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Land-use change is a predictable and principal driving force of potential environmental changes on all spatial and temporal scales. A land-use change model is a tool that supports the analysis of the sources and consequences of land-use dynamics. This study aims to assess the spatiotemporal land-use changes that occurred during 1990–2020 in the municipal council limits of Batticaloa. A land change modeler has been used as an innovative land planning and decision support system in this study. The main satellite data were retrieved from Landsat in 1990, 2000, 2010, and 2020. For classification, the supervised classification method was employed, particularly with the medium resolution satellite images. Land-use classes were analyzed by the machine learning algorithm in theland change modeler. The Markov chain method was also used to predict future land-use changes. The results of the study reveal that only one land-use type, homestead, has gradually increased, from 12.1% to 34.1%, during the above-mentioned period. Agriculture land use substantially declined from 26.9% to 21.9%. Bare lands decreased from 11.5% to 5.0%, and wetlands declined from 13.9% to 9.6%.

Detecting coastal morphodynamics is a crucial task for monitoring shoreline changes and coastal zone management. However, modern technology viz., Geoinformatics paves the way for long-term monitoring and observation with precise output. Therefore, this study aimed to produce explicit shoreline change maps and analyze the historical changes of the coastline at the east coast of the Ampara District in Sri Lanka. The histogram threshold method is used to extract data from satellite images. The time-series satellite images, acquired from 1987 to 2017, toposheet, and Google Earth historical images were compared having adjusted with the ground-truth to find the seashore changes in the study area. The histogram threshold method is used on band 5 (mid-infrared) for separating land from water pixels which means that the water pixel values were classified to one (1) and land pixel values to zero (0). The extracted shoreline vectors were associated with each other to determine the dynamics of changing shoreline of the study area. The Digital Shoreline Analysis System (DSAS) was used to find shoreline movements for each period of time. As a result, it was observed by the cross-section analysis within 100 m shoreline—seaward range along the study area—in which severe erosion has occurred northward of the Oluvil Harbor and anomalous accretion southward of the harbor because of the breakwaters constructed in the port entrance which hinder the long shore sediment transport along the study area. This situation has resulted in many ramifications to the coastal zone of the study area in socio-economic and environmental aspects in which the coastal protection mechanisms have not been well implemented to curb such issues.

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