Evaluation of SPOT imagery for the estimation of grassland biomass.
2015
Dusseux, Pauline | Hubert-Moy, Laurence | Corpetti, Thomas | Vertès, Francoise | Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes) ; Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG) ; Université de Caen Normandie (UNICAEN) ; Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE) ; Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN) ; Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN) ; Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE) ; Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN) ; Université de Nantes (UN)-Université de Nantes (UN) | Sol Agro et hydrosystème Spatialisation (SAS) ; Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST | French National Research Agency SYSTERRA-ACASSYA program (ANR-08-STRA-0); Lannion-Tregor District Council
International audience
Afficher plus [+] Moins [-]anglais. In many regions, a decrease in grasslands and change in their management, which are associated with agricultural intensification, have been observed in the last half-century. Such changes in agricultural practices have caused negative environmental effects that include water pollution, soil degradation and biodiversity loss. Moreover, climate-driven changes in grassland productivity could have serious consequences for the profitability of agriculture. The aim of this study was to assess the ability of remotely sensed data with high spatial resolution to estimate grassland biomass in agricultural areas. A vegetation index, namely the Normalized Difference Vegetation Index (NDVI), and two biophysical variables, the Leaf Area Index (LAI) and the fraction of Vegetation Cover (fCOVER) were computed using five SPOT images acquired during the growing season. In parallel, ground-based information on grassland growth was collected to calculate biomass values. The analysis of the relationship between the variables derived from the remotely sensed data and the biomass observed in the field shows that LAI outperforms NDVI and fCOVER to estimate biomass (R2 values of 0.68 against 0.30 and 0.50, respectively). The squared Pearson correlation coefficient between observed and estimated biomass using LAI derived from SPOT images reached 0.73. Biomass maps generated from remotely sensed data were then used to estimate grass reserves at the farm scale in the perspective of operational monitoring and forecasting.
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