Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community

Accurate maps of surface water extent are of paramount importance for water management, satellite data processing and climate modeling. Several maps of water bodies based on remote sensing data have been released during the last decade. Nonetheless, none has a truly (90 ∘ N/90 ∘ S) global coverage while being thoroughly validated. This paper describes a global, spatially-complete (void-free) and accurate mask of inland/ocean water for the 2000–2012 period, built in the framework of the European Space Agency (ESA) Climate Change Initiative (CCI). This map results from the synergistic combination of multiple individual SAR and optical water body and auxiliary datasets. A key aspect of this work is the original and rigorous stratified random sampling designed for the quality assessment of binary classifications where one class is marginally distributed. Input and consolidated products were assessed qualitatively and quantitatively against a reference validation database of 2110 samples spread throughout the globe. Using all samples, overall accuracy was always very high among all products, between 98 % and 100 % . The CCI global map of open water bodies provided the best water class representation (F-score of 89 % ) compared to its constitutive inputs. When focusing on the challenging areas for water bodies’ mapping, such as shorelines, lakes and river banks, all products yielded substantially lower accuracy figures with overall accuracies ranging between 74 % and 89 % . The inland water area of the CCI global map of open water bodies was estimated to be 3.17 million km 2 ± 0.24 million km 2 . The dataset is freely available through the ESA CCI Land Cover viewer

Le travail politique dans la transmission des coopératives de production.

National audienc

A Unified Cropland Layer at 250 m for Global Agriculture Monitoring

Accurate and timely information on the global cropland extent is critical for food security monitoring, water management and earth system modeling. Principally, it allows for analyzing satellite image time-series to assess the crop conditions and permits isolation of the agricultural component to focus on food security and impacts of various climatic scenarios. However, despite its critical importance, accurate information on the spatial extent, cropland mapping with remote sensing imagery remains a major challenge. Following an exhaustive identification and collection of existing land cover maps, a multi-criteria analysis was designed at the country level to evaluate the fitness of a cropland map with regards to four dimensions: its timeliness, its legend, its resolution adequacy and its confidence level. As a result, a Unified Cropland Layer that combines the fittest products into a 250 m global cropland map was assembled. With an evaluated accuracy ranging from 82% to 95%, the Unified Cropland Layer successfully improved the accuracy compared to single global products

Seasonal metrics and anomaly detection based on spot-vegetation archive in Europe

Vegetation phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating photosynthesis and other ecosystem processes, as well as competitive interactions and feedbacks to the climate system [1]. Assessing how environmental changes affect the distribution and dynamics of vegetation and animal populations is becoming increasingly important for terrestrial ecologists to enable better predictions of the effects of global warming, biodiversity reduction or habitat degradation[2]. However, despite the existence of long term Earth Observation time series, phenology information is not easily accessible to all scientists. The European Infrastructure for Biodiversity and Ecosystem Research, LifeWatch, provides a set of distributed services to the scientific community in biodiversity research. In this study, we present the development of indices of the vegetation seasonality that are related to ecosystem functioning and animal life traits. These indices aim at describing the average vegetation cycle using a small set of metrics and detecting the anomalies in these cycles in near real-time

Strengths and weaknesses of multi-year Envisat ASAR backscatter measurements to map permanent open water bodies at global scale

The mapping of water bodies at global scale has been undertaken primarily using multi-spectral optical Earth Observation data. Limitations of optical data associatedwith non-uniform and temporally variable spectral signatures suggested investigating alternative approaches towards a more consistent and reliable detection of water bodies. Multi-year (2005-2012) observations of SAR backscattered intensities at moderate resolution from the Envisat Advanced Synthetic Aperture Radar (ASAR) instrument were used in this study to generate an indicator of open permanent water bodies (SAR-WBI) for the year 2010 time frame and for all land surfaces excluding Antarctica and the Greenland ice sheet. A first map of potential water bodies with a spatial resolution of 150 m was obtained with a global detection algorithm based on a set of thresholds applied to multi-temporal metrics of the SAR backscatter (temporal variability, TV, and minimum backscatter, MB). Local refinements were then used to reduce systematic commission and omission errors (4.6% of the total area mapped) due to the similarity of TV and MB over open water bodies and other land surface types primarily in cold and arid environments. The refinement rules are here explained by means of a detailed signature analysis of the SAR backscatter in such environments. The accuracy of the SAR-WBIwas 80%when compared against 2078 manually interpreted footprints with a size of 150 × 150 m2. Omission errors were primarily observed along coast- and shorelines whereas commission errors were associated with (i) ephemeral water bodies, (ii) seasonally inundated areas, and (iii) an incorrect choice of the local refinement

Snow cover anomalies from 2000 to 2014: highlight of two exceptional years over Europe

Snow covers soil and plants several months per year over large parts of Eurasia and North America [1]. This seasonal phenomenon, like many other climatic phenomena, has large impacts on the environment and on ecosystem processes [2]. Snow melt provides water for many mid latitude populations [3] and the freshwater discharge into the ocean modulates ocean circulation [4]. Snow also influences plants and animals species. The population dynamic of different animal species, such small rodents [5] or butterflies [6] can be influenced by snow. In this context, a broad range of users need snow cover information in order to better understand its impacts on the environment. Within the LifeWatch Infrastructure (European Infrastructure Consortium for biodiversity and ecosystem research), we aim to provide meaningful information to the research communities about biodiversity and ecosystems. The objective of this study is to extract metrics and anomalies related to the snow cover in Europe based on remote sensing instead of interpolated meteorological data

object-based automatice change detection in forested areas of poland between 2000 and 2006 using NDVI times series at moderate resolution

Object-based automatic change detection in forested areas of Poland between 2000 and 2006 using NDVI times series at moderate resolution

PROBA-V time series for monitoring vegetation dynamics and snow cover

Vegetation phenology monitoring by remote sensing is of crucial importance for understanding ecosystem behavior. Animals and plant are affected by vegetation phenology which reflects effects of climate change or meteorological extreme events. In this study, we focus on the integration of PROBA-V data to continue the SPOT-VGT time series. In the frame of the European infrastructure Lifewatch, our goal is to provide information about ecosystem dynamics to researchers. Phenology is therefore important because it has a strong impact on animal behavior, and it has been linked to changes in geographical distribution of animal species over time. Since PROBA-V has acquired the possibility to get data in the north of Europe during winter, it becomes interesting to analyze the snow classification of PROBA-V for two main reasons. First, data from the northern regions are crucial to monitor anomalies of snow and their effects over populations. Second, PROBA-V has a higher spatial resolution than MODIS even if the latter has a good overall snow classification