Change and Time Series Analysis
Change and Time Series analysis identifies and quantifies change and its impacts. IDRISI includes an extensive set of tools for measuring change at both the local and global scales, including tools for pairwise image comparison, multiple image comparison, and predictive modeling and assessment.
IDRISIís distinctive image comparison tools include image differencing, image ratioing, regression differencing, change vector analysis, and qualitative data analysis. Multiple image comparison techniques look at trends and anomalies across multiple images (time series) and include tools for time series analysis using Principal Components analysis, time series Fourier analysis, spatial/temporal correlation and image profiling over time.
A suite of tools is also provided for predictive land cover change modeling as well as the assessment of those predictions, utilizing knowledge of past changes. These tools include Markov Chain Analysis, Cellular Automata, logistical regression and multinomial logistical regression, GEOMOD, and Artificial Neural Networks. The latter has been incorporated into the Land Change Modeler, a vertical application integrated within IDRISI, which provides tools for the assessment of land cover change, the identification of driving forces of change, and the use of that information to predict future scenarios.