Clémence Vannier

Multiscale ecological assessment of remote sensing images

In landscape ecology, the importance of map extent and resolution on the value of landscape indices is widely discussed, but the information content of the map, mostly derived from remote sensing images, is not. In this study, we sought (1) to understand the influence of changes in maps’ spatial and spectral resolution of agricultural landscape elements, taking hedgerow networks as a case study, and (2) to explore how predictions of species distribution might be affected by maps’ resolutions, taking two carabid species as a case study. To do so, we compared maps from different remote sensors, derived two landscape characterization variables from the maps related to patterns known to drive ecological processes, and analyzed their predictive power on biological data distribution to assess the information content of these maps. The results show that (1) the use of several methods, including landscape metrics, was useful to assess map validity; (2) the spatial resolution of satellite images is not the only important factor; changes in spectral resolution significantly alter maps; (3) the relevant definition of “hedgerow” to construct functional maps is species and process specific; thus the different maps are not either good or bad, but rather provide complementary information; (4) the more a species responds to network structure and over small areas, the less the different maps can be substitutable one to another.

Markov chains–cellular automata modeling and multicriteria analysis of land cover change in the Lower Nhecolândia subregion of the Brazilian Pantanal wetland

Abstract. The dynamics of land use/land cover change in the Lower Nhecolândia wetland are marked by deforestation for pasture expansion, resulting in a real threat to the ecological stability. The aim of our work was to analyze the spatial distribution of land cover changes in the Lower Nhecolândia from 1985 to 2013 and to predict changes in trends for 2040. The mapping of land cover changes was developed using Landsat satellite images of 1985, 1999, 2007, and 2013, based on geographic object-based image analysis approach. This study uses integrated Markov chains and cellular automata modeling and multicriteria evaluation techniques to produce transition probability maps and describe the trajectory analysis methodology to construct a continuity of spatial and temporal changes for the wetland. The results of the multitemporal change detection classification show that, from 1985 to 2013, the forest woodland decreased by 6.89% and the grassland class increased by 18.29%. On the other hand, all water bodies showed a reducing trend, while the bare soil class increased compared to 1985, but did not present a regular trend of increase or decrease. From the present day, the trend for the future is a reduction of almost 6.4% by 2040. We found that deforestation actions will be concentrated in the areas with the highest concentration of saline lakes, constituting a serious threat to the natural functioning of this environmental system.

Multiscale comparison of remote-sensing data for linear woody vegetation mapping

Wooded hedgerows do not cover large areas but perform many functions that are beneficial to water quality and biodiversity. A broad range of remotely sensed data is available to map these small linear elements in rural landscapes, but only a few of them have been evaluated for this purpose. In this study, we evaluate and compare various optical remote-sensing data including high and very high spatial resolution, active and passive, and airborne and satellite data to produce quantitative information on the hedgerow network structure and to analyse qualitative information from the maps produced in order to estimate the true value of these maps. We used an object-based image analysis that proved to be efficient for detecting and mapping thin elements in complex landscapes. The analysis was performed at two scales, the hedgerow network scale and the tree canopy scale, on a study site that shows a strong landscape gradient of wooded hedgerow density. The results (1) highlight the key role of spectral resolution on the detection and mapping of wooded elements with remotely sensed data; (2) underline the fact that every satellite image provides relevant information on wooded network structures, even in closed landscape units, whatever the spatial resolution; and (3) indicate that light detection and ranging data offer important insights into future strategies for monitoring hedgerows.