This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown at six dates staggered over the crop season. We monitored ten varieties of wheat, grown in trial micro-plots in the South-West of France. For each date, we acquired multiple views in four spectral bands corresponding to blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, both generic and date-specific. Therefore, we established a robust and stable generic relationship between, in one hand, leaf area index and NDVI and, in the other hand, nitrogen uptake and GNDVI. Due to a high amount of noise in the data, it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships.

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In tropical rainforests, the sustainability of selective logging is closely linked to the extent of collateral stand damage. The capacity to measure the extent of such damage is essential for calculating carbon emissions due to forest degradation under the Reducing Emissions from Deforestation and Forest Degradation (REDD+) process. The use of remote sensing to detect canopy gaps in tropical rainforests is an attractive alternative to ground surveys, which are laborious and imprecise. In French Guiana, the detection of logging-related gaps using very high spatial resolution optical satellite images produced by the Système Pour l'Observation de la Terre (SPOT) 5 sensor is carried out by Office National des Forêts (ONF) (French National Forestry Agency). Gaps are detected using a segmentation method based on computer-assisted photointerpretation. Detection has been automated to improve and accelerate the process. We developed an automatic method, which involves estimating segmentation thresholds using a statistical approach. The principle of the method presented in this article is to model the forest's spectral signature by using a Gaussian distribution and calculate a divergence between that theoretical signature and the image histogram in order to detect gaps that constitute a reduction of forest cover. The segmentation threshold between gap and forest is thus no longer defined in the original radiometric area but as a discrepancy between theoretical distribution and histogram. Computing the divergence to define the threshold made it possible to efficiently automate the detection of all gaps and skid trails with a surface area greater than 100 m2. The proportion of misclassified points measured during field surveys is 12%, which is a high level of precision. The proportion of misclassified points obtained is 12%. This tool could be used to assess the quality of logging operations or biomass loss in other areas where the forest is undergoing deterioration while still remaining predominant in the landscape. (Résumé d'auteur

International audienceSynthetic Aperture Radar (SAR) is the most widely used sensor for ship detection from space but optical sensors are increasingly used in addition of these. The combined use of these sensors in an operational framework becomes a major stake of the efficiency of the current systems. It becomes also a source of the increased complexity of these systems. Optical and SAR signals of a maritime scene have many similarities. These similarities allow us to define a common detection approach presented in this paper. Beyond the definition of a single algorithm for both types of data, this study aims to define an algorithm for the detection of vessels of any size in any resolution images. After studying the signatures of vessels, this second goal leads us to define a detection strategy based on multi-scale processes. It has been implemented in a processing chain into two major steps: first targets that are potentially vessels are identified using a Discrete Wavelet Transform (DWT) and Constant False Alarm Rate (CFAR) detector. Second among these targets, false alarms are rejected using a multi-scale reasoning on the contours of the targets. The definition of this processing chain is made with respect to three constraints: the detection rate should be 100%, the false alarm rate should be as low as possible and finally the processing time must be compatible with operations at sea. The method was developed and tested on the basis of a very large data set containing real images and associated detections. The obtained results validate this approach but with limitations mainly related to the sea state

In the context of improving the responsiveness of a maritime surveillance service, we propose a ship detection algorithm in optical satellite imagery. The major objective is the detection of vessels of any sizes in any resolution images. Signatures of ships and false alarm sources are analyzed and the state of the art studied. The wavelet transform is used for extracting at different scales details corresponding to vessels. At each scale, details coefficients are processed to provide an improved signal to noise ratio data that can be segmented by a conventional adaptive thresholding method. Contours from these segmentations are assembled in trees which are then processed in the discrimination step. From one scale to the other changes in the contour of a same target are analyzed to determine if it corresponds to a vessel. The detection capabilities at the base scale are improved, and application to other scales allows the detection of any size of vessel.