Utilisation des aménagements agroforestiers linéaires par les mammifères en milieu agricole intensif

L'intensification agricole entraîne une vaste perte d'habitat et de connectivité du paysage. Les espèces subsistantes dans ces paysages dominés par l'agriculture utilisent souvent des éléments du paysage linéaires et minces, comme des haies brise-vent et des fossés végétalisés, en tant qu’habitat ou comme corridor de déplacement entre les parcelles d’habitat. Toutefois, la compréhension de l’utilisation de ces aménagements agroforestiers linéaires (AAL) par la faune est limitée et pourrait profiter de l’utilisation de données de télédétection à haute résolution, qui sont non biaisées, détaillées et reproductibles. Le but de cette étude est d’évaluer les caractéristiques qui affectent l’utilisation des AAL par les mammifères de moyenne et grande taille, avec des données in situ et de télédétection, dans un paysage dominé par l’agriculture dans le sud du Québec. Vingt-trois AAL ont été sélectionnés et caractérisés, à la fois par des relevés terrain et des analyses de télédétection (entre autres métriques LiDAR et indices de végétation). La fréquentation de chaque AAL par les mammifères a été mesurée à l'aide de pièges photographiques, de la fin du printemps au début de l'automne 2018. Nous avons obtenu 431 détections de mammifères, tous les AAL combinés. Parmi ces détections, sept espèces ont été répertoriées, toutes opportunistes et bien adaptées au milieu agricole. Nos résultats démontrent qu’il y a des différences significatives dans l'utilisation des AAL par les mammifères, liées à l'influence unique de l’assemblage des caractéristiques considérés. Une dizaine de modèles de régression ont été testés et le modèle retenu basé sur l'AICc comprend plusieurs caractéristiques, tant locales que du paysage. Les coefficients de ce modèle indiquent une relation positive entre l’utilisation des AAL par les mammifères et leur longueur, le couvert arborescent et la quantité d’habitat environnant, alors que cette relation est négative avec la largeur et les perturbations anthropiques. Les données dérivées de télédétection ont contribué à ce modèle final, rappelant leur utilité dans les études sur les habitats fauniques. Ces résultats indiquent que de nombreux facteurs semblent influencer l’utilisation des AAL dans le sud du Québec, que ce soit comme corridor ou comme habitat pour les mammifères. Les informations fournies par cette étude ont généré des suggestions pour une gestion favorable des AAL et la conservation de la faune sauvage en milieu agricole.Agricultural intensification causes habitat modification, sometimes leading to habitat loss and subsequent loss of connectivity. Remaining species in these agriculture-dominated landscapes often use hedgerows, such as windbreaks or riparian strips, as movement corridors or even as habitats. However, the understanding of the use of these hedgerows by mammals is limited and could be improved with the use of high-resolution remote sensing data, which are unbiased, detailed and repeatable. The aim of this study was to assess the attributes that affect medium- and large-sized mammals’ use of hedgerows, with in situ and remotely sensed data (including LiDAR and multispectral images) in an agriculture-dominated landscape in southern Québec. Twenty-three hedgerows were selected and characterized with both field surveys and remote sensing analyses, like LiDAR metrics and vegetation indices. Wildlife frequentation of each hedgerow was measured using camera traps, from late spring to early fall in 2018. 431 mammal detections were obtained among all 23 hedgerows. From this, seven species were recorded, all of them opportunistic and well adapted to agricultural environment. Results showed significant differences in mammal use of hedgerows. Coefficients of the better-ranked models based on AICc indicated a positive relationship between hedgerow length and their use by mammals, and a negative relationship with the hedgerow width. Hedgerow use by mammals also increased as tree cover increased, as habitat became more available and as human disturbance decreased. These results characterized for the first time the variables influencing hedgerow use by a broad set of medium- and large-sized mammal species and confirmed their use as both movement corridor and habitat. This study also confirmed the complementary usefulness of variables derived from remote sensing combined with field data. The low explanatory power of variables often cited in the literature (e.g. NDVI, canopy height) also highlights the need to further explore their specific influence on mammals. The information provided by this study supports the beneficial role played by hedgerows for wildlife conservation in intensive agricultural landscapes. Management guidelines are provided as well as future research avenues

Coupling SAR C-band and optical data for soil moisture and leaf area index retrieval over irrigated grasslands

International audienceThe objective of this study was to develop an approach for estimating soil moisture and vegetation parameters in irrigated grasslands by coupling C-band polarimetric Synthetic Aperture Radar (SAR) and optical data. A huge dataset of satellite images acquired from RADARSAT-2 and LANDSAT-7/8, and in situ measurements were used to assess the relevance of several inversion configurations. A neural network (NN) inversion technique was used. The approach for this study was to use RADARSAT-2 and LANDSAT-7/8 images to investigate the potential for the combined use of new data from the new SAR sensor SENTINEL-1 and the new optical sensors LANDSAT-8 and SENTINEL-2. First, the impact of SAR polarization (mono-, dual- and full-polarizations configurations) and the Normalized Difference Vegetation Index (NDVI) calculated from optical data for the estimation error of soil moisture and vegetation parameters was studied. Next, the effect of some polarimetric parameters (Shannon entropy and Pauli components) on the inversion technique was also analyzed. Finally, configurations using in situ measurements of the fraction of absorbed photosynthetically active radiation (FAPAR) and the fraction of green vegetation cover (FCover) were also tested.The results showed that HH polarization is the SAR polarization most relevant to soil moisture estimates. An RMSE for soil moisture estimates of approximately 6 vol.% was obtained even for dense grassland cover. The use of in situ FAPAR and FCover only improved the estimate of the leaf area index (LAI) with an RMSE of approximately 0.37 m²/m². The use of polarimetric parameters did not improve the estimate of soil moisture and vegetation parameters. Good results were obtained for the biomass (BIO) and vegetation water content (VWC) estimates for BIO and VWC values lower than 2 and 1.5 kg/m², respectively (RMSE is of 0.38 kg/m² for BIO and 0.32 kg/m² for VWC). In addition, a high under-estimate was observed for BIO and VWC higher than 2 and 1.5 kg/m², respectively (a bias of -0.65 kg/m² on BIO estimates and -0.49 kg/m² on VWC estimates). Finally, the estimation of vegetation height (VEH) was carried out with an RMSE of 13.45 cm

Développement et automatisation de méthodes de classification à partir de séries temporelles d'images de télédétection : application aux changements d'occupation des sols et à l'estimation du bilan carbone

La quantité de données de télédétection archivées est de plus en plus importante et grâce aux nouveaux et futurs satellites, ces données offriront une plus grande diversité de caractéristiques : spectrale, temporelle, résolution spatiale et superficie de l'emprise du satellite. Cependant, il n'existe pas de méthode universelle qui maximise la performance des traitements pour tous les types de caractéristiques citées précédemment; chaque méthode ayant ses avantages et ses inconvénients. Les travaux de cette thèse se sont articulés autour de deux grands axes que sont l'amélioration et l'automatisation de la classification d'images de télédétection, dans le but d'obtenir une carte d'occupation des sols la plus fiable possible. En particulier, les travaux ont portés sur la la sélection automatique de données pour la classification supervisée, la fusion automatique d'images issues de classifications supervisées afin de tirer avantage de la complémentarité des données multi-sources et multi-temporelles et la classification automatique basée sur des séries temporelles et spectrales de référence, ce qui permettra la classification de larges zones sans référence spatiale. Les méthodes ont été testées et validées sur un panel de données très variées de : capteurs : optique (Formosat-2, Spot 2/4/5, Landsat 5/7, Worldview-2, Pleiades) et radar (Radarsat,Terrasar-X), résolutions spatiales : de haute à très haute résolution (de 30 mètres à 0.5 mètre), répétitivités temporelles (jusqu'à 46 images par an) et zones d'étude : agricoles (Toulouse, Marne), montagneuses (Pyrénées), arides (Maroc, Algérie). Deux applications majeures ont été possibles grâce à ces nouveaux outils : l'obtention d'un bilan carbone à partir des rotations culturales obtenues sur plusieurs années et la cartographie de la trame verte (espaces écologiques) dans le but d'étudier l'impact du choix du capteur sur la détection de ces élémentsAs acquisition technology progresses, remote sensing data contains an ever increasing amount of information. Future projects in remote sensing like Copernicus will give a high temporal repeatability of acquisitions and will cover large geographical areas. As part of the Copernicus project, Sentinel-2 combines a large swath, frequent revisit (5 days), and systematic acquisition of all land surfaces at high-spatial resolution and with a large number of spectral bands.The context of my research activities has involved the automation and improvement of classification processes for land use and land cover mapping in application with new satellite characteristics. This research has been focused on four main axes: selection of the input data for the classification processes, improvement of classification systems with introduction of ancillary data, fusion of multi-sensors, multi-temporal and multi-spectral classification image results and classification without ground truth data. These new methodologies have been validated on a wide range of images available: various sensors (optical: Landsat 5/7, Worldview-2, Formosat-2, Spot 2/4/5, Pleiades; and radar: Radarsat, Terrasar-X), various spatial resolutions (30 meters to 0.5 meters), various time repeatability (up to 46 images per year) and various geographical areas (agricultural area in Toulouse, France, Pyrenean mountains and arid areas in Morocco and Algeria). These methodologies are applicable to a wide range of thematic applications like Land Cover mapping, carbon flux estimation and greenbelt mappin

Investigating the Use of Street-Level Imagery and Deep Learning to Produce In-Situ Crop Type Information

The creation of crop type maps from satellite data has proven challenging and is often impeded by a lack of accurate in situ data. Street-level imagery represents a new potential source of in situ data that may aid crop type mapping, but it requires automated algorithms to recognize the features of interest. This paper aims to demonstrate a method for crop type (i.e., maize, wheat and others) recognition from street-level imagery based on a convolutional neural network using a bottom-up approach. We trained the model with a highly accurate dataset of crowdsourced labelled street-level imagery using the Picture Pile application. The classification results achieved an AUC of 0.87 for wheat, 0.85 for maize and 0.73 for others. Given that wheat and maize are two of the most common food crops grown globally, combined with an ever-increasing amount of available street-level imagery, this approach could help address the need for improved global crop type monitoring. Challenges remain in addressing the noise aspect of street-level imagery (i.e., buildings, hedgerows, automobiles, etc.) and uncertainties due to differences in the time of day and location. Such an approach could also be applied to developing other in situ data sets from street-level imagery, e.g., for land use mapping or socioeconomic indicators

The integration of freely available medium resolution optical sensors with Synthetic Aperture Radar (SAR) imagery capabilities for American bramble (Rubus cuneifolius) invasion detection and mapping.

Doctoral Degree. University of KwaZulu- Natal, Pietermaritzburg.The emergence of American bramble (Rubus cuneifolius) across South Africa has caused severe ecological and economic damage. To date, most of the efforts to mitigate its effects have been largely unsuccessful due to its prolific growth and widespread distribution. Accurate and timeous detection and mapping of Bramble is therefore critical to the development of effective eradication management plans. Hence, this study sought to determine the potential of freely available, new generation medium spatial resolution satellite imagery for the detection and mapping of American Bramble infestations within the UNESCO world heritage site of the uKhahlamba Drakensberg Park (UDP). The first part of the thesis determined the potential of conventional freely available remote sensing imagery for the detection and mapping of Bramble. Utilizing the Support Vector Machine (SVM) learning algorithm, it was established that Bramble could be detected with limited users (45%) and reasonable producers (80%) accuracies. Much of the confusion occurred between the grassland land cover class and Bramble. The second part of the study focused on fusing the new age optical imagery and Synthetic Aperture Radar (SAR) imagery for Bramble detection and mapping. The synergistic potential of fused imagery was evaluated using multiclass SVM classification algorithm. Feature level image fusion of optical imagery and SAR resulted in an overall classification accuracy of 76%, with increased users and producers’ accuracies for Bramble. These positive results offered an opportunity to explore the polarization variables associated with SAR imagery for improved classification accuracies. The final section of the study dwelt on the use of Vegetation Indices (VIs) derived from new age satellite imagery, in concert with SAR to improve Bramble classification accuracies. Whereas improvement in classification accuracies were minimal, the potential of stand-alone VIs to detect and map Bramble (80%) was noteworthy. Lastly, dual-polarized SAR was fused with new age optical imagery to determine the synergistic potential of dual-polarized SAR to increase Bramble mapping accuracies. Results indicated a marked increase in overall Bramble classification accuracy (85%), suggesting improved potential of dual-polarized SAR and optical imagery in invasive species detection and mapping. Overall, this study provides sufficient evidence of the complimentary and synergistic potential of active and passive remote sensing imagery for invasive alien species detection and mapping. Results of this study are important for supporting contemporary decision making relating to invasive species management and eradication in order to safeguard ecological biodiversity and pristine status of nationally protected areas

How can GIS support the evaluation and design of biodiverse agroecosystems and landscapes? Applying the Main Agroecological Structure to European agroecosystems

Agrobiodiversity plays a critical role in fostering the stability, resilience, and sustainability of European farming systems. Nonetheless, there is currently a lack of comprehensive methods to describe its spatial distribution within farms, its connectivity with the surrounding landscape, and, most crucially, how the perceptions and actions of human communities affect it. The Main Agroecological Structure (MAS) has recently been proposed as an environmental index aiming to tackle such challenges by promoting a dialogue between landscape ecology and agroecology, encompassing criteria that focus on both landscape parameters and cultural variables. Geographic information systems (GIS) can play a key role in the measurement of the index by leveraging public geodata and engaging with the direct participation of communities to map the territories they inhabit and cultivate. Nevertheless, their use in this context has not yet been studied. We propose here a new GIS-based approach for estimating the Main Agroecological Structure: landscape criteria are assessed through the hybrid use of free and open-source GIS tools, field samplings, and participative mapping methods; cultural parameters are evaluated through semi-structured interviews. Contextually to the definition of such methodological foundations, the present study tests the relevance of the index to European agroecological contexts by applying the proposed workflow to three Italian farms characterized by different territorial and organizational forms. Along with a few modifications to the original proposal, we highlight the relevance of GIS in making agrobiodiversity visible at a landscape level within the context of the index. We also suggest some potential future applications related to local empowerment and agroecosystem mapping

Investigation into the bio-physical constraints on farmer turn-out-date decisions using remote sensing and meteorological data.

ThesisDoctoral thesisAccepted versionGrass is the most common landcover in Ireland and covers a bigger percentage (52%) of the country than any other in Europe. Grass as fodder is Ireland’s most important crop and is the foundation of its most important indigenous industry, agriculture. Yet knowledge of its distribution, performance and yield is scant. How grass is nationally, on a farm by farm, year by year basis managed is not known. In this thesis the gaps in knowledge about grassland performance across Ireland are presented along with arguments on why these knowledge gaps should be closed. As an example the need for high spatial resolution animal stocking rate data in European temperate grassland systems is shown. The effect of high stocking density on grass management is most apparent early in the growing season, and a 250m scale characterization of early spring vegetation growth from 2003-2012, based on MODIS NDVI time series products, is constructed. The average rate of growth is determined as a simple linear model for each pixel, using only the highest quality data for the period. These decadal spring growth model coefficients, start of season cover and growth rate, are regressed against log of stocking rate (r2 19 = 0.75, p<0.001). This model stocking rate is used to create a map of grassland use intensity in Ireland, which, when tested against an independent set of stocking data, is shown to be successful with an RMSE of 0.13 Livestock Unit/ha for a range of stocking densities from 0.1 to 3.3 Livestock Unit/ha. This model provides the first validated high resolution approach to mapping stocking rates in intensively managed European grassland systems. There is a demonstrated a need for a system to estimate current growing conditions. Using the spring growth model constructed for estimating stocking density a new style of grass growth progress anomaly map in the time-domain was developed. Using the developed satellite dataset 1 and 12 years of ground climate station data in Ireland, NDVI was modelled against time as a proxy for grass growth This model is the reference for estimating current seasonal progress of grass growth against a ten year average. The model is developed to estimate Seasonal Progress Anomalies in the Time domain (SPAT), giving a result in terms of “days behind” and “days ahead” of the norm. SPAT estimates for 2012 and 2013 are compared to ground based estimates from 30 climate stations and have a correlation coefficient of 0.897 and RMSE of 15days. The method can successfully map current grass growth trends compared to the average and present this information to the farmer in simple everyday language. This is understood by the author to be the first validated growth anomaly service, and the first for intensive European grasslands. The decisions on when to turn out cattle (the turn out date (TOD)) from winter housing to spring grazing is an important one on Irish dairy farms which has significant impacts on operating costs on the farm. To examine the relationship of TOD to conditions, the National Farm Survey (NFS) of Ireland database was geocoded and the data on turn out dates from 199 farms across Ireland over five years was used. A fixed effects linear panel data model was employed to explore the association between TOD and conditions, as it allows for unobserved variation between farmers to be ignored in favour of modelling the variance year on year. The environmental variables used in the analysis account for 38% of the variance in the turn out dates on farms nationwide. National seasonal conditions dominate over local variation, and for every week earlier grass grows in spring, farmers gain 3.7 days in grazing season but ignore 3.3 days of growth that could have been used. Every 100mm extra rain in spring means TOD is a day later and every dry day leads to turn out being half a day earlier. A well-drained soil makes TOD 2.5 days earlier compared to a poorly drained soil and TOD gets a day later for every 16km north form the south coast. This work demonstrates that precision agriculture 1 driven by optical and radar satellite data is closer to being a reality in Europe driven by enormous amounts of free imagery from NASA and the ESA Sentinel programs coupled with open source meteorological data and models and new developments in data analytics

Entwicklung einer übertragbaren, synergistischen Methode zur Kartierung von Biotoptypen anhand von hochauflösenden optischen und Radar-basierten Daten

Das übergeordnete Ziel der Arbeit war es zu evaluieren, in welchem Umfang die synergistische Verwendung von modernen Erdbeobachtungsdaten und -methoden zur Kartierung von Biotoptyp- und Landnutzungsinformationen beitragen kann. Anhand einer umfangreichen Literaturrecherche wurden die traditionellen Methoden der Biotoptypenkartierung und der Stand der Forschung im Bereich der Verwendung von Fernerkundungsinformationen für die Biotoptypenkartierung analysiert und Forschungsdefizite aufgezeigt, sowie Ansatzpunkte für eine Weiterentwicklung definiert. Hieraus ergaben sich die folgenden vier übergeordneten Forschungs- beziehungsweise Arbeitsschwerpunkte, welche im Verlauf der Arbeit noch weiter unterteilt wurden: 1. Die Analyse und Extraktion von potenziellen Informationen (Merkmalen) aus den vorliegenden Geoinformationen und die anschließende Reduktion der potenziellen Merkmale auf die relevanten Merkmale für die Kartierung der Biotoptyp- und Landnutzungsinformationen. 2. Die Entwicklung eines Klassifikationsansatzes für die Erfassung der Biotoptypen- und Landnutzungsinformationen anhand eines Entwicklungsdatensatzes. 3. Die Evaluation der Robustheit der Methode mittels Übertragung auf zwei weitere Datensätze. 4. Die Evaluation der Synergie der zugrundliegenden Geoinformationen. Es konnte gezeigt werden, dass das Ziel der Entwicklung einer übertragbaren, synergistischen Methode zur Kartierung von Biotoptypen anhand von hochauflösenden optischen und Radar-basierten Daten erreicht werden konnte. Die entstandenen Karten können als Hilfe für die Entscheidungsfindung im Bereich der Anforderungen der nationalen und internationalen Naturschutzrichtlinien dienen. Die gezeigten Ergebnisse im Bereich der Übertragbarkeit lassen darauf hoffen, dass die entwickelte Methode und die daraus entstehenden Ergebnisse auch in anderen Ökoregionen einsetzbar sind