Land cover change monitoring is essential for sound policymaking and keeping track of what effects policies have over time. Automatic land cover change detection allows for rapid identification of hotspots of activity over large areas, which allows deriving change alerts for law enforcement and policymakers.
Multiple methods have been developed to detect breaks in time series, which is a proxy for land cover change. In addition, machine learning algorithms suitable for change detection have been emerging. Recently there has been a growth in land cover change datasets, which allows for a more direct comparison between land cover change detection methods based on real world cases of land cover change.
The different methods are based on different assumptions and different philosophy. More research is needed to discover the strengths and weaknesses of each method and their suitability for change detection at a global scale.
- Develop a method for assessing the accuracy of different land cover change detection algorithms
- Compare chosen land cover change detection methods in terms of accuracy, applicability, input requirements and output
- Zhu, Zhe. “Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications”. ISPRS Journal of Photogrammetry and Remote Sensing 130 (2017): 370–84.
- Geo Scripting
- Affinity work with large scale remote sensing datasets
Theme(s): Integrated Land Monitoring