ABSTRACT
Vulnerability assessments of landscapes were done to highlight risks and sensitivity due to prevailing hazards and anthropogenic reservoir management and dam building activities. Here, a quantitative and qualitative approach was used to generate physical vulnerability assessment index maps. An appropriate progression for identifying the spatial and temporal variation in urbanization was found to be done using cellular automata techniques.
The vulnerability assessment was based on selected parameters like slope, slope profile, slope aspects, relative relief, curvature, soil texture, lithology, river morphometric, precipitation, land use and land cover (LULC), mass movement, floods, geological elements, earthquake occurrences, and anthropogenic activity such as hydroelectric projects. To analyze the combined effect of each causative factor on urbanization change and to develop a set of transition rules, prediction models were adopted (MLR for P, D r, and D B).
The annual comparison of the classified images showed an almost 34% increase in urban pixel count from 2002 to 2019, while official reports affirm an urban growth of 43.2% until 2019. Individual analysis for each causative factor showed that the distance from major roads and railways plays the most important role in increasing urbanization, which can be directly correlated to population change.
The major reasons for the increase in urban areas were recognized as increasing population, resident requirements, and commercialization. The proximity matrix and increasing population were evaluated as per different causative factors apart from social and political tools.
These vulnerabilities were forecast out to 2050 giving managers and decision-makers a framework to understand future growth and needs of land and water.
