MODELISATION 3D DE ROUTES PAR TELEMETRIE LASER EMBARQUEE POUR LA MESURE DE DISTANCE DE VISIBILITE

National audienceLa plateforme LARA-3D, développée au Centre de Robotique de Mines Paris, est un système de télémétrie laser embarquée sur véhicule permettant de numériser des environnements routiers ou urbains à la vitesse du déplacement, et d'obtenir ainsi des nuages de points 3D denses. Nous présentons l'utilisation de ce système à des fins de mesure de distances de visibilité sur des routes, effectuée dans le cadre du projet SARI/VIZIR, en partenariat avec le LRPC de Strasbourg

Towards a multi-scale approach for an Earth observation-based assessment of natural resource exploitation in conflict regions

The exploitation of resources, if not properly managed, can lead to spoiling natural habitats as well as to threatening people’s health, livelihoods and security. The paper discusses a multi-scale Earth observation-based approach to provide independent information related to exploitation activities of natural resources for countries which are experiencing armed conflict. The analyses are based on medium to very high spatial resolution optical satellite data. Object-based image analysis is used for information extraction at these different scales. On a subnational level, conflict-related land cover changes as an indication of potential hot spots for exploitation activities are classified. The regional assessment provides information about potential activity areas of resource exploitation, whereas on a local scale, a site-specific assessment of exploitation areas is performed. The study demonstrates the potential of remote sensing for supporting the monitoring and documentation of natural resource exploitation in conflict regions

VHR imagery to quantify crop response to fertilizer and develop business services for smallholders

Food needs arising from the demographic explosion of sub-Saharan Africa can only be met through agricultural intensification. Smallholder systems feature enormous yield gaps, which may be reduced through ISFM and other sustainable intensification practices. However, today’s huge variability in farming practices and returns on investments is likely to exacerbate in the future. Monitoring changes in productivity across scales is a significant challenge in heterogeneous systems, where overall low SOM and nutrient deficiencies prevail. Fortunately, remote sensing can help monitor crop performance at levels of granularity increasingly compatible with smallholder farming. This opens support applications for precision agriculture, allowing the exploitation – rather than the mitigation – of spatial heterogeneity, and the demonstration that enhanced productivity and livelihoods are possible in complex cropping systems

A Global Human Settlement Layer from optical high resolution imagery - Concept and first results

A general framework for processing of high and very-high resolution imagery for creating a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 millions of square kilometres of the Earth surface spread over four continents, corresponding to an estimated population of 1.3 billion of people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS-2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye-1, QuickBird-2, Ikonos-2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, by band, by resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.JRC.G.2-Global security and crisis managemen

Crop monitoring by advanced SAR remote sensing techniques : modelling and experimental analysis

Satellite remote sensing is a key source of frequent information for agricultural production forecasting. Only Synthetic Aperture Radar (SAR) sensor can provide systematic observation thanks to the microwave insensitivity to the cloud cover. This research investigates actual information requirements for agricultural monitoring, current information sources and advanced SAR potentialities. Performances of advanced SAR techniques to crop type identification and crop growth estimation in operational conditions were studied through experimental and modelling research. Operational complementarities between SAR and optical high resolution images for crop type discrimination are first demonstrated leading to actual use by the Belgian Ministry of Agriculture. The efficiency concept is introduced as an alternative indicator of classification performance in the context of crop area control. Simulated ENVISAT Wide Swath time series was also found promising for crop identification at regional scale. Crop growth is demonstrated to be best monitored by 1-day interferometric coherence. Indeed canopy cover and plant height were found very highly correlated to signal coherence for 4 different crops and weakly influenced by soil water content. In order to further understand backscattered signal from crops a unique and very dedicated field data set was collected for maize and wheat. A new method of soil roughness measurement based on photogrammetric and geostatistical techniques also allowed describing the agricultural soil anisotropy by a bi-dimensional roughness characterisation. Two fully polarimetric radiative transfer models are validated using SAR observations acquired in various configurations (i.e. polarisations and incidence angles) simultaneously with the field measurements. Based on these validated simulations polarimetric indices correlated to maize leaf area index and biomass are developed to dissociate the vegetation backscattering from the soil water content contribution.Doctorat en sciences agronomiques et ingénierie biologique (AGRO 3) -- UCL, 200

Retrieving crop parameters based on tandem ERS 1/2 interferometric coherence images

One-day interval coherence images derived from pairs of ERS SAR tandem acquisitions are suitable for crop monitoring. Coherence images were analyzed and compared to field measurements of four crops, i.e., winter wheat, sugar beet, potato and maize, taken during the satellite overpass. First, the sensitivity of the coherence to the plant height and the canopy cover was statistically investigated. Regression analyses were computed and the coefficients of determination (R-2) ranged from 0.64 to 0.92. The shape of these relationships varied according to the geometric factors which are crop-type dependent. A prediction model of the wheat height was calculated and was able to estimate the plant height with a mean absolute error of approximately 7 cm. While this high performance obtained on the average matched the observed range of the field height for a given date, it was not sufficient for monitoring at the field level. However, such a performance level may meet the information requirements for an operational crop monitoring system at the regional level, which includes a much larger diversity of growing conditions. Moreover, the soil roughness change associated with the sowing practices that occurred between the two tandem acquisitions strongly decreased the coherence signal. This dataset also indicated that variation in the soil moisture influenced the backscattering coefficient more than it influenced the coherence signal. This result enhanced the InSAR coherence potentialities to estimate the crop parameters during the growing season. (C) 2003 Elsevier Inc. All rights reserved

Characterizing Bidimensional Roughness of Agricultural Soil Surfaces for SAR Modeling

In the description of agricultural soil roughness, the hypothesis of surface isotropy is currently admitted, and linear measurements are often used to characterize the soil roughness considered as a single-scale process. However, multiscale roughness is frequently observed, and tillage practices created oriented roughness. This paper presents a new technique to measure precisely the bidimensional soil roughness. Digital elevation model derived using photogrammetric technique reproduces the millimeter-scale height variations of three different soil surfaces (ploughed, smoothed, and row structured field) over about 8 m(2). A single surface measurement is sufficient to accurately measure the soil roughness parameters. Geostatistic parameterization allows the measurement of the roughness anisotropy. For smooth surface, a two-scale roughness is observed. Anisotropy is observed in the larger scale roughness. The proposed method allows the computation of the bidimensional correlation function, which is required by the integral equation method model for the simulation of the SAR signal over anisotropic soil surfaces

Efficiency of crop identification based on optical and SAR image time series

This paper assessed the use of optical and SAR imagery for crop identification in an operational context with a particular emphasis on actual crop diversity and information delivery time. Fifteen ERS and Radarsat and 3 optical images were used to discriminate agricultural crop types based on dedicated per-parcel classification and photo interpretation schemes. For crop area control, the efficiency concept was introduced as a complementary indicator of classification performance. A set of 6571 parcels were classified into 39 crop types from various combinations of images. The efficiency computed from an independent set of 899 parcels peaked based on a combination of optical images and 3 to 5 SAR images. Moreover, the delivery time of the relevant information was improved when SAR data was included. The hierarchical classification strategy based on nested classifications also improved the operational crop control system for all image combinations. Finally, this research documented the respective contributions of optical and SAR time series for any control system of agricultural land. (c) 2005 Elsevier Inc. All rights reserved