La question simple : « La localisation cree-t-elle des predispositions a l’echec ou a la reussite scolaire ? » se complexifie lorsque l’on prend en compte la residence de l’eleve, l’etablissement, la relation entre ces deux lieux, l’ensemble de la spatialite de l’eleve, un espace local incluant eleves, etablissements et vie sociale quotidienne et des espaces elargis accessibles par une mobilite moins frequente. Dans ce texte, on tente d’y repondre, en insistant sur les espaces a faible densite et sur les situations d’isolement et en menant une enquete de terrain sur des lieux tres types en ce sens 1, en posant une question plus specifique : jusqu’a quel point ces configurations peuvent-elles constituer un handicap pour la reussite scolaire des eleves ? Cet article tire sa substance d’un projet de recherche 2 mene en 2019-2020 et en donne les resultats sans expliciter, faute de place, l’ensemble des methodes et des techniques a la fois quantitatives (utilisant les vastes ressources de la DEPP) et qualitatives (une enquete par entretiens et focus-groups portant sur les eleves, les parents et les enseignants).
To allow for the production of timber while preserving conservation values, forestry regulations in the Congo Basin have made Forest Management Plans (FMP) mandatory in logging concessions. This paper uses original high-resolution maps of forest-cover changes and official records on the activities of logging concessions to analyze the impact of FMP on deforestation in this region. We apply quasi-experimental and difference-in-difference approaches to evaluate the change in deforestation in concessions managed under an approved FMP. We find that between 2000 and 2010, deforestation was 74% lower in concessions with an FMP compared to others. Building on a theory of change, further analyses revealed that this decrease in deforestation takes time to occur and is highest around communities located in and nearby logging concessions, and in areas close to previous deforestation. These findings suggest that FMP help avoid deforestation by allowing logging companies to rotate cycles of timber extraction, thereby avoiding the overexploitation of areas that were previously logged, and by the better regulation of access to concessions by closing former logging roads to limit illegal activities such as shifting agriculture, hunting and the illegal harvest of timber or fuel-wood.
Morphological attribute profiles (APs) are among the most effective spatial-spectral methods to perform multilevel image description based on hierarchical tree-based representation. They have been widely applied to the processing and characterization of remote sensing images, in particular to tackle classification task, in the literature. Recently, a novel extension of APs called FPs has been proposed by replacing pixel gray-levels with some statistical and geometrical features when forming the output profiles. FPs have been proved to be more efficient than the standard APs when generated from both inclusion and partition trees. The motivation of this article is to conduct a comparative study of APs and FPs using different attributes including some novel ones that have not been used in the literature. We also present our developed library called Broceliande, which proposes efficient implementation of APs and FPs to perform remote sensing image classification, with various choices of tree structures as well as attributes. We perform our experiments on two high resolution optical image data sets and provide comparative results of APs and FPs, showing and confirming their effectiveness to describe and classify remote sensing images.
Land cover mapping has benefited a lot from the introduction of the Geographic Object-Based Image Analysis (GEOBIA) paradigm, that allowed to move from a pixelwise analysis to a processing of elements with richer semantic content, namely objects or regions. However, this paradigm requires to define an appropriate scale, that can be challenging in a large-area study where a wide range of landscapes can be observed. We propose here to conduct the multiscale analysis based on hierarchical representations, from which features known as differential attribute profiles are derived over each single pixel. Efficient and scalable algorithms for construction and analysis of such representations, together with an optimized usage of the random forest classifier, provide us with a semi-supervised framework in which a user can drive mapping of elements such as Small Woody Features at a very large area. Indeed, the proposed open-source methodology has been successfully used to derive a part of the High Resolution Layers (HRL) product of the Copernicus Land Monitoring service, thus showing how the GEOBIA framework can be used in a big data scenario made of more than 38,000 Very High Resolution (VHR) satellite images representing more than 120 TB of data.
