Une chaîne de traitement automatique pour l'estimation de la rugosité des sols agricoles par photogrammétrie 3D à partir de photographies prises sans contraintes pour le suivi radiométrique des sols

Une chaîne de traitement automatique pour l'estimation de la rugosité des sols agricoles par photogrammétrie 3D à partir de photographies prises sans contraintes pour le suivi radiométrique des sols. 11. Journées d'Etude des Sol

Mapping tillage operations over a peri-urban region using combined SPOT4 and ASAR/ENVISAT images

This study aimed at assessing the potential of combining synchronous SPOT4 and ENVISAT/ASAR images (HH and HV polarizations) for mapping tillage operations (TOs) of bare agricultural fields over a peri-urban area characterized by conventional tillage system in the western suburbs of Paris (France). The reference spatial units for spatial modeling are 57 within-field areas named "reference zones" (RZs) homogeneous for their soil properties, constructed in the vicinity of 57 roughness measurement locations, spread across 20 agricultural fields for which TOs were known. The total RZ dataset was half dedicated to successive random selections of training/validating RZs, the remaining half (29 RZs) being kept for validating the final map results. Five supervised per-pixels classifiers were used in order to map 2 TOs classes (seedbed&harrowed and late winter plough) in addition to 4 landuse classes (forest, urban, crops and grass, water bodies): support vector machine with polynomial kernel (pSVM), SVM with radial basis kernel (rSVM), artificial neural network (ANN), Maximum Likelihood (ML), and regression tree (RT). All 5 classifiers were implemented in a bootstrapping approach in order to assess the uncertainty of map results. The best results were obtained with pSVM for the SPOT4/ASAR pair with producer's and user's mean validation accuracies (PmVA/UmVA) of 91.7%/89.8% and 73.2%/73.3% for seedbed&harrowed and late winter plough conditions, respectively. Whatever classifier, the SPOT4/ASAR pair appeared to perform better than each of the single images, particularly for late winter plough: PmVA/UmVA of 61.6%/53.0% for the single SPOT4 image; 0%/6% for the single ASAR image. About 73% of the validation agricultural fields (79% of the RZs) were correctly predicted in terms of TOs in the best pSVM-derived final map. Final map results could be improved through masking non-agricultural areas with land use identification system layer prior to classifying images. Such knowledge of agricultural operations is likely to facilitate the mapping of agricultural systems which otherwise proceed from time-consuming surveys to farmers

Outil Cadastre_NH 3 : Evaluer les pratiques réduisant les émissions d’ammoniac au champ

International audienceL'outil Cadastre_NH3 réalise des cadastres dynamiques des émissions d’ammoniac liées à la fertilisation azotée aux échelles régionale et nationale. Ces informations fines sont indispensables pour améliorer les outils opérationnels de prédiction de la qualité de l’air en France, comme pour évaluer l’influence des différentes pratiques de fertilisation sur ces émissions

Potential of Ultra-High-Resolution UAV Images with Centimeter GNSS Positioning for Plant Scale Crop Monitoring

To implement agricultural practices that are more respectful of the environment, precision agriculture methods for monitoring crop heterogeneity are becoming more and more spatially detailed. The objective of this study was to evaluate the potential of Ultra-High-Resolution UAV images with centimeter GNSS positioning for plant-scale monitoring. A Dji Phantom 4 RTK UAV with a 20 MPixel RGB camera was used, flying at an altitude of 25 m (0.7 cm resolution). This study was conducted on an experimental plot sown with maize. A centimeter-precision Trimble Geo7x GNSS receiver was used for the field measurements. After evaluating the precision of the UAV’s RTK antenna in static mode on the ground, the positions of 17 artificial targets and 70 maize plants were measured during a series of flights in different RTK modes. Agisoft Metashape software was used. The error in position of the UAV RTK antenna in static mode on the ground was less than one centimeter, in terms of both planimetry and elevation. The horizontal position error measured in flight on the 17 targets was less than 1.5 cm, while it was 2.9 cm in terms of elevation. Finally, according to the RTK modes, at least 81% of the corn plants were localized to within 5 cm of their position, and 95% to within 10 cm

Soil surface roughness measurement: A new fully automatic photogrammetric approach applied to agricultural bare fields

This work develops a fully automatic photogrammetric approach for measuring soil surface roughness from pictures taken in the field with a simple digital camera, without geometric constraints. On each site, 13 overlapping photographs of the soil surface were taken from different angles, under the shade of an umbrella. Millimeter accuracy 3D soil models were calculated from these pictures and were used to derive 11 roughness indexes. The whole procedure was implemented in a fully automatic Python program. The system accuracy was determined on artificial models built with polystyrene, the positional and elevation accuracies of which were about 1.5 mm, while the error on the surface area estimation was less than 0.76% of the site surface area. This approach was successfully applied to an agricultural field experiment in which four soil tillage levels have been generated. These levels were correctly identified using two indices for 96% of the 32 measurement sites. These results show that two roughness indices, the surface tortuosity index and the mean value of height, are most efficient to discriminate agricultural soil tillage levels

The mapping of soil carbon content using spectroscopy and very high resolution images

International audienc

Retrospective 70 y-spatial analysis of repeated vine mortality patterns using ancient aerial time series, Pléiades images and multi-source spatial and field data

For any wine estate, there is a need to demarcate homogeneous within-vineyard zones (‘terroirs’) so as to manage grape production, which depends on vine biological condition. Until now, the studies performing digital zoning of terroirs have relied on recent spatial data and scant attention has been paid to ancient geoinformation likely to retrace past biological condition of vines and especially occurrence of vine mortality. Is vine mortality characterized by recurrent and specific patterns and if so, are these patterns related to terroir units and/or past landuse? This study aimed at performing a historical and spatial tracing of vine mortality patterns using a long time-series of aerial survey images (1947–2010), in combination with recent data: soil apparent electrical conductivity EM38 measurements, very high resolution Pléiades satellite images, and a detailed field survey. Within a 6 ha-estate in the Southern Rhone Valley, landuse and planting history were retraced and the map of missing vines frequency was constructed from the whole time series including a 2015-Pléiades panchromatic band. Within-field terroir units were obtained from a support vector machine classifier computed on the spectral bands and NDVI of Pléiades images, EM38 data and morphometric data. Repeated spatial patterns of missing vines were highlighted throughout several plantings, uprootings, and vine replacements, and appeared to match some within-field terroir units, being explained by their specific soil characteristics, vine/soil management choices and the past landuse of the 1940s. Missing vines frequency was spatially correlated with topsoil CaCO3 content, and negatively correlated with topsoil iron, clay, total N, organic C contents and NDVI. A retrospective spatio-temporal assessment of terroir therefore brings a renewed focus on some key parameters for maintaining a sustainable grape production

Mapping tillage operations over peri-urban croplands using a synchronous SPOT4/ASAR ENVISAT pair and soil roughness measurements

Geophysical Research Abstracts Vol. 16, EGU2014-3896, poster abstractabsen

Potential of Ultra-High-Resolution UAV Images with Centimeter GNSS Positioning for Plant Scale Crop Monitoring

To implement agricultural practices that are more respectful of the environment, precision agriculture methods for monitoring crop heterogeneity are becoming more and more spatially detailed. The objective of this study was to evaluate the potential of Ultra-High-Resolution UAV images with centimeter GNSS positioning for plant-scale monitoring. A Dji Phantom 4 RTK UAV with a 20 MPixel RGB camera was used, flying at an altitude of 25 m (0.7 cm resolution). This study was conducted on an experimental plot sown with maize. A centimeter-precision Trimble Geo7x GNSS receiver was used for the field measurements. After evaluating the precision of the UAV’s RTK antenna in static mode on the ground, the positions of 17 artificial targets and 70 maize plants were measured during a series of flights in different RTK modes. Agisoft Metashape software was used. The error in position of the UAV RTK antenna in static mode on the ground was less than one centimeter, in terms of both planimetry and elevation. The horizontal position error measured in flight on the 17 targets was less than 1.5 cm, while it was 2.9 cm in terms of elevation. Finally, according to the RTK modes, at least 81% of the corn plants were localized to within 5 cm of their position, and 95% to within 10 cm

Digital zoning of South African viticultural terroirs using bootstrapped decision trees on morphometric data and multitemporal SPOT images

Digital zoning of South African viticultural terroirs using bootstrapped decision trees on morphometric data and multitemporal SPOT images. 9. International Congress of Vitivinicultural Terroir