L'Attentat d'Orsini, étude de psycho-pathologie historique ...

Thèse de médecine. Paris. 1934. N° 776

Winter Wheat Seedtime Monitoring through Satellite Remote Sensing Data

Part 1: Simulation, Optimization, Monitoring and Control TechnologyInternational audienceWinter wheat seedtime is important for wheat growth. It affects the wheat yield and quality. The objective of this study is monitoring the winter wheat seedtime through remote sensing imagery. Two HJ-1B images and one Landsat5 TM image were used in this study. Three Vegetation Indices, DVI, SAVI and RDVI were calculated. The correlation about the wheat seedtime and VIs were analyzed. The result indicated that wheat growth was negatively correlated with seedtime. Wheat VIs during 40-60 days after sowing is best for seedtime monitoring. The correlation coefficient for DVI of November 22th HJ-1B and seedtime reached -0.51. The seedtime for whole wheat plant field of Beijing area was inverted through the model. The results show that area for seeded before Sept 30th was about 12.7 thousands ha, 31. 8 thousands ha between Oct 1th and Oct 8th, and 16.2 thousands ha seeded after Oct 8th during 2009-2010 year

Temporal and Spatial Relationships between within-field Yield variability in Cotton and High-Spatial Hyperspectral Remote Sensing Imagery

Traditional remote sensing methods for yield estimation rely on broadband vegetation indices, such as the Normalized Difference Vegetation Index, NDVI. Despite demonstrated relationships between such traditional indices and yield, NDVI saturates at larger leaf area index (LAI) values, and it is affected by soil background. We present results obtained with several new narrow-band hyperspectral indices calculated from the Airborne Visible and Near Infrared (AVNIR) hyperspectral sensor flown over a cotton (Gossypium hirsutum L.) field in California (USA) collected over an entire growing season at 1-m spatial resolution. Within-field variability of yield monitor spatial data collected during harvest was correlated with hyperspectral indices related to crop growth and canopy structure, chlorophyll concentration, and water content. The time-series of indices calculated from the imagery were assessed to understand within-field yield variability in cotton at different growth stages. A K means clustering method was used to perform field segmentation on hyperspectral indices in classes of low, medium, and high yield, and confusion matrices were used to calculate the kappa ({kappa}) coefficient and overall accuracy. Structural indices related to LAI [Renormalized Difference Vegetation Index (RDVI), Modified Triangular Vegetation Index (MTVI), and Optimized Soil-Adjusted Vegetation Index (OSAVI)] obtained the best relationships with yield and field segmentation at early growth stages. Hyperspectral indices related to crop physiological status [Modified Chlorophyll Absorption Index (MCARI) and Transformed Chlorophyll Absorption Index (TCARI)] were superior at later growth stages, close to harvest. From confusion matrices and class analyses, the overall accuracy (and kappa) of RDVI at early stages was 61% ({kappa} = 0.39), dropping to 39% ({kappa} = 0.08) before harvest. The MCARI chlorophyll index remained sensitive to within-field yield variability at late preharvest stage, obtaining overall accuracy of 51% ({kappa} = 0.22).The authors gratefully acknowledge the NASA/USDA jointly funded AG20/20 project for funding image acquisitions.Peer reviewe