Land cover and land use maps
Classical production approaches
- supervised: areas for which the land cover is known are used as learning examples;
- unsupervised: the image pixels are grouped by similarity and the classes are identified afterwards.
Supervised classification often yields better results, but it needs reference data which are difficult or costly to obtain (field campaigns, photo-interpretation, etc.).
What time series bring
If one wants to build generic (independent from the geographic sites and therefore also from the target nomenclatures) and operational systems, regular and frequent image acquisitions have to be ensured. This will soon be made possible by the Sentinel-2 mission, and it is right now already the case with demonstration data provided by Formosat-2 and SPOT4 (Take 5). Furthermore, it can be shown that having a high temporal resolution is more interesting than having a high spectral diversity. For instance, the following figure shows the classification performance results (in terms of \( \kappa \) index, the higher the better) as a function of the number of images used. Formosat-2 images (4 spectral bands) and simulated Sentinel-2 (13 bands) and Venµs (12 bands) data have been used. It can be seen that, once enough acquisitions are available, the spectral richness is caught up by a fine description of the temporal evolution.
What we can expect from Sentinel-2
- 290 km. swath;
- 10 to 60 m. spatial resolution depending on the bands;
- 5-day revisit cycle with 2 satellites;
- 13 spectral bands.
Systems with similar spatial resolution (SPOT or Landsat) have longer revisit periods and fewer and larger spectral bands. Systems with similar temporal revisit have either a lower spatial resolution (MODIS) or narrower swaths (Formosat-2). The kind of data provided by Sentinel-2 allows to foresee the development of land cover map production systems which should be able to update the information monthly at a global scale. The temporal dimension will allow to distinguish classes whose spectral signatures are very similar during long periods of the year. The increased spatial resolution will make possible to work with smaller minimum mapping units. However, the operational implementation of such systems will require a particular attention to the validation procedures of the produced maps and also to the huge data volumes. Indeed, the land cover maps will have to be validated at the regional or even at the global scale. Also, since the reference data (i.e. ground truth) will be only available in limited amounts, supervised methods will have to be avoided as much as possible. One possibility consists of integrating prior knowledge (about the physics of the observed processes, or via expert rules) into the processing chains. Last but not least, even if the acquisition capabilities of these new systems will be increased, there will always be temporal and spatial data holes (clouds, for instance). Processing chains will have to be robust to this kind of artefacts.
Ongoing work at CESBIO