Télédétection de la trame verte arborée en haute résolution par morphologie mathématique

International audienceCet article pointe d'abord les causes et/es enjeux des verrous cartographiques dans !a mise en oeuvre de la politique de la trame verte et bleue (TVB). Une connaissance de l'emprise précise de la trame verte arborée par les acteurs locaux apparaît incontournable. Nous proposons ensuite une méthode de télédétection associée à des algorithmes de morphologie mathématique pour extraire la trame verte arborée à l'échelle métrique à partir d'une image à très haute résolution spatiale. La méthode proposée est une séquence de quatre étapes : 1) analyse en composantes principales (ACP), 2) segmentation par la transformation chapeau haut de forme, 3) élimination du bruit morphologique, 4) restauration des contours arborés par dilatation géodésique. Enfin, une discussion sur les perspectives en termes de conséquences sur les méthodologies de télédétection et sur les politiques publiques environnementales termine l'article

Kalideos OSR MiPy : un observatoire pour la recherche et la démonstration des applications de la télédétection à la gestion des territoires

International audienceCes dernières années, le CESBIO a mis en place un Observatoire Spatial Régional 'OSR' : un dispositif d'observation couplant mesures de terrain et télédétection dans le sud-ouest de la France. L'OSR se base sur des acquisitions mensuelles de données satellitaires à résolution décamétrique depuis 2002 et sur des sites expérimentaux lourdement instrumentés (mesures en continu de flux d'eau et de carbone) à partir de 2004. Ce dispositif a été reconnu service d'observation par l'INSU/CNRS en 2007 et site KALIDEOS par le CNES fin 2009 : 'KALIDEOS OSR MiPy'. Le site atelier correspond à une emprise d'image SPOT, soit environ 50x50 km et couvre une grande diversité de milieux (pédologie, topographie), d'occupation et d'utilisation des sols, de pratiques et de modalités de gestion (agricole, forestière...) et de conditions climatiques (fort gradient de déficits hydriques estivaux). Pour la télédétection, ce site a servi la préparation de SMOS, et il soutient maintenant en priorité à la préparation des missions VENμS et Sentinel-2. Les aspects radar, imagerie thermique et les approches multi-capteurs se développent depuis peu. Le traitement du signal, la physique de la mesure et l'amélioration de la qualité des données constituent le premier axe de recherche. Au niveau thématique, le CESBIO a pour priorité les suivis et les modélisations des agrosystèmes de grandes cultures. L'implication récente d'autres partenaires scientifiques ou gestionnaires a permis d'initier des travaux sur d'autres aspects, comme la biodiversité, l'aménagement du territoire, le suivi de l'extension urbaine, les risques environnementaux, la santé des forêts, l'enfrichement, la diversité et la productivité des prairies. La valorisation des 10 années d'archives 2002-2011 débute et semble très pertinente pour la caractérisation en haute et en basse résolution des conséquences d'années climatiques atypiques (2003, 2011) sur les éco-agro-systèmes. L'extrapolation des résultats obtenus sur ce site atelier à toute la région Midi-Pyrénées ou à la chaine des Pyrénées est aussi initiée

Arqueo 2.0. Renovación metodológica de la prospección arqueológica de zonas de montaña

Hicieron falta varias décadas de interacción y diálogo entre las ciencias humanas y las ciencias medioambientales, además de una serie de avances metodológicos, para que los espacios de altura fueran considerados algo más que inmutables y átonos. Sometidos a un cuestionamiento interdisciplinario integrado, revelan no una, sino muchas historias y dinámicas complejas. Historizar las zonas de montaña permite invertir el punto de vista global sobre las sociedades pirenaicas y reconsiderar los sistemas de valles. Es evidente que los equipos que participan en esta línea de investigación se enfrentan al problema de adquirir información primaria. Siendo requisito indispensable de toda investigación arqueológica, la prospección en alta montaña presenta especificidades propias y desafíos metodológicos particulares. Ahora es posible pensar en nuevos procedimientos de adquisición de información arqueológica basados en cinco grandes avances: la diversificación y miniaturización de los sensores, que permiten transportarlos en drones; la reducción significativa de los costes de adquisición de los vehículos aéreos; un entorno de vuelo cada vez más seguro; las herramientas de tratamiento de datos y el trabajo sobre la ergonomía informática, y la apertura de posibilidades que ofrece la inteligencia artificial, que permite prever posibilidades futuras para optimizar la detección de restos arqueológicos en entornos de gran altitud

Determination of the crop row orientations from Formosat-2 multi-temporal and panchromatic images

This paper presents a technique developed for the retrieval of the orientation of crop rows, over anthropic lands dedicated to agriculture in order to further improve estimate of crop production and soil erosion management. Five crop types are considered: wheat, barley, rapeseed, sunflower, corn and hemp. The study is part of the multi-sensor crop-monitoring experiment, conducted in 2010 throughout the agricultural season (MCM'10) over an area located in southwestern France, near Toulouse. The proposed methodology is based on the use of satellite images acquired by Formosat-2, at high spatial resolution in panchromatic and multispectral modes (with spatial resolution of 2 and 8 m, respectively). Orientations are derived and evaluated for each image and for each plot, using directional spatial filters (45 and 135 ) and mathematical morphology algorithms. ''Single-date'' and ''multi-temporal'' approaches are considered. The single-date analyses confirm the good performances of the proposed method, but emphasize the limitation of the approach for estimating the crop row orientation over the whole landscape with only one date. The multi-date analyses allow (1) determining the most suitable agricultural period for the detection of the row orientations, and (2) extending the estimation to the entire footprint of the study area. For the winter crops (wheat, barley and rapeseed), best results are obtained with images acquired just after harvest, when surfaces are covered by stubbles or during the period of deep tillage (0.27 > R2 > 0.99 and 7.15 > RMSE > 43.02 ). For the summer crops (sunflower, corn and hemp), results are strongly crop and date dependents (0 > R2 > 0.96, 10.22 > RMSE > 80 ), with a well-marked impact of flowering, irrigation equipment and/or maximum crop development. Last, the extent of the method to the whole studied zone allows mapping 90% of the crop row orientations (more than 45,000 ha) with an error inferior to 40 , associated to a confidence index ranging from 1 to 5 for each agricultural plot

Overview Of A Decade Of Yearly Land Cover Classifications Derived From Multi-Temporal Optical Satellite Images

International audienceThis article presents a monitoring of land cover/use by satellite images over an 11-year period (2006-2016), over a study site located in southwestern France near Toulouse. Time series of optical data are acquired by Spot and Landsat, which deliver images in multispectral mode with high spatial resolution (10-30 m). The detection of the different types of land cover/use (crops, grasslands, water, urban and wood) is produced every year. It is based on national reference geographical data and a random forest algorithm. The classifications are characterized by a high level of performance, with an average kappa of 0.83 (OA=0.85). The performance by land cover/use type is related to their representativeness, dates and number of acquisitions, and the resolution of satellite images. The results allow analyzing the evolution of the three main crops (wheat, sunflower and corn)

Towards an Improved Inventory of N2O Emissions Using Land Cover Maps Derived from Optical Remote Sensing Images

International audienceAgricultural soils are the primary anthropogenic source of N2O emissions, one of the most important greenhouse gases, because of the use of nitrogen (N) fertilizers. The proposed method provides access to an inventory of potential N2O emissions (the term potential refers to possible but not yet actual) at a fine scale, with an annual update, without a heavy deployment linked to a collection of field measurements. The processing chain is applied to optical satellite images regularly acquired at a high spatial resolution during the 2006–2015 period, allowing a better spatial and temporal resolution of the estimates of potential N2O emissions from crops. The yearly potential N2O emissions inventory is estimated over a study site located in southwestern France, considering seven main seasonal crops (i.e., wheat, barley, rapeseed, corn, sunflower, sorghum and soybean). The first step of the study, that is the land use classification, is associated with accurate performances, with an overall accuracy superior to 0.81. Over the study area, the yearly potential budget of N2O emissions ranges from 97 to 113 tons, with an estimated relative error of less than 5.5%. Wheat, the main cultivated crop, is associated with the maximum cumulative emissions regardless of the considered year (with at least 48% of annual emissions), while maize, the third crop regarding to the allocated area (grown on less than 8% of the study site), has the second highest cumulative emissions. Finally, the analysis of a 10-year map of the potential N2O budget shows that the mainly observed crop rotation (i.e., alternating of wheat and sunflower) reaches potential emissions close to 16 kg N2O emitted per hectare, while the monoculture maize is associated with the maximum value (close to 28.9 kg per hectare)

Improved Early Crop Type Identification By Joint Use of High Temporal Resolution SAR And Optical Image Time Series

High temporal and spatial resolution optical image time series have been proven efficient for crop type mapping at the end of the agricultural season. However, due to cloud cover and image availability, crop identification earlier in the season is difficult. The recent availability of high temporal and spatial resolution SAR image time series, opens the possibility of improving early crop type mapping. This paper studies the impact of such SAR image time series when used in complement of optical imagery. The pertinent SAR image features, the optimal working resolution, the effect of speckle filtering and the use of temporal gap-filling of the optical image time series are assessed. SAR image time series as those provided by the Sentinel-1 satellites allow significant improvements in terms of land cover classification, both in terms of accuracy at the end of the season and for early crop identification. Haralik textures (Entropy, Inertia), the polarization ratio and the local mean together with the VV imagery were found to be the most pertinent features. Working at at 10 m resolution and using speckle filtering yield better results than other configurations. Finally it was shown that the use of SAR imagery allows to use optical data without gap-filling yielding results which are equivalent to the use of gap-filling in the case of perfect cloud screening, and better results in the case of cloud screening errors

In-Season Mapping of Irrigated Crops Using Landsat 8 and Sentinel-1 Time Series

Numerous studies have reported the use of multi-spectral and multi-temporal remote sensing images to map irrigated crops. Such maps are useful for water management. The recent availability of optical and radar image time series such as the Sentinel data offers new opportunities to map land cover with high spatial and temporal resolutions. Early identification of irrigated crops is of major importance for irrigation scheduling, but the cloud coverage might significantly reduce the number of available optical images, making crop identification difficult. SAR image time series such as those provided by Sentinel-1 offer the possibility of improving early crop mapping. This paper studies the impact of the Sentinel-1 images when used jointly with optical imagery (Landsat8) and a digital elevation model of the Shuttle Radar Topography Mission (SRTM). The study site is located in a temperate zone (southwest France) with irrigated maize crops. The classifier used is the Random Forest. The combined use of the different data (radar, optical, and SRTM) improves the early classifications of the irrigated crops (k = 0.89) compared to classifications obtained using each type of data separately (k = 0.84). The use of the DEM is significant for the early stages but becomes useless once crops have reached their full development. In conclusion, compared to a “full optical” approach, the “combined” method is more robust over time as radar images permit cloudy conditions to be overcome

Identification and characterization of agro-ecological infrastructures by remote sensing

National audienceAgro-Ecological Infrastructures (AEIs) include many semi-natural habitats (hedgerows, grass strips, grasslands, thickets…) and play a key role in biodiversity preservation, water quality and erosion control. Indirect biodiversity indicators based on AEISs are used in many national and European public policies to analyze ecological processes. The identification of these landscape features is difficult and expensive and limits their use. Remote sensing has a great potential to solve this problem. In this study, we propose an operational tool for the identification and characterization of AEISs. The method is based on segmentation, contextual classification and fusion of temporal classifications. Experiments were carried out on various temporal and spatial resolution satellite data (20-m, 10-m, 5-m, 2.5-m, 50-cm), on three French regions southwest landscape (hilly, plain, wooded, cultivated), north (open-field) and Brittany (farmland closed by hedges). The results give a good idea of the potential of remote sensing image processing methods to map fine agro-ecological objects. At 20-m spatial resolution, only larger hedgerows and riparian forests are apparent. Classification results show that 10-m resolution is well suited for agricultural and AEIs applications, most hedges, forest edges, thickets can be detected. Results highlight the multi-temporal data importance. The future Sentinel satellites with a very high temporal resolution and a 10-m spatial resolution should be an answer to AEIs detection. 2.50-m resolution is more precise with more details. But treatments are more complicated. At 50-cm resolution, accuracy level of details is even higher; this amplifies the difficulties previously reported. The results obtained allow calculation of statistics and metrics describing landscape structures