A remote sensing-based methodology to assess the vulnerability, versatility, and vitality (3Vs) of rural towns: Bayerisch Eisenstein and Tuchenbach, Germany

This chapter aims to formulate a methodology to carry out a comparative analysis of the resilience of a rural town that is in proximity to a large city to a remotely located town far away from urban agglomeration taking the case study of two towns in Germany. The resilience is measured with respect to their vulnerability, versatility, and vitality (3V), as a means to “socialize the pixel.” That is, to explore to what extent remotely sensed data can portray the social reality of both rural areas one at the periphery of urban agglomeration and the other located far away from a large city. A land use classification using a support vector machine (SVM) algorithm is performed on selected rural towns of Bayerisch Eisenstein and Tuchenbach in Bavaria, Germany. Each case study has its unique characteristics in terms of scale, demography, and physical location. The first study area (Bayerisch Eisenstein) is a rural town located away from the large city and facing challenges such as an aging population, depopulation, migration, and closing of industries in the region impacting the economy. The second study area (Tuchenbach) is a town in proximity to a large city, Nuremberg, and due to the emergence of industries near the town or having the advantage of being on the edge of the metropolitan region, it is adapting to the metropolitan lifestyle. Spatio-temporal trends are observed for four different periods of the two rural towns. To evaluate the effectiveness of the methodology, we rely on Remote Sensing and Machine Learning techniques to extract information from high-resolution orthophotos of 40 cm resolution of selected land use classes to assess the 3Vs. Results show that the built-up area for the town of Tuchenbach has expanded over the period and has shown more resilience to the change by adopting alternate sources of energy like increase an in the usage of solar energy. On the other hand, there haven't been any significant changes in the built-up in the town of Bayerisch Eisenstein even though there has been an increase in the use of solar panels. This methodology can be applied to countries where there is a lack of socio-economic statistical data and difficulty in conducting field surveys. However, combining geostatistical and statistical datasets with the remote sensing data would improve the classification results.