# Rationale We would like to enable semi-automated change detection between two or more scenes acquired at different time stamps, thus allowing absolute change to be quantified. Visible changes represented in a change map are always the result of a spatio-temporal process, whose dynamics are varying in space and time. If the temporal distance between two images is large, information about the dynamics of change is lost. Therefore, to allow better informed change analysis, this application provides information about the variability between two time stamps together with the change map. ![Figure 1][1] *Figure 1: Calculation principle of temporal variability of an area.* Figure 1 (source: [Braun & Hochschild, 2017][2]) depicts an example of these dynamics, expressed as variability v, based on natural resources (NR), developed by [Hagenlocher, Lang & Tiede, 2012][3]. The overall change $C_o$ consider only the first and last image, the annual changes $C_a$ considers the internal temporal variability within the unit of analysis. # Software architecture and implementation The application is implemented in Trimble cCognition. Details can be found in the software implementation wiki [here][4]. # Proof-of-concept tbd. # References [Braun, A. and Hochschild, V., 2017. A SAR-Based Index for Landscape Changes in African Savannas. Remote Sensing, 9(4), p.359.][5] [Hagenlocher, M., Lang, S. and Tiede, D., 2012. Integrated assessment of the environmental impact of an IDP camp in Sudan based on very high resolution multi-temporal satellite imagery. Remote Sensing of Environment, 126, pp.27-38.][6] [1]: http://www.mdpi.com/remotesensing/remotesensing-09-00359/article_deploy/html/images/remotesensing-09-00359-g005.png [2]: http://www.mdpi.com/2072-4292/9/4/359/html [3]: https://www.sciencedirect.com/science/article/pii/S0034425712003227 [4]: https://osf.io/xz8by/wiki/home/ [5]: http://www.mdpi.com/2072-4292/9/4/359/html [6]: https://www.sciencedirect.com/science/article/pii/S0034425712003227