Bernard Etlicher

Exploring spatial patterns of vulnerability for diverse biodiversity descriptors in regional conservation planning

In this study, we developed a multi-criteria assessment of spatial variability of the vulnerability of three different biodiversity descriptors: sites of high conservation interest by virtue of the presence of rare or remarkable species, extensive areas of high ecological integrity, and landscape diversity in grid cells across an entire region. We assessed vulnerability in relation to (a) direct threats in and around sites to a distance of 2 km associated with intensive agriculture, building and road infrastructure and (b) indirect effects of human population density on a wider scale (50 km). The different combinations of biodiversity and threat indicators allowed us to set differential priorities for biodiversity conservation and assess their spatial variation. For example, with this method we identified sites and grid cells which combined high biodiversity with either high threat values or low threat values for the three different biodiversity indicators. In these two classes the priorities for conservation planning will be different, reduce threat values in the former and restrain any increase in the latter. We also identified low priority sites (low biodiversity with either high or low threats). This procedure thus allows for the integration of a spatial ranking of vulnerability into priority setting for regional conservation planning.

An Image Segmentation Process Enhancement for Land Cover Mapping from Very High Resolution Remote Sensing Data Application in a Rural Area

In this chapter, we describe a procedure for enhancing the automatic image segmentation for land cover mapping from Very High Resolution images. The increased need for large scale mapping (1:10000) for local territorial monitoring led to think about mapping production. Nowadays mapping production for land cover and land use (LUC) is mainly performed with human photo-interpretation. This approach can be extremely time consuming, expensive and tedious for data producers. This is confirmed from the evidence of rural areas where the use of the GIS database for LUC is less numerous than the GIS urban database. In the last decade, Geographic Object-Based Image Analysis (GEOBIA) has been developed by the image processing community. This new paradigm builds on theory, methods and tools for replicating the human photo-interpretation process from remote sensing data (Hay and Castilla 2008). However, the GEOBIA community is still fragile and suffers from a lack of protocols and standards for operational LUC mapping applications. Currently, human photo-interpretation seems a safer option. The objective of this research is to find an alternative to this time consuming and expensive use of human expertise. We explored the limits of GEOBIA to propose an automatic image segmentation enhancement for an operational mapping application. Questions behind this study were: What is a good segmentation? How can we obtain it?