Developing a method to map coconut agrosystems from high-resolution satellite images

https://icaci.org/files/documents/ICC_proceedings/ICC2015/papers/38/fullpaper/T38-504_1427765394.pdfInternational audienceOur study aims at developing a generalizable method to exploit high resolution satellite images(VHR) for mapping coconut-based agrosystems, differentiating them from oil palm agrosystems.We compared two methods of land use classification. The first one is similar to that described byTeina (2009), based on spectral analysis and watershed segmentation, which we simplified byusing the NDVI vegetation index. The second one is the semi-automatic classification based ontexture analysis (PAPRI method of Borne, 1990). These methods were tested in two differentenvironments: Vanua Lava (Vanuatu; heterogeneous landscape, very ancient plantations) andIvory Coast (Marc Delorme Research Station, monoculture, regular spacing, oil palm plantations);and their results were evaluated against manually digitized photo-interpretation maps.In both situations, the PAPRI method produced better results than that of Teina (global kappa of0.60 vs. 0.40). Spectral signatures do not allow a sufficiently accurate mapping of coconut and donot differentiate it from oil palm, despite their different NDVI signatures. The PAPRI methoddifferentiates productive coconut from mixed plantations and other vegetation, either high or low(70% accuracy). In both situations, Teina’s method allows counting 65% of the coconut treeswhen they are well spaced. To increase the method accuracy, we suggest (1) field surveys (forsmall scale studies) and/or finer image resolution, allowing a high precision in manual mappingwith a better discrimination between coconut and oil palm, thus limiting the proportion of mixedpixels. (2) A phenological monitoring could improve the distinction between coconut and oil palmagrosystems. (3) Hyper-spectral images should allow extracting more precisely the respectivesignatures of both species. Another possibility would be (4) an object-oriented analysis asproposed by the eCognition software. Finally, (5) coupling the Lidar system with watershedanalysis would allow a better characterization of coconut varietal types

Using multi-temporal satellite images to evaluate the changes of vegetation index of land cover in Thai Binh Province

224pThis chapter describes the current status of Thai Binh province in Vietnam and its agricultural development plans for 2010. The environmental and economic impacts of pig production are discussed. The various stakeholders and their active involvement in agricultural production are analysed. In addition, an innovative approach to sustainable development of animal produce commodity chains in northern Vietnam, is described. The 12-month E3P Project (Environmental Protection and Pig Production) was aimed to establish baseline work for designing and implementing a geographical information system. A large proportion of unknown factors concerning the issue of effluents in the province was studied at the farm, communal and district, and on a scientific levels. These unknown factors justify the regional diagnosis presented by the E3P Project

Salvage use of allogeneic hematopoietic stem cell transplantation after reduced intensity conditioning from unrelated donors in multiple myeloma. A study by the Plasma Cell Disorders subcommittee of the European Group for Blood and Marrow Transplant Chronic Malignancies Working Party

Non peer reviewe

Methodes numeriques de reconnaissance de paysages, application a la region du lac Alaotra, Madagascar

INIST T 74596 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

Description et segmentation de motifs d'organisation spatiale. Application à l'obtention d'esquisses paysagères

National audienc

Deep Mangoes: from fruit detection to cultivar identification in colour images of mango trees

International audienceThis paper presents results on the detection and identification mango fruits from colour images of trees. We evaluate the behaviour and the performances of the Faster R-CNN network to determine whether it is robust enough to "detect and classify" fruits under particularly heterogeneous conditions in terms of plant cultivars, plantation scheme, and visual information acquisition contexts. The network is trained to distinguish the 'Kent', 'Keitt', and 'Boucodiekhal' mango cultivars from 3,000 representative labelled fruit annotations. The validation set composed of about 7,000 annotations was then tested with a confidence threshold of 0.7 and a Non-Maximal-Suppression threshold of 0.25. With a F1-score of 0.90, the Faster R-CNN is well suitable to the simple fruit detection in tiles of 500x500 pixels. We then combine a multi-tiling approach with a Jaccard matrix to merge the different parts of objects detected several times, and thus report the detections made at the tile scale to the native 6,000x4,000 pixel size images. Nonetheless with a F1-score of 0.56, the cultivar identification Faster R-CNN network presents some limitations for simultaneously detecting the mango fruits and identifying their respective cultivars. Despite the proven errors in fruit detection, the cultivar identification rates of the detected mango fruits are in the order of 80%. The ideal solution could combine a Mask R-CNN for the image pre-segmentation of trees and a double-stream Faster R-CNN for detecting the mango fruits and identifying their respective cultivar to provide predictions more relevant to users' expectations

Myopericarditis revealing adult-onset Still's disease

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

La visualisation des paysages pour l'aménagement agroforestier

23 ref.National audienc

Couplage de données radar SIR-C à un modèle architectural de croissance des arbres : évaluation de paramètres forestiers sur la maquette et simulation 3D d'un paysage

Spaceborne SAR remote sensing and tree architectural growth model are new tools under development and validation, towards the retrieval and mapping of forest parameters through a GIS. On one hand, L-band SAR data (SIR-C) is useful for the retrieval of some forest parameters such as age and woody biomass; on the other hand, tree growth model allow to retrieve many forest parameters at the tree level, and to simulate a 3D view by image synthesis both at the tree and landscape levels. The coupling through a GIS of these tools is to drive the growth model by parameters obtained using SAR data or other sources. A methodology is proposed and illustrated on a simple forest ecosystem, that is a Austrian pine forest over hilly terrain in South France. Preliminary results show the potentialities and interests in using such tools.Les nouveaux outils que sont la télédétection RSO (radar à synthèse d'ouverture) spatiale et les modèles architecturaux de croissance présentent des potentialités et des intérêts complémentaires pour l'obtention d'informations forestières géolocalisées, par l'intermédiaire de leur couplage avec un système d'information à références spatiales (SIRS). Les données RSO bande L (SIR-C) permettent d'accéder à certaines caractéristiques du couvert forestier comme le volume, alors que les modèles architecturaux permettent une bonne estimation de plusieurs paramètres au niveau de l'arbre, en plus d'une simulation visuelle 3D au niveau de l'arbre et du paysage. Le couplage possible des deux outils est le pilotage du modèle architectural par un paramètre estimé par radar, et ce au sein du SIRS, à des fins de spatialisation de l'information forestière extraite. Une méthodologie est proposée et illustrée par des résultats encourageants obtenus sur une forêt de résineux en terrain accidenté, dans le département de la Lozère dans le sud de la France, permettant d'entrevoir les perspectives d'utilisation de ces outils combinés via un SIRS

A First Attempt to Combine NIRS and Plenoptic Cameras for the Assessment of Grasslands Functional Diversity and Species Composition

International audienceGrassland represents more than half of the agricultural land. Numerous metrics (biomass, functional trait, species composition) can be used to describe grassland vegetation and its multiple functions. The measures of these metrics are generally destructive and laborious. Indirect measurements using optical tools are a possible alternative. Some tools have high spatial resolutions (digital camera), and others have high spectral resolutions (Near Infrared Spectrometry NIRS). A plenoptic camera is a multifocal camera that produces clear images at different depths in an image. The objective of this study was to test the interest of combining plenoptic images and NIRS data to characterize different descriptors of two Mediterranean legumes mixtures. On these mixtures, we measured biomass, species biomass, and functional trait diversity. NIRS and plenoptic images were acquired just before the field measurements. The plenoptic images were analyzed using Trainable Weka Segmentation ImageJ to evaluate the percentage of each species in the image. We calculated the average and standard deviation of the different colors (red, green, blue reflectance) in the image. We assessed the percentage of explanation of outputs of the images and NIRS analyses using variance partition and partial least squares. The biomass Trifolium michelianum and Vicia sativa were predicted with more than 50% variability explained. For the other descriptors, the variability explained was lower but nevertheless significant. The percentage variance explained was nevertheless quite low, and further work is required to produce a useable tool, but this work already demonstrates the interest in combining image analysis and NIRS