Automated Land Cover Mapping and Independent Change Detection in Tropical Forest Using Multi-temporal High Resolution Data Set

An automatic method for land cover mapping and for detecting forest change has been designed for high resolution couples of image. The work is done on 20x20 km samples of 30 m resolution Landsat imagery. The methodology has been developed for two dates extracts but several couple of images can be compared. An automatic multi-date segmentation is applied on extracts pairs. Segmentation parameters are tuned, thanks to an iterative procedure, in order to provide image-objects of 1 hectare. In dense moist forest areas, 1 hectare guarantees a pure land cover for each object-segment. These image-objects will be the units of the classification and change detection work. An unsupervised classification is performed for each image, grouping image-objects into clusters. For each image, a tree cover mask is made based on a land cover map of reference with a coarser scale. Each cluster is then automatically labelled with this tree cover mask. Small image-objects are aggregated into image-objects with a minimum mapping unit of 5 hectares thanks to a second segmentation level. Two independent things are then produced, a land cover map for each date of interest and a set of objects flagged as changed between the two dates. The forest change detection is obtained by running a statistical outlier detection method on the difference of both images. The detection is done under the union of the tree cover mask of both dates in order to work with an homogeneous set of objects. The accuracy of this methodology is assessed for ten pairs of images, visually validated by experts. * Corresponding author