• Rural Digital Europe
• Other research productsclose
• 2014-2023close
• CemOA close

Accurate estimation of leaf chlorophyll content (Cab) from remote sensing is of tremendous significance to monitor the physiological status of vegetation or to estimate primary production. Many vegetation indices (VIs) have been developed to retrieve Cab at the canopy level from meter- to decameter-scale reflectance observations. However, most of these VIs may be affected by the possible confounding influence of canopy structure. The objective of this study is to develop methods for Cab estimation using millimeter to centimeter spatial resolution reflectance imagery acquired at the field level. Hyperspectral images were acquired over sugar beet canopies from a ground-based platform in the 400-1000 nm range, concurrently to Cab, green fraction (GF), green area index (GAI) ground measurements. The original image spatial resolution was successively degraded from 1 mm to 35 cm, resulting in eleven sets of hyperspectral images. Vegetation and soil pixels were discriminated, and for each spatial resolution, measured Cab values were related to various VIs computed over four sets of reflectance spectra extracted from the images (soil and vegetation pixels, only vegetation pixels, 50% darkest and brightest vegetation pixels). The selected VIs included some classical VIs from the literature as well as optimal combinations of spectral bands, including simple ratio (SR), modified normalized difference (mND) and structure insensitive pigment index (SIPI). In the case of mND and SIPI, the use of a blue reference band instead of the classical near-infrared one was also investigated. For the eleven spatial resolutions, the four pixel selections and the five VI formats, similar band combinations are obtained when optimizing VI performances: the main bands of interest are generally located in the blue, red, red edge and near-infrared domains. Overall,mNDblue[728,850] defined as (R440

Beyond some scientific and technological challenges, this presentation will underline several other criteria to consider in order to give the key for a real market development of agricultural robots in close relationship with the operational needs and the constraints of farmer end-users.

Estimating exact 3D distribution of canopy components using terrestrial lidar in forest is limited by signal occlusion. We propose a method to address this limitation: it uses voxels, beam returns and beam propagation through the scene. The proposed method was validated using simulated forest scenes and a lidar simulator.

This report presents a method for assessing economic impacts of available water resources for irrigation at farm, food processing sector and territory levels. It is mainly based on the example of the study commissioned by the Direction Régionale de l'Agriculture, de l'Alimentation et de la Forêt de la région Rhône Alpes in Drôme des collines , while comparing it with similar studies conducted in other regions in France. / Ce rapport présente une méthode d'évaluation des impacts économiques de la réduction des volumes disponibles pour l'irrigation à l'échelle des exploitations agricoles, des filières et du territoire irrigué. Il s'appuie notamment sur l'exemple de l'étude réalisée en Drôme des collines à la demande de la Direction Régionale de l'Agriculture, de l'Alimentation et de la Forêt de la région Rhône Alpes, en le mettant en perspective avec des études similaires menées dans d'autres régions.

This paper describes a new approach for yield sampling in viticulture. It combines approaches based on auxiliary information and path optimization to offer more consistent sampling strategies, integrating statistical approaches with computer methods. To achieve this, groups of potential sampling points, comparable according to their auxiliary data values are created. Then, an optimal path connecting several points, one from each group of potential sampling points and minimizing the route distance is created. This part is performed using constraint programming, a programming paradigm offering tools to deal efficiently with combinatorial problems. The paper presents the formalization of the problem, as well as the tests performed on real fields. Results show that combining target sampling and path optimization can reduce by 45% the average sampling circuit length compared to previous methods based on auxiliary data while being almost equivalent in yield prediction error.

In the Provence Alpes Côte d'Azur region (France), some forest lands are converted to agricultural use, in particular in the wine-growing sector. In this region agricultural lands have to cope with urbanization and agricultural land abandonment. But this trend raises the question of natural and forest lands preservation. The issue is also to understand the conditions of these changes in land use. We carried out an exploratory study in order to quantify this evolution and provide information on the agriculture and forest stakes. This work was based on a bibliographic study and some interviews among local stakeholders. We also conducted a more precise study in the Var department using the CRIGE-PACA database to quantify the changes in land use regarding vineyards and forests (conversion of forest land to vineyards and vice-versa) between 2006 and 2014. We showed that the surfaces of forest lands to vineyards was very small. The requests for authorization to clear forest land often concerned plots which were formerly cultivated. However the context could accelerate this process, especially due to outside investors entering the wine-growing sector as well as the surface growth of som AOC/AOP, and the strong willingness of agricultural organizations to develop the wine-growing sector and more widely the agricultural sector. A more comprehensive study will be needed using spatial data to locate the impacted territories and assess the possible effects on forests. / En région Provence-Alpes-Côte d'Azur (France), des terres sont récupérées sur des espaces forestiers par des agriculteurs, particulièrement dans le secteur viticole. Dans une région où les espaces agricoles peinent à se maintenir face au développement urbain et à la déprise, cette tendance pose cependant la question de la préservation des espaces naturels et forestiers et des conditions dans lesquelles s'effectuent ces changements d'occupation du sol (lieu, superficie et types de forêts concernées, projets de valorisation de ces espaces...). Nous avons réalisé une étude exploratoire visant à quantifier cette évolution et à en préciser les enjeux pour l'agriculture et pour les espaces forestiers. Ce travail a été conduit à partir de la bibliographie existante et d'entretiens avec quelques acteurs du territoire. Un travail plus précis a été réalisé sur le département du Var, pour lequel nous avons utilisé des bases de données du CRIGE-PACA pour quantifier les transformations d'occupation du sol concernant la viticulture et la forêt (transformation de forêts en vignobles, et vice versa) entre 2006 et 2014. Les analyses montrent que les superficies de forêts converties en vignobles aux dépens d'espaces forestiers ont été très limitées au cours de la période étudiée. Les demandes de défrichement concernent souvent des parcelles anciennement cultivées. Cependant certains éléments de contexte pourraient conduire à une accélération de cette tendance, notamment l'entrée d'investisseurs extérieurs au secteur viticole apportant des capitaux importants et souhaitant les valoriser, l'extension des aires d'appellations de certaines AOC/AOP, et la volonté affirmée des organisations professionnelles agricoles de développer le secteur viticole provençal et plus largement l'agriculture. Le travail réalisé devra être poursuivi par des études spatialisées permettant de localiser les territoires impactés (ou pouvant être impactés) par cette tendance et d'évaluer les effets possibles sur les forêts concernées.

Mediterranean countries are undergoing dietary and nutritional changes that affect their inhabitants' health, while facing massive environmental challenges. The increasing demand of water in agriculture, the capacity to maintain local food production, and the growing dependence on food imports are interconnected issues that must be addressed to ensure food security and nutrition in the Mediterranean region. Here, we present the conceptual framework and methodologies developed by the MEDINA-Study Group for rethinking food systems toward sustainable consumption and production modes. Based on its multidisciplinary expertise, the MEDINA-Study Group designed a "fork-to-farm" multi-scale approach, stemming from current dietary habits and examining how some options to nutritionally improve these habitsmight affect the food systems. This approach was developed for research activities in the South of France and Tunisia, two areas with very different diet-agriculture-environment nexus. The conceptual framework is based on the analysis of elements of the food systems (from consumption to production) at different levels (individual, household, regional and national levels). The methods include: (i) modeling options of dietary changes at different scales, in order to nutritionally optimize food consumption-production without increasing the environmental impact, (ii) translating the best-choice changes into possible policy actions, (iii) testing the acceptability and feasibility of these actions with several stakeholders, and (iv) producing guidelines for sustainable food choices and production. The MEDINA-Study Group identified additional issues that could be included in a future framework to help designing ambitious agricultural, food and health policies in the Mediterranean region.

Study Region Garonne Basin, France. Study Focus This study analyses water mass variations for the whole Garonne basin (50,000 km2 drainage area). To do so, Total Water Storage Anomalies (TWSA) from seven global solutions based on the Gravity Recovery And Climate Experiment (GRACE) satellite mission measurements (˜300 km spatial resolution) are inter-compared with TWSA from two hydrological models, SAFRAN-ISBA-MODCOU (SIM) and Soil and Water Assessment Tool (SWAT), between January 2003 and December 2010. New Hydrological Insights for the Region Despite the small size of the Garonne basin compared to GRACE spatial resolution, good agreement between GRACE solutions and hydrological model TWSA has been found (maximum correlation coefficient ˜0.9 and Nash-Sutcliffe Efficiency, NSE, ˜0.7). These datasets showed that TWSA in the Garonne basin is mainly due to water stored in the first dozen meters of soil and in the shallow aquifer. To a smaller extent, snow also influences Garonne TWSA. Open surface water TWSA is quite small and TWSA from deep aquifer is negligible. The most important drought period occurred in 2011/2012, due to low precipitation during the two hydrological years and ETR close to previous years. Important precipitation in 2013/2014 helps to refill the water stocks. This study also showed that GRACE and models mismatches should be due to GRACE poor spatial resolution, but also to its monthly time resolution (rarely shown in previous studies).

The main purpose of this study is to investigate the performance of two radar backscattering models; the calibrated integral equation model (CIEM) and the modified Dubois model (MDB) over an agricultural area in Karaj, Iran. In the first part, the performance of the models is evaluated based on the field measurement and the mentioned backscattering models, CIEM and MDB performed with root mean square error (RMSE) of 0.78 dB and 1.45 dB, respectively. In the second step, based on the neural networks (NNS), soil surface moisture is estimated using the two backscattering models, based on neural networks (NNs), from single polarization Sentinel-1 images over bare soils. The inversion results show the efficiency of the single polarized data for retrieving soil surface moisture, especially for VV polarization.

Nowadays, remote sensing technologies produce huge amounts of satellite images that can be helpful to monitor geographical areas over time. A satellite image time series (SITS) usually contains spatio-temporal phenomena that are complex and difficult to understand. Conceiving new data mining tools for SITS analysis is challenging since we need to simultaneously manage the spatial and the temporal dimensions at the same time. In this work, we propose a new clustering framework specifically designed for SITS data. Our method firstly detects spatio-temporal entities, then it characterizes their evolutions by mean of a graph-based representation, and finally it produces clusters of spatio-temporal entities sharing similar temporal behaviors. Unlike previous approaches, which mainly work at pixel-level, our framework exploits a purely object-based representation to perform the clustering task. Object-based analysis involves a segmentation step where segments (objects) are extracted from an image and constitute the element of analysis. We experimentally validate our method on two real world SITS datasets by comparing it with standard techniques employed in remote sensing analysis. We also use a qualitative analysis to highlight the interpretability of the results obtained.