indonesia is mandated by Indonesia space law of 2013 to carry out remote sensing activities that are not only strategic nationally in nature but also charged being benefits economically. This study investigated the intangible economic benefits of remote sensing activities in Indonesia. This research explored qualitatively the advantage of the use of remote sensing information by users obtained from the results of questionnaires and in-depth interviews. The conceptual framework of intangible benefit is based on the information system success model by the De-Lone and McLean Information Value model. The results showed that the quality of remote sensing was good. This can be seen from most responses of user respondents on the Perceived System Quality and Information Quality indicators giving a good rating. The response of the user respondents caused a sense of satisfaction and also caused the use of the system to be more intense. These had a good impact on the performance of respondents, both individually and organizationally. Strengthened by in-depth interviews showed that economically provide benefits with an average of 64% increasing in the efficiency of the performance by Agriculture and Palm Oil sectors. In short, space agency in Indonesia is proven to provide intangible economic benefits on remote sensing activities.

Landslide hazard zone mapping using Information Value model: the case of Gidole Landslide, Southern Ethiopia

<p>Landslide hazard is becoming serious environmental constraints for the developmental activities in the highlands of Ethiopia. With the current infrastructure development, urbanization, rural development, and with the present landslide management system, it is predictable that the frequency and magnitude of landslide and losses due to such hazards would continue to increase. In the present study landslide hazard zone mapping were carried out in and around Gidole Town in Southern Ethiopia. The main objective of the study was to map landslide hazard zone using Information Value Bi-variant statistical model.  For landslide hazard zonation of the study area six causative factors namely; aspect, slope angle, elevation, Lithology, Normalized Deference Vegetation Index (NDVI) and land-use and land-cover were considered. The landslide inventory mapping for the present study area was carried out through field observations and Google Earth image interpretation. Later, Information value was calculated based on the influence of causative factors on past landslide. The distribution of landslide over each causative factor maps was obtained and analyzed. Weights for the class with in these causative factor maps was obtained using information value model. Distribution of landslide in the study area was largely governed by aspect of southwest facing, slope angel of 30-45<sup>o</sup>, elevation of 1815–2150m, NDVI of 0.27−0.37, Lithology of colluvial deposit and land-use and land-cover of agricultural land. The landslide hazard zonation map shows that 78.38km<sup>2</sup> (36.3%) area fall within very low hazard (VLH) zone, 72.85km<sup>2</sup> (34.2%) of the area fall within low hazard (LH) zone, 12.78 km<sup>2</sup> (6.6%), 32.72 km<sup>2</sup> (15.4%) and 15.89 km<sup>2</sup> (7.5%) of the area falls into very high hazard (VHH), high hazard (HH) and moderate hazard (MH), respectively. Further, validation of LHZ map with past landslide inventory data shows that 92.3% of the existing landslides fall in very high hazard (VHH) and high hazard (VHH) zone. Thus, it can safely be concluded that the hazard zones delineated in the present study validates with the past landslide data and the potential zone depicted can reasonably be applied for the safe planning of the area.</p><p><strong>Key words</strong>: Landslide, Gidole, Landslide hazard zone, Information Value model</p>