Object-based characterization of vegetation heterogeneity with sentinel images proves efficient in a highly human-influenced National Park of Côte d'Ivoire.

Forest monitoring requires more automated systems to analyze high ecosystem heterogeneity. The traditional pixel-based detection method has proven to be less and less effective. A novel change detection method is therefore proposed to detect changes in forest cover using satellite images at very high spatial resolution. This is object-oriented classification, which groups pixels into interpreted objects, based on their spectral values, spatial, and textural properties. Using sentinel and Lansat images, we tested for the first time in the West African rainforest zone the effectiveness of this method for better detection, delineation, and analysis of land use and occupation types. The mean shift algorithm was used in both the segmentation and classification processes. Next, we compared the proposed object-oriented method with a pixel-based image classification detection method by implementing both methods under the same conditions. High detection accuracy (> 90%) and an overall Kappa greater than 0.90 were obtained by the object-oriented method, which is about 20% higher than the pixel-based method. The object-based method was free of salt and pepper effects and was less prone to image misregistration in terms of change detection accuracy and mapping results. This study demonstrates that the object-based classifier is a much better approach than the classical pixel-based classifier. In addition, it shows the problems of detecting heterogeneous landscapes and explains the observed confusions between the types of vegetation formations specific to tropical wetlands. The results obtained are encouraging and the contribution of high-resolution images and the object-based method to better discrimination of tropical wetland vegetation is discussed.

Agricultural practices in grasslands detected by spatial remote sensing.

The major decrease in grassland surfaces associated with changes in their management that has been observed in many regions of the earth during the last half century has major impacts on environmental and socio-economic systems. This study focuses on the identification of grassland management practices in an intensive agricultural watershed located in Brittany, France, by analyzing the intra-annual dynamics of the surface condition of vegetation using remotely sensed and field data. We studied the relationship between one vegetation index (NDVI) and two biophysical variables (LAI and fCOVER) derived from a series of three SPOT images on one hand and measurements collected during field campaigns achieved on 120 grasslands on the other. The results show that the LAI appears as the best predictor for monitoring grassland mowing and grazing. Indeed, because of its ability to characterize vegetation status, LAI estimated from remote sensing data is a relevant variable to identify these practices. LAI values derived from the SPOT images were then classified based on the K-Nearest Neighbor (KNN) supervised algorithm. The results points out that the distribution of grassland management practices such as grazing and mowing can be mapped very accurately (Kappa index = 0.82) at a field scale over large agricultural areas using a series of satellite images.