التفاصيل البيبلوغرافية
| العنوان: |
Satellite Image Time Series to detect and monitor agricultural large-scale land acquisitions (LSLAs): Senegal case study |
| المؤلفون: |
Ngadi Scarpetta, Yasmine, Lebourgeois, Valentine, Dieye, Mohamadou, Bourgoin, Jeremy, Bégué, Agnès, Laques, Anne-Elisabeth |
| المساهمون: |
UMR 228 Espace-Dev, Espace pour le développement, Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de la Nouvelle-Calédonie (UNC)-Université de Guyane (UG)-Université des Antilles (UA)-Université de Montpellier (UM), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Montpellier (UM), Département Environnements et Sociétés (Cirad-ES), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Bureau d’analyses macro-économiques de l'ISRA (BAME-ISRA), Institut de Recherche pour le Développement (IRD), Projet TOSCA-VISAGE (CNES), Université de Montpellier, Leibniz Centre for Agricultural Ladscape Research (ZALF), ANR-16-CONV-0004,DIGITAG,Institut Convergences en Agriculture Numérique(2016) |
| المصدر: |
Landscape 2021 ; https://hal.science/hal-04445968 ; Landscape 2021, Leibniz Centre for Agricultural Ladscape Research (ZALF), Sep 2022, Berlin, Germany |
| بيانات النشر: |
CCSD |
| سنة النشر: |
2022 |
| المجموعة: |
Université de Perpignan: HAL |
| مصطلحات موضوعية: |
MODIS NDVI, SITS analysis, BFAST, Land use - land cover change, [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph] |
| جغرافية الموضوع: |
Berlin, Germany |
| الوصف: |
International audience ; Large scale land acquisitions (LSLAs), often referred as "land grabbing", refer to the control of larger than locally-typical amounts of land by any physical/moral person for agricultural purposes, logging, tourism, conservation, mining, urban expansion or large infrastructural works. LSLAs are highly dynamic and complex land use systems, that are rapidly transforming ecosystems and societies in many lowincome countries of the world, bringing on one hand sustainability challenges and, on the other hand, undermining the right of peoples to self-determination over natural resources. Consequently, monitoring of those large-scale agricultural expansions has appeared to be of paramount importance. International initiatives such as the Land Matrix relying on publicly available sources, have emerged in response to that need. However, because information on those acquisitions is opaque and scarce, systems allowing near real-time LSLAs detection, characterization and monitoring are needed (1). With the increasing availability of global satellite data products, technological development in cloud computing, image and data mining analysis, remote sensing has appeared to be an interesting tool for the detection and characterization of such land use systems. Their repetitive coverage at short intervals and consistent image quality, combined with the free-of-cost availability of dense temporal series of satellite images, have explained their wide use in land use and land cover change detection. While LSLAs are not directly observable from remote sensing images (no one-to-one relation between land cover and functionality), they may be inferred from observable land cover, structural elements in the landscape and spatio-temporal characteristics at different scales (2). This study deals with the detection of agricultural LSLAs (~80% of LSLAs) across Senegal. Its strong north-south gradient of rainfall, makes of Senegal an interesting study case for the detection of LSLAs under different environmental ... |
| نوع الوثيقة: |
conference object |
| اللغة: |
English |
| الاتاحة: |
https://hal.science/hal-04445968 |
| رقم الانضمام: |
edsbas.6DB324A6 |
| قاعدة البيانات: |
BASE |