Flavescence dorée is a grapevine disease affecting European vineyards which has severe economic consequences and containing its spread is therefore considered as a major challenge for viticulture. Flavescence dorée is subject to mandatory pest control including removal of the infected vines and, in this context, automatic detection of Flavescence dorée symptomatic vines by unmanned aerial vehicle (UAV) remote sensing could constitute a key diagnosis instrument for growers. The objective of this paper is to evaluate the feasibility of discriminating the Flavescence dorée symptoms in red and white cultivars from healthy vine vegetation using UAV multispectral imagery. Exhaustive ground truth data and UAV multispectral imagery (visible and near-infrared domain) have been acquired in September 2015 over four selected vineyards in Southwest France. Spectral signatures of healthy and symptomatic plants were studied with a set of 20 variables computed from the UAV images (spectral bands, vegetation indices and biophysical parameters) using univariate and multivariate classification approaches. Best results were achieved with red cultivars (both using univariate and multivariate approaches). For white cultivars, results were not satisfactory either for the univariate or the multivariate. Nevertheless, external accuracy assessment show that despite problems of Flavescence dorée and healthy pixel misclassification, an operational Flavescence dorée mapping technique using UAV-based imagery can still be proposed.

An index-based insurance solution was developed to estimate and monitor near real-time forage production using the indicator Forage Production Index (FPI) as a surrogate of the grassland production. The FPI corresponds to the integral of the fraction of green vegetation cover derived from moderate spatial resolution time series images and was calculated at the 6 km × 6 km scale. An upscaled approach based on direct validation was used that compared FPI with field-collected biomass data and high spatial resolution (HR) time series images. The experimental site was located in the Lot and Aveyron departments of southwestern France. Data collected included biomass ground measurements from grassland plots at 28 farms for the years 2012, 2013 and 2014 and HR images covering the Lot department in 2013 (n = 26) and 2014 (n = 22). Direct comparison with ground-measured yield led to good accuracy (R² = 0.71 and RMSE = 14.5%). With indirect comparison, the relationship was still strong (R² ranging from 0.78 to 0.93) and informative. These results highlight the effect of disaggregation, the grassland sampling rate, and irregularity of image acquisition in the HR time series. In advance of Sentinel-2, this study provides valuable information on the strengths and weaknesses of a potential index-based insurance product from HR time series images.

Dans les milieux de savanes, l'identification des surfaces brûlées et leur suivi sont des informations essentielles pour assurer une bonne gestion et conservation de ces écosystèmes. L'approche choisie repose sur l'analyse de séries temporelles d'images de télédétection à moyenne résolution spatiale. Les savanes étudiées sont situées sur le bassin versant de Marovoay au nord-ouest de Madagascar : elles présentent la particularité d'un taux de recouvrement de la végétation faible. Les développements méthodologiques ont été concentrés sur deux thèmes : la signature spectrale des surfaces brûlées et la méthode de cartographie de ces surfaces. Dans un premier temps, une analyse spectrale est menée pour évaluer la capacité de six indices spectraux (NDVI, SAVI, BAI, GEMI, Albédo et Indice de Brillance) à discriminer les surfaces brûlées. Les résultats montrent que l'Indice de Brillance est le plus performant. Dans un second temps, une méthode de cartographie des surfaces brûlées annuellement est développée. Elle s'appuie sur le traitement d'une série temporelle de synthèses MODIS à huit jours sur la période 2000–2007. À partir de l'analyse des variations intra-annuelles de l'Indice de Brillance, deux indicateurs spatiaux – annuel et saisonnier – sont élaborés pour localiser les surfaces brûlées et déterminer la période de passage du feu. L'utilisation des courbes ROC pour le seuillage de l'indicateur de surface brûlée permet de définir la valeur optimale pour laquelle les erreurs d'omission et de commission sont minimisées. Les résultats sont évalués par comparaison avec des cartes de surfaces brûlées produites à partir d'images à haute résolution spatiale. La qualité d'identification des surfaces brûlées est bonne, avec un indice Kappa entre 0,74 et 0,76 et une précision cartographique entre 76% et 79%. In savannah areas where fire is a predominant practice, the localization and the monitoring of burned areas are essential information to develop an accurate management system of this ecosystem. This problem is addressed through the analysis of time series of medium spatial-resolution remotely sensed images. The authors studied the savannah localized on the Marovoay watershed, on the north-west coast of Madagascar. These present the particularity of a low vegetation cover. The methodological developments proposed in this article concern more specifically two domains: the spectral signature of burned areas and the method for mapping these areas. First, a spectral analysis is undertaken to assess the capacity of six spectral indexes (NDVI, SAVI, BAI, GEMI, Albedo and Brightness Index) to discriminate burned areas. Results show that the Brightness Index is the best. Then, a method for mapping annual burned areas at the scale of the watershed is developed. It is based on the processing of a MODIS eight-day composite times series over the period 2000–2007. Analysis of the intra-annual variations of the Brightness Index leads to the definition of two spatial indicators – annual and seasonal – elaborated to localize the burned areas and to determine the period at which the fire occurs within the fire season. The ROC curves are used as a threshold tool to define the optimal value of the burned area indicator that minimizes both omission and commission errors. The validation of the MODIS burned areas maps consists of the comparison with burned areas maps produced with high spatial-resolution remotely sensed images. The quality of the results is good, with a Kappa index between 0.74 and 0.76 and a cartographic index between 76% and 79%.