About predictions in spatial autoregressive models

We address the problem of prediction in the spatial autoregressive SAR model for areal data which is classically used in spatial econometrics. With the Kriging theory, prediction using Best Linear Unbiased Predictors is at the heart of the geostatistical literature. From the methodological point of view, we explore the limits of the extension of BLUP formulas in the context of the spatial autoregressive SAR models for out-of-sample prediction simultaneously at several sites. We propose a more tractable \almost best" alternative and clarify the relationship between the BLUP and a proper EM-algorithm predictor. From an empirical perspective, we present data-based simulations to compare the efficiency of the classical formulas with the best and almost best predictions

About predictions in spatial autoregressive models

We address the problem of prediction in the spatial autoregressive SAR model for areal data which is classically used in spatial econometrics. With the Kriging theory, prediction using Best Linear Unbiased Predictors is at the heart of the geostatistical literature. From the methodological point of view, we explore the limits of the extension of BLUP formulas in the context of the spatial autoregressive SAR models for out-of-sample prediction simultaneously at several sites. We propose a more tractable \almost best" alternative and clarify the relationship between the BLUP and a proper EM-algorithm predictor. From an empirical perspective, we present data-based simulations to compare the efficiency of the classical formulas with the best and almost best predictions

About predictions in spatial autoregressive models: Optimal and almost optimal strategies

We address the problem of prediction in the classical spatial autoregressive lag model for areal data. In contrast with the spatial econometrics literature, the geostatistical literature has devoted much attention to prediction using the Best Linear Unbiased Prediction approach. From the methodological point of view, we explore the limits of the extension of BLUP formulas in the context of the spatial autoregressive lag models for in sample prediction as well as out-of-sample prediction simultaneously at several sites. We propose a more tractable “almost best” alternative. From an empirical perspective, we present data-based simulations to compare the efficiency of the classical formulas with the best and almost best predictions

Modélisation du mouvement des chevreuils dans un paysage bocager simulé : premiers résultats, projets

Les tiques, dont Ixodes ricinus, espèce la plus répandue en Europe, sont vecteurs de nombreux agents pathogènes, protozoaires, bactéries ou virus, qui peuvent être responsables de maladies touchant l’Homme (Borreliose de Lyme) ou l’animal(babésiose bovine). En vue d’identifier les zones à risque vis-à-vis de ces maladies, il est important de connaître la distribution spatiale des tiques. Cette distribution dépend d’une part des conditions locales de température et d’humidité, d’autre part des mouvements des hôtes des tiques(Estrada-Peña, 2002). Les chevreuils sont notamment reconnus pour influencer fortement la densité de tiques(Ruiz-Fons et Gilbert 2010) et se déplacer sur de longues distances. Dans le cadre de l’estimation spatiale des risques, il est nécessaire de disposer d’un modèle de déplacement des hôtes en fonction des caractéristiques du paysage, dont le développement n’a pas été réalisé à ce jour. Dans un premier temps, une approche théorique a été privilégiée. Un modèle du paysage a été développé via une tesselation de Voronoï et un processus de marquage. Au sein de ce paysage modélisé, le mouvement du chevreuil est modélisé par des équations différentielles stochastiques. Ce mouvement se décompose donc en deux termes : un de dérive, qui dépend d’une fonction de potentiel reliée aux différents habitats qui composent le paysage, et un terme de diffusion. A partir d’une première fonction potentielle, il est donc possible de simuler le déplacement d’un individu dans un paysage modélisé. Les développements actuels visent dans un premier temps à tester différentes fonctions de potentiel en fonction de nos connaissances sur le comportement du chevreuil. L’étape suivante consistera à développer des méthodes d’inférence afin d’estimer les paramètres à partir de données simulées ou observées. Par la suite le prototype obtenu pourra être utilisé pour tester l’influence des caractéristiques du paysage sur le mouvement des chevreuils. Enfin, un couplage avec un modèle de dynamique de population de tiques (Hoch et al, 2010) fournira des aires de répartition simulées des vecteurs

A statistical approach for predicting grassland degradation in disturbance-driven landscapes

Maintaining a land base that supports safe and realistic training operations is a significant challenge for military land managers which can be informed by frequent monitoring of land condition in relation to management practices. This study explores the relationship between fire and trends in tallgrass prairie vegetation at military and non -military sites in the Kansas Flint Hills. The response variable was the longterm linear trend (2001-2010) of surface greenness measured by MODIS NDVI using BFAST time series trend analysis. Explanatory variables included fire regime (frequency and seasonality) and spatial strata based on existing management unit boundaries. Several non-spatial generalized linear models (GLM) were computed to explain trends by fire regime and/or stratification. Spatialized versions of the GLMs were also constructed. For non-spatial models at the military site, fire regime explained little (4%) of the observed surface greenness trend compared to strata alone (7% - 26%). The non-spatial and spatial models for the non -military site performed better for each explanatory variable and combination tested with fire regime. Existing stratifications contained much of the spatial structure in model residuals. Fire had only a marginal effect on surface greenness trends at the military site despite the use of burning as a grassland management tool. Interestingly, fire explained more of the trend at the nonmilitary site and models including strata improved explanatory power. Analysis of spatial model predictors based on management unit stratification suggested ways to reduce the number of strata while achieving similar performance and may benefit managers of other public areas lacking sound data regarding land usage

Is there a solution to the spatial scale mismatch between ecological processes and agricultural management?

The major limit to develop robust landscape planning for biodiversity conservation is that the spatial levels of organization of landscape management by local actors rarely match with those of ecological processes. This problem, known as spatial scale mismatch, is recognized as a reason of lack of effectiveness of agri-environment schemes. We did a review to describe how authors identify the problem of spatial scale mismatch in the literature. The assumption is made that the solutions proposed in literature to conciliate agricultural management and conservation of biodiversity are based on theoretical frameworks that can be used to go towards an integration of management processes and ecological processes. Hierarchy Theory and Landscape Ecology are explicitly mobilized by authors who suggest multiscale and landscape scale approaches, respectively, to overcome the mismatch problem. Coordination in management is proposed by some authors but with no theoretical background explicitly mentioned. The theory of organization of biological systems and the theories of Social-Ecological Systems use the concept of coordination and integration as well as concepts of organization, adaptive capabilities and complexity of systems. These theories are useful to set up a new framework integrating ecological processes and agricultural management. Based on this review we made two hypotheses to explain difficulties to deal with spatial scale mismatch: (1) authors generally do not have an integrated approach since they consider separately ecological and management processes, and (2) an inaccurate use of terminology and theoretical frameworks partially explain the inadequacy of proposed solutions. We then specify some terms and highlight some ‘rules’ necessary to set up an integrative theoretical and methodological framework to deal with spatial scale mismatch.(Presentation des résumés n°186, p. 95-96, non paginé

Mapping health status of chestnut forest stands using Sentinel-2 images

In many parts of France, health status of chestnut forest stands is a crucial concern for forest managers. These stands are made vulnerable by numerous diseases and sometimes unadapted forestry practices. Moreover, since last years, they were submitted to several droughts. In Dordogne province, the economic stakes are important. About 2/3 of the chestnut forest area are below the optimal production level. The actual extent of chestnut forest decline remains still unknown. Sentinel-2 time series show an interesting potential to map declining stands over a wide area and to monitor their evolutions. This study aim to propose a method to discriminate healthy chestnut forest stands from the declining ones with several levels of withering intensity over the whole Dordogne province. The proposed method is the development of a statistical model integrating in a parsimonious manner several vegetation indices and biophysical parameters. The statistical approach is based on an ordered polytomous regression to which are applied various technics of models’ selection. We aim to map 3 classes of predictive health status. In this study, Sentinel-2 images (10 bands at 10 and 20 m spatial resolution) acquired during the growing season of 2016 have been processed. Due to insufficient data quality related to atmospheric conditions, only 2 cloud-free images could be analyzed (one in July and one in September). About 36 vegetation indices were calculated from THEIA-MAJA L2A products and 5 biophysical parameters (Cover fraction of brown vegetation, Cover fraction of green vegetation, Fraction of Absorbed Photosynthetically Active Radiation, Green Leaf Area Index, Leaf water content) were processed from ESA level 1C product. These last parameters have been obtained with the Overland software (developed by Airbus DS Geo-Intelligence) by inverting a canopy reflectance model. This software couples the PROSPECT leaf model and the scattering by arbitrary inclined leaves (SAIL) canopy model. Calibration and validation of the predictive model are based on the health status of chestnut forest stands data survey. About 50 plots have been surveyed by foresters describing the chestnut trees health status by using two protocols (ARCHI and expert knowledge). Model stability over time and space will be further analyzed with Sentinel-2 time series during 2017 and 2018 on other different chestnut forest stands

Health status diagnosis of chestnut forest stands using Sentinel-2 images.

The Theia workshop for Sentinel-2 L2A MAJA products was held in Toulouse on the 13th and 14th of June 2018. About 80 people participated either on the 13th or 14th, and nearly 70 participants attended each day of this workshop, whose object was to collect feedback and share experiences on the quality, use and applications of the L2A surface reflectance products delivered by Theia from Sentinel-2 data

Military training and fire regime impacts on tallgrass prairie vegetation degradation

The relationship between fire and long-term trends in tallgrass prairie vegetation was assessed at Fort Riley and Konza Prairie Biological Station (KPBS) in Kansas. Linear trends of surface greenness were previously estimated using BFAST and MODIS MOD13Q1 NDVI composite images from 2001 to 2010. To explain trends, fire frequency and seasonality (fire regime) was determined and each site was divided into spatial strata using administrative or management units. Generalized linear models (GLM) were used to explain trends by fire regime and/or stratification. Spatialized versions of GLMs were also computed address unexplained spatial components. Non-spatial models for FRK showed fire regime explained only 4% of trends compared to strata (7-26%). At KPBS, fire regime and spatial stratification explained 14% and 39%, respectively. At both sites, improvements in performance were minimal using both fire and strata as explanatory variables. Model spatialization resulted in a 5% improvement at FRK, but with weak spatial structure in the residuals, and was not necessary at KPBS as the existing stratification most of the spatial structure in model residuals. All models at KPBS performed better for each explanatory variable and combination tested. Fire has only a marginal effect on vegetation trends at FRK despite its widespread use as a grassland management tool to improve vegetation health, and explains much more of the trends at KPBS. Analysis of predictors from spatial models with existing stratification yielded an approach with fewer strata but similar performance and may provide insight about additional explanatory variables omitted from this analysis

Detection of "Flavescence dorée" Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery

Flavescence dorée is a grapevine disease affecting European vineyards which has severe economic consequences and containing its spread is therefore considered as a major challenge for viticulture. Flavescence dorée is subject to mandatory pest control including removal of the infected vines and, in this context, automatic detection of Flavescence dorée symptomatic vines by unmanned aerial vehicle (UAV) remote sensing could constitute a key diagnosis instrument for growers. The objective of this paper is to evaluate the feasibility of discriminating the Flavescence dorée symptoms in red and white cultivars from healthy vine vegetation using UAV multispectral imagery. Exhaustive ground truth data and UAV multispectral imagery (visible and near-infrared domain) have been acquired in September 2015 over four selected vineyards in Southwest France. Spectral signatures of healthy and symptomatic plants were studied with a set of 20 variables computed from the UAV images (spectral bands, vegetation indices and biophysical parameters) using univariate and multivariate classification approaches. Best results were achieved with red cultivars (both using univariate and multivariate approaches). For white cultivars, results were not satisfactory either for the univariate or the multivariate. Nevertheless, external accuracy assessment show that despite problems of Flavescence dorée and healthy pixel misclassification, an operational Flavescence dorée mapping technique using UAV-based imagery can still be proposed