A Spatial Model of Landslides with A Micro-Topography and Vegetation Approach for Sustainable Land Management in the Volcanic Area

Abstract

This study aims to produce a spatial model for sustainable land management in landslide-prone areas, based on exploring non-stationary relationships between landslide events, geomorphological and anthropogenic variables on tropical hillsides, especially in Taji Village, Jabung District, East Java Province, Indonesia. A series of approaches combine in this research, and methods are used to construct independent and dependent variables so that GWR can analyze them to obtain the best model. Transformation of categorical data on microtopography, landform, and land cover variables was carried out. When modelled, landscape metrics can explain landslide events in the study area better than distance metrics with adj. R2 = 0.75 and AICc = 2526.38. Generally, local coefficient maps for each variable are mapped individually to reveal their relationship with landslide events, but in this study they are integrated to make it more intuitive and less confusing. From this map, it was found that most of the variables that showed the most positive relationship to the occurrence of landslides in the study area were the divergent footslopes. At the same time, the negative one was plantation land. It was concluded that the methodological approach offered and implemented in this study provides significant output results for the spatial analysis of the interaction of landslide events with geomorphological and anthropogenic variables locally, which cannot be explained in a global regression. This study produces a detailed scale landslide-prone conservation model in tropical hill areas and can be reproduced under the same geo-environmental conditions.

Cost-Benefit Analysis of Minor Irrigation Tank Rehabilitation Using Run-Off and Storage Capacity: A Case Study from Ambuliyar Sub-Basin, Tamil Nadu, India

“…The method divides the study area into regions according to the proximity of rain gauge stations, providing a practical approach to identifying micro-watersheds under the influence of a particular rain gauge station. Accordingly, the Thiessen polygon approach was used to identify micro-watersheds affected by rainfall stations, as shown in Table 4 [38]. The mean rainfall was then allocated to specific micro-watersheds based on their proximity to rain gauge stations (Figure 5).…”