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GIS server and desktop GIS technologies support scientific work at all levels, from data collection and data processing to data management and data visualisation. Here we present how the development and publication of scalable WebGIS data services supports the ESA DUE Globpermafrost (2016-2018), and the ESA CCI+ Permafrost (2018-2021) projects, specifically in the interaction with the permafrost community. Within ESA DUE programs, user feedback is essential to improve the remote sensing products. This is why ESA GlobPermafrost had to focus on methods and infrastructure for data presentation, and established PerSYS (Permafrost Information System). PerSYS became the ESA GlobPermafrost geospatial information service for publishing and visualisation of information and data products to the public. Data products are described and searchable in the PerSYS Data Catalogue, a core component of the Arctic Permafrost Geospatial Centre (APGC), established within the framework of ERC PETA-CARB at AWI. All GlobPermafrost data products will be DOI-registered and archived in the data archive PANGAEA provided by AWI. The data visualisation employs AWI’s WebGIS-infrastructure maps@awi (http://maps.awi.de), a highly scalable data visualisation unit within AWI’s data workflow framework O2A (from Observation to Archive). GIS services have been created and designed using ArcGIS for Desktop (latest Version) and finally published as a Web Map Service (WMS), an internationally standardized format (Open Geospatial Consortium (OGC)), using ArcGIS for Server. The project-specific data WMS as well as a resolution-specific background map WMS are embedded into a GIS viewer application based on Leaflet, an open-source JavaScript library. The GIS viewer application was adapted to interlink all GlobPermafrost WebGIS projects, and especially to enable their direct accessibility via the GlobPermafrost Overview WebGIS. The PerSys WebGIS is accessible via the GlobPermafrost project webpage and linked to the respective product groups as well as to maps@awi. WebGIS technology within maps@awi supports the project-specific visualisation of raster and vector data products of diverse spatial resolutions and remote sensing sources. This is a prerequisite for the visualisation of the wide range of GlobPermafrost remote sensing products like: Landsat multispectral index trends (Tasseled Cap Brightness, Greeness, Wetness; Normalized Vegetation Index NDVI), Arctic land cover (e.g., shrub height, vegetation composition), lake ice grounding, InSAR-based land surface deformation, rock glacier velocities and a spatially distributed permafrost model output with permafrost probability and ground temperature per pixel. All WebGIS projects are adapted to the products’ specific spatial scales. For example, the WebGIS ‘Arctic’ visualises the Circum-Artic products. Higher spatial resolution products for rock glacier movements are visualised on regional scales in the WebGIS projects ‘Alps’, ‘Andes’ or ‘Central Asia’. The PerSYS WebGIS also visualises the locations of the WMO GCOS ground monitoring networks of the permafrost community: the Global Terrestrial Network for Permafrost GTN-P managed by the International Permafrost Association IPA. The PerSYS WebGIS has been presented on several User workshops and at conferences, and is being continuously adapted in close interaction with the IPA.

Permafrost is an Essential Climate Variable (ECV) within the Global Climate Observing System (GCOS), which is characterized by subsurface temperatures and the depth of the seasonal thaw layer. Complementing ground-based monitoring networks, the Permafrost CCI project funded by the European Space Agency (ESA) 2018-2021 will establish Earth Observation (EO) based products for the permafrost ECV spanning the last two decades. Since ground temperature and thaw depth cannot be directly observed from space-borne sensors, we will ingest a variety of satellite and reanalysis data in a ground thermal model, which allows to quantitatively characterize the changing permafrost systems in Arctic and High-Mountain areas. As recently demonstrated for the Lena River Delta in Northern Siberia, the algorithm uses remotely sensed data sets of Land Surface Temperature (LST), Snow Water Equivalent (SWE) and landcover to drive the transient permafrost model CryoGrid 2, which yields ground temperature at various depths, in addition to thaw depth. For the circumpolar CCI product, we aim for a spatial resolution of 1km, and ensemble runs will be performed for each pixel to represent the subgrid variability of snow and land cover. The performance of the transient algorithm crucially depends on the correct representation of ground properties, in particular ice and organic contents. Therefore, the project will compile a new subsurface stratigraphy product which also holds great potential for improving Earth System Model results in permafrost environments. We present simulation runs for various permafrost regions and characterize the accuracy and ability to reproduce trends against ground-based data. Finally, we evaluate the feasibility of future “permafrost reanalysis” products, exploiting the information content of various satellite products to deliver the best possible estimate for the permafrost thermal state over a range of spatial scales.

Permafrost regions are highly sensitive to climate changes. The monitoring of key variables and identification of relevant-processes is of topmost importance in these environments. ESA DUE GlobPermafrost (www.globpermafrost.info) provides a remote sensing data service for permafrost research and applications. This service was extended by permafrost modelling (time series), implemented in the new ESA CCI+ Permafrost project (2018-2021). The service comprises of the generation of remote sensing products for various regions and spatial scales as well as the specific infrastructures for visualisation, dissemination and access to datasets - PerSys. PerSys is the ESA GlobPermafrost geospatial information service for publishing and visualisation of information and data products to the public. Data products are described and searchable in the PerSys data catalogue (apgc.awi.de), and data visualisation employs the AWI WebGIS-infrastructure maps@awi (http://maps.awi.de), a highly scalable data visualisation unit within the AWI data-workflow framework O2A, from Observation to Archive. maps@awi WebGIS technology supports the project-specific visualisation of raster and vector data products of any spatial resolution and remote sensing origin. This is a prerequisite for the visualisation of the wide range of GlobPermafrost remote sensing products like: Landsat multispectral index trends (Tasseled Cap Brightness, Greeness, Wetness; Normalized Vegetation Index NDVI), Arctic land cover (e.g. shrub height, vegetation composition), lake ice grounding, InSAR-based land surface deformation, rock glacier velocities and a spatially distributed permafrost model output with permafrost probability and ground temperature per pixel. We established several WebGIS projects for the adaption to products specific spatial scales. For example, the WebGIS ‘Arctic’ visualises the Circum-Artic products. Highly resolved data products for rock glacier movements are visualised on regional scales in the WebGIS projects ‘Alps’, ‘Andes’ or ‘Central Asia’. The PerSYS WebGIS also visualises the stations of the WMO GCOS ground monitoring networks of the permafrost community: the Global Terrestrial Network for Permafrost GTN-P managed by the International Permafrost Association IPA. The PerSYS WebGIS has been continuously adapted in close co-operation with user at user workshops and at conferences and the International Permafrost Association (IPA).

ESA GlobPermafrost (www.globpermafrost.info) provides a remote sensing data service for permafrost research and applications. This service comprises data product generation for various regions and spatial scales as well as specific infrastructures for visualisation and access to datasets. PerSys is the open access geospatial information system for dissemination and visualisation of remote sensing data derived within the ESA GlobPermafrost project. The data products are visualised in the PerSys WebGIS and are described and searchable in the PerSys Data Catalogue. The PerSys Data Catalogue is a core component of the Arctic Permafrost Geospatial Centre (APGC), set up within ERC PETA-CARB at AWI1. The visualization employs the AWI WebGIS infrastructure maps@awi (http://maps.awi.de), relying on OGC-standardized Web Mapping/Feature Services (WMS, WFS). The WebGIS supports the project specific visualisation of raster and vector data products such as land cover, Landsat multispectral index trends, InSAR-based land surface deformations, rock glacier velocities, and permafrost model outputs. The WebGIS projects are adapted to the products specific spatial scales, e.g. the Arctic WebGIS visualizes Circum-Artic products as well as other large-scale data products. Rock-glacier data products of higher spatial resolution are visualised on regional scale in the WebGIS projects Alps, Andes and Central Asia. PerSys is accessible via the GlobPermafrost project webpage.

ESA GlobPermafrost (2016-2019, http://www.globpermafrost.info) provides a remote sensing service for permafrost research and applications. The service comprises of data product generation for various regions and spatial scales as well as specific infrastructure allowing visualization and access to datasets. Based on an online user survey conducted within the project, the user community extensively applies GIS software to handle remote sensing-derived datasets and requires preview functionalities before accessing them. In response, we developed the Permafrost Information System PerSys which is conceptualized as an Open Access geospatial data dissemination and visualization portal for Earth Observation, i.e. remote sensing-derived datasets produced within the ESA GlobPermafrost project. The prototype and final remote sensing products and metadata will be visualized in the PerSys WebGIS and are described and searchable via the PerSys Data Catalogue. The WebGIS visualization is managed via the AWI WebGIS infrastructure maps@awi (http://maps.awi.de) relying on OGC-standardized Web Mapping Service (WMS) and Web Feature Service (WFS) technologies for data display and visualization. The PerSys WebGIS projects allow visualization of raster and vector products such as land cover classification, Landsat multispectral index trend datasets, lake and wetland extents, InSAR-based land surface deformation maps, rock glacier velocity fields, spatially distributed permafrost model outputs, and land surface temperature datasets. Each of these WebGIS projects is adapted to the spatial scale of the specific products, ranging from local to hemispherical coverage. The PerSys Data Catalogue will provide the metadata and the access to all mature-state and final-state GlobPermafrost products. PerSys can be accessed through the GlobPermafrost project webpage. PerSys is also a core component of the Arctic Permafrost Geospatial Centre (APGC), a geodata portal for permafrost launched within the framework of the ERC PETA-CARB project at the Alfred-Wegener-Institut Helmholtz-Zentrum für Polar and Meeresforschung AWI. The APGC framework features a range of permafrost-specific geospatial data projects, including PerSys, and will allow searching for project-specific geospatial data by tags, keywords, data type and format, licence type, or by location. PerSys will be launched within APGC in early 2017. Long-term availability of PerSys and APGC is guaranteed through AWI. PerSys will also integrate remote sensing products from previous European permafrost observing programs using earth observation, in particular from ESA DUE Permafrost, ESA STSE ALANIS Methane and EU FP7 PAGE21. We aim also to catalogue and visualize external permafrost-related satellite-derived products. The Open Access data library PANGAEA serves as permanent archive for the final GlobPermafrost products, providing permanent Digital Object Identifiers (DOIs) for each dataset archived. Final product data sets of ESA DUE Permafrost project are already published under DOI doi:10.1594/PANGAEA.780111.

The GlobPermafrost project focuses on the accessibility of remote sensing data. This comprises of data product generation as well as on specific infrastructure to give information on and access to data. Further information regarding project status and events are available from www.globpermafrost.info. An online user survey conducted within the project highlights that GIS software is applied by a great deal of the user community. Additionally, data preview was requested by the majority of the survey participants. The Permafrost Information System PerSys will be conceptualized as an open access geospatial data dissemination and visualization portal. PerSys will allow raster and vector products visualisation resulting from GlobPermafrost such as land cover classifications, Landsat/Sentinel Trend datasets, lake and wetland extents, InSAR-based land surface deformation maps, block glaciers’ velocity fields, spatial permafrost model outputs, LST datasets, and many more. The data will be published as WebGIS services relying on OGC-standardized Web Mapping Service (WMS) and Web Feature Service (WFS) technologies for data display and visualization. The technical WebGIS environment will be hosted at AWI where a geodata infrastructure has been implemented comprising of ArcGIS for Server 10.4, PostgreSQL 9.2 and a browser-driven data viewer unit based on Leaflet (http://leafletjs.com). Independently, we will provide an ‘Access - Restricted Data Dissemination Service’, which will be available to users for testing frequently updated versions of project datasets. In addition, the European Research Council (ERC) funded PETA-CARB project (http://www.awi.de/) developing the Arctic Permafrost Geospatial Centre (APGC) where PerSys will become a core project. The APGC Data Catalogue will contain all final products of GlobPermafrost and links to the derived permanent DOI-based ESA remote sensing products archived in PANGAEA data repository.

In order to define the information needs in support to climate science, the Global Climate Observing System established a list of Essential Climate Variables (ECV), selected to be critical for a full understanding of the climate system and currently ready for global implementation on a systematic basis. In response to the ECV list, ESA initiated a new program - namely the Climate Change Initiative (CCI) –to develop global monitoring datasets to contribute in a comprehensive and timely manner to the need for long-term satellite-based products in the climate domain. Among the 14 ESA CCI components respectively addressing the atmospheric, oceanic and terrestrial domains, the ESA CCI Land Cover (CCI-LC) project is dedicated to land cover (LC) characterization. LC is indeed referred to as one of the most obvious and commonly used indicators for land surface and the associated human induced or naturally occurring processes, while also playing a significant role in climate forcing. This project builds on the ESA-GlobCover projects experiences (Arino et al. 2008, Defourny et al. 2009). It aims at revisiting all algorithms required for the generation of a global LC product from various Earth Observation (EO) instruments that matches the needs of key users’ belonging to the climate modelling community. First, a user requirements analysis was completed with this community to identify its specific needs in terms of satellite-based global LC products. This analysis highlighted a set of requirements in terms of thematic content, spatial and temporal resolution, stability and accuracy that are not met by existing global products. One finding of particular interest was the priority for both stable and consistent LC products over time. Some interest was also expressed for more dynamic information reflecting LC change and vegetation phenology. However, the most recent series of global land cover products are specifically pointing out this inconsistency issue as a quite difficult one (Friedl et al. 2010, Bontemps et al. 2011). Yet, it must be recognized that the land cover cannot, at the same time, be defined as the physical and biological cover on the Earth’s surface (Herold et al. 2009, Di Gregorio 2005) and remains stable and consistent over time as expected by most users. This conclusion calls for the development of a new land cover ontology, which explicitly addresses the issue of inconsistency between annual land cover products and/or of products sensitivity to the observation period. The proposed land cover ontology assumes that the land cover is organized along a continuum of temporal and spatial scales and that each land cover type is defined by a characteristic scale, i.e. by typical spatial extent and time period over which its physical traits are observed (Miller 1994). This twofold assumption requires introducing the time dimension in the land cover characterization, which contributes to define the land cover in a more integrative way. Accounting for the time dimension allows distinguishing between the stable and the dynamic component of land cover. The stable component refers to the set of land elements which remain stable over time and thus define the land cover independently of any sources of temporary or natural variability. Conversely, the dynamic component is directly related to this temporary or natural variability that can induce some variation in land observation over time but without changing the land cover feature in its essence. This LC-condition is typically driven by biogeophysical processes and encompasses different observable variables such as the green vegetation phenology, snow coverage, open water presence and burnt scars. The CCI-LC project aims at delivering four global LC-condition products: the Normalized Vegetation Index (NDVI), snow, burnt area (BA) and water condition products. On a per pixel basis, these LC-conditions reflect, along the year, the average trajectory, also called climatology profile, and the intra-annual variability of a land surface feature over the 1998-2012 period. They are expressed as 7-day time profiles of the mean and standard deviation for continuous variables (NDVI) or as temporal series of occurrence probabilities for discrete variables (snow, BA and water). These products are complementary to the three CCI-LC global maps products characterizing the stable component of the land cover for the same period (see Bontemps et al. – also submitted to the same conference). The condition products are built from existing global datasets which beneficiate from high temporal frequency (capacity to depict the intra-annual variability) and long-term dataset. The NDVI condition product v1.0 is therefore built from the time series of SPOT-Vegetation surface reflectance datasets (1km) over the 1999-2011 period. The BA condition product v1.0 covers the 1998-2012 period combining for the time being the GlobCarbon.v2 and the Global Fire Emissions Database version 3 (GFED.v3). Its spatial resolution is 500m. As soon as available, this condition product will be computed from the Level 3 CCI-Fire disturbance product generated by the ESA CCI Fire project. Each LC condition product is delivered in 52 files (1 file per 7-day time interval), each file including measurements and quality flag layers. The snow and water conditions are still in process. The major challenge but also the main added-value beyond the compilation of these already existing data sets is the consistency through space/time, with the LC and between conditions. It is indeed intended to describe the whole dynamic of the terrestrial surface in a meaningful way over time. As the respective production of the different data sets was fully independent and sometimes sensor-dependent, discrepancies and incompatibility were clearly observed and highlighted. A detailed analysis of the discrepancies, the mismatching and the inconsistency distribution allows developing a new method to build the consistency. These condition products should be considered as a first version of an integrative description of the terrestrial surface. The sensor-independent processing algorithms of these products and a better consistency between sensors, such as the one expected among Sentinel instruments, allow expecting major improvements in consistency, spatial and temporal resolution. Of course, other conditions could be also considered such as the Leaf Area Index (LAI) or Land Surface Temperature (LST) and help enrich the land surface characterization and modelling. The public release of the CCI global LC condition products is planned for October 2013. These products will be delivered with the three CCI global maps products which will depict the stable component of the land cover. References • Arino O., Bicheron P., Achard F., Latham J., Witt R. and Weber J. L., 2008, Globcover: the most detailed portrait of Earth, ESA Bulletin 136, 24–31 • Bontemps S., Herold M., Kooistra L., van Groenestijn A., Hartley A., Arino O., Moreau I. and Defourny P., 2012, Revisiting land cover observation to address the needs of the climate modeling community, Biogeosciences, 9, 2145–2157 • Defourny P.et al., 2009, Accuracy assessment of a 300-m global land cover map: the GlobCover experience, in: Proceedings of the 33rd International Symposium on Remote Sensing of Environment, Stresa, Italy, 4–8 May 2009, PS-B1-3 (Ref 863) • Defourny P. and Bontemps S., 2012. Revisiting Land-Cover Mapping Concepts. In: Remote Sensing of Land Use and Land Cover : Principles and Applications, 2012, p. 49-63. 978-1-4200-7074-3. http://hdl.handle.net/2078/114554 • Di Gregorio, A. (Ed.). (2005). Land Cover Classification System: Classification Concepts and User Manual: LCCS (No. 8). FAO. • Friedl, M. A., Sulla-menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., & Huang, X. (2010). • Remote Sensing of Environment MODIS Collection 5 global land cover : Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1), 168–182. doi:10.1016/j.rse.2009.08.016 • Herold, M. (2009). Land Cover. Assessment of the status of the development of the standards for the Terrestrial Essential Climate Variables (p. 36). Rome: Global Terrestrial Observing System.

A technique was developed for the mapping of selective logging in tropical forest using PALSAR imagery acquired before and after the logging operations. The technique is based on automatic detection of new forest roads and unsupervised classification of textural features, which were computed from a backscatter ratio image. The technique was adapted for TerraSAR-X and ENVISAT/ASAR data. The accuracy of a PALSAR-based map of selective logging was assessed against reference plot data interpreted from GeoEye-1 imagery. The user's accuracy of selective logging was 95 %. The overall accuracy was 70.4 %. This is affected by the limited size of the area, which was chosen to include a large proportion of selectively logged forest. The accuracy of a TerraSAR-X-based map of selective logging was 53.6 %, but the user's accuracy of selective logging was 100 %. ALOS-type L-band radar data could be applied in an operational system over country-wide datasets to map newly constructed roads. The 40 % underestimation does not make direct reliable mapping of the area of selective logging possible. Instead, L-band derived maps of selectively logged area and new roads can be used in a wider system to pin-point areas of recent logging activity (as a proxy for forest degradation). The detected areas can then be covered by satellite or airborne optical data or ground surveys. The L-band map of areas with logging activity can also be used for stratification for sampling in a statistical area assessment