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Chilika Lake, a RAMSAR site, is an environmentally and ecologically pivotal coastal lagoon in India facing significant emerging environmental challenges due to anthropogenic activities and natural processes. Traditional in situ water quality monitoring methods are often labor intensive and time consuming. This study presents a novel approach for ex situ water quality monitoring in Chilika Lake, located on the east coast of India, utilizing Google Earth Engine (GEE) and spectral indices, such as the Normalized Difference Turbidity Index (NDTI), Normalized Difference Chlorophyll Index (NDCI), and total suspended solids (TSS). The methodology involves the integration of multi-temporal satellite imagery and advanced spectral indices to assess key water quality parameters, such as turbidity, chlorophyll-a concentration, and suspended sediments. The NDTI value in Chilika Lake increased from 2019 to 2021, and the Automatic Water Extraction Index (AWEI) method estimated the TSS concentration. The results demonstrate the effectiveness of this approach in providing accurate and comprehensive water quality assessments, which are crucial for the sustainable management of Chilika Lake. Maps and visualization are presented using GIS software. This study can effectively detect floating algal blooms, identify pollution sources, and determine environmental changes over time. Developing intuitive dashboards and visualization tools can help stakeholders engage with data-driven insights, increase community participation in conservation, and identify pollution sources. [ABSTRACT FROM AUTHOR]

Modeling the prediction of land use/land cover (LULC) change is necessary for sustainable management of natural resources and effective planning for proper urban development. The Markov chain model is the most commonly used prediction model for assessing LULC changes. This study aims to assess changes in LULC in Cuttack City from 1990 to 2020 and to predict LULC modification in 2050. For the analysis, Landsat satellite data were collected from the USGS website for 1990, 2005, and 2020. The satellite images were masked with our area of interest and classified using the maximum likelihood classifier tool in ArcGIS 10.2. The result of the multi‐temporal classification shows that the built‐up area percentage reached 45.927% in 1990 and 57.827% in 2020. Most areas of vegetation and water bodies were affected by this impromptu growth. Using the chi‐square test, the results of the Markov model are validated, which shows no significant differences between the classified and simulated areas. This model can, therefore, be accepted for our prediction. Built‐up growth has already achieved its peak in Cuttack City. There may be further growth in built‐up areas in the city shortly. These findings draw the attention of urban planners and policymakers to the stress of urban sprawl and prompt them to take stringent action to deal with this pressing issue. [ABSTRACT FROM AUTHOR]

The integrative approach of remote sensing and geographical information system (GIS) has been a very useful tool for the delineation of groundwater potential zones in any terrain. Various natural factors such as lithology, geomorphology, land use land cover, slope, drainage density, lineament density, etc. control the groundwater recharge and storage. Individual thematic layers have been developed through GIS assigning proper weightage based on storage capacity. The groundwater potential zones in the study region are divided into three classes such as poor, good, and exceptional. The aforesaid methodology has been applied in the selected part of Kotagarh Block of Kandhamal District, Odisha to delineate the possible groundwater potential zones. The present study revealed that the Kotagarh region being a rugged topography is good for groundwater occurrence and storage. [ABSTRACT FROM AUTHOR]