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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.

The purpose of this paper is to give an exhaustive assessment of water depth estimation in shallow inland and coastal water environments with the assistance of optical remote detecting procedures, utilizing satellite/airborne multispectral information. Remote sensing has been used to map bathymetry for several decades. Inaccessibility, large‐scale depth mapping, very shallow areas, and cost constraint are some of the reasons why remote sensing technique is a boon for bathymetry. Researchers have come a long way since the remote sensing‐based bathymetry has been used. Fourfold increase in spatial, spectral, radiometric and temporal resolution of sensors have also increased the accuracy. There are two types of remote sensing techniques; Active and Passive. Active remote sensing includes the use of ground penetrating radar and bathymetric lidar, which are expensive but precise surveys successful up to 70 m depth. On the other hand, passive remote sensing deals with water photogrammetry and radiometric methods done by capturing imagery with space borne/air borne platforms having depth penetration up to 25 m. The latter technique includes finding of a logarithmic relationship between river depth and image values. This technique is most widely used because of its cost effectiveness. In this paper, the emphasis has been given to derive bathymetric techniques using multispectral data as these methods have proved reasonably successful when water quality, bottom reflectance, and atmospheric effects are all invariant. At a specific area, the detection of possible changes in water flow and levels indicates flow path changes which can be used to predict potential surface level flooding for better water management and policy decisions.

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