Littoral, Environnement, Télédétection, Géomatique

Abstract Aim Species distribution modelling, a family of statistical methods that predicts species distributions from a set of occurrences and environmental predictors, is now routinely applied in many macroecological studies. However, the reliability of evaluation metrics usually employed to validate these models remains questioned. Moreover, the emergence of online databases of environmental variables with global coverage, especially climatic, has favoured the use of the same set of standard predictors. Unfortunately, the selection of variables is too rarely based on a careful examination of the species' ecology. In this context, our aim was to highlight the importance of selecting ad hoc variables in species distribution models, and to assess the ability of classical evaluation statistics to identify models with no biological realism. Innovation First, we reviewed the current practices in the field of species distribution modelling in terms of variable selection and model evaluation. Then, we computed distribution models of 509 European species using pseudo‐predictors derived from paintings or using a real set of climatic and topographic predictors. We calculated model performance based on the area under the receiver operating curve (AUC) and true skill statistics (TSS), partitioning occurrences into training and test data with different levels of spatial independence. Most models computed from pseudo‐predictors were classified as good and sometimes were even better evaluated than models computed using real environmental variables. However, on average they were better discriminated when the partitioning of occurrences allowed testing for model transferability. Main conclusions These findings confirm the crucial importance of variable selection and the inability of current evaluation metrics to assess the biological significance of distribution models. We recommend that researchers carefully select variables according to the species' ecology and evaluate models only according to their capacity to be transfered in distant areas. Nevertheless, statistics of model evaluations must still be interpreted with great caution.

Abstract In the last two decades river restoration has become increasingly a field of research asking a series of complex questions related not just to science but also to society. Why should we restore ecosystems? Is restoration always beneficial? When is it beneficial? What should be the target reference states? What is success and when can it be evaluated? Our objective is to chronicle and discuss the fundamental concepts of reference versus objective, state versus process‐based actions, nature versus culture and ecosystem integrity versus ecosystem benefits driven restoration. We discuss the dynamic and yet unresolved definition of a reference state. Although the desire to re‐create the past is tempting, science has shown that river systems follow complex trajectories frequently making it impossible to return to a previous state. Therefore, restoration goals are moving away from explicitly defining a reference state because of the difficulty of attaining that reference state. We argue that the reference‐based strategy should be progressively replaced by an objective‐based strategy that reflects the practical limitations of developing sustainable landscapes and the emerging importance of accounting for human services of the target ecosystem. After a decade during which natural processes have been the focus of restoration, it appears that particular processes are not equally valuable everywhere and that regional complexity must be better understood to adjust restoration actions. Copyright © 2009 John Wiley & Sons, Ltd.

BACKGROUND: Heat waves have a drastic impact on urban populations, which could increase with climate change. OBJECTIVES: We evaluated new indicators of elderly people's exposure to heat in Paris, from a public health prevention perspective, using satellite thermal images. METHODS: We used a time series of 61 images from the satellites of the National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) taken from 1 to 13 August 2003 to produce thermal indicators of minimum, maximum, and mean surface temperatures and diurnal temperature amplitude, with different lags between the meteorological data and the health impact. Health data came from a case-control study involving 241 people ≥ 65 years of age who died in the city of Paris or the nearby suburban area of Val-de-Marne during the August 2003 heat wave, and 241 controls who were matched to cases on age, sex, and residential zone. For each person, we integrated the thermal indicators in a conditional logistic regression model, adjusted for age and other potential confounders. We computed odds ratios (ORs) comparing the 90th and 50th percentiles of the temperature differences between cases and controls for various indicators. RESULTS: Mortality risk was significantly associated with exposure for two indicators: minimum temperatures averaged for 1-13 August [for a 0.41°C increase, OR = 2.17; 95% confidence interval (CI): 1.14, 4.16] and minimum temperature averaged on the day of death and the 6 preceding days (for a 0.51°C increase: OR = 2.24; 95% CI: 1.03, 4.87). CONCLUSIONS: Our results support the influence of night temperatures on the health impact of heat waves in urban areas. Urban heat exposure indicators based on satellite imagery have the potential to identify areas with higher risk of death, which could inform intervention decisions by key stakeholders.

For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

International audience

Abstract Studies of coastal vulnerability due to climate change tend to focus on the consequences of sea level rise, rather than the complex coastal responses resulting from changes to the extreme wave climate. Here we investigate the 2013/2014 winter wave conditions that severely impacted the Atlantic coast of Europe and demonstrate that this winter was the most energetic along most of the Atlantic coast of Europe since at least 1948. Along exposed open‐coast sites, extensive beach and dune erosion occurred due to offshore sediment transport. More sheltered sites experienced less erosion and one of the sites even experienced accretion due to beach rotation induced by alongshore sediment transport. Storm wave conditions such as were encountered during the 2013/2014 winter have the potential to dramatically change the equilibrium state (beach gradient, coastal alignment, and nearshore bar position) of beaches along the Atlantic coast of Europe.

International audience

Unmanned aerial vehicles (UAVs) and satellite constellations are both essential Earth Observation (EO) systems for monitoring land surface dynamics. The former is frequently used for its acquisition flexibility and its ability to supply imagery with very high spatial resolution (VHSR); the latter is interesting for supplying time-series data over large areas. However, each of these data sources is generally used separately even though they are complementary and have strong and promising potential synergies. Data fusion is a well-known technique to exploit this multi-source synergy, but in practice, UAV and satellite synergies are more specific, less well known and need to be formalized. In this article, we review remote sensing studies that addressed both data sources. Current approaches were categorized to distinguish four strategies: “data comparison”, “multiscale explanation”, “model calibration” and “data fusion”. Analysis of the literature revealed emerging trends, the supply of these distinct strategies for several applications and allowed to identify key contributions of UAV data. Finally, the high potential of this synergy seems currently under-exploited; therefore a discussion is proposed about the related implications for data interoperability, machine learning and data sharing to reinforce synergies between UAVs and satellites.

Agriculture in Brazilian Amazonia is going through a period of intensification. Crop mapping is important in understanding the way this intensification is occurring and the impact it is having. Two successive classifications based on MODIS (MODerate Resolution Imaging Spectroradiometer)-TERRA/EVI (Enhanced Vegetation Index) time series are applied (1) to map agricultural areas and (2) to identify five crop classes. These classes represent agricultural practices involving three commercial crops (soybean, maize and cotton) planted in single or double cropping systems. Both classifications are based on five steps: (1) analysis of the MODIS/EVI time series, (2) application of a smoothing algorithm, (3) application of a feature selection/extraction process to reduce the data set dimensionality, (4) application of a classifier and (5) application of a post-classification treatment. The first classification detected 95% of the agricultural areas (5 617 250 ha during the 2006–2007 harvest) and correlation coefficients with agricultural statistics exceeded 0.98 for the three crop classes at municipality level. The second classification (overall accuracy = 74% and kappa index = 0.675) allowed us to obtain the spatial variability mapping of agricultural practices in the state of Mato Grosso. A total of 30% of the total planted area was cultivated through double cropping systems, especially along the BR163 highway and in the Parecis plateau region.

Coral reefs are diverse ecosystems that support millions of people worldwide by providing coastal protection from waves. Climate change and human impacts are leading to degraded coral reefs and to rising sea levels, posing concerns for the protection of tropical coastal regions in the near future. We use a wave dissipation model calibrated with empirical wave data to calculate the future increase of back-reef wave height. We show that, in the near future, the structural complexity of coral reefs is more important than sea-level rise in determining the coastal protection provided by coral reefs from average waves. We also show that a significant increase in average wave heights could occur at present sea level if there is sustained degradation of benthic structural complexity. Our results highlight that maintaining the structural complexity of coral reefs is key to ensure coastal protection on tropical coastlines in the future.

Abstract Summer warming trends in Western Europe are increasing the incidence, intensity and duration of heat waves. They are especially deadly in large cities owing to population density, physical surface properties, anthropogenic heat and pollutants. In August 2003, for 9 consecutive days, the Paris metropolitan area experienced an extreme heat wave that caused 4867 estimated heat‐related deaths. A set of 61 NOAA‐AVHRR (advanced very high‐resolution radiometer) images and one SPOT‐high resolution visible (HRV) image were used to analyse the spatial variations of land surface temperature (LST) over the diurnal cycle during the heat wave. The LST patterns were markedly different between daytime and night‐time. A heat island was centred downtown at night, whereas multiple temperature anomalies were scattered in the industrial suburbs during the day. The heat wave corresponded to elevated nocturnal LST compared to normal summers. The highest mortality ratios matched the spatial distribution of the highest night‐time LSTs, but were not related to the highest daytime LSTs. LSTs were sampled from images at the addresses of 482 elderly people (half were deceased persons and half were control ones) to produce daily and cumulative minimal, maximal and mean thermal indicators, over various periods of time. These indicators were integrated into a conditional logistic regression model to test their use as heat exposure indicators, based on risk factors. Over the period 1–13 August, thermal indicators taking into account minimum nocturnal temperatures averaged over 7 days or over the whole period were significantly linked to mortality. These results show the extent of the spatial variability in urban climate variables and the impact of night‐time temperatures on excess mortality. These results should be used to inform policy and contingency planning in relation to heat waves, and highlight the role that satellite remote sensing can play in documenting and preventing heat‐related mortality. Copyright © 2010 Royal Meteorological Society

ABSTRACT The rivers of the world are undergoing accelerated change in the Anthropocene, and need to be managed at much broader spatial and temporal scales than before. Fluvial remote sensing now offers a technical and methodological framework that can be deployed to monitor the processes at work and to assess the trajectories of rivers in the Anthropocene. In this paper, we review research investigating past, present and future fluvial corridor conditions and processes using remote sensing and we consider emerging challenges facing fluvial and riparian research. We introduce a suite of remote sensing methods designed to diagnose river changes at reach to regional scales. We then focus on identification of channel patterns and acting processes from satellite, airborne or ground acquisitions. These techniques range from grain scales to landform scales, and from real time scales to inter‐annual scales. We discuss how remote sensing data can now be coupled to catchment scale models that simulate sediment transfer within connected river networks. We also consider future opportunities in terms of datasets and other resources which are likely to impact river management and monitoring at the global scale. We conclude with a summary of challenges and prospects for remotely sensed rivers in the Anthropocene. © 2019 John Wiley & Sons, Ltd.

Building bridges between environmental and political agendas is essential nowadays in face of the increasing human pressure on natural environments, including wetlands. Wetlands provide critical ecosystem services for humanity and can generate a considerable direct or indirect income to the local communities. To meet many of the sustainable development goals, we need to move our trajectory from the current environmental destructive development to a wiser wetland use. The current article contain a proposed agenda for the Pantanal aiming the improvement of public policy for conservation in the Pantanal, one of the largest, most diverse, and continuous inland wetland in the world. We suggest and discuss a list of 11 essential interfaces between science, policy, and development in region linked to the proposed agenda. We believe that a functional science network can booster the collaborative capability to generate creative ideas and solutions to address the big challenges faced by the Pantanal wetland.

International audience

Monitoring grassland plant communities is crucial for understanding and managing biodiversity. Previous studies indicate that mapping these natural habitats from single-date remotely sensed imagery remains challenging because some communities have similar physiognomy. The recently launched Sentinel-2 satellites are a promising opportunity for monitoring vegetation. This article assesses the advantages of Sentinel-2 time-series for discriminating plant communities in wet grasslands. An annual Sentinel-2 time-series was compared respectively to single-date and single-band datasets derived from this time-series for mapping grassland plant communities in a temperate floodplain located near Mont-Saint-Michel Bay, which is included in the long-term ecological research network "ZA Armorique" (France). At this 475 ha site, 123 vegetation relevs were collected and assigned to seven plant communities to calibrate and validate the Sentinel-2 data. Satellite images were classified using support vector machine (SVM) and random forest (RF) classifiers. Results show that the SVM classifier performs slightly better than the RF classifier (overall accuracy 0.78 and 0.71, respectively). They highlight that accuracy is lower when using single-date (0.67) or single-band images (0.70). The results also reveal that discrimination of plant communities is more sensitive to temporal resolution ( = 0.34 in overall accuracy) than spectral resolution ( = 0.12 in overall accuracy).

Faced with sea level rise and the intensification of extreme events, human populations living on the coasts are developing responses to address local situations. A synthesis of the literature on responses to coastal adaptation allows us to highlight different adaptation strategies. Here, we analyze these strategies according to the complexity of their implementation, both institutionally and technically. First, we distinguish two opposing paradigms – fighting against rising sea levels or adapting to new climatic conditions; and second, we observe the level of integrated management of the strategies. This typology allows a distinction between four archetypes with the most commonly associated governance modalities for each. We then underline the need for hybrid approaches and adaptation trajectories over time to take into account local socio-cultural, geographical, and climatic conditions as well as to integrate stakeholders in the design and implementation of responses. We show that dynamic and participatory policies can foster collective learning processes and enable the evolution of social values and behaviors. Finally, adaptation policies rely on knowledge and participatory engagement, multi-scalar governance, policy monitoring, and territorial solidarity. These conditions are especially relevant for densely populated areas that will be confronted with sea level rise, thus for coastal cities in particular.

Abstract: The African continent hosts some of the largest freshwater systems worldwide, characterized by a large distribution and variability of surface waters that play a key role in the water, energy and carbon cycles and are of major importance to the global climate and water resources. Freshwater availability in Africa has now become of major concern under the combined effect of climate change, environmental alterations and anthropogenic pressure. However, the hydrology of the African river basins remains one of the least studied worldwide and a better monitoring and understanding of the hydrological processes across the continent become fundamental. Earth Observation, that offers a cost-effective means for monitoring the terrestrial water cycle, plays a major role in supporting surface hydrology investigations. Remote sensing advances are therefore a game changer to develop comprehensive observing systems to monitor Africa's land water and manage its water resources. Here, we review the achievements of more than three decades of advances using remote sensing to study surface waters in Africa, highlighting the current benefits and difficulties. We show how the availability of a large number of sensors and observations, coupled with models, offers new possibilities to monitor a continent with scarce gauged stations. In the context of upcoming satellite missions dedicated to surface hydrology, such as the Surface Water and Ocean Topography (SWOT), we discuss future opportunities and how the use of remote sensing could benefit scientific and societal applications, such as water resource management, flood risk prevention and environment monitoring under current global change. Article Highlights: The hydrology of African surface water is of global importance, yet it remains poorly monitored and understoodComprehensive review of remote sensing and modeling advances to monitor Africa's surface water and water resourcesFuture opportunities with upcoming satellite missions and to translate scientific advances into societal applications.

Even though urban land-use change simulations provide useful information for decision makers, planning is generally weakly integrated into land-change modelling. However, the increasingly digitally available zoning data from statutory planning offers new opportunities. This study aims to reveal the potential effectiveness of statutory planning in terms of sustainable urban development by integrating zoning regulations in a multi-scenario simulation. Specifically, the gross floor area that can be built per parcel, as defined in the zoning plan, supports the allocation of varying degrees of urban densities. Using the CLUmondo modelling framework that couples cellular automata and multivariate logistic regression, we simulated urban growth in Madrid, Barcelona, Valencia, and Zaragoza Spanish Functional Urban Areas from 2012 to 2030 in four scenarios. The scenarios reflect the degree of planning intervention, ranging from high intervention to unrestricted development, while consider Spanish legislation and urban agenda 2030 sustainability goals. Simulations shows that by shifting growth to zones with urbanization projects almost 4200 ha of grassland and cropland could be saved from overbuilding, and 3800 ha by shifting it to zones without urbanization project. The simulation results provide critical information to support decision-makers and planners in revising plans and designing new plans to achieve sustainable urban development.

The arid coasts of North Africa, extending over 4633 km from the Gulf of Tunis to the Nile Delta, are undergoing pronounced shoreline retreats and coastal floodings that are reported as a consequence of the ongoing sea level rise resulting from global warming. Of particular interest are the abnormal shoreline dynamics for deltaic and sandy beaches, which are severely impacted by abrupt decadal variabilities in both climatic and anthropogenic drivers resulting in their increased vulnerability to disturbances from coastal hazards. Unfortunately, the evolution, distribution and impacts of these drivers remain largely unquantified, let alone understood, for these extensive arid coasts that harbor the major portion of North Africa's population as well as unique and fragile marine ecosystems. To address this deficiency, we use GIS-based multi-criteria approaches combined with analytic hierarchy process to map the Coastal Vulnerability Index and the Socioeconomic Vulnerability Index along these coasts to investigate the amplitude and extent of shoreline deterioration resulting from sudden fluctuations in sediment transport to the coastline. We use the western bay of the Gulf of Tunis, the coasts of Tripoli and the Nile Delta as three validation sites for our vulnerability assessment. The statistical Integrated Coastal Vulnerability Index map reveals that 47% of arid North African coasts are characterized by high to very high vulnerability. In particular, we observe that the densely populated deltaic coasts in both Tunisia and Egypt are 70% more vulnerable than any others coast in the eastern Mediterranean Basin. These abnormally high-vulnerability extensive areas are also correlated with significant deterioration of coastal aquifers and hence in crop production, compromising local food security and resulting in increasing outflow migration trends. Both Tunisia and Egypt observed dramatic increases in the net population outflow migration by respectively 62% and 248% between 2000 and 2016, mostly from coastal areas. Our source analysis of the amplitude and extent of these high coastal vulnerabilities suggests that they result from the anthropogenic drivers of damming and rapid urban growth over the last few decades rather than the effects of global warming.

Abstract. The relationships between rainfall, hydrology and landslide movement are often difficult to establish. In this context, ground-water flow analyses and dynamic modelling can help to clarify these complex relations, simulate the landslide hydrological behaviour in real or hypothetical situations, and help to forecast future scenarios based on environmental change. The primary objective of this study is to investigate the possibility of including more temporal and spatial information in landslide hydrology forecasting, by using a physically based spatially distributed model. Results of the hydrological and geomorphological investigation of the Super-Sauze earthflow, one of the persistently active landslide occurring in clay-rich material of the French Alps, are presented. Field surveys, continuous monitoring and interpretation of the data have shown that, in such material, the groundwater level fluctuates on a seasonal time scale, with a strong influence of the unsaturated zone. Therefore a coupled unsaturated/saturated model, incorporating Darcian saturated flow, fissure flow and meltwater flow is needed to adequately represent the landslide hydrology. The conceptual model is implemented in a 2.5-D spatially distributed hydrological model. The model is calibrated and validated on a multi-parameters database acquired on the site since 1997. The complex time-dependent and three-dimensional groundwater regime is well described, in both the short- and long-term. The hydrological model is used to forecast the future hydrological behaviour of the earthflow in response to potential environmental changes.