In ArcGIS Pro, you can perform LULC classification for the past 30 years or decade-wise using remote sensing data and appropriate classification techniques, such as supervised, unsupervised, or machine learning algorithms. These classifications can then be analyzed using the Trend analysis tools to identify patterns and rates of change over time. The Model Builder in ArcGIS Pro allows you to integrate various data layers, including socio-economic and environmental factors, and apply analysis rules or equations to develop predictive models. Techniques like Markov Chain analysis, Cellular Automata models, and regression analysis can be employed to project historical land use trends into the future.
Alternatively, TerrSet, a software specifically designed for land change science, offers a comprehensive suite of tools for LULC prediction. It includes modules for image processing, change analysis, and modeling, as well as a dedicated Land Change Modeler (LCM) for projecting future LULC scenarios. TerrSet's LCM utilizes a hybrid modeling approach that combines empirical modeling techniques, such as logistic regression and multi-layer perceptron neural networks, with spatial modeling techniques like Markov Chain analysis and Cellular Automata. This hybrid approach allows for a comprehensive analysis of the driving factors and spatial patterns influencing LULC changes.
Regardless of the software chosen, the process involves validating and calibrating the predictive models by comparing their outputs to actual recent LULC data, and making necessary adjustments to improve accuracy. Once the model is calibrated, it can be run to generate a predictive LULC map for the desired future timeframe, incorporating the complex interactions and driving factors influencing land use dynamics in your study area.