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Urban air mobility (UAM) extends urban transportation to low-altitude airspace using electric vertical take-off and landing (eVTOL) vehicle to reduce traffic congestion. The vertical take-off and landing (VTOL) site connecting ground and air transport is the critical infrastructure of the UAM. Determining its locations is essential for the design and operation of the air route. This study focuses on the problem of the location of the VTOL site, using Shenzhen as the study area, and establishes an integer programming model with the objective of maximizing travel cost savings to identify the optimal locations of the VTOL sites. This study is different from existing ones in that it explicitly considers the three-dimensional spatial availability of VTOL sites. Geographic information system (GIS) tools are used to identify locations that satisfy two-dimensional (2D) planar availability, and an obstacle assessment model of the approach/departure and transitional surfaces of the VTOL site is built to further screen the locations. The selected potential sites are used as input to the integer programming model, ensuring that the locations identified to establish the VTOL site are optimal. The impact of the number of VTOL sites, the user's transfer time at the VTOL sites, and the eVTOL pricing on the model solution is also discussed. Although this study uses Shenzhen as a research object, the proposed methodology is generalized and applicable to any other city or region, providing recommendations and references for initial planning and related operations of the UAM in selected areas.

The increasing deterioration of urban traffic congestion has led to the emergence of Urban Air Mobility (UAM). UAM seeks to reduce congestion by offering flexible and efficient aerial transportation using electric vertical take-off and landing aircraft (eVTOL) and unmanned aerial vehicles (UAV). As UAM evolves, the guarantee of the effectiveness and safety of UAM aircraft has become an essential task. Thus, UAM airspace design and risk assessment are vital. This paper reviews and synthesizes the latest research on UAM airspace design and risk assessment. Initially, it outlines the ideas of UAM airspace categorization and geofencing. Subsequently, it presents three approaches to organizing UAM airspace: gridding, stratification, and networking, emphasizing their benefits, limitations, and uses. In addition, it contrasts four commonly used UAM airspace structure models. In the next step, the article introduces relevant risk assessment principles and analyzes four prominent collision risk assessment models, pointing out their merits and drawbacks. In conclusion, the paper highlights the current research challenges and proposes future research directions.

Depuis le milieu du vingtième siècle, les paysages bocagers du nord-ouest de la France subissent d’importantes mutations. Sous l’effet de la transformation foncière des exploitations agricoles, les parcellaires sont rationnalisés, remembrés et les haies sont fréquemment arasées. Ces mutations ont pour répercussion de multiplier les transferts hydro-sédimentaires et les connectivités hydrologiques entre les parcelles agricoles et le cours d’eau, impactant l’environnement (érosion des sols, pertes en sol, la pollution des milieux aquatiques). Ces conséquences déjà pressantes et pesantes pourraient devenir récurrentes dans un contexte de changement climatique. À l’heure où les enjeux liés à l’aménagement et à la préservation des territoires bocagers sont à repenser, il est intéressant de s’interroger sur la « vertueusité » des paysages passés au-regard des transferts hydro-sédimentaires. Cet article étudie l’évolution des connectivités hydrologiques depuis deux siècles sur différents bassins versants bocagers, afin d’offrir des perspectives quant à la gestion durable et future des territoires.

Data mining has achieved great success in air traffic management as a technology for learning knowledge from historical data that benefits people. However, data mining can rarely be embedded into the trajectory optimization process since regular optimization algorithms cannot utilize the functional and implicit knowledge extracted from historical data in a general paradigm. To tackle this issue, this research proposes a novel data mining-based trajectory generation method that is compatible with existing optimization algorithms. Firstly, the proposed method generates trajectories by combining various maneuvers learned from operation data instead of reconstructing trajectories with generative models. In such a manner, data mining-based trajectory optimization can be achieved by solving a combinatorial optimization problem. Secondly, the proposed method introduces a majorization–minimization-based adversarial training paradigm to train the generation model with more general loss functions, including non-differentiable flight performance constraints. A case study on Guangzhou Baiyun International Airport was conducted to validate the proposed method. The results illustrate that the trajectory generation model can generate trajectories with high fidelity, diversity, and flyability.

Aircraft sequencing has the potential to decrease flight delays and improve operational efficiency at airports. This paper presents the aircraft sequencing problem (ASP) on multiple runways with complex interactions by allocating flights on runways and optimizing landing times, take-off times, and crossing times simultaneously in a uniform framework. The problem was formulated as a mixed-integer program considering realistic operational constraints, including runway assignment preferences based on the entry/exit fixes of the terminal maneuvering area (TMA), minimum runway separation, time window, and arrival crossing rules. Variable-fixing strategies were applied, to strengthen the formulation. A first-come-first-served (FCFS) heuristic was proposed for comparison. Various instances from the literature and from realistic data sets were tested. Our computational study showed that the solution approach optimizes runway schedules, to achieve significantly fewer flight delays, taking runway assignment preferences and arrival crossings into account.

The bocage landscape of north-western France has undergone major changes since the middle of the twentieth century, its structure having been altered by land consolidation, the rationalization and enlargement of plots, and the levelling of hedgerows. The environmental consequences of these landscape changes are significant: water erosion, pollution of aquatic environments and loss of biodiversity. The aim of this article is to study the evolution of an agricultural area that is representative of the changes that have affected bocage landscapes over the last two centuries. Using the example of a small, historically bocage watershed in Calvados (Le Tortillon), the methodology is based on a geohistorical approach, combining the use of archives, particularly land registry records, and aerial images within a Geographic Information System (GIS). Although land registers provide precise information on land use in 19th-century landscapes, the article questions the relevance of their representation in terms of landscape structure. The results of two centuries of landscape trajectory show the evolution of land use and bocage structure. In particular, there has been a steady decline in the number of hedgerow lines since the 1940s, in line with the opening up of the landscape, land consolidation and the meteoric increase in the size of agricultural plots. The results of these centuries of change shed new light on the trajectory of a bocage landscape over the last two centuries, and help us to understand the landscape of tomorrow.

As demand for air transport continues to grow, the complexity of the operating environment of air traffic has intensified. Air Traffic Controllers (ATCos) are faced with the critical task of making timely decisions in response to rapidly changing air traffic. In this context, maintaining Situation Awareness (SA) becomes critical, directly influencing ATCos’ decision making and preventing potential traffic accidents or incidents. This paper presents a systematic review of the theoretical development of SA measurement techniques for ATCos. Firstly, the measurement techniques developed to assess individual and team SA are discussed and summarized. Additionally, some recently developed novel measurement methods are introduced. Four specific techniques applied to evaluate ATCos’ SA are highlighted. Second, the article analyzes the comprehensive utilization of neurophysiological measurement techniques, with a particular focus on eye movement and EEG methodologies. These techniques exhibit promising potential in measuring ATCos’ SA and are explored in-depth. Furthermore, four distinct types of sensitivity factors that primarily affect ATCos’ SA are summarized. This paper provides some insight into the current state of SA measurement techniques for ATCos. It concludes with recommendations for future research, specifically addressing the evaluation of ATCos SA in high-automation environments.

Despite their remarkable performance, deep learning models still lack robustness guarantees, particularly in the presence of adversarial examples. This significant vulnerability raises concerns about their trustworthiness and hinders their deployment in critical domains that require certified levels of robustness. In this paper, we introduce an information geometric framework to establish precise robustness criteria for l2 white-box attacks in a multi-class classification setting. We endow the output space with the Fisher information metric and derive criteria on the input–output Jacobian to ensure robustness. We show that model robustness can be achieved by constraining the model to be partially isometric around the training points. We evaluate our approach using MNIST and CIFAR-10 datasets against adversarial attacks, revealing its substantial improvements over defensive distillation and Jacobian regularization for medium-sized perturbations and its superior robustness performance to adversarial training for large perturbations, all while maintaining the desired accuracy.

In an airway network, some critical links exist that are vital for the structural integrity and performance of the network. The detection of such links may assist with improving the imbalance between the limited airspace capacity and the ever-increasing traffic demand, which elicit flight delays, significant economic losses, etc. However, it is challenging to identify such links as they evolve (both in space and time) with changing traffic flow dynamics. This paper proposes a complex network approach for spatial-temporal critical links detection in a given airway network. First, flight track data is employed to characterize the airway network as weighted spatial-temporal networks. Then edge centrality and network percolation metrics are adopted to detect the critical links in each snapshot of the spatial-temporal networks. Afterward, the critical links detected by the two metrics are spatially overlapped to determine the final critical links over time. To examine the operational validity of the proposed method, we carry out a case study on the Southeast Asia airway network derived from one-month flight track data. Results demonstrate that the spatial distribution of the critical links varies over different traffic scenarios, and most of the identified critical links are found in the transition sectors with complex traffic situations. Four links, which are parts and at crosses of major trunk airways connecting to major navigation aids (VOR/DME) in the studied network, appear highly in all examined traffic scenarios. The unavailability of such links may lead to traffic flow disruptions. Observations by subject matter experts from air-traffic data visualizations demonstrate that the complex network-based methods can dynamically identify airway links that are operationally critical under time-evolving air-traffic scenarios. With good traffic flow prediction tools in the future, this method can be adopted to predict critical links in airway networks to better assist controllers in real-time air traffic management.

Climate impact models of the non-CO2 emissions of aviation are still subject to significant uncertainties. Condensation trails, or contrails, are one of these non-CO2 effects. In order to validate the contrail simulation models, a dataset of observations covering the entire lifetime of the contrails will be required, as well as the characteristics of the aircraft which produced them. This study carries on the work on contrail observation from geostationary satellite by proposing a new way to track contrails and identify the flight that produced it using geostationary satellite infrared images, weather data as well as air traffic data. It solves the tracking and the identification problem as one, each process leveraging information from the other to achieve a better overall result. This study is a new step towards a consistent contrail dataset that could be used to validate contrail models.

The number of vehicles equipped with radars on the road has been increasing for years and is expected to reach 50% of cars by 2030. This rapid rise in radars will likely increase the risk of harmful interference, especially since radar specifications from standardization bodies (e.g., ETSI) provide requirements in terms of maximum transmit power but do no mandate specific radar waveform parameters nor channel access scheme policies. Techniques for interference mitigation are thus becoming very important to ensure the long-term correct operation of radars and upper-layer ADAS systems that depend on them in this complex environment. In our previous work, we have shown that organizing the radar band into time-frequency resources that do not interfere with each other vastly reduces the amount of interference by facilitating band sharing. In this paper, a metaheuristic is presented to find the optimal resource sharing between radars, knowing their relative positions and thereby the line-of-sight and non-line-of-sight interference risks during a realistic scenario. The metaheuristic aims at optimally minimizing interference while minimizing the number of resource changes that radars have to make. It is a centralized approach where everything about the system is known (e.g., the past and future positions of the vehicles). This and the high computational load induce that this algorithm is not meant to be used in real-time. However, the metaheuristic approach can be extremely useful for finding near optimal solutions in simulations, allowing for the extraction of efficient patterns, or as data generation for machine learning.

Land use and landscape structure play an important role in the functioning of flash floods on the arable plateaus of northern France. Landscape structures have changed considerably over the last decades with an increase in runoff-producing surfaces and an enlargement of the plots. This evolution results in an increase in runoff volumes accompanied by an increasingly easy diffusion of runoff on the slopes. There is therefore potentially an increasingly frequent and important spatial connectivity from the plots to the thalwegs, which can lead to the formation of flash floods. This study proposes to model this diachronic evolution of connectivity in a small agricultural basin of Seine Maritime using a multiagent system (MAS) and to develop synthetic indicators characterising these spatial links in the flow processes. The model outputs show that spatial connectivity has been steadily increasing over the past 70 years due to the enlargement of the parcel grid and the growth of runoff surfaces. For example, for the same 20 mm/h rainfall, the connectivity indicator increases from 40.99% (in 1947) to 78.33% (in 2015). This observation is observed for all levels of rainfall intensity, including the lowest. This modelling, carried out for a 116 ha basin in arable farming, can be transposed to all small agricultural basins.

A multiple-airport system (MAS) consists of more than two airports in a metropolitan area under a large block of terminal airspace that is managed by one or two air traffic control units. When the capacity of an airport or of the terminal airspace drops, flight delays occur in the MAS system. A quick estimation and predication of traffic congestion in the MAS is important yet challenging. This paper aims to develop a queuing network model of MAS using point-wise stationary queues. The model analyzes the changes of non-stationary queues under the principle of flow conservation to capture flight delay propagation in the system. Regression analyses are performed to examine the relationship between the arrival and departure efficiencies of different airports. The model is validated with the data of Guangdong–Hong Kong–Macao Greater Bay Area airports. Simulation results show that the model can effectively estimate flight delays in the MAS.

During the COVID-19 period and particularly during lockdown, deviations from nominal operations have shown to become more frequent. To confirm this observation this paper proposes to evaluate the impact of COVID-19, and more generally of crises that lead to a sharp drop in traffic, on the pilot/controller system, especially during the critical approach and landing phases. To study the influence of this type of crisis on flight operations at Charles De Gaulle airport, an existing energy atypicality metric is applied on a reference period before COVID-19 and compared to the COVID-19 period. Whereas the traffic at Charles De Gaulle airport has decreased by around 90% on April 2020, the obtained statistics underlined an increase in the atypical flight ratio of around 50%. This trend can be explained in part by the appearance of glide interceptions from above as a result of trajectory shortenings, and an increase in the proportion of high speed approaches.

This paper presents a new air traffic complexity metric based on linear dynamical systems, of which the goal is to quantify the intrinsic complexity of a set of aircraft trajectories. Previous works have demonstrated that the structure and organization of air traffic are essential factors in the perception of the complexity of an air traffic situation. Usually, they were not able to explicitly address trajectory pattern organization. The new metric, by identifying the organization properties of trajectories in a traffic pattern, captures some of the key factors involved in ATC complexity. The key idea of this work is to find a linear dynamical system which fits a vector field as closely as possible to the observations given by the aircraft positions and speeds. This approach produces an aggregate complexity metric that enables one to identify high (low) complexity regions of the airspace and compare their relative complexity. The metric is very appropriate to compare different traffic situations for any scale (sector or country) by associating a complexity index to each trajectory sample in the airspace. For instance, to compute the complexity for a sector, one must just sum-up the complexity for trajectory samples intersecting such a sector. This computation can also be extended in the time dimension in order to estimate the average complexity in a given airspace for a period of time.

This paper addresses the generation of aircraft emergency trajectories with obstacle avoidance. After presenting in detail the fast marching tree algorithm, in this paper we propose an improvement of its performance. First, the free space checking function is sped up. Then, the algorithm is used twice, firstly with the sampling of a few points to generate an approximate trajectory, and secondly with a sampling of points close to the first computed trajectory to refine it. The proposed method significantly reduces the computing time of the emergency geometric trajectory generation.

The COVID-19 pandemic has had a significant impact on the air transportation system worldwide. This paper aims at analyzing the effect of the travel restriction measures implemented during the COVID-19 pandemic from a passenger perspective on the US air transportation system. Four metrics based on data generated by passengers and airlines on social media are proposed to measure how the travel restriction measures impacted the relation between passengers and airlines in close to real-time. The proposed metrics indicate that each airline has reacted differently to the COVID-19 travel restriction measures from a passenger perspective, therefore they can be used by airlines and passengers to improve their decision making process. This report comes ahead of official data related to the same sequence of events, thereby showing the value of passenger-borne data in an industry where corporate priorities, institutional prudence, and passenger satisfaction come close together.

This paper presents a methodology to minimize the airspace congestion of aircraft trajectories based on slot allocation techniques. The traffic assignment problem is modeled as a combinatorial optimization problem for which a selective simulated annealing has been developed. Based on the congestion encountered by each aircraft in the airspace, this metaheuristic selects and changes the time of departure of the most critical flights in order to target the most relevant aircraft. The main objective of this approach is to minimize the aircraft speed vector disorder. The proposed algorithm was implemented and tested on simulated trajectories generated with real flight plans on a day of traffic over French airspace with 8800 flights.

Air traffic controllers have to make quick decisions to keep air traffic safe. Their behaviors have a significant impact on the operation of the air traffic management (ATM) system. Automation tools have enhanced the ATM system’s capability by reducing the controller’s task-load. Much attention has been devoted to developing advanced automation in the last decade. However, less is known about the impact of automation on the behaviors of air traffic controllers. Here, we empirically tested the effects of three levels of automation—including manual, attention-guided, and automated—as well as varying traffic levels on eye movements, situation awareness and mental workload. The results showed that there are significant differences in the gaze and saccade behaviors between the attention-guided group and automated group. Traffic affected eye movements under the manual mode or under the attention-guided mode, but had no effect on eye movements under the automated mode. The results also supported the use of automation for enhancing situation awareness while reducing mental workload. Our work has potential implications for the design of automation and operation procedures.

With the continuous air traffic growth and limits of resources, there is a need for reducing the congestion of the airspace systems. Nowadays, several projects are launched, aimed at modernizing the global air transportation system and air traffic management. In recent years, special interest has been paid to the solution of the dynamic airspace configuration problem. Airspace sector configurations need to be dynamically adjusted to provide maximum efficiency and flexibility in response to changing weather and traffic conditions. The main objective of this work is to automatically adapt the airspace configurations according to the evolution of traffic. In order to reach this objective, the airspace is considered to be divided into predefined 3D airspace blocks which have to be grouped or ungrouped depending on the traffic situation. The airspace structure is represented as a graph and each airspace configuration is created using a graph partitioning technique. We optimize airspace configurations using a genetic algorithm. The developed algorithm generates a sequence of sector configurations for one day of operation with the minimized controller workload. The overall methodology is implemented and successfully tested with air traffic data taken for one day and for several different airspace control areas of Europe.

Paris Charles de Gaulle Airport was the second European airport in terms of traffic in 2019, having transported 76.2 million passengers. Its large infrastructures include four runways, a large taxiway network, and 298 aircraft parking stands (131 contact) among three terminals. With the current pandemic in place, the European air traffic network has declined by −65% flights when compared with 2019 traffic (pre-COVID-19), having a severe negative impact on the aviation industry. More and more often taxiways and runways are used as parking spaces for aircraft as consequence of the drastic decrease in air traffic. Furthermore, due to safety reasons, passenger terminals at many airports have been partially closed. In this work we want to study the effect of the reduction in the physical facilities at airports on airspace and airport capacity, especially in the Terminal Manoeuvring Area (TMA) airspace, and in the airport ground side. We have developed a methodology that considers rare events such as the current pandemic, and evaluates reduced access to airport facilities, considers air traffic management restrictions and evaluates the capacity of airport ground side and airspace. We built scenarios based on real public information on the current use of the airport facilities of Paris Charles de Gaulle Airport and conducted different experiments based on current and hypothetical traffic recovery scenarios. An already known optimization metaheuristic was implemented for optimizing the traffic with the aim of avoiding airspace conflicts and avoiding capacity overloads on the ground side. The results show that the main bottleneck of the system is the terminal capacity, as it starts to become congested even at low traffic (35% of 2019 traffic). When the traffic starts to increase, a ground delay strategy is effective for mitigating airspace conflicts; however, it reveals the need for additional runways.

Fire regimes are important components of environmental dynamics, but our understanding of them is limited. Despite recent advances in the methodologies used to remotely sense and map fires and burned areas and new case studies that shed light on local fire use and management practices, the scientific community still has much to learn about anthropogenic fire regimes. We identify two areas for improvement: first, the fine-scale heterogeneity of fire dynamics for specific regions is often masked by global-scale approaches, and second, barriers between the disciplines focusing on fire impacts hamper the development of knowledge of the human dimensions of fire regimes. To address the “blind spot” that these limitations create, we present a simple dynamic model of fire ignition in savanna systems. The aim is to connect the local and global scales of fire regimes by focusing on human fire management (anthropogenic fire). Our dynamical model is based on a study area in Western Burkina Faso and integrates biophysical elements (climate and soil data), land cover, and fire management scenarios based on field surveys. The simulation results offer contrasting views of the impact of local fire management practices on regional fire regimes observed in savannas. Fire density and frequency are local variables that clearly change the fire regimes despite a complex and constrained biophysical system. This experience, drawing from fieldwork and modelling, may be a way to integrate some key aspects of anthropogenic fire research in savanna systems.

The capture of a target spacecraft by a chaser is an on-orbit docking operation that requires an accurate, reliable, and robust object recognition algorithm. Vision-based guided spacecraft relative motion during close-proximity maneuvers has been consecutively applied using dynamic modeling as a spacecraft on-orbit service system. This research constructs a vision-based pose estimation model that performs image processing via a deep convolutional neural network. The pose estimation model was constructed by repurposing a modified pretrained GoogLeNet model with the available Unreal Engine 4 rendered dataset of the Soyuz spacecraft. In the implementation, the convolutional neural network learns from the data samples to create correlations between the images and the spacecraft’s six degrees-of-freedom parameters. The experiment has compared an exponential-based loss function and a weighted Euclidean-based loss function. Using the weighted Euclidean-based loss function, the implemented pose estimation model achieved moderately high performance with a position accuracy of 92.53 percent and an error of 1.2 m. The in-attitude prediction accuracy can reach 87.93 percent, and the errors in the three Euler angles do not exceed 7.6 degrees. This research can contribute to spacecraft detection and tracking problems. Although the finished vision-based model is specific to the environment of synthetic dataset, the model could be trained further to address actual docking operations in the future.

This paper proposes an effective approach for modelling and assessing the risks associated with unmanned aerial vehicles (UAVs) integrated into national airspace system (NAS). Two critical hazards with UAV operations are considered and analyzed, which are ground impacts and midair collisions. Threats to fatalities that result from the two hazards are the focus in the proposed method. In order to realize ground impact assessment, a multifactor risk model is designed by calculating system reliability required to meet a target level of safety for different UAV categories. Both fixed-wing and rotary-wing UAVs are taken into account under a real scenario that is further partitioned into different zones to make the evaluation more precise. Official territory and population data of the operation scenario are incorporated, as well as UAV self-properties. Casualty area of impacting debris can be obtained as well as the probability of fatal injuries on the ground. Sheltering factors are not neglected and defined as four types based on the real scenario. When midair collision fatality risk is estimated, a model of aircraft collisions based on the density of civil flight in different regions over Chinese airspace is proposed. In the model, a relative collision area and flying speed between UAVs and manned aircraft are constructed to calculate expected frequency of fatalities for each province correspondingly. Truthful data with different numbers of UAVs is incorporated in the model with the expected number of fatalities after a collision is included. Experimental simulations are made to evaluate the ground impacts and midair collisions when UAVs operate in the NAS.

The bottleneck of today’s airspace is the Terminal Maneuvering Areas (TMA), where aircraft leave their routes to descend to an airport or take off and reach the en-route sector. To avoid congestion in these areas, an efficient design of departure and arrival routes is necessary. In this paper, a solution for designing departure and arrival routes is proposed, which takes into account the runway configuration, the surroundings of the airport and operational constraints such as limited slopes or turn angles. The routes consist of two parts: a horizontal path in a graph constructed by sampling the TMA around the runway, to which is associated a cone of altitudes. The set of all routes is optimized by the Simulated Annealing metaheuristic. In the process and at each iteration, each route is computed by defining adequately the cost of the arcs in the graph and then searching a path on it. The costs are chosen so as to avoid zigzag behaviors as much as possible. Two tests were performed, one on an instance taken from the literature and the other on an artificial problem designed specifically to test this approach. The obtained results are satisfying with regard to the current state of air operations management and constraints.

Hydrosedimentary fluxes that run from agricultural plots down to the water streams are currently associated with many social, economic and environmental issues such as diffuse pollution, soil infertility, soil loss, floods, etc. While the genesis of these transfers are well-known at the scale of the agricultural plot, flow dynamics occurring on slopes remain poorly understood. This is especially the case in the small basins located in the north-western part of France, where the landscape mosaic, which includes linear networks (hedges, roads, ditches...), tends to complicate the processes. Analyzing the transfer conditions is important for a further integrated watershed management. Hence, in this paper, we propose to use a multi-agent system as a support decision tool to understand the role of anthropogenic linear networks on runoff pathways. We carried out a so-called bottom-up approach, to measure the effects of the local interactions between each linear element on the global hydrological behavior of the watershed. A first spatial analysis index, characterizing the impact of linear networks in the path of runoff, is constructed here from numerical simulations. The results of this study also tend to demonstrate the relevance of Multi-Agent Systems as diagnostic tools to the hydrosedimentary transfers and processes.

In the region of Basse-Normandie, the process of “agricultural modernisation” started in the 1950s has led to an unprecedented reorganisation of rural spaces and landscapes. The irregularity of the fabric of land plots was corrected by successive land consolidation schemes the stated objective of which was to “do away with the past”. However, in spite of the significant reduction in the number of land consolidation schemes since the 1990s, the rationalisation of the bocage landscapes has been continued. In this article we propose a method for interpreting the transformation of the bocage landscapes, while attempting to provide a detailed description of the underlying mechanisms causing this transformation. A spatial analysis of the dynamics in the landscape was conducted and completed by a survey among farmers. In spite of public statements and actions in favour of maintaining and even restoring bocage landscapes, the results of this study suggest that the changes in the structure of farms (larger establishments, their concentration and the specialisation of production) has contributed to the erosion of these landscapes which are emblematic of these western rural areas and has led to the emergence of new forms of landscapes.

Cotton countryside in the Western Burkina Faso has rapidly evolved over the last fifty years. Agrarian and demographic transition has led to an important decline in bush areas. A phase of photo interpretation was conducted on a spatial extent comprising a dozen villages along the protected area of the Tui. After a description of spatial patterns with the help of various indicators (shape, structure ...), a summary of the interactions between the protected area and humanized places is proposed. This study shows that even if this area has been protected for 50 years, rules of access to this area have evolved. The rights for cropping of in the 1950’s and 60’s did not exist, and fields eviction were accelerated in the 1990’s. The agrarian transition coupled with strong desire of protection produce then a recent differentiation of two units, one agricultural and one of nature conservation. On this scale of observation, space competition seems to be important. A second approach based on crop/fallow cycles on a finer scale then distinguishes different spatial and dynamics patterns. This broader scale shows that the large increase in cultivated area does not lead to bush extinction. Some farm units continue the practice of shifting cultivation. Ultimately, this work highlights the importance of the relationship between spatial and temporal scales for studying dynamics in this region.

These study analyze the role of protected areas on landscape evolution in western Burkina Faso. The analysis focuses on material change in landscape but also on representations due to those new protected areas. From aerial photography and investigations we try to understand how this area is observed by farmers. Different practices and perceptions of the landscape by farmers were identified with several examples (fallow, soil and agricultural change). Access to land is strongly linked to local context (social position of the individual and land pressure) and influenced practices and representations of landscape. Despite the significant changes we observed shapes and behaviors (housing, temporary crop, sacred woods ...) showing the importance of local dynamics in these Bwa area.

In the areas with a dairy production and in a hedgerow landscape surrounding, farms appear as the elementary units of the structuration of rural spaces and landscape. The study of the logics of organization of the farmers’ practices thus appears as a pertinent grid to analyze the dynamics of the transformations of agricultural spaces and landscapes. For the dairy farms of the Western Atlantic Europe, the factors linked to the morphology of the fields play a dominating part (plots pulverization, acreage of the farm). From this angle, the analysis of the part played by the distance of the fields from the farm buildings itself as far as the organization of the farmers’ practices is concerned, takes on a central place. How can we take this distance into account and measure it too?The methods coming from the development of the Geographical Information Systems bring along, in this field, interesting advances. Making a grid based on distance criteria to read farmers’ practices highlights the diversity of land use organization of dairy farms in various agricultural and spatial contexts (Galicia, Normandy and England). This diversity then sends back to the dynamics of changes of agricultural systems relating to the land concentration (increase of the farms’ size, land consolidation), relating to the production system (specialization, intensification) and relating to the farms functioning (mechanization, automation, development of cooperative societies).

Classical morphometric indexes have been the first quantitative methods created by geomorphologists to compare the morphometric characteristics of many catchments and to quantify their theoretical hydrologic behavior. Even if many indices still remain quoted in numerous hydrologic books, a few authors have recently underlined the drawbacks of such methods. They are mainly static and not synthetics, and so, they are not sufficient to predict the hydrological response. So, we choose creating relevant indexes basing our approach on new distributed simulation tools as cellular automata (RuiCells) to calculate synthetic indexes based on numerical experiences. Simulations on 422 varied catchments lead a few interesting results. Some efficient spatial configurations are detected at a local scale while at the global scale such behavior was not observed. An index of spatial efficiency is proposed on each cell in order to quantify the efficiency of the network structure in every point within the catchment. The greater values identify the points with the stronger theoretical response.

The level of risk is defined by two standards : uncertainty and vulnerability. The latter is commonly estimated by an overlay of the different information layers, in order to obtain a local quantitative measure of the exposed properties. This approach is convenient for hazardous events clearly delimited and without any spatial diffusion, such as landslides or soil pollution. This approach is however soon bounded in the case of a dynamic process, such as the hydrological hazard. The overlay of different data gives a valuable appreciation of the streaming surfaces, but this remains insufficient to express the spatial dynamics of the flow. The global sensibility of a basin is not merely the sum of the local sensibilities. The global hazard is defined by the spatial organisation of the production or infiltration areas, and by their mutual relations, which validates the cellular automata approach to measure the level of organisation of the sensitive areas and to model the diffusion of the flows between these areas.