Contribution à l'amélioration de l'expertise en situation de crise par l'utilisation de l'informatique distribuée : application aux crues à cinétique rapide

Co-encadrement de la thèse : Pierre-Alain AyralFlash floods, as it happened on September, the 8th and 9th 2002 in the Gard territory, can yield to important economic and human damages. After this catastrophic hydrological situation, a reform of flood warning services (SAC) was initiated. Thus, this political reform has transformed the 52 existing flood warning service into 22 flood forecasting service (SPC) which are more hydrologically consistent, and has created a national central service, the SCHAPI. A new effective hydrological forecasting mission has been assigned to these services. In the context of flash floods, the flood forecasting service Grand Delta (SPC-GD) uses the application ALHTAÏR integrating hydrometeorological data and real-time modeling capabilities of mountainous rivers' discharges. However, this service a limited hydrological forecast ability. Hence, this PhD attempts to enhance this forecasting competency using the purpose of the innovative Grid technology to provide on demand computing resources and collaborative environment. The methodology and experimentations have enabled to implement a hydrological spatial decision support system (G-ALHTAÏR) based on the resources of EGEE (Enabling Grids for E-science) grid architecture. Promising results demonstrate a capacity to model an important number of hydrological forecasts scenarios in an operational context and suitable with SPC-GD requirements.Les crues à cinétique rapide, telles que l'évènement du 8 et 9 septembre 2002 dans le département du Gard, causent régulièrement d'importants dommages socio-économiques. A la suite de cette catastrophe, une réforme des services d'annonce des crues (SAC) a été initiée, et a engendré la transformation des 52 services existants en 22 services de prévision des crues (SPC) et la création d'un service central au niveau national, le SCHAPI. Plus cohérent hydrologiquement, ces services se sont vus assigner une nouvelle mission de prévision des crues. Dans le cadre des prévisions des crues à cinétique rapide, le service de prévision des crues " Grand Delta " (SPC-GD) utilise l'application ALHTAÏR capable d'intégrer des données hydrométéorologiques et la modélisation " temps réel " des débits des cours d'eau des bassins versants amont. Cependant, ce service ne dispose pas à l'heure actuelle d'une véritable capacité de prévision des crues à cinétique rapide. Cette thèse s'attache donc à perfectionner cette capacité de prévision en utilisant la vocation de la technologie grille, une technologie informatique innovante, à fournir de ressources informatiques à la demande et un environnement collaboratif. La méthodologie et les expérimentations ont permis de développer un système spatial d'aide à la décision hydrologique (G-ALHTAÏR) basé sur les ressources de l'architecture de grille européenne EGEE (Enabling Grids for E-science). Des résultats prometteurs montrent une capacité à modéliser un nombre important de scénarios de prévision hydrologique dans un contexte opérationnel, et en adéquation avec les besoins du SPC-GD

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National audienc

Theia OSO Land Cover Map 2018

Land cover map of France based on Sentinel-2 satellite images with iota² chain (https://framagit.org/iota2-project/iota2/

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National audienc

Estimating two centuries of forest landscape changes at different spatio-temporal scales: pressures vs. Mitigation

International audienceHistorical perspectives increase our understanding of the dynamic nature of landscapes and provide a framework of reference for assessing patterns and processes. There is a direct relationship between the space and time scales appropriate for observing different aspects of pattern and processes. Interactions between individual plants may be observed on a yearly basis, but the dynamics of forest stands can only be perceived on a scale of tens to hundreds of years. Nevertheless in order to capture changes of landscapes’ “essential characteristics”, we can use past land transition information in tandem with environmental variables information. We present a hierarchical approach to understand forest changes and pressures from 1840 up to 2010. Changes analyzed at different temporal scales and at different resolutions provide a general trend to forest changes while providing inputs on structural changes to monitor in detail landscape level dynamics. We present an example on the French Alps, considered a “hot spot” of biodiversity for Europe and also part of a LTER site. We develop a methodology to: i) provide a real-time, cost-effective evaluation of landscape alterations and changes in the continuity of the natural communities, ii) improve scenario development that has direct application to improve forest management, conservation and mitigation measures, iii) provide a holistic approach that can be considered towards the interplay between biodiversity value and the needs of forestry activities. Vis-a-vis of results and participatory work, which concepts, methodologies and tools can be validated on strong scientific grounds that can be proposed to the actors charged to implement policies and actions on the ground? One of the most important challenges for future research will be to integrate research across different scales, including spatio-temporal scales within an interdisciplinary and multidisciplinary framework. If we manage to follow this route, science will be able to move from analytical to actionable knowledge

Quality assessment of MODIS vegetation continuous fields of tree cover over France

International audienceThe MODIS VCF is the unique product that enable to map tree cover at globalscale and assess its dynamic for almost two decades. It is used for a wide rangeof environmental studies and provide essential knowledge on the distribution oftrees on earth. The product has already been validated in different areas ofthe world. However, information on its accuracy remains very patchy and theunderstanding of the spatial distribution of errors limited.In this study, we investigate the accuracy of the MODIS VCF percent treecover layer (collection 5, 250-m spatial resolution) over France using the mostdetailed topographical vector database available at the national scale (BDTopo,French mapping agency IGN; minimum mapping unit of 500 m2). We alsocompare the VCF data with a new national land cover map (the OSO product)based on a fully automatic processing 191 chain using Sentinel-2 image time series.In a first time, we estimate for each VCF pixel (n = 5,909,620), the corresponding tree cover percentage of the two reference datasets, while ensuring consistency between the production years. Then, a set of multiple independentvariables are computed including some environmental factors (such as elevationand slope), the level of fragmentation of woody vegetation, and the land covercomposition in the VCF pixel. Finally, uncertainty is quantified from classicalstatistical indicators (Root-Mean-Squared Error, Mean Bias Error, Mean Abso-lute Error) and differences in tree cover are mapped, to understand the spatialpattern and possible sources of errors.Preliminary results show significant differences between the VCF tree coverand the reference products despite the high positive correlation (Spearman’srho correlation = 0.75 for VCF vs BDTopo, and rho = 0.74 for VCF vs OSO;p<0.001). Negative bias with high variability was measured relative to BDTopo(MBE = -12.04 ± 22.41%; RMSE = 25.44%) and OSO (MBE = -6.59 ± 22.34%;RMSE=23.29%). Understimation of VCF tree cover is mainly observed from25% to 75% of cover. Overestimation also appears, mainly in pixels dominatedby grasslands. These findings corroborate previous studies, except for sparselytree areas. No relationship is observed with the quality level of the VCF product.Differences are only slightly explained by slope and elevation.This study contributes to the effort of independant validation of the VCFproduct and may help to better understand the classification errors in variouslandscape contexts

Using the new French Land Cover Map (OSO) as spatial inputs in forest ecological modeling

International audienceSince 2016, a new French Land Cover Map (the OSO product) is available at a country scale with 17 land cover classes representing main land cover types (urban, agricultural and semi-natural). It is based on a fully automatic processing chain using Sentinel-2 image time series and Landsat images. The classification accuracy is around 90\% and reasonably enables to use this map as spatial inputs in species-habitat model. However, information on potential spatial uncertainty effects in these ecological models remain limited yet.In this study, we explore the impact of spatial uncertainty on a Generalized Linear Model (GLM) that investigates area and connectivity effects of 48 woodlands on the species richness of forest specialist hoverflies. We compared three different models calibrated from the three OSO products available (10m raster, 20m raster and the 20m vectorized product).Firstly, AREA and CONNECTIVITY were derived for all detected sampled forest fragments in each forest product. Then, a statistical protocol was defined in order to evaluate performances and sensitivity of each model : (1) a Leave One Out Cross Validation (LOOCV) was applied. Then, we computed mean and standard deviation for indicators usually analyzed in ecological statistical models outputs : the deviance explained (D2), the significance of regression coefficients (p-value) and the predictive capability (Root Mean Squared Error - RMSE) ; (2) We used a boostrapping technique (1000 runs) to approximate the distribution and test the stability of regression coefficients estimates for each model. Therefore, for each k-1 cross validation run, we also analyzed the standard deviation of the RMSE.Preliminar results show that spatial inputs derived from OSO products had no impacts on the predictive capability of the models (Rho = 0.85, p < 0.001). Nevertheless, we observed strong impacts on the quantity of Deviance explained (Mean Absolute Error (D2) = -0.12 ± 0.08) and the significance of the coefficients. AREA effect on the response variable remains significant with the « raster 10 m » (p-value <0.05) product but disappears in the other model’s configurations. The significance level of the connectivity variable also decreases with the increase of spatial resolution and/or simplification. It became strictly non-existent in the model based 192 on variables from the « vectorized product"

French land cover map based on Sentinel-2 time series images to model species richness of hoverflies

International audienceA refreshed knowledge of the land cover is crucial for many scientific and operational applications. In this sense, it provides data useful to derivate several essential biodiversity variables (EBV), such as ecosystem extend and fragmentation, and habitat structure, well-known as linked to landscape biodiversity. Land cover is thus an essential input of predictive models or landscape modeling approaches relatives to landscape ecology researches. Nowadays, several global land cover map databases exist, such as Corine Land Cover (CLC) at the European scale or BD TOPO® (IGN) at the French national scale. These two databases offer powerful capabilities to describe land cover with rich nomenclature on large areas. However, they suffer of a lack of timelines. For example, CLC 2012 was published in 2015. In parallel, the BD TOPO® (IGN) database accurately describes permanent classes of landscape but annual classes (e.g. annual crops) are not present or well described. The new availability of Sentinel-2 time series images with its 5-day revisit cycle with 2 satellites and 10m decametric spatial resolution on the whole of Earth’s surface give new opportunities in producing accurate and up-to-date land cover maps on large areas. Its frequent revisiting capability makes possible to analyze temporal dynamics of classes and thus improve their discrimination while improving timeliness. In the framework of Land Cover Scientific Expertise Centre (CES OSO) of French Theia Land Data Centre, CESBIO with contributions from Dynafor (INRA) developed an operational supervised classification methodology (iota2) for the fully automatic production of land cover maps at country scale using Sentinel-2 and Landsat-8 images. The produced map, called CES OSO land cover map, has 17 land cover classes representing main land cover types (urban, agricultural and semi-natural) with a 10m spatial resolution and a minimal mapping unit (MMU) of 0.01 ha. The classification accuracy around 90% enables its use in operational and scientific decision-making context. Firstly, this presentation will describe the characteristics of this product, the methodology to produce it and its statistical accuracy. Secondly, and not the least, the spatial uncertainty of this product, which can affect dependant ecological modeling, will be tackled. A comparative study has been indeed developed between predictive models based on forest map digitized by hand and several forest maps extracted from CES OSO land cover map (raw and generalized maps). The original predictive model (one based on hand-made map) is a species-habitat model which investigates effect of woodland area, structural heterogeneity and connectivity on the species richness of forest-specialist hoverflies. Results seem to show negligible impact of geometrical inaccuracies on models performance while automatic land cover mapping (from remote sensing methods) provides a new interesting perspective to analyze the effect of the whole of landscape matrix on species richness

Nationwide operational mapping of grassland mowing events combining machine learning and Sentinel-2 time series

Grasslands cover approximately 40% of the Earth's land area, encompassing nearly 70% of the global agricultural land area, and are distributed on all continents and across all latitudes (Suttie et al., 2005; White et al., 2000). Grassland dynamics influence global ecosystem functioning, and their impact is widely modulated by management practices intensity on these landscapes (Zhao et al., 2020). Management practices are primarily driven by grassland landscape maintenance, as well as by ecosystem service of provisioning offered by the grasslands. Grasslands are subject to management practices such as mowing or grazing or a combination of both. Therefore, monitoring grassland management practices is essential for assessing management intensity level, which in turn plays a critical role in studies related to biodiversity (XXXX), water (XXXXX) and carbon (XXXXX) cycling and others topics (XXXX). In France, the National Observatory of Mowed Grassland Ecosystems conducts birdlife monitoring in mowed grasslands, with a particular focus on the rise in breeding failures attributed to increasingly early mowing. Early mowing intercepts birds' reproductive period and interrupts their breeding process (Broyer et al., 2012). Usually, responsible agencies conduct occasional observation campaigns to support ecosystem-related public policies, but ground observations are not spatially exhaustive and are time-consuming. As an alternative source, synoptic remote sensing data enables regular and global-scale monitoring, enabling tracking of vegetation dynamics. Currently, Sentinel-2 mission provides cost-free high resolution data at 10m spatial resolution with a 5-day temporal frequency (10 days before 2017), allowing intra-plot level observations. Grassland mowing events timing and intensity have already been mapped using remote sensing-based time series, mainly from features sensitive to vegetation status, such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI) and more. There have been several methods used to detect mowing events from satellite time series. These methods were mainly based on temporal changes in time series using threshold-based methods and anomalies detection approach. More recently, deep learning-based architectures were also used to detect mowing events timing. Estel et al. (2018) assessed grassland use intensity spatial patterns across Europe. To extract annual mowing frequency, a temporal change analysis based on spline-adjusted MODIS NDVI time series was used. Their approach involved identifying mowing events as instances where a local minima exhibited a change, relative to its preceding peak, exceeding 10% of growing season amplitude. The results showed an overall accuracy of 80%, which decreases as the frequency of events increases. In northern Switzerland, Kolecka et al. (2018) also estimated mowing frequency employing similar temporal change analysis, but based on raw Sentinel-2 NDVI time series. Here, a drop in NDVI greater than 0.2, between two consecutive cloud-free acquisition dates, was counted as a mowing event. Their method accurately identified 77% of observed events and highlighted that false detection can occur due to residual cloud presence, while sparse time series led to the omission of mowing events. Regarding Griffiths et al. (2020), mowing events frequency and timing were mapped in Germany using 10-day composite Harmonized Landsat-Sentinel NDVI time series. Discrepancies between a hypothetical bell-shaped curve and the current polynomial-fitted curve were evaluated. An event was counted when the difference exceeded 0.2 NDVI. Findings revealed consistent spatial patterns in mowing frequency (indicating extensive and intensive management). However, estimated dates exhibited significant discrepancies compared to observed dates (MAE > 50 days), which could be due to lower temporal resolution of Sentinel-2 before 2017 and the absence of reliable ground data for calibration and validation. Stumpf et al. (2020) mapped grassland management (grazing or mowing) and its intensity based on biomass productivity and management frequency, respectively. The latter were extracted from n-day composite Landsat ETM + and Landsat OLI NDVI time series. As in previous cases, a management event was counted when NDVI loss is higher than a threshold, which was based on the probability density function of all NDVI changes across the time series and was specified for p = 0.01. Their approach yielded management patterns consistent with several management-related indicators (species richness, nutrient supply, slope, etc). Recently, Watzig et al. (2023) estimated mowing events in Austria, using Sentinel-2 NDVI time series and implementing discrepancy analysis between a idealized unmowed trajectory and actual NDVI values. An event was recorded if the difference exceeded-0.061. Commission errors due to residual clouds were reduced via a subsequent binary classification of each estimated event using a gradient boosting algorithm trained over cloudy plots. Findings indicated an overall accuracy of 80% in correct event detection, with estimated dates closely aligning with observed dates (MAE < 5 days). Vroey et al. (2022) developed a algorithm for detecting mowing events across Europe. Here, raw Sentinel-2 NDVI and Sentinel-1 VH-coherence time series were used separately

Classification de la physionomie paysagère de montagne par classification supervisée orientée « objet »

International audienceMontane landscape physiognomy consists mainly of natural and semi-natural elements where ecological variability, extreme meteorological conditions and anthropogenic influence make it highly heterogeneous on multi-scale levels. Thus, very high spatial resolution (VHSR) optical satellite images appear indispensable for describing the physiognomic composition of mosaic-like landscape and for facilitating land cover mapping on large areas. This chapter considers this mosaic-like landscape as open environments starting at the subalpine zone. It presents an application of object-based classification for montane landscape physiognomy mapping. Automatic remote sensing detection of montane vegetation physiognomy is based on VHSR images. The chapter considers a pansharpening RCS method implemented in the Orfeo Toolbox (OTB) library. It presents a geographic information system method in order to use a vector layer of samples produced independently from a pre-produced segmentation, that is by manual digitization and photo-interpretation