Quantitative Analysis of Land Use and Land Cover Dynamics using Geoinformatics Techniques: A Case Study on Kolkata Metropolitan Development Authority (KMDA) in West Bengal, India

Abstract

One of the most valuable approaches in spatial analysis for a better understanding of the hydrological response of a region or a watershed is certainly the analysis of the well-known land use land cover (LULC) dynamicity. The present case study delves deeper into the analysis of LULC dynamicity by using digital Landsat TM and Landsat OLI data to classify the Kolkata Metropolitan Development Authority (KMDA) into seven classes with over 90% classification accuracy for decadal level assessments of 30 years (for the years 1989, 1999, 2009, and 2019). The change index, the Dematel method for analyzing the cause-effect relationship among the LULC classes, the Jaccard Similarity Index for measuring the nature of similarity among the LULC classes, and the Adherence Index for measuring the consistency of the LULC classes after the transition was used in this study to analyze the LULC transformation. In more detail, the present study considers how urban land use is altering at the expense of other land uses. Besides the shifting pattern of mean centers of the LULC classes through time, also gives a very significant insight into the LULC dynamics over 30 years of span. The current study of LULC dynamicity and transformation patterns over the 30 years of the KMDA area is expected to assist land and urban planners, engineers, and administrators in sustainable decisions and policies to ensure inclusive urbanization that accommodates population growth while minimizing the impact on potential natural resources within the whole study area.

Study on the Characteristics and Influencing Factors of Land Use Changes in the Metropolitan Fringe Area: The Case of Shenzhen Metropolitan Area in China

“…The factor of light index and distance from highway also has a significant effect on urban expansion in the study area, with Wald statistics of 233.082 and 212.479, indicating that the higher the intensity of non-agricultural industrial activities and the closer the distance from the highway, the more significant the expansion of urban land is. The regression coefficient of distance from the administrative boundary is positive, and the Wald statistic is smaller, only 16.915, which indicates that from the perspective of the whole area, the closer to the administrative boundary of Shenzhen City, the less significant urban land expansion, but this factor has little influence compared with other indicators.…”

“…Many studies have investigated various aspects of metropolitan area characteristics, such as population growth [8], spatial expansion [9,10], driving forces [11,12], and metropolitan governance [13,14]. Recently, an increasing number of studies have attempted to explore the spatial changes in metropolitan areas, and a large number of studies have quantitatively measured various spatial change processes, dynamics, and patterns within metropolitan areas through the use of remote sensing image identification techniques and the establishment of assessment indicators [15,16]. However, there are fewer studies on land use changes in the metropolitan fringe areas, and a few studies focus on identifying the urbanrural fringe of metropolitan areas [17,18].…”

Spatiotemporal Conflict Analysis and Prediction of Long Time Series Land Cover Changes in the Black Soil Region of Northeast China Using Remote Sensing and GIS

“…Spatial analysis is the quantitative study of geospatial phenomena, and it is the core of remote sensing and GIS. By using spatial analysis, we can describe the evolutionary process and spatiotemporal association of land cover adequately [105,106]. Meanwhile, the transfer matrix can define the important processes of changes in land cover [107]; we can obtain the temporal and spatial changes in different land cover types and understand the overall status of regional ecosystem service functions from land cover change.…”

Hydro-Climatic and Vegetation Dynamics Spatial-Temporal Changes in the Great Lakes Depression Region of Mongolia

“…KC is a measure of agreement between predefined producer ratings and user-assigned ratings. The calculation is based on the difference between how much agreement is present ("observed" agreement) and how much agreement would be expected to be present by chance alone ("expected" agreement) [29].…”