Context and objectives
The main objective objective of PROBASIM-LUC is to demonstrate the usefulness of simulated Proba-V data for land use change detection projects. Three spectral bands of SPOT data will be used to simulate Proba-V data. The simulation is based on re-sampling to convert the original SPOT data spatial high resolution to the much coarser Proba-V spatial resolution. Three sites in different parts of the globe are selected as test sites to evaluate in practice the usefulness of Proba-V data. For each of the three sites land cover classification based on simulated Proba-V data will be performed. The classification schema will be kept the same for all test sites and comparable to the well known Corine's nomenclature. A time series with three points in time will be constructed, i.e. for the years 2000, 2005 and 2010. Change detection analysis will be performed throughout these years. The same analysis will be performed with the original SPOT-VEGETATION data. The results will be compared with the results obtained based on simulated Proba-V data. The results will be assessed in terms of thematic accuracy and usefulness of classes produced. The overall procedure will be documented in full extend in the final report.
Expected scientific results
The main result of PROBASIM-LUC is the final report that comments on the usefulness on simulated Proba-V data for land use change detection projects. Moreover, it will be demonstrated that Proba-V will be suitable to continue the change detection projects which were started with SPOT-VEGETATION data sets. The strength of the PROBASIM-LUC project is primarily that the report will be based on three test sites that are selected in three very different parts of the globe, with different types of land cover as well as different patterns of land cover change. Best practices and optimal methods to conduct land use and land cover change detection based on Proba-V data are going to be established. Other aspects that might present interesting outcome are
- change detection using indexes (e.g. NDVI, urban index)
- Land cover change sensitivity with respect to scale variation.