Rémi Lecerf

Preprocessing of Low-Resolution Time Series Contaminated by Clouds and Shadows

Monitoring changes in the vegetation cover during the intercrop season is of special interest in intensive agricultural region, such as the Brittany region in France, to locate bare soils and control their influence to the environment. The presence of bare soils leads to detrimental environmental effects such as soil erosion or water quality degradation. Therefore, identification and monitoring of bare soils at a regional scale in the winter season are required for any agricultural management program. Data from the Moderate Resolution Imaging Spectroradiometer have been selected for this paper due to their low spatial resolution, which decreases the cost of processing and storage, and the high revisit frequency, which increases the probability to acquire scenes free of clouds and shadows during the winter season. Unfortunately, few images per season only are free of cloud contamination and associated shadows. Therefore, the specific objective of this paper is to develop and implement a preprocessing method to recover spectral values of contaminated data by weather conditions for subsequent bare-soil mapping. In the context of this paper, Kohonens self-organizing map (SOM) is used for recovering data contaminated by weather conditions that have been considered as erroneous data. This nonparametric regression procedure has proved its success to deal with missing-values problem. Hence, the erroneous-values problem, reflectance values contaminated by clouds or shadows, has been converted to the missing-values problem by using a cloud and shadow (outlier) detector. The SOM algorithm was tested also on the erroneous data directly, but better results were found with the ldquomissing valuesrdquo formulation. The idea is to, first, train SOM onto clear temporal profiles free of clouds and shadows during the winter season. Second, erroneous values are converted to missing values by an outlier detector which operates on each temporal profile (set of colocated pixels acquired at different dates). Finally, the SOM algorithm for missing values is used to estimate contaminated reflectance values.

Monitoring land use and land cover changes in oceanic and fragmented landscapes with reconstructed MODIS time series

Image time series from medium resolution sensors such as NASA EOS/MODIS are frequently used to monitor vegetation phenology at regional and global scales. Facing the limitations of high resolution sensors, that is small coverage areas and low revisit frequencies, data from medium resolution sensors are now assessed to monitor subtle vegetation changes at meso or large scales, even in fragmented landscapes. However, monitoring of subtle changes is difficult to perform with such data without important pre-processing steps. Previous studies showed that time series extracted from original images are often corrupted and hence not exploitable, due to atmospheric and geometric distortions and others artifacts (angle variations, clouds, aerosols for example). In this paper we present an approach to reconstruct high accurate NASA EOS/MODIS time series. Firstly, we propose a method to correct images from atmospheric and geometric distortions. The comparison between different pre-processed NDVI MODIS images and SPOT HRVIR high resolution data points out significant differences, highlighting the necessity of properly pre-processing time serie data. Moreover, on the basis of these first results obtained in using pre-processed series of MODIS images through the smoothing technique developed here to recover the winter vegetation phenology, it is now possible to undertake the identification of subtle changes on land surfaces.

Environmental determinants of the urinary concentrations of herbicides during pregnancy: The PELAGIE mother–child cohort (France)

Herbicides are generally the most extensively used of the pesticides applied to agricultural crops. However, the literature contains little evidence useful in assessing the potential sources of the general populations exposure to herbicides, including by residential proximity to crops. The objective of this study was to take advantage of data from the PELAGIE mother-child cohort to identify the main determinants of the body burden of exposure to the chloroacetanilide and triazine herbicides commonly used on corn crops in Brittany, France, before 2006. Urine samples from a randomly selected subcohort of women in the first trimester of pregnancy (n=579) were assayed for herbicide metabolites. The residential exposure resulting from proximity to corn crops was assessed with satellite-image-based scores combined with meteorological data. Data on diet, drinking tap water (from the public water supply), occupations, and household herbicide use were collected by questionnaires. Herbicides were quantified in 5.3% to 39.7% of urine samples. Alachlor and acetochlor were found most frequently in the urine of women living in rural areas. The presence of dealkylated triazine metabolites in urine samples was positively associated with residential proximity to corn crops (OR=1.38, 95% CI: 1.05-1.80). Urinary metabolites of both atrazine and dealkylated triazine were correlated with tap water consumption (OR=2.94, 1.09-7.90, and OR=1.82, 1.10-3.03, respectively); hydroxylated triazine metabolites were correlated with fish intake (OR=1.48, 1.09-1.99). This study reinforces previous results that suggest that environmental contamination resulting from agricultural activities may contribute to the general populations exposure to herbicides.

Comparison of Multitemporal MODIS-EVI Smoothing Algorithms and its Contribution to Crop Monitoring

Time series of MODIS vegetation indices are widely used to map vegetation. However, some noise can affect the temporal profiles. Thus, many techniques have been developed to smooth them. Four algorithms are applied on crop pixels in the Brazilian Amazonian State of Mato Grosso. Comparisons led to the selection of the Weighted Least Squares (WLS) algorithm and the Savitzky-Golay (SG) filter. Those techniques were computed on MODIS data in order to detect six crop classes. Tests of separability show that the smoothed data improved the potential of separability at each MODIS sub-period. Moreover, supervised classifications were then realized. The WLS data refined efficiently the classification result when using C4.5 decision tree. When using the Maximum Likelihood and Spectral Angle Mapper classifiers, the smoothed data did not improve the classification results as compared with those obtained through original MODIS data. However, it required fewer input MODIS images to reach good results. The SG filter led to better results than the WLS algorithm when using those classifiers.

Estimating Biophysical Variables at 250 M with Reconstructed EOS/MODIS Time Series to Monitor Fragmented Landscapes

The objective of this study is to identify changes in the vegetation cover in estimating crop fraction cover in the Brittany region over the 2000-2006 period. Biophysical products derived from current coarse and medium resolution sensors allow the detection of changes over landscapes made of large patches. In fragmented landscapes, the 1 km spatial resolution is not suited to identify changes in vegetation cover due to mixing effects. Higher spatial resolution is thus required to detect vegetation changes. We present an original method to estimate biophysical variables and particularly fCOVER at 250 m spatial resolution from EOS/MODIS-AM1 (Terra) sensor in using the combined PROSPECT-SAIL model The results obtained from this study highlight that MODIS fCOVER data at 250 m spatial resolution estimated with the PROSPECT+ SAIL model is related to field observations of green vegetation cover fraction.

Green and blue belt networks: Mapping connectivity areas at a regional scale using medium and high spatial resolution satellite images

The distribution of ecosystem sites, their connectivity and their evolution are key links between ecology and society, as these landscape patterns determine the regional sustainability for biodiversity and are subject to human interventions. While little attention has been paid to procedures for mapping connectivity among numerous “natural” landscape elements over large areas, the principal objective of this study was to map ecological corridors at a regional scale using remotely sensed data. To achieve this goal, we developed an approach including three stages: first, permanent “natural” landscape elements were identified from Landsat TM images; second, permanent grassland connected to these elements was extracted from a multitemporal series of MODIS images and aggregated to them; third, connectivity was depicted by least-cost modeling. The regional connectivity map can be analyzed at a regional scale to highlight large corridors, but also at a local scale to determine areas for conservation intervention.

MONITORAMENTO DE SECAS NA BRETANHA reconstituição histórica e abordagem por teledetecção

Brittany, as many French territories, experiences sometimes drought issues which can vary in intensity and duration. The aim of this study is to determine the impacts of droughts on soil water resources since the early XIXth century. Thus, a soil water balance suited to regional scales was used and applied to different cities of Brittany and surroundings such as Rennes, Plougonvelin and Nantes. The evaporation deÞ ciency obtained at a monthly scale revealed droughts intensity and inter-annual variability. At a yearly scale a positive tendency of the deÞ ciency was noticed. At a monthly scale the inter-annual variability was clearly shown and revealed a 4-year period rhythm with soils lacking of water for one summer month. Finally, we used the NDVI calculated from SPOT-Vegetation images for monitoring the spatial extent of drought in Brittany. For 2003 (the last great drought observed in western France), we found a good relationship between the NDVI and the evapotranspiration but bidirectional res ectance effects, angular values and compositing’s procedures may also have a great impact on observed values of NDVI.

Self-Organizing Map for surface characterization in time series

Monitoring changes in the vegetation cover during the intercrop season is of a special interest in intensive agricultural regions. The presence of bare soils leads to detrimental environmental effects such as soil erosion or water quality degradation. To identify and monitor winter land cover at a regional scale in the Brittany region in France, data from the moderate resolution imaging spectroradiometer (MODIS) low resolution time series were used. This study examines the self-organizing map (SOM) for classifying winter land cover with MODIS time series in agricultural and fragmented landscapes. Field data and high resolution images have been used to generate two sets of pixels for training and validation. A comparison between the suggested method and NDVI thresholding is presented.