Hamidreza Keshtkar

TL;DR: In this paper, the authors analyzed the spatiotemporal variations of land use and land cover and quantified the change in three important ecosystem services (food production, carbon storage, and habitat quality) in the Koshi River Basin, Nepal during 1996-2016 by using freely available data and tools such as, Landsat satellite images and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model.

TL;DR: The past and present land-use/land-cover (LULC) changes and urban expansion pattern for the cities of the Kathmandu valley and their surroundings using Landsat satellite images from 1988 to 2016 is explored.

TL;DR: The results suggest that the use of Landsat time-series archive images and the CA–Markov model are the best options for long-term spatiotemporal analysis and achieving an acceptable level of prediction accuracy.

TL;DR: In this paper, an integrated Cellular Automata-Markov Chain land change model was carried out to simulate the future landscape change during the period of 2020-2050, and the predictive power of the model was successfully evaluated using Kappa indices.

TL;DR: This study found that the object-based SVM classifier is the most accurate with an overall classification accuracy of 93.54% and a kappa value of 0.88 when compared to pixel-based random forest and decision tree classifier methods.