Monitoring of land use land cover dynamics and prediction of urban growth using Land Change Modeler in Delhi and its environs, India

Abstract

In the recent decades, cities have been expanding at a great pace which changes the landscape rapidly as a result of inflow of people from rural areas and economic progression. Therefore, understanding spatiotemporal dynamics of human induced land use land cover changes has become an important issue to deal with the challenges for making sustainable cities. This study aims to determine the rate of landscape transformations along with its causes and consequences as well as predicting urban growth pattern in Delhi and its environs. Landsat satellite images of 1989, 2000, 2010 and 2020 were used to determine the changes in land use land cover using supervised maximum likelihood classification. Subsequently, Land Change Modeler (LCM) module of TerrSet software was used to generate future urban growth for the year 2030 based on 2010 and 2020 dataset. Validation was carried out by overlaying the actual and simulated 2020 maps. The change detection results showed that urban and open areas increased by 13.44% and 2.40%, respectively, with a substantial decrease in crop land (10.88%) from 1989 to 2020 and forest area increased by 3.48% in 2020 due to restoration programmes. Furthermore, the simulated output of 2030 predicted an increase of 24.30% in urban area and kappa coefficient 0.96. Thus, knowledge of the present and predicted changes will help decision-makers and planners during the process of formulating new sustainable policies, master plans and economic strategies for rapidly growing cities with urban blue-green infrastructures.

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

“…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. In addition, the use of long time series data also provides opportunities for the prediction of land cover in the future, such as the dynamics of land system (DLS) model [65], land change evaluation model [66], CA-Markov model [68,108,109] [111]; however, the above methods also had some limitations. The DLS model required multiple simulations to determine the optimal model parameters.…”

Role of landscape change mapping in ecosystem services analysis: a case study of Delhi-NCR region

“…The use of geo-information technology to create practical-based theoretical models of urban thermal environments can aid in regional planning and decision-making [17]. Urban expansion and population growth have put a strain on public open spaces, leading to a decline in the quality of the urban environment [18,19]. The amount of airborne particulate matter is a major driver of changes in LULC [20].…”

An Integrated assessment on LULC changes and Groundwater Quality in Ambattur Zone of Metropolitan Chennai, India

“…True colour composite maps were produced using the red, green, and blue bands (RGB) of each scene in the module "RGB Composite". Subsequently, false colour maps (FCC) using the bands Green, Red, and Infra-red (RGIR) were produced in the same module "RGB Composite" for both scenes (16,17) . These two composite maps are used later in assigning the classes for each cluster of classified images.…”