Two-Dimensional and 3D Change Detection in Urban Area Using Very High-Resolution Satellite Data and Impact of Urbanization over LST and NDVI

High-resolution satellite data are an excellent way to monitor the growth of an urban area in terms of vertical and horizontal growth. Time-series data over two different zones from the same satellite sensor or contemporary sensors act as a good test bed for change detection. In most of the cases, 2D images of different time frames are spatially registered, and pixel difference is calculated which enables the detection of change in horizontal growths. Three-dimensional change detection to mark a change in the vertical direction can also be computed by comparing high-resolution digital surface models (DSM) of two different times and detect changes in topography. Using accurate DSM and derived digital terrain models (DTM) information from DSM, exact and accurate heights of the building footprints can be extracted. Using 3D city models, information about horizontal growth and vertical growth of the city can be assessed using change detection over the temporal data. Three-dimensional change detection can also enable district and state administration to discern the planned growth of the city, illegal constructions and future planning of the city, especially in the projects like smart cities. In this study, we are comparing 2D raster images of different time frames to assess change in horizontal direction, very high-resolution DSMs and DTMs datasets of two different time zones to assess change in vertical directions and visualizing 3D change detection of Ahmedabad city, in terms of its horizontal and vertical changes in urban growth area. We are also making the assessment of the growth of the city (5% change in building structures) and population in the studied area. It is inferred that the city population in the year 2018 is more than 35% as compared to the population in the year 2011. Further, we are calculating geophysical parameters of land surface temperature (LST) and normalized difference vegetation index (NDVI) over a time using satellite datasets, which provides a proxy observation for the changes in the urban growth. Using satellite data, it is concluded that NDVI is reduced over the study area whereas there is an increase in LST temperature at night time during the winter season. We concluded that increased urbanization and population (> 35%) are also contributing for rise in the LST temperature at nights in the city apart from the other big environmental parameters such as global warming, etc.