Predictive modelling of land use land cover dynamics for a tropical coastal urban city in Kerala, India

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.…”

“…The results showed that Landsat 8 had greater advantages over Sentinel 2 in the monitoring of forests, herbaceous vegetation, and water; the former was more accurate [64]. The use of long time series data also provided opportunities for the forecasting of land cover and desert greening in the future, such as a dynamics of land system (DLS) model [65], land change evaluation model [66], CA-Markov model [44,67,68], and GM (1,1) model [69,70]. Among them, the GM (1,1) model can build mathematical models and make forecasts based on a small amount of incomplete information and data by considering the law of the past and present development of objective things [71].…”