Radiometric landscape: a new conceptual framework and operational approach for landscape characterisation and mapping

Lemettais Louise, Alleaume Samuel, Luque Sandra, Laques Anne-Elisabeth, Alim Yonas, Demagistri Laurent, Bégué Agnès. 2024. Radiometric landscape: a new conceptual framework and operational approach for landscape characterisation and mapping. Geo-spatial Information Science, 23 p.

https://doi.org/10.1080/10095020.2024.2314558

Article de revue ; Article de recherche ; Article de revue à facteur d'impact

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Url - autre : https://catalogue.theia-land.fr / Url - autre : https://appeears.earthdatacloud.nasa.gov / Url - autre : https://worldcover2020.esa.int/download / Url - jeu de données - Entrepôt autre : https://doi.org/10.57745/DBGRDI

Résumé : Landscape mapping has the potential to address some of the most pressing research issues of our time, including climate change, sustainable development, and human well-being. In this paper, we propose an original method that lays the foundations for landscape mapping and overcomes some of the major limitations of existing biophysical methods. Based on the assumption that the primary components of the landscape can be extracted directly from the radiometric information of satellite image time series, this paper presents a new approach to landscape characterization and mapping based solely on remote sensing data. The approach relies on a conceptual model, which links the description, characteristics, structure and functions of the landscape to a set of Remote Sensing-based Essential Landscape Variables (RS- ELVs). The RS-ELVs are then processed according to geographic object-based image analysis (GEOBIA) approach to produce a radiometric landscape map. The model and the remote sensing data processing chain are tested on a case study in central Madagascar (about 13 000 km2) composed of contrasting landscapes resulting from different climatic conditions and agricultural practices. The RS-ELVs are extracted from MODIS image time series for the temporal and spectral variables, and from MODIS and Sentinel-2 images for the texture variables. The parameterization of the segmentation and clustering algorithms is determined by statistical optimization. The final result is a radiometric landscape map in six classes. The landscape classes are then characterized using an independent set of remote sensing variables, a global land cover map and ground observations. The approach successfully identifies and delineates the gradient and major landscape types of the complex region of central Madagascar, confirming our initial hypothesis. The production of such radiometric landscape maps opens the way for integrated territorial development, including the planning and protection of the living environment and human well-being, and the implementation of sectoral policies.

Mots-clés Agrovoc : télédétection, paysage, cartographie de l'occupation du sol, imagerie par satellite, changement climatique, analyse d'image, cartographie de l'utilisation des terres, utilisation des terres, couverture végétale, imagerie multispectrale, cartographie, développement durable, facteur climatique

Mots-clés géographiques Agrovoc : Madagascar, France

Mots-clés libres : Remote Sensing, MODIS, Sentinel-2, Essential variables, Satellite Image Time Series, Madagascar

Agences de financement hors UE : Institut de Recherche pour le Développement, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Centre National d'Etudes Spatiales

Projets sur financement : (FRA) TOSCA CES PAYSAGE

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