Temporal consistency of land cover time series and detection of major land cover change are key requirements from the users’ community to describe terrestrial surface over time. Land cover was listed as an Essential Climate Variables (ECVs) by the Global Climate Observing System (GCOS) as critical information to further understand the climate system and support climate feedback modelling. However, only global land cover maps from the same instruments have been produced till now, keeping the time series rather short. In the framework of the Climate Change Initiative (CCI) supported by the European Space Agency (ESA) climate modelling and remote sensing teams joined forces to design, implement and deliver global datasets matching the climate science needs for long-term global products.

Building on the ESA-GlobCover experiences, this research first revisited the land cover definition into a stable component and seasonal component and designed a new approach to produce consistent land cover time series decoupling consistent mapping and change detection. Then, the archives of several satellite missions including ENVISAT Meris FR and RR, SPOT-Vegetation, the more recent PROBA-V, and the 90s’ archive of 1 km AVHRR, were reprocessed using state-of-the art methods to produce weekly surface reflectance composite and quality flags throughout the years. According to a stratification splitting the world into 22 equal-reasoning areas from ecological and remote sensing points of view, different seasonal composites were compiled to enhance the land cover discrimination. A typology of 22 land cover classes was defined based on the UN Land Cover Classification System and its classifiers to support the further conversion into Plant Functional Types distribution required by the Earth System Models (ESM). The classification processes combined first machine learning and unsupervised algorithms at 300 m resolution from the whole MERIS FR archives using most of the MERIS bands to establish the land cover baseline. Based on similar algorithms annual global land cover maps from 1 km AVHRR HRPT, 1 km SPOT-Vegetation data sets and from 300 m PROBA-V time series were then produced to serve as input in the land cover change detection algorithm. Systematic analysis of the temporal trajectory of each pixel allows depicting the main change for a simplified land cover typology matching the IPCC classes. This new land cover change detection method was found quite reliable for SPOT-Vegetation and PROBA-V thanks to their excellent temporal co-registration. In contrast, the poorer radiometric and geometric quality of AVHRR HRPT time series only provided major change in contrasted landscapes. The change detected at 1 km was then disaggregated at 300 m whenever higher resolution imagery was available. Finally, these products were validated from an independent reference dataset built by a network of international experts.

All land cover maps can be visualized from an interactive web interface and downloaded along with an aggregation tool, enabling re-projection and re-sampling as well as the translation from LC classes into Plant Functional Types for different climate models. Three major ESM successfully completed several joint experiments based on the CCI land cover products.

The Copernicus Global Land Service is the component of the European Copernicus service which ensures a global systematic monitoring of the Earth’s land surface. It provides bio-geophysical variables in near real time describing the daily state, and changes in state, of vegetation, land surface processes and is currently preparing the release of a Moderate resolution Dynamic Global Land Cover layer.

In this presentation the methodology and rationale behind the Moderate resolution Dynamic Global Land cover layer is explained. This layer complements several global land cover ‘epoch’ datasets which have been created at medium (and high) spatial resolution during the last decade by providing a yearly dynamic land cover layer at 100m resolution. We will present the sub-product covering continental Africa for the year 2015. To build this global land cover layer, 100m spatial resolution PROBA-V data are used as primary EO data. Data fusion techniques are applied for areas with insufficient 5-daily PROBA-V100 m data and daily 300 m datasets are fused in. Next, time series metrics together with ancillary data sets (e.g. other Copernicus global land service biophysical products) are used in a supervised classification approach. Finally, at a third level, we build upon the success of previous global mapping efforts and focus on the improvement in areas where the thematic accuracy of the respective maps was insufficient to perform the final classification of each pixel. The map uses a hierarchical legend based on the United Nations Land Cover Classification System (LCCS). Compatibility with existing global land cover products is hereby taken into account, and extended by providing several cover layers. Training data have been collected from multiple sources, among others by using existing reference datasets (e.g. GOFC-GOLD) and by collecting reference data through Geo-Wiki (http://geo-wiki.org/). The product has been validated by local experts. The validation sample design has been random stratified where each sample site has been classified by visual interpratation of a high resolution imagery (Google and Bing).

GlobeLand30 is an open-access 30-m resolution global land cover (GLC) data product with 10 major classes for years 2000 and 2010. Since its first release on the 22 Sept, 2014, it has been utilized by users from about 120 countries and found applications in many Societal Benefit Areas. At the same time, the users have put forward new demands, such as providing more land cover classes, up-to-dateness and time-series. This has led to an international validation and the preparation of the updating of GlobeLand30.

The validation of 30-m GLC data products is facing several critical challenges related to the high spatial heterogeneity of land cover in the entire earth land surface, and the lacking of standardized approaches and efficient on-line tools to support collaborative practices. With the support of GEO and UN-GGIM, a technical specification of validation has been formulated and a web-based validation system has been developed. About 40 GEO-UN_GGIM members have participated in the joint validation of GlobeLand30.

The updating of 30-m GlobeLand30 is different than its original creation, and aims to produce a 2015’s version product. From the technical point of view, change detection with remote sensed imagery is the major approach and the rapidly increasing crowdsourcing information provides another valuable resource. Due to the extreme complexity of spectral heterogeneity of land cover classes, no one change detection algorithm could be universally applicable to all kinds of imagery and geographic regions. In order to support efficient updating with the consideration of the existing land cover data sets, a specific on-line system was developed to facilitate the design and execution of suitable change detection workflow with the help of a domain knowledge-based service relation model and dynamic service composition.

In the context of the Climate Change Initiative supported by ESA, the Land Cover team aims to map the whole Africa based on the entire archive of Sentinel-2 mission. To address the requirement of a high spatial resolution LC map expressed by the climate science community, the research team will generate a prototype map at 10 m resolution over the whole Africa with a consistent legend of 10 classes. This pioneer experiment faces several challenges including the big data management issues, the preprocessing chain development for improved and cloud screened Sentinel-2 surface reflectance, the precise definition of a scalable land cover typology, the land cover processing chain development and the design of reference database collection for validation.
The increase in spatial resolution requires indeed significant methodological adjustments and innovations to the processing chains developed for medium spatial resolution imagery at global scale. For the pre-processing, for example, the topography and adjacency effects have to be taken into account in the atmospheric correction. In addition, due to the lower revisiting capacity of high spatial resolution sensors such as Sentinel-2, the spatial consistency of surface reflectance between few images becomes a critical aspect in the production of high spatial resolution composites.

Sentinel-2 imagery requires several quality control procedures in order to be processed by large scale processing facilities. From a data management point of view, 72 TB of Sentinel-2 data have been downloaded and preprocessed into surface reflectance L3. A surface reflectance product covering one month of acquisition corresponds to 10 TB and continental surface reflectance mosaic reaches 2 TB of data.

For the LC classification, challenges are of a different nature. The decametric resolution captures the landscape elements diversity and distinct evolution through time due to slightly different seasonality and ecological gradients. Specific effort is made to ensure the consistency between this decametric map and the medium resolution global LC maps already developed within the Climate Change Initiative. While the rich literature on LC mapping at high resolution supports the processing chain development, the data flow provided by Sentinel-2 forces to revisit the classification strategy to map the LC consistently over space and time.

A review of various regional and global mapping efforts completed at different scales from 30 to 300 m resolution, a land cover typology is proposed and currently tested over different regions. The compilation and harmonization of all available land cover maps have been completed in order to support the processing strategy. A set of 10 test sites widely distributed in Africa and representative of different EO conditions and ecoregions allows benchmarking several automated methods. These benchmarking results support the processing chain development in order to optimize the performances of the each step. Finally, results over the whole Africa as well as regional results are presented, highlighting the potential of Sentinel-2 for global land cover mapping and the challenges ahead.

The new global policy framework for the sustainable development of urban areas calls for timely, consistent and accurate information on human settlements. Free and open earth observation data (e.g. Landsat, Sentinel) offer a great potential for large area mapping of human settlements. The Global Human Settlement Layer (GHSL) is the first open and free information layer describing the spatial evolution of human settlements in the past 40 years. It has been produced from Landsat image collections (1975, 1990, 2000 and 2014) and publically released on the JRC open data portal. The recent availability of Sentinel-1 and Sentinel-2 data is expected to bring land cover mapping and monitoring to an unprecedented level. With the great advantage of being free and immediately available for the users, Sentinel data can provide up-to-date global information on the status and evolution of human settlements. With the shift to Sentinel imagery, regular updates and incremental improvements of the GHSL will become more feasible and reliable. This study presents the recent developments in global mapping of human settlements with Sentinel-1 data. Taking advantage of the capabilities offered by the Symbolic Machine learning approach and the functionalities of JRC Big Data infrastructure, the challenges posed by the processing and analytics of the Sentinel-1 global coverage were effectively addressed. In view of the future deployment of the GHSL framework on Sentinel-2 data, a benchmark experiment over selected European cities has been performed in order to assess the added-value of Sentinel-1 and Sentinel-2 with respect to Landsat for improving global high-resolution human settlement mapping. The results show that noticeable improvement could be gained from the increased spatial detail and from the thematic contents of Sentinel-2 compared to the Landsat derived product as well as from the complementarity between Sentinel-1 and Sentinel-2 images.

From the beginning of the years 2000, more than half of the global population is living in urban environments and the dynamic trend of urbanization is growing at an unprecedented speed. Accordingly, an effective monitoring of urbanization represents a key issue to analyze and understand the complexity of human settlements and ensure their sustainable development.

To this purpose, starting from the last decade different global maps outlining urban areas have started being produced. In this framework, the two currently most largely employed are JRC’s Global Human Settlement Layer (GHSL) derived at 38m spatial resolution from Landsat data and, especially, DLR’s Global Urban footprint (GUF) derived at 12m spatial resolution from TanDEM-X/TerraSAR-X data. However, it is worth noting that, despite generally accurate, these layers still exhibit both some over- and underestimation issues. Specifically, this is mostly due to the fact that they have been generated by means of: i) single-date scenes (which can be strongly affected by the specific acquisition conditions) and ii) solely using either optical or radar data, which are sensible to different structures on the ground (e.g., with optical imagery bare soil and sand generally tend to be misclassified as urban, while this does not occur with radar data; on the contrary, with radar imagery complex topography areas or forested regions can be wrongly categorized as urban areas, whereas this does generally not happen if optical data are employed).

In order to overcome these limitations, in the framework of the ESA SAR4Urban project, we have developed a novel methodology that jointly exploits multitemporal optical and radar data for automatically outlining urban areas. In particular, the basic assumption of the intended approach is that the temporal dynamics of urban settlements over time are sensibly different than those of all other non-urban classes. Hence, given all the multitemporal images available over the region of interest in the selected time interval we first extract key temporal statistics (i.e., temporal mean, minimum, maximum, etc.) of: i) the original backscattering value in the case of radar data; and ii) different spectral indices (e.g., vegetation index, built-up index, etc.) derived after performing cloud masking in the case of optical imagery. Then, different classification schemes based on Support Vector Machines are separately applied to the optical and radar temporal features, respectively, and, finally, the two outputs are properly combined together.

At present, the technique is being employed for generating the so-called GUF+ 2015, a global map of urban areas at 10m spatial resolution derived jointly using the TimeScan-Landsat 2015 product (i.e., a dataset including temporal statistics for several spectral indexes derived from ~420,000 Landsat-7/8 scenes produced within the ESA Urban-TEP platform) and temporal statistics of Sentinel-1 IW GRDH data computed globally using Google Earth Engine. The whole classification activities are supported by the Urban-TEP infrastructure and the GUF+ is expected to be completed by March 2017.

Experimental results are extremely promising and confirm the great potential of combining optical and radar imagery and the higher accuracy of the GUF+ compared to the other existing layers.

Ten years of operations of the Envisat ASAR instrument have generated an invaluable archive of repeated observations of the SAR backscatter over land masses. The potential of such data is still being unravelled in applications despite the Envisat mission ended in 2012. The CCI Water Bodies dataset is one example of a global thematic dataset encompassing the ASAR data archive. More in general, thematic applications over land have been possible in spite of an uncoordinated acquisition strategy. Only at coarse resolution (1,000 m), the complement of all ASAR acquisition modes yielded wall-to-wall repeated coverage throughout the Envisat mission. Multi-temporal metrics of the SAR backscatter were found to overcome typical confusion between water and land occurring in single images under windy or frozen conditions. Nonetheless, such observables where not totally unique over water since specific land surface types such as glaciers and sand dunes were characterized by similar values under specific imaging conditions.

With the Sentinel-1 mission, it is envisaged that several caveats identified when mapping inland water bodies with ASAR are somewhat overcome. This appears to be the case after two years of operations and the operations of two satellites. Repeated acquisitions are planned according to a predefined strategy aiming at maximizing the information content in the scene observed. Stacks of multi-temporal observations, often in dual-polarization, are being created. The 6-12 days acquisition rate over Europe and other intensively observed regions even opens possibility to track water seasonality, which was only possible with the ASAR mission locally or for a short time period.

Here, two global scale applications of the Envisat ASAR data archive to map water bodies and some examples of the contribution of Sentinel-1 to map water bodies are presented.

The SAR Water Body Indicator derived to support the CCI Water Body Dataset is briefly reviewed. We then present the contribution of ASAR multi-year observations (2005-2012) to capture inland water dynamics at 1,000 m with a weekly time step. A novel approach based on the functional relationship between ASAR backscatter and local incidence angle is applied. The water seasonality appears to be well identified in the northern hemisphere thanks to the very dense ASAR observations in time. In regions characterized by small water bodies and dynamics, or when the data sampling was irregular, the dynamics appear to be underrepresented.

While the detection of water bodies with ASAR had to rely on a sophisticated construct and required multi-temporal observations, the availability of cross-polarized backscatter from the Sentinel-1 satellites relaxes the constraints on the input data source and allows for improved thematic accuracy. In boreal landscapes, the detection of water bodies using a simple threshold-based approach on a summer mean of cross-polarized backscatter images performed at 20 m with over 90% accuracy when compared to samples interpreted in high-resolution images. We are currently extending our investigations to other landscapes in Europe and Africa, here with a focus to complement the land cover mapping activities based on Sentinel-2 within the CCI Land Cover Project. Results will be presented at the conference.

The location and persistence of surface water is both affected by climate and human activity and affects climate, biological diversity and human wellbeing.
Global datasets documenting surface water location and seasonality have been produced, but measuring long-term changes at high resolution remains a challenge.
To address the dynamic nature of water, the European Commission’s Joint Research Centre (JRC), working with the Google Earth Engine (GEE) team, has processed each single pixel acquired by Landsat 5, 7, and 8 between 16th March 1984 and 10th October 2015 (> 3,000,000 Landsat scenes, representing > 1,823 Terabytes of data).
The produced dataset records the months and years when water was present across 32 years, where occurrence changed and what form changes took in terms of seasonality and persistence, and documents intra-annual persistence, inter-annual variability, and trends.
This validated dataset shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered showing how surface water is altered by human activities.
Freely available, we anticipate that this dataset will provide valuable information to those working in areas linked to security of water supply for agriculture, industry and human consumption, for assessing water-related disaster reduction and recovery, and for the study of waterborne pollution and disease spread. The maps will also improve surface boundary condition setting in climate and weather models, improve carbon emissions estimates, inform regional climate change impact studies, delimit wetlands for biodiversity and determine desertification trends. Issues such as dam building (and less widespread dam removal), disappearing rivers, the geopolitics of water distribution and coastal erosion are also addressed.

Amongst all land use change processes, the agricultural area change due to spatial expansion or cultivated lands abandonment is one of the most dynamic land cover change. Furthermore, cropland or agriculture areas corresponds to very diverse land features which vary over time as the interaction result of crop management practices and seasonal weather conditions. In order to capture the cropland evolution, the JECAM network has adopted a restrictive definition of cropland which corresponds to annually cultivated lands; in spite of its remote sensing perspective this definition still makes the cropland and crop type mapping particularly challenging.

Early 2017, the Sentinel-2 (S2) mission will reach the optimal capacity for cropland mapping and agriculture monitoring in terms of resolution (10-20 m), revisit frequency (5 days with two satellites) and systematic coverage (global). In order to exploit these new capabilities, specific methods for dynamic cropland mapping and main crop type classification have been developed in the framework of the Sentinel-2 for Agriculture project funded by ESA.

Dynamic Cropland masks correspond to a set of successive masks to depict annually cultivated areas. The production can rely on two alternative approaches depending on the availability or not of in-situ data. Both methods are based on a random forest classifier, trained in the first case with in-situ data and in the second case, with samples collected from an existing reference land cover map. The Crop Type map classifies the main crop groups, i.e. irrigated versus rainfed and summer versus winter crops. The map is produced at the half and at the end of the season using a random forest classifier over a combination of S2 and L8 time series.

During the 2016 growing season, these methods were applied at national scale over Ukraine, Mali and South Africa covering more than 500 000 km² in each country, where fast track nationwide field campaigns were organized by national partners. These demonstration cases delivered a new type of land mask delineating the cropland at 10 m resolution over the entire country for a given season and delivered less than few weeks after the last observation.

The mapping results obtained over Ukraine for 2016 have been thoroughly validated through an independent accuracy assessment. National workshops are also organized with various key users in order to discuss the timeliness and relevance of the products accuracy for different operational applications. A similar approach is going on for Mali and South Africa. The accuracy assessment results are very high for Ukraine and will be available for Mali and South Africa.

Beyond these very encouraging results of the Sen2Agri system, such an automatic production of high resolution land map obtained from freely and continuously available time series changes completely the classical remote sensing approach. Indeed this system is designed to deliver products on a yearly basis and to run over very large areas providing a new capability for regional to continental mapping. Based on the lessons learnt from the Sen2Agri system demonstration, challenges ahead are discussed towards a more general land cover mapping system.

Rice is the most important food crop in Asia and the mapping and monitoring of paddy rice fields is an important task in the context of food security, food trade policy, water management and greenhouse gas emissions modelling. Asia’s biggest rice producers are facing increasing pressure in terms of food security due to population and economic growth while agricultural areas are confronted with urban encroachment and the limits of yield increase. At the same time demand for rice imports is increasing, spurred by global population growth.

Despite the importance of knowledge about rice production the countries official land cover products and rice production statistics are of varying quality and sometimes even contradict each other. Available remote sensing studies focused either on time-series analysis from optical sensors or from Synthetic Aperture Radar (SAR) sensors. We try to address the sensor specific limitations by proposing a paddy rice mapping approach that combines medium spatial resolution, temporally dense time-series from the optical MODIS sensors and high spatial resolution time-series from the Sentinel-1 A/B SAR sensors.

We developed a method to use MODIS time-series and a one-class classifier to create medium resolution rice maps [1]. In a next step we used these medium resolution rice maps to mask Sentinel-1 Interferometric Wide Swath images, which limits the amount of data to process and allows efficient rice mapping over larger areas. The high resolution rice masks are then created by segmentation of multi-temporal SAR images into objects, from which backscatter time-series are derived and classified. We created 10m resolution rice-maps that also allow seasonality extraction, given enough Sentinel-1 acquisitions. This method allows concurrent, accurate and high resolution mapping of paddy rice areas from freely available data. Results of our paddy rice classification will be presented for selected study sites in Asia.

1. Clauss, K.; Yan, H.; Kuenzer, C. Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series. Remote Sensing. 2016, 8, 434.

The need for accurate information on the state and evolution of tropical forest types at regional and continental scales is widely recognized, particularly to analyze the forest diversity and dynamics, to assess degradation and deforestation processes and to better manage these natural resources (Achard et al. 2014).

A few global and continental land cover or forest cover products have been derived from Landsat satellite imagery at 30m resolution: they either contain detailed thematic information without temporal dynamics (Chen et al. 2015, Giri and Long 2010) or contain information on forest-cover changes over long time periods (10 to 30 years) without thematic classes such as the discrimination of evergreen forests (Kim et al. 2014, Hansen et al. 2013, Potapov et al. 2015).

The objective of this study is to map undisturbed evergreen and semi-deciduous forests at 30m resolution over the full tropical humid domain and to better characterize the changes and disturbances which occurred during the last 33 years in these forests. Therefore we exploited the full archive of Landsat imagery between 1984 and 2016 and developed a pixel-based automatic methodology which includes four steps: (i) pre-processing of the Landsat time series with cloud masking and filtering of sensor artefacts, (ii) single-date image classification (driven by a large spectral library) into three basic classes (evergreen forest, vegetative non-forest cover and poorly/non vegetated cover), (iii) creating forest/non-forest maps for three epochs based on the occurrence of non-forest classes, and (iv) production of a final map of detailed forest types based on the temporal succession of observed basic classes from 1984 to 2016.

The resulting map includes six classes: undisturbed forest cover, old and young vegetation regrowths, old or recent deforested areas (during last 10 years), recently disturbed areas (during last 3 years) and other land cover. This map at 30 m resolution allows the identification of small linear features such as gallery forests and of small disturbance events such as skid trails and logging decks. The use of a 33-year Landsat time series allows (i) to identify most deforestation and degradation events (when > 0.1 ha) that occurred during this period, (ii) to provide the dates of the forest disturbances, and (iii) to considerably reduce the confusion that usually occurs with small scale agricultural fields (shifting cultivation, tree plantation and irrigated crops…). Finally we characterize the deforested and disturbed classes by providing their timing and occurrence (date of first and last events, number of events).

The accuracy of the forest map was assessed over Africa from an independent sample of reference data (3830 plots) created through visual expert interpretation of Landsat imagery at several dates and finer resolution satellite imagery with an overall agreement of 90%. The pan-tropical map and the accuracy assessment results over Africa will be presented at the conference.

It is intended in the future to adapt and apply the methodology on Sentinel-2 data for a better characterization of forest-cover disturbances.

Remote sensing is an important tool for understanding the spatial and temporal dynamics of forest disturbances over large areas. The most recent developments in disturbance detection algorithms utilize dense time series information that enables the detailed characterization of abrupt and gradual disturbance events. These advances allow the mapping of a wide variety of disturbance agents, including harvest, fire, blowdown, and insect attacks. However, current algorithms have primarily been developed and tested in the forest ecosystems of North America, which are characterized by relatively homogeneous coniferous forests, little to none management, and medium to large-scale disturbance patches. Those algorithms have not been validated for mapping forest disturbances in Europe yet, where forest ecosystems are much more variable in terms of species composition, landscape structure, and forest management. Consequently, our aim was to evaluate current disturbance detection algorithms for mapping forest disturbance in Europe by i) comparing spectral-temporal characteristics of forest disturbances across a sample of five protected forest areas in the temperature forests of central Europe; and by ii) comparing disturbance characteristics inside the protected forests to the surrounding unprotected forests in order to understand the compound effect of natural and management disturbances on spectral-temporal characteristics. We utilized dense Landsat time series from the USGS and ESA archives covering the time period 1985 to 2016. We mapped forest disturbance characteristics (i.e., magnitude and duration) using state-of-the-art time series tools in conjunction with random forests classification and a set of photo-interpreted reference plots. Primary results show that disturbances were detected with high accuracies (>90% overall accuracy) across all sites. Spectral-temporal characteristics varied substantially within and outside protected forests. In particular, unmanaged areas showed more long-term disturbances – likely related to blowdown and following bark beetle mortality – whereas disturbances in managed forests were mainly short and of high magnitude. However, temporal patterns of disturbances were similar inside and outside the protected areas, suggesting that spectral disturbance patters outside protected forests are superimposed by a strong management signal. Our analysis improves the understanding of forest disturbances – and how to map them using remote sensing – for European temperate forests, a forest ecosystem of high economic and ecological value. As such, this study paves the way for using long and dense time series of optical satellite data, i.e. Landsat and Sentinel-2, for mapping and understanding forest dynamics across Europe.

Wetlands are amongst the planet‘s most productive ecosystems providing a wealth of ecosystem services, e.g., nutrition, flood control and protection, or support of biodiversity, to name a few. Nevertheless, wetlands are exposed to multiple threats due to climate change, agricultural pressure, hydrological modifications, fragmentations, etc. Thus, a consistent mapping and monitoring of global wetland ecosystems is very important to track changes and trends aiming at supporting wetland conservation and sustainable management. Although EO data are ideal for large scale inventorying of wetlands, the large diversity of them makes remote detection particular challenging. This diversity and resulting challenge has been tackled by many researchers applying different sensors (optical and radar) and mapping techniques to delineate wetland from non-wetland areas. A global and homogenous inventory, however, is still not available and scope of ongoing research.

Herein, we present an innovative and operational water and wetness product building on data from Sentinel-1 SAR and Sentinel-2 MSI complemented with historical data from the Landsat missions. Rather than trying to detect wetlands in the ecological sense, we derive wetlands in the physical meaning identifying the wetness of the underlying land surface.

Using a hybrid sensor approach, i.e., combining optical and radar observations, provides a more robust wetland delineation with optical imagery being more sensitive to the vegetation cover and radar imagery to soil moisture content. Additionally, the higher frequency of observations stemming from the combined data streams contributes to a better characterization of seasonal dynamics which is important so that seasonal and temporary changes do not lead to false conclusions of the overall long-term trend in wetland extent. Within the domain of optical remote sensing, the identification of wetlands is based on the enhancement of the spectral signature using bio-physical indices sensitive to water and wetness and subsequent derivation of a water and wetness probability index. The radar-based algorithm builds on geophysical parameters, surface soil moisture dynamics and water bodies, derived from historical Envisat ASAR and Sentinel-1 backscatter time series to identify permanent/temporary wet and flooded areas. In addition, it is possible to identify flooded vegetation according to the double-bounce scattering principle in densely vegetated wetlands. The non-flood prone areas are masked using the Height Above Nearest Drainage (HAND) index. After the separate processing of the optical and radar imagery, the data is fused into a combined water and wetness product. With our methods we aim at detecting the current status of wetland areas, but also to capture the historic evolution taking into account the past 25 years in a fully automated manner.

The above described methods are currently applied for several large regional sites throughout Africa within the GlobWetland Africa project and for the Pan-European production of the “water and wetness” High Resolution Layer of the Copernicus Land Monitoring Service. We will further present a thorough validation of the product for different wetland ecosystems and discuss remaining issues, mainly due to global data availability and coverage.

Land-cover and Land Use information are important parameters in most of the studies related to natural environment, ecosystem services and many other important disciplines. However despite its importance and the many efforts toward data harmonization (especially for LC) do not exist an accepted model on how to link and functionally correlate those two information. On the contrary there is often a contamination of LCLU terms in many LC nomenclatures (Anderson, Corine, etc) and surprising also in some LU classifications (UNFCC, NLUD, etc). Even when the two information are keep correctly clearly distinct (E.U. Inspire spatial data infrastructure) no effort is made to model/describe their functional relationship. FAO (and UNEP) have a long prominent role on the efforts to develop standardized LCLU classification and data harmonization. Especially in LC the development of the LCCS parametric method and subsequently the “object oriented” approach underlining the LCML (Land Cover Meta-Language) model (ISO Standard 19144-2) has open a new path forward for the representation/harmonization of LC information. Based on this experience and using as base part of the original LCML UML schema, a new model is under development, the LCHML (Land Characterization Meta-Language). LCHML not only propose a revised LC and a new LU model but also try to create a comprehensive standardized framework were is possible to create an exhaustive and functional correlation of both biophysical and human related activities. LCHML therefore try to integrate in a unique model both LC and LU. The objective is to create a standardized framework were it is possible to describe any geographic area from different perspectives: pure LC, pure LU, or a functional combination of the two called (tentatively) LCH (Land Characterization).

The Copernicus Programme, headed by the European Commission (EC) in partnership with the European Space Agency (ESA), offers Earth observation-based services for six core thematic areas: Land, Atmosphere, Oceans, Climate Change, Emergency and Security. Among these services – mainly based on Earth Observation (EO) data provided by ESA through the Copernicus Space Component – the Copernicus Land Monitoring Service delivers products on local, continental and global levels. As part of the pan-European Copernicus Land Service, coordinated by the European Environment Agency (EEA), the High Resolution Layers (HRLs) map multi-temporal land cover characteristics for five thematic areas (Imperviousness, Forest, Grassland, Water/Wetness, Small Woody Features) in 20 meters spatial resolution and in a consistent manner for 39 European countries. All thematic HRLs contain specific information on current environmental conditions and temporal variance of major land cover types with thematic accuracies exceeding 80–90% (depending on the product). The HRL products are tailored towards a multi-user community and are freely provided for download on the Copernicus website.

With the current production for the 2015 reference year, the HRLs are entering the era of big data multi-temporal image processing, incorporating large data volumes from different sensors in a decentralized processing framework in a network of industrial service providers. Our contribution will describe the framework, methodology and first results of the current HRL 2015 production comprising the update of the existing (2012) pan-European HRLs Imperviousness and Forest, including 2012–2015 change products, as well as 2015 mapping of other newly defined HRLs (Grassland, Water/Wetness, Small Woody Features).

Primary information source are multi-temporal, high-resolution satellite images from Sentinel-1 and -2, as well as data from SPOT, Resourcesat and Landsat contributing missions. Whereas the 2015 Forest and Imperviousness HRLs will be produced based on optical time series imagery, the newly defined Grassland and Water/Wetness products will benefit from innovative approaches on the basis of a fusion of optical and synthetic aperture radar (SAR) time series data. The novel HRL on Small Woody Features is the HRL using very high resolution (VHR) data as primary input. The VHR data sets will also be used for reference data collection and validation alongside national and pan-European in-situ data sets.

The full chain of image and in-situ data acquisition, pre-processing, generation of biophysical variables, multi-temporal image classification and validation will be demonstrated, and first results will be presented for all five HRLs. Semi-automatic classification techniques based on multi-temporal pixel-based as well as segment-based approaches are applied specifically tailored towards each HRL, resulting in raster products in full 20m resolution, as well as vector products on 1:5000 scale for the Small Woody Features.

The Copernicus HRLs are designed for a broad user community as basis for environmental and regional geo-spatial analyses as well as for supporting political decision-making. With future updates, the HRLs will significantly benefit from ESA’s growing Sentinel-1/-2 archive, further improving the products’ consistency, timeliness and accuracy. An outlook concludes on the potential usability of the presented methods and products for future European to global LC/LU applications on a HR scale.

The National Land Cover Database (NLCD) is an example of a national land cover change monitoring system that incorporates user requirements, scientific advances and the results from rigorous accuracy assessments to provide accurate and current data products that are useful to land managers and the public. It is managed by a consortium of United States governmental agencies, the Multi-Resolution Land Characteristics (MRLC) consortium, that require land cover information to assess environmental quality and promote the sustainable use of natural resources. This consortium is a collaborative forum, where members share research, methodological approaches, and data to establish protocols promoting the development and use of integrated land cover data products. The NLCD began as a one-time land cover thematic mapping effort of the conterminous US in 1992 and now encompasses four epochs (1992, 2001, 2006 and 2011) of thematic land cover data, as well as continuous field datasets such as percent impervious surface, required for water quality assessments and percent canopy cover, required for biodiversity, biomass and carbon sequestration assessments. All datasets are derived from Landsat imagery and so have a spatial resolution of 30 metres by 30 metres. Monitoring land cover change is accomplished by integrating a remote sensing image change analysis with a knowledge-based system on land change. This system consists of rules and/or attributes derived from the spectral, spatial, and temporal characteristics of remote sensing data and historical knowledge on land cover change and its trajectories. All of the datasets have undergone rigorous accuracy assessments that have been used to guide continuous improvements in the data, as well as advancing remote sensing science. The consortium has stressed advancing the technological aspects of land cover data production, including data preprocessing, classification methodologies and advancing an integrated database paradigm that enables change monitoring over time. The result has been a dramatic decrease in the amount of time required to create a dataset and its cost, as well as the identification of new compatible datasets such as the newly formulated percent bareground and shrub cover. The results of the consortium’s efforts have been a suite of land cover data products that provide valuable, tangible societal benefits. These benefits include: water quality monitoring and assessment; identifying potential relationships between land cover patterns and human well-being; assessing the impacts of natural disasters on ecosystem services; and influencing federal energy policies on land use change and potential environmental degradation that may arise from land use change.

A component of the EU-funded ECOPOTENTIAL project funded under the Horizon 2020 Program (Reference 641762) has been the development of the EO Data for Ecosystem Monitoring (EODESM) system. This system allows classification of land covers according to the Food and Agricultural Organisation (FAO) Land Cover Classification System (LCCS-2) taxonomy from EO-derived biophysical and thematic information. The presentation will a) demonstrate the EODESM system, giving examples from protected areas in Europe, Africa and the Middle East, and its wider application (including at the global level), and b) convey the uncertainty associated with including layers generated at different scales within the classification.

The EODESM system has been developed such that LCCS classifications can be generated from pre-prepared (including global) data layers as well as satellite data (e.g., as acquired by the Sentinel’s and Landsat). These layers relate to the broader LCCS Level 3 categories (natural/semi-natural and cultivated/managed landscapes, natural and artificial bare areas, and water) and include additional information (including lifeform, structure, phenology, hydroperiod, and urban density). Inputs can also include existing thematic information (e.g., cadastral and infrastructure maps), modeled outputs (e.g., hydrological) or knowledge. These layers are used to generate the component LCCS codes (e.g., A3 or A4 for trees or shrubs, D1 or D2 for broadleaved or evergreen), which are then combined. The system has also been adapted to consider changes in these codes but also biophysical and other attributes (including those unrelated to the classification; e.g., above ground biomass), and can attribute changes to specific causes and indicate or suggest consequences and the impacts on, for example, protected areas. An advantage is that it can be applied at any scale (local to global)and also integrate data from sensors operating in different modes (lidar, radar, optical) and spatial and temporal resolutions. Furthermore, consistent and highly detailed land cover classifications are provided together with a diverse range of information describing landscapes (e.g., biomass, hydrology, Leaf Area Index, soil moisture). The system is also well suited to follow the LCCS-3 or Land Cover Macro Language (LCML) and is being modified accordingly. In the context of ECOPOTENTIAL, the EODESM approach has been applied to a wide range of protected areas and their surrounds with the intention of providing timely and historical information to ensure protection and facilitate restoration of ecosystem services. The EODESM system has potential for global application and this will be conveyed within the presentation with reference to other land cover classification schemes. The EODESM system will be made available through a Virtual Laboratory being prepared by the ECOPOTENTIAL project and integrated within GEOSS. Land cover maps and change maps will also be accompanied by comprehensive metadata including data quality documentation.

Traditionally remote sensing of land cover has focused on mapping land cover types, with monitoring being devoted to finding the conversions between land cover types. As time series of observations have become available, it is now possible to detect more subtle characteristics about landscapes, including trends in ecosystem health and condition. Using many observations it is possible to separate effects related to seasonality from those providing longer term indications of the condition of ecosystems. For example, it is possible to observe trends indicative of growth in forests, decline related to pests or degradation, and interannual variability related to climate. One particularly interesting case is to monitor the recovery of ecosystems following disturbance. Additionally, it is possible to begin to identify events that influence ecosystems, such as extreme weather. The net effect is the ability to derive more subtle information about land cover that will prove helpful in both ecosystem modeling efforts and land management.