Many areas of science and policy depend on knowledge of

the tree cover in Europe. Sentinel-2 is a new (launched in

2015) satellite with a higher spatial resolution compared to

previous satellites. In the present study a new algorithm for

mapping tree cover from Sentinel-2 is developed, an analysis

of which bands should be used for tree cover mapping is

made, the accuracy of the mapping is assessed, and the tree

cover from the present approach is compared with previous

estimates. Firstly, the feasibility of the present algorithm is

demonstrated. Secondly, it is shown that only ten band

combinations have good performance in four selected

Sentinel-2 tiles and that the bands 3, 5, 6, 12 appear in most

combinations. Thirdly, the accuracy is assessed to be high,

and lastly it is shown that the relative difference between the

tree cover of the present study and the tree cover of previous

studies is between -14% and 68

BURGER ARMIN

CORBAN CHRISTINA

DIJKSTRA LEWIS

FLORCZYK ANETA

KEMPER THOMAS

MAFFENINI LUCA

PESARESI MARTINO

POLITIS PANAGIOTIS

RODRIGUEZ ASERETTO ROQUE DARIO

SABO FILIP

SOILLE PIERRE

SYRRIS VASILEIOS

VASILEV VESELIN

This paper presents a new paradigm for extracting information from large databases of remote sensing images. It aims at improving any task applied to image time series by exploiting
properties related to their temporal cross-dependence. Images part of the same time series are casually related to each other. As a consequence, the results of the tasks are mutually entangled. The proposed paradigm exploits this property and validates the results of the tasks one to each other to improve the overall performance. The paradigm is general and has relevant implications in Big Data analysis because it is suitable to archives containing not only Earth Observed images but any time-varying quantity or feature. Preliminary results show that change detection accuracy improves after the evaluation of the conservative property within the image time
series

L. Bruzzone

M. Bertoluzza

F. Bovolo

Archivio della ricerca - Fondazione Bruno Kessler

A New Paradigm for the Exploitation of the Semantic Content of Large Archives of Satellite Remote Sensing Images

Semantic segmentation for synthetic aperture radar (SAR)

imagery is a rarely touched area, due to the specific image

characteristics of SAR images. In this research, we propose a

dataset which consists of three data sources: TerraSAR-X images,

Google Earth images and OpenStreetMap data, with the

purpose of performing SAR and optical image semantic segmentation.

By using fully convolutional networks and deep

residual networks with pre-trained weights, we investigate the

accuracy and mean IOU values of semantic segmentation for

both SAR and optical image patches. The best segmentation

accuracy results for SAR and optical data are around 60% and

82%. Moreover, we study SAR models by combining multiple

data sources: Google Earth images and OpenStreetMap

data

Yao, Wei

Marmanis, Dimitrios

Datcu, Mihai

Institute of Transport Research:Publications

Semantic segmentation using the fully convolutional networks for SAR and optical image pairs

Continuous global-scale mapping of human settlements in support to international agreements calls for massive volumes of multi-source, multi-temporal and multi-scale Earth Observation (EO) data. In this paper, the latest developments in terms of processing EO datacubes for the purpose of improving the Global Human Settlement Layer (GHSL) data are presented. Two workflows with Sentinel-1 and Landsat data collections were run leveraging on the Joint Research Centre (JRC) Earth Observation and Processing Platform (JEODPP). The paper presents the processing workflows and the results of the two main workflows giving insights into the enhanced mapping capabilities gained by analyzing Sentinel-1 and Landsat datasets, and the lessons learnt in terms of handling and processing Earth Observation datacubes.JRC.E.1-Disaster Risk Managemen

JRC Publications Repository

MASS PROCESSING OF SENTINEL-1 AND LANDSAT DATA FOR MAPPING HUMAN SETTLEMENTS AT GLOBAL LEVEL

Ottosen, Thor-Bjørn

Petch, Geoffrey

Hanson, Mary

Skjøth, C.

University of Worcester Research and Publications

TOWARDS A MAP OF THE EUROPEAN TREE COVER BASED ON SENTINEL-2 Thor-Bjørn Ottosen1, Geoffrey Petch1, Mary Hanson1 and Carsten Ambelas Skjøth1 1Institute of Science and the Environment, University of Worcester, Worcester, UK  ABSTRACT Many areas of science and policy depend on knowledge of the tree cover in Europe. Sentinel-2 is a new (launched in 2015) satellite with a higher spatial resolution compared to previous satellites. In the present study a new algorithm for mapping tree cover from Sentinel-2 is developed, an analysis of which bands should be used for tree cover mapping is made, the accuracy of the mapping is assessed, and the tree cover from the present approach is compared with previous estimates. Firstly, the feasibility of the present algorithm is demonstrated. Secondly, it is shown that only ten band combinations have good performance in four selected Sentinel-2 tiles and that the bands 3, 5, 6, 12 appear in most combinations. Thirdly, the accuracy is assessed to be high, and lastly it is shown that the relative difference between the tree cover of the present study and the tree cover of previous studies is between -14% and 68%. Index Terms— Algorithm design, spectral bands, broadleaved trees, coniferous trees, Intel DAAL. 1. INTRODUCTION The European tree cover is important for many areas of science and planning such as climate [1], atmospheric composition [2, 3] and socio-economic values [4]. Until recently, the best estimate of the European tree cover has been 1.47 ∙ 10଺݇݉ଶ േ 0.02 ∙ 10଺݇݉ଶ based on either a combination of MODIS images and LISS-3 images [5] or Landsat images [6] with a spatial resolution of 25 m – 30 m. In old cultural landscapes, like the UK and other European countries, trees are often located in small linear features or groups and along roads and rivers, thus making them difficult to detect using satellites such as Landsat. It is thus likely that the actual tree cover is higher than previously thought and that woodland is more widely distributed.  With the launch of Sentinel-2, a new tool has become available with a high spatial resolution and a band combination specifically designed for vegetation studies. However, a number of methodological questions remain to be answered before a European tree map can be produced from Sentinel-2. In this paper we explore a methodology for tree cover mapping from Sentinel-2, we analyse the performance of all 8100 potential band combinations for tree mapping, assess the accuracy of the methodology and compare with previous estimates for four Sentinel-2 tiles. 1. METHODS 1.1. Mapping tree cover The tree mapping algorithm consists of a number of steps carried out sequentially:  1. Clouds and other artifacts are removed using the accompanying masks.  2. All bands are resampled to 10 m × 10 m. 3. All bands are normalized by mean centering and division with the standard deviation. This is done to normalize the weight of the individual bands.  4. A K-means unsupervised classification is done using Intel Data Analytics Abstraction Library (DAAL) with the number of classes set to 25.  5. The NDVI values of the pixels corresponding to forests in Corine Land Cover (CLC) [7] are extracted from the image.  6. Pixels from the entire image are subsequently removed if their NDVI value is less than µ-2 of the distribution of values extracted in step 5. In this way, non-vegetation pixels are efficiently removed.  7. The dominating classes for respectively broadleaved and coniferous forests are labelled using CLC by K-means clustering the clusters from the first clustering into two classes (dominating and non-dominating). This is done iteratively starting from the largest polygons within the Sentinel-2 tile proceeding to the smallest polygon until convergence. Convergence is defined when the largest change in a class is smaller than 1%.   1.2. Analysis of band combinations To analyze whether all spectral bands were needed for this algorithm, and if not, which spectral bands provided the best performance, the algorithm was applied to all 8100 possible band combinations of size 3 to 13 on four Sentinel-2 tiles covering selected areas of Northern Europe. These tiles are named 30UWC, 30VUH, 32NVH and 33VUC. The kappa-coefficient for the wall-to-wall comparison with the respective national forest inventory was subsequently calculated and the performance of each band combination Data Processing, Analytics, and Vizualisation [Poster Session]Proc. of the 2017 conference onBig Data from Space (BiDS’17) doi: 10.2760/383579402 Toulouse, France28–30 November 2017ranked by summing the kappa coefficient over the four images. 1.3. Accuracy assessment The accuracy assessment of the present study was performed on tile 30UWC. 1.3.1. Sampling design 1000 pixels were selected across the image using stratified random sampling [8]. The pixels were stratified into broadleaved trees, coniferous trees and no trees to prevent the non-forest category dominating the results. 1.3.2. Response design The primary land use class of each pixel was subsequently manually determined using Google Earth. The interpreter did not have access to the classified map during the manual classification to avoid biasing the classification (blind interpretation). To enhance consistency among interpreters, a written guide to the classification procedure was produced and 99 points were classified by all interpreters. As well as broadleaved trees, coniferous trees and non-forest, the interpreter had opportunity to classify a pixel as unclassified and unclassified trees. Data points that were classified to be in the last two categories were subsequently excluded from the analysis. 2. PRELIMINARY RESULTS AND DISCUSSION 2.1. Mapping tree cover Figure 1 shows an example of a result of the labelling procedure of the 25 classes produced by the unsupervised classification algorithm. The blue bars are the areas marked as broadleaved forest in CLC and the orange bars are the areas marked as coniferous forest in CLC. In this case, classes 18 and 22 dominate broadleaved forests, whereas classes 8 and 12 dominate coniferous forests. It is likewise evident, that these four classes are the dominant ones and therefore the ones labelled as forests since a complete separation cannot be achieved as the CLC land cover by definition will miss small woodland areas outside forests and miss small bare patches inside forest regions.  Figure 1 Example of a result of the labelling procedure.   Figure 2 Example of a tree map on the border between Herefordshire and Worcestershire, UK (tile 30UWC) superimposed on a RGB-image from Sentinel-2. The scale is 1:75,000.  An example of a classification result is shown in Figure 2 superimposed on a RGB-image from Sentinel-2. The area is dominated by broadleaved forests with only small patches of coniferous forests. It is evident from this figure, that the algorithm allows the mapping of trees with a high degree of detail. This is seen from the many small features in the left side of the image and the trees mapped in the urban area in the right side of the image. These small features are by definition not a part of the CLC land cover or the more detailed UK tree cover map. Furthermore the map also contains a separation into coniferous and broadleaved trees. 0.000.050.100.150.200.250.300.351 3 5 7 9 11 13 15 17 19 21 23 25Proportion of land coverClass numberCLC BroadleavedCLC ConiferousFinal coniferous Final broadleaved trees[Poster Session] Data Processing, Analytics, and VizualisationProc. of the 2017 conference onBig Data from Space (BiDS’17) doi: 10.2760/383579403 Toulouse, France28–30 November 20172.2. Analysis of band combinations Table 1 shows the ten band combinations that appear among the top-5% combinations in all four images. It is evident that bands 2, 3, 6 and 12 appear in many of the combinations. Band 2 is the blue band (496.6 nm), band 3 is the green band (560.0 nm), band 6 is a red-edge band (740.2 nm), and band 12 is a short-wave infrared band (2202.4 nm). Using USGS Spectral Characteristics Viewer it can be seen that these bands are particularly suitable to separate different types of vegetation.  Table 1 Band combinations appearing in top-1500 in all four images and their corresponding kappa-sum. ෍ߢ௜   Combination   2.755  1, 2, 3, 4, 5, 7, 9, 12   2.803  2, 3, 6, 12   2.774  1, 3, 5, 6, 12   2.771  1, 2, 3, 5, 6, 12   2.800  1, 3, 4, 5, 6, 11, 12   2.797  2, 4, 5, 6, 12   2.799  3, 5, 6, 11, 12   2.797  2, 3, 4, 5, 6, 11   2.757  3, 4, 5, 6, 7, 9, 11, 12    Out of these combinations, the combination 2, 3, 6 and 12 was chosen, since it has a good performance in all four images. 2.3. Accuracy assessment Table 2 Confusion matrix for the satellite map for tile 30UWC versus google earth. 0 is non-forest, 1 is broadleaved forest, 2 is coniferous forest, n=941.  Classified data   Producer’s Acc. (%) Reference data  0  1  2   0  303  67  18  0.78 1  23  228  182  0.53 2  3  10  107  0.89 User’s Acc. (%)  0.92  0.75  0.35  0.68  The results of the accuracy assessment are shown in Table 2. Compared to the commonly used success criteria of 85% accuracy, an overall accuracy of 68% is low. The algorithm is especially challenged in separating broadleaved trees from coniferous trees. This is natural, since these two classes are spectrally much more similar compared to the non-forest class. This shows that the map tends to overestimate the cover of coniferous forest. Part of this is the result of shadows on forest edges, forest roads etc. that gives the trees a darker color. Future work should aim at reducing this effect. A group of points are classified as forests, and referenced as non-forest. This is largely caused by green fields being spectrally similar to forests. In the future we will implement a temporal averaging procedure over several images, which we hope will reduce this effect. The accuracy for the map (area × accuracy) is 0.76 or 0.90 depending on whether the user’s accuracy or the producer’s accuracy is used. Averaging the two numbers gives 83%, which is very close to the commonly used limit. The map accuracy is 88% if classified as only trees and no trees. Considering that the accuracy assessment is done on 10 m × 10 m resolution, which is higher than previous maps, this result must be considered acceptable. 2.4. Comparison with previous datasets Table 3 compares the amount of tree cover in the dataset of [5] with the tree cover mapped from Sentinel-2 for four tiles in Northern Europe. As can be seen, the relative difference is between -14% and 68% with Sentinel-2 often estimating a larger tree cover. Generally, this looks like a realistic result. Widespread clouds, where the accompanying cloud mask does not remove some of them, influence 32VNH. It is thus plausible that this value is an overestimation. That Sentinel-2 has the lower of the two numbers in 33VUC is likewise an effect of high clouds that have not been removed by the cloud mask. This demonstrates the ability of Sentinel-2 to detect smaller groups of trees compared to previous satellites. It is expected that using several images over the same area will either completely remove or at least reduce the effect of clouds, thus obtaining more accurate estimates of the tree cover in cultural landscapes over very large areas like Europe.   Table 3 Comparison between the tree cover in our map and the tree cover in the map of [5]. Tile code:  Location:  Our map (km2) Previous maps (km2) 30UWC  Worcester, UK  1148  909 30VUH  Scotland  2204  1407 32VNH  West Denmark  2387  1178 33VUC  East Denmark   2175  2515  3. CONCLUSION AND OUTLOOK The present study developed an algorithm for automatic tree cover mapping based on Sentinel-2 and demonstrated that this is a feasible approach to tree cover mapping, analyzed the performance of 8100 band combinations, assessed the accuracy, and demonstrated that Sentinel-2 can contribute to more accurate tree cover maps of Europe, in particular by identifying smaller woodlands that are important in some areas. In the future, this algorithm will be extrapolated to the entire European domain, to obtain a complete estimate of the European tree cover divided into broadleaved and coniferous trees. Data Processing, Analytics, and Vizualisation [Poster Session]Proc. of the 2017 conference onBig Data from Space (BiDS’17) doi: 10.2760/383579404 Toulouse, France28–30 November 20174. ACKOWLEDGEMENTS The present study has been supported by the BBSRC funded project New approaches for the early detection of tree health pests and pathogens, projectID: BB/L012286/1. 5. REFERENCES [1] Bonan, G.B., “Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests.” Science (80-. ). 320, 1444 LP-1449, 2008.  [2] Kesselmeier, J., Staudt, M., “Biogenic Volatile Organic Compounds (VOC): An Overview on Emission, Physiology and Ecology.” J. Atmos. Chem. 33, pp. 23–88, 1999.   [3] Pauling, A., Rotach, M.W., Gehrig, R., Clot, B., “A method to derive vegetation distribution maps for pollen dispersion models using birch as an example”. Int J Biometeorol 56, pp. 949–958, 2012.  [4] FAO, Global Forest Resources Assessment 2015, FAO Forestry, 2015.  [5] Kempeneers, P., Sedano, F., Seebach, L., Strobl, P., San-Miguel-Ayanz, J., “Data fusion of different spatial resolution remote sensing images applied to forest-type mapping.” IEEE Trans. Geosci. Remote Sens. 49, pp. 4977–4986, 2011.  [6] Hansen, M.C., Potapov, P. V, Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S. V, Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., Townshend, J.R.G.,. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science (80-. ). 342, 850 LP-853, 2013.  [7] EEA, CORINE land cover technical guide – Addendum 2000, EEA, Copenhagen, 2000.  [8] Stehman, S. V., “Sampling designs for accuracy assessment of land cover”. Int. J. Remote Sens. 30, 5243–5272. 2009. [Poster Session] Data Processing, Analytics, and VizualisationProc. of the 2017 conference onBig Data from Space (BiDS’17) doi: 10.2760/383579405 Toulouse, France28–30 November 2017

Towards a Map of the European Tree Cover based on Sentinel-2

Historical satellite data are of high value for climate research as an independent source to validate the output of climate models. Of special interest are data from the AVHRR sensor in 1km spatial resolution (LAC), which are available from 1981 until 2017 (2022). The data archived at different centers in Europe need consolidation (filling data gaps, removing redundant orbits etc.) and harmonization (generating the same format and meta-files) before transferred to ESA facilities to make a long time series accessible for scientific use. In the frame of ESA's Long Term Data Preservation program the archived AVHRR data will be stored and maintained for the next +50 years. We will present the different parts of the ESA-UniBern AVHRR-LTDP project and recommendations for the next processing steps needed to generate essential climate variables (e.g. snow extent, lake surface water temperature).<br/

Wunderle, Stefan

Neuhaus, Christoph

Brooks, Andrew

Hüsler, Fabia

Lonie, Neil

Albani, Mirko

Folco, Sergio

Leone, Rosemarie

University of Dundee Online Publications

                                                              University of DundeePreservation and harmonization of historical AVHRR LAC data to serve the needs ofusers in climate researchWunderle, Stefan ; Neuhaus, Christoph ; Brooks, Andrew; Hüsler, Fabia ; Lonie, Neil; Albani,Mirko ; Folco, Sergio ; Leone, RosemariePublished in:Proceedings of the 2017 conference on Big Data from Space (BIDS' 2017)DOI:10.2760/383579Publication date:2017Document VersionPublisher's PDF, also known as Version of recordLink to publication in Discovery Research PortalCitation for published version (APA):Wunderle, S., Neuhaus, C., Brooks, A., Hüsler, F., Lonie, N., Albani, M., ... Leone, R. (2017). Preservation andharmonization of historical AVHRR LAC data to serve the needs of users in climate research. In Proceedings ofthe 2017 conference on Big Data from Space (BIDS' 2017) (pp. 154). ESA Publications Division.https://doi.org/10.2760/383579General rightsCopyright and moral rights for the publications made accessible in Discovery Research Portal are retained by the authors and/or othercopyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated withthese rights. • Users may download and print one copy of any publication from Discovery Research Portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain. • You may freely distribute the URL identifying the publication in the public portal.Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.PRESERVATION AND HARMONIZATION OF HISTORICAL AVHRR LAC DATA TO SERVE THE NEEDS OF USERS IN CLIMATE RESEARCH Stefan Wunderle1, Christoph Neuhaus2, Fabia Hüsler1, Andrew Brooks3, Neil Lonie3, Mirko Albani4,Sergio Folco4, Rosemarie Leone4 1 Oeschger Center for Climate Change Research (OCCR), Department of Geography, University of Bern, Switzerland. 2 Department of Geography, University of Bern, Switzerland 3 Dundee Satellite Receiving Station (DSRS), University of Dundee, UK 4 ESA-ESRIN, Frascati, Italy  ABSTRACT Historical satellite data are of high value for climate research as an independent source to validate the output of climate models. Of special interest are data from the AVHRR sensor in 1km spatial resolution (LAC), which are available from 1981 until 2017 (2022). The data archived at different centers in Europe need consolidation (filling data gaps, removing redundant orbits etc.) and harmonization (generating the same format and meta-files) before transferred to ESA facilities to make a long time series accessible for scientificuse. In the frame of ESA’s Long Term Data Preservation program the archived AVHRR data will be stored and maintained for the next +50 years. We will present the different parts of the ESA-UniBern AVHRR-LTDP projectand recommendations for the next processing steps needed to generate essential climate variables (e.g. snow extent, lakesurface water temperature). Index Terms— AVHRR-LAC data, ESA Long Term Data Preservation (LTDP), data consolidation and harmonization 1. INTRODUCTIONDuring the development phase of the AVHRR sensor in the early 70ies, no one could imagine that the system would become very attractive for climate research. This is due to its almost unchanged sensor during the last 35 years on the NOAA-series that makes it the longest time-series of optical satellite imagery in a daily resolution.  Furthermore, with the cooperation between NOAA and EUMETSAT, under theJoint Polar Satellite Systems (JPSS) agreement, the sensor will be in orbit until 2022 on MetOp. A unique data set forclimate research covering a period from 1981 – 2022, whichis the basis for many investigations, in addition to being used as a complementary and independent source to validate climate models. While many projects use the globally available AVHRR Global Area Coverage (GAC) data with a degraded spatial resolution, higher spatial resolution is mostly needed. Therefore, the AVHRR Local Area Coverage(LAC) data with a ground sampling distance (GSD) of 1.1km in nadir are of special interest. Due to limited on-board storage capability of the NOAA-satellites the availability of LAC data relies on local receiving stations and archiving facilities. Depending on financial resources and used hard- and software the archived data exhibit temporal and spatial data gaps, different file formats (HRPT, NOAA-level 1a, SHARP, etc.) and processing levels, different archiving and storage techniques. In addition, many of the local archives atUniversities are managed as a “one-man-show” relying onenduring interest of a few scientists. Contrarily, professional data centers or meteorological services had the resources to keep the data useable but political decisions often resulted in a complete loss of AVHRR data, especially from the years 1981 – 1995. Hence, there is a strong need by scientists to get access to a homogenized and consolidated AVHRR LAC archive.        2. DATA AVAILABILITYUnder the circumstances of the financial and administrative restriction mentioned above it is remarkable that University of Bern (UoB) has one of the longest AVHRR LAC archives (1989 – 2017), which is accessible for scientific use without any restrictions (figure 1). Covering whole Europe in a dailyresolution the UoB archive was defined as a European Master Data Set. Figure 1: archived AVHRR LAC data at UniBern Analysing the Temporal DimensionProc. of the 2017 conference onBig Data from Space (BiDS’17) doi: 10.2760/383579154 Toulouse, France28–30 November 2017In the framework of ESA’s Long Term Data Preservation (LTDP) program the AVHRR LAC data received and archived at ESA facilities (Maspalomas, Frascati, Tromso) are read from old tapes and stored in fast accessible archives (figure 2).    Figure 2: archived AVHRR LAC data at ESA facilities  The aim of ESA-UoB project AVHRR-LTDP is to use the UoB European Master Data Set and to combine it with ESA-AVHRR data. Furthermore, Dundee Satellite Receiving Station (DSRS) has systematically received and archived AVHRR LAC data since the first sensor was launched in the late 1970's. It maintains extensive and continuous archives (1978 - 2017) which will be used to extend and supplement the European Master Data Set.     Figure 3: Subset of AVHRR LAC data archived at DSRS (1981 – 1988) to be included in the harmonized AVHRR data archive.   After consolidation and harmonization of the time series all of the data will be transferred to ESA facilities to make them accessible for the next 50+ years.   3. PRESERVATION OF AVHRR DATA It has been decided to compile a data archive based on data of the lowest level (raw data) to maintain the option of extracting all necessary meta data useable for quality checks etc. Hence, the first step of the LTDP-AVHRR project was an analysis of all available data sets at ESA facilities and UoB in terms of satellite, coverage, temporal resolution and data format. This information formed the basis to detect gaps at the UoB Master data set and to screen the ESA holdings for potential gap filling. Only a few minor data gaps for the period 1989 – 2016 were found. However, the different formats at UoB (HRPT, NOAA level 1b) and ESA (SHARP) may cause some additional effort in data processing / re-formatting. Moreover, there is a need to fill the previous years, at least from 1985 – 1989, to fulfill the WMO requirement of a climate period (30 years minimum), which offers climate modelers a long time series for sound statistical analysis. The only archive with the needed AVHRR LAC data of this period is that of the Dundee Satellite Receiving Station (DSRS) – agreement has been reached for DSRS to support the project and include also its data in the European Master Data Set. In the framework of the project the content of the archives is documented to start re-processing for harmonization.  4. HARMONIZATION Some of the re-processing steps were defined with support of an established AVHRR advisory group. This included decisions on the final data format, stitching of images from same orbit but with different length, exclude identical data sets in the user accessible area but keep the image as backup, homogenize file names, etc. After these decisions the reprocessing at UoB Processing and Archiving Facility (PAF) has started. The needed ESA data are transferred to UoB to guarantee a consistent reprocessing of all data (> 200.000 data sets/scenes) and generate quality flags, Quicklooks and all meta-data needed to compile the final archiving format at ESA (EO-SIP). . 5. STEPS TOWARDS SERVING THE NEEDS OF SCIENTISTS IN CLIMATE RESEARCH In previous studies related to the usability of AVHRR data many users pointed out their interest in long time series of products (essential climate variables) and their limited capability to process raw AVHRR data. Hence, a careful pre-processing including calibration and geocoding is foreseen to be the next step after the compilation of the European AVHRR Master data set. 5.1. Calibration The AVHRR calibration process of the shortwave and thermal channels varies with respect to the availability of adequate calibration information [1]. For the thermal channels, an onboard calibration information based on a view of stable blackbody and deep-space reference is provided, which is utilized to convert raw counts to a meaningful physical quantity: the brightness temperature. In contrast to the thermal channels, the visible and near-infrared channels are only calibrated pre-launch, which complicates the calibration procedure as their signal was observed to decrease Analysing the Temporal DimensionProc. of the 2017 conference onBig Data from Space (BiDS’17) doi: 10.2760/383579155 Toulouse, France28–30 November 2017over time. To account for this fact, time-dependent correction using updated calibration coefficients is required. Here, we currently use the updated coefficients suggested by Heidinger et al. 2010 [2] and Heidinger et al. 2014 [3]. Based on these updated coefficients, channels 1, 2 and 3A are calibrated to top-of-atmosphere (TOA) reflectance as described in the NOAA User’s Guides.   Figure 4: Time series of mean monthly TOA reflectance (a (channel 1), b (channel 2)) and brightness temperature of channel 4 (c) of La Crau. (Hüsler et al. 2011)  5.2. Geocoding Accurate geolocation remains critical for generating long-term data records for climate studies of land surface and atmospheric parameters. Inaccurate co-registration of consecutive images may lead to biases in time series since every single scene contributes to the final product.  The implemented geocoding and orthorectification procedure SAPS (Science Systems and Applications, Inc. AVHRR Processing System) at University of Bern has been developed by Khlopenkov at al. 2010 [5]. The processing system relies on 250m MODIS monthly composites as reference images and 500m grid spacing SRTM digital surface model. The final accuracy of the AVHRR images after geocoding/orthorectification is 1/3 pixels on average [1].                Figure 5: Mean x-y-shift (pixel) from 32 image pairs (NOAA-18, NOAA-19 and MetOp-A). from Aksakal et al. 2015 5.3. Product retrieval The benefit of long time series based on AVHRR data will be shown at the end of the presentation with two examples: Snow Extent (SE) for the European Alps and Lake Surface Water Temperature (LSWT) of some smaller lakes in Europe. 6. CONCLUSION The combination of three major AVHRR data archives in Europe, namely University of Bern, European Space Agency and Dundee Satellite Receiving System will result in a homogeneous and consolidated time series with high impact in climate change studies. The final data set covers Europe from 1980 – 2017 with daily observations and a spatial resolution of 1km (nadir). Some effort is needed to generate a useful level 1c data set including calibration and geocoding. 7. REFERENCES  [1] Aksakal, Sultan; Neuhaus, Christoph; Baltsavias, Emmanuel; Schindler, Konrad (2015). Geometric Quality Analysis of AVHRR Orthoimages. Remote sensing, 7(3), pp. 3293-3319. Molecular Diversity Preservation International MDPI 10.3390/rs70303293  [2] Hüsler, Fabia; Fontana, Fabio; Neuhaus, Christoph; Riffler, Michael; Musial, Jan; Wunderle, Stefan (2011). AVHRR Archive and Processing Facility at the University of Bern: A comprehensive 1-km satellite data set for climate change studies. EARSeL eProceedings, 10(2), pp. 83-101. Oldenburg: BIS Verlag  [3] Heidinger, A.K., William C. Straka III, Christine C. Molling, Jerry T. Sullivan & Xiangqian Wu (2010): Deriving an inter-sensor consistent calibration for the AVHRR solar reflectance data record, International Journal of Remote Sensing, 31:24, 6493-6517;   [4] Heidinger, A. K., Foster, M. J., Walther, A., and Zhao, X.: 2014. The pathfinder atmospheres-extended AVHRR climate dataset, Bulletin of the American Meteorological Society, 95, 909–922.  [5] Khlopenkov, K.V., A.P. Trishchenko, Y. Luo (2010): Achieving subpixel georeferencing accuracy in the Canadian AVHRR processing system. IEEE Transactions on Geoscience and Remote Sensing, 48 (4) (2010), pp. 2150-2161. Analysing the Temporal DimensionProc. of the 2017 conference onBig Data from Space (BiDS’17) doi: 10.2760/383579156 Toulouse, France28–30 November 2017

Preservation and harmonization of historical AVHRR LAC data to serve the needs of users in climate research

The JRC Earth Observation Data and Processing Platform (JEODPP) is a versatile petabyte scale platform that serves the needs of a wide variety of projects.  This is achieved by providing a cluster environment for batch processing, a web-based remote desktop access with a variety of software suites, and a web-based interactive visualisation and analysis ecosystem.  These three layers are complementary and are all relying on common hardware layer wherre the data is co-located with the processing.  The versatility of the platform is illustrated by a series of applications running on the JEODPP.JRC.I.3-Text and Data Minin

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Big Data from Space refers to Earth and Space observation data collected by space-borne and ground- based sensors. Whether for Earth or Space observation, they qualify being called 'big data' given the sheer volume of sensed data (archived data reaching the exabyte scale), their high velocity (new data is acquired almost on a continuous basis and with an increasing rate), their variety (data is delivered by sensors acting over various frequencies of the electromagnetic spectrum in passive and active modes), as well as their veracity (sensed data is associated with uncertainty and accuracy measurements). Last but not least, the value of big data from space depends on our capacity to extract information and meaning from them.  The goal of the Big Data from Space conference is to bring together researchers, engineers, developers, and users in the area of Big Data from Space. 

The 2017 "Big Data from Space" conference (BiDS'17) was held from the 28th to the 30th of November 2017 at the Pierre Baudis conference Centre (Toulouse, France).  BiDS'17 was co-organised by the European Space Agency (ESA), the Joint Research Center of the European Commission (JRC), and the European Union Satellite Centre (SatCen) and hosted by CNES, the French Government Space Agency.  
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The response from the user community has shown a great need for the Forestry Thematic Exploitation Platform (Forestry-TEP) that entered into the pre-operational phase in summer 2017. Forestry TEP offers a one-stop-shop for forestry remote sensing services. It makes data value adding quicker and smoother than traditional approaches that the users have applied. The platform offers access to imagery, computing infrastructure, ready-made value adding services and an opportunity for users to develop and upload their own applications to the platform. It forms a worldwide marketing channel for commercial remote sensing services and a networking forum for users. Ten thematic processing services are presently available on the Forestry TEP - from computing vegetation indices and mapping of land and forest cover to biomass and change estimation. In addition, a user can utilize all the features of the Sentinel Application Platform (SNAP) image processing application, the Monteverdi/Orfeo toolbox, and the open source QGIS tool. The platform will move to an operational phase in 2018

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                                                                    University of DundeePreservation and harmonization of historical AVHRR LAC data to serve the needs ofusers in climate researchWunderle, Stefan ; Neuhaus, Christoph ; Brooks, Andrew; Hüsler, Fabia ; Lonie, Neil; Albani,MirkoPublished in:Proceedings of the 2017 conference on Big Data from Space (BIDS' 2017)DOI:10.2760/383579Publication date:2017Document VersionPublisher's PDF, also known as Version of recordLink to publication in Discovery Research PortalCitation for published version (APA):Wunderle, S., Neuhaus, C., Brooks, A., Hüsler, F., Lonie, N., Albani, M., Folco, S., & Leone, R. (2017).Preservation and harmonization of historical AVHRR LAC data to serve the needs of users in climate research.In Proceedings of the 2017 conference on Big Data from Space (BIDS' 2017) (pp. 154). 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Apr. 2023PRESERVATION AND HARMONIZATION OF HISTORICAL AVHRR LAC DATA TO SERVE THE NEEDS OF USERS IN CLIMATE RESEARCH Stefan Wunderle1, Christoph Neuhaus2, Fabia Hüsler1, Andrew Brooks3, Neil Lonie3, Mirko Albani4,Sergio Folco4, Rosemarie Leone4 1 Oeschger Center for Climate Change Research (OCCR), Department of Geography, University of Bern, Switzerland. 2 Department of Geography, University of Bern, Switzerland 3 Dundee Satellite Receiving Station (DSRS), University of Dundee, UK 4 ESA-ESRIN, Frascati, Italy  ABSTRACT Historical satellite data are of high value for climate research as an independent source to validate the output of climate models. Of special interest are data from the AVHRR sensor in 1km spatial resolution (LAC), which are available from 1981 until 2017 (2022). The data archived at different centers in Europe need consolidation (filling data gaps, removing redundant orbits etc.) and harmonization (generating the same format and meta-files) before transferred to ESA facilities to make a long time series accessible for scientificuse. In the frame of ESA’s Long Term Data Preservation program the archived AVHRR data will be stored and maintained for the next +50 years. We will present the different parts of the ESA-UniBern AVHRR-LTDP projectand recommendations for the next processing steps needed to generate essential climate variables (e.g. snow extent, lakesurface water temperature). Index Terms— AVHRR-LAC data, ESA Long Term Data Preservation (LTDP), data consolidation and harmonization 1. INTRODUCTIONDuring the development phase of the AVHRR sensor in the early 70ies, no one could imagine that the system would become very attractive for climate research. This is due to its almost unchanged sensor during the last 35 years on the NOAA-series that makes it the longest time-series of optical satellite imagery in a daily resolution.  Furthermore, with the cooperation between NOAA and EUMETSAT, under theJoint Polar Satellite Systems (JPSS) agreement, the sensor will be in orbit until 2022 on MetOp. A unique data set forclimate research covering a period from 1981 – 2022, whichis the basis for many investigations, in addition to being used as a complementary and independent source to validate climate models. While many projects use the globally available AVHRR Global Area Coverage (GAC) data with a degraded spatial resolution, higher spatial resolution is mostly needed. Therefore, the AVHRR Local Area Coverage(LAC) data with a ground sampling distance (GSD) of 1.1km in nadir are of special interest. Due to limited on-board storage capability of the NOAA-satellites the availability of LAC data relies on local receiving stations and archiving facilities. Depending on financial resources and used hard- and software the archived data exhibit temporal and spatial data gaps, different file formats (HRPT, NOAA-level 1a, SHARP, etc.) and processing levels, different archiving and storage techniques. In addition, many of the local archives atUniversities are managed as a “one-man-show” relying onenduring interest of a few scientists. Contrarily, professional data centers or meteorological services had the resources to keep the data useable but political decisions often resulted in a complete loss of AVHRR data, especially from the years 1981 – 1995. Hence, there is a strong need by scientists to get access to a homogenized and consolidated AVHRR LAC archive.        2. DATA AVAILABILITYUnder the circumstances of the financial and administrative restriction mentioned above it is remarkable that University of Bern (UoB) has one of the longest AVHRR LAC archives (1989 – 2017), which is accessible for scientific use without any restrictions (figure 1). Covering whole Europe in a dailyresolution the UoB archive was defined as a European Master Data Set. Figure 1: archived AVHRR LAC data at UniBern Analysing the Temporal DimensionProc. of the 2017 conference onBig Data from Space (BiDS’17) doi: 10.2760/383579154 Toulouse, France28–30 November 2017In the framework of ESA’s Long Term Data Preservation (LTDP) program the AVHRR LAC data received and archived at ESA facilities (Maspalomas, Frascati, Tromso) are read from old tapes and stored in fast accessible archives (figure 2).    Figure 2: archived AVHRR LAC data at ESA facilities  The aim of ESA-UoB project AVHRR-LTDP is to use the UoB European Master Data Set and to combine it with ESA-AVHRR data. Furthermore, Dundee Satellite Receiving Station (DSRS) has systematically received and archived AVHRR LAC data since the first sensor was launched in the late 1970's. It maintains extensive and continuous archives (1978 - 2017) which will be used to extend and supplement the European Master Data Set.     Figure 3: Subset of AVHRR LAC data archived at DSRS (1981 – 1988) to be included in the harmonized AVHRR data archive.   After consolidation and harmonization of the time series all of the data will be transferred to ESA facilities to make them accessible for the next 50+ years.   3. PRESERVATION OF AVHRR DATA It has been decided to compile a data archive based on data of the lowest level (raw data) to maintain the option of extracting all necessary meta data useable for quality checks etc. Hence, the first step of the LTDP-AVHRR project was an analysis of all available data sets at ESA facilities and UoB in terms of satellite, coverage, temporal resolution and data format. This information formed the basis to detect gaps at the UoB Master data set and to screen the ESA holdings for potential gap filling. Only a few minor data gaps for the period 1989 – 2016 were found. However, the different formats at UoB (HRPT, NOAA level 1b) and ESA (SHARP) may cause some additional effort in data processing / re-formatting. Moreover, there is a need to fill the previous years, at least from 1985 – 1989, to fulfill the WMO requirement of a climate period (30 years minimum), which offers climate modelers a long time series for sound statistical analysis. The only archive with the needed AVHRR LAC data of this period is that of the Dundee Satellite Receiving Station (DSRS) – agreement has been reached for DSRS to support the project and include also its data in the European Master Data Set. In the framework of the project the content of the archives is documented to start re-processing for harmonization.  4. HARMONIZATION Some of the re-processing steps were defined with support of an established AVHRR advisory group. This included decisions on the final data format, stitching of images from same orbit but with different length, exclude identical data sets in the user accessible area but keep the image as backup, homogenize file names, etc. After these decisions the reprocessing at UoB Processing and Archiving Facility (PAF) has started. The needed ESA data are transferred to UoB to guarantee a consistent reprocessing of all data (> 200.000 data sets/scenes) and generate quality flags, Quicklooks and all meta-data needed to compile the final archiving format at ESA (EO-SIP). . 5. STEPS TOWARDS SERVING THE NEEDS OF SCIENTISTS IN CLIMATE RESEARCH In previous studies related to the usability of AVHRR data many users pointed out their interest in long time series of products (essential climate variables) and their limited capability to process raw AVHRR data. Hence, a careful pre-processing including calibration and geocoding is foreseen to be the next step after the compilation of the European AVHRR Master data set. 5.1. Calibration The AVHRR calibration process of the shortwave and thermal channels varies with respect to the availability of adequate calibration information [1]. For the thermal channels, an onboard calibration information based on a view of stable blackbody and deep-space reference is provided, which is utilized to convert raw counts to a meaningful physical quantity: the brightness temperature. In contrast to the thermal channels, the visible and near-infrared channels are only calibrated pre-launch, which complicates the calibration procedure as their signal was observed to decrease Analysing the Temporal DimensionProc. of the 2017 conference onBig Data from Space (BiDS’17) doi: 10.2760/383579155 Toulouse, France28–30 November 2017over time. To account for this fact, time-dependent correction using updated calibration coefficients is required. Here, we currently use the updated coefficients suggested by Heidinger et al. 2010 [2] and Heidinger et al. 2014 [3]. Based on these updated coefficients, channels 1, 2 and 3A are calibrated to top-of-atmosphere (TOA) reflectance as described in the NOAA User’s Guides.   Figure 4: Time series of mean monthly TOA reflectance (a (channel 1), b (channel 2)) and brightness temperature of channel 4 (c) of La Crau. (Hüsler et al. 2011)  5.2. Geocoding Accurate geolocation remains critical for generating long-term data records for climate studies of land surface and atmospheric parameters. Inaccurate co-registration of consecutive images may lead to biases in time series since every single scene contributes to the final product.  The implemented geocoding and orthorectification procedure SAPS (Science Systems and Applications, Inc. AVHRR Processing System) at University of Bern has been developed by Khlopenkov at al. 2010 [5]. The processing system relies on 250m MODIS monthly composites as reference images and 500m grid spacing SRTM digital surface model. The final accuracy of the AVHRR images after geocoding/orthorectification is 1/3 pixels on average [1].                Figure 5: Mean x-y-shift (pixel) from 32 image pairs (NOAA-18, NOAA-19 and MetOp-A). from Aksakal et al. 2015 5.3. Product retrieval The benefit of long time series based on AVHRR data will be shown at the end of the presentation with two examples: Snow Extent (SE) for the European Alps and Lake Surface Water Temperature (LSWT) of some smaller lakes in Europe. 6. CONCLUSION The combination of three major AVHRR data archives in Europe, namely University of Bern, European Space Agency and Dundee Satellite Receiving System will result in a homogeneous and consolidated time series with high impact in climate change studies. The final data set covers Europe from 1980 – 2017 with daily observations and a spatial resolution of 1km (nadir). Some effort is needed to generate a useful level 1c data set including calibration and geocoding. 7. REFERENCES  [1] Aksakal, Sultan; Neuhaus, Christoph; Baltsavias, Emmanuel; Schindler, Konrad (2015). Geometric Quality Analysis of AVHRR Orthoimages. Remote sensing, 7(3), pp. 3293-3319. Molecular Diversity Preservation International MDPI 10.3390/rs70303293  [2] Hüsler, Fabia; Fontana, Fabio; Neuhaus, Christoph; Riffler, Michael; Musial, Jan; Wunderle, Stefan (2011). AVHRR Archive and Processing Facility at the University of Bern: A comprehensive 1-km satellite data set for climate change studies. EARSeL eProceedings, 10(2), pp. 83-101. Oldenburg: BIS Verlag  [3] Heidinger, A.K., William C. Straka III, Christine C. Molling, Jerry T. Sullivan & Xiangqian Wu (2010): Deriving an inter-sensor consistent calibration for the AVHRR solar reflectance data record, International Journal of Remote Sensing, 31:24, 6493-6517;   [4] Heidinger, A. K., Foster, M. J., Walther, A., and Zhao, X.: 2014. The pathfinder atmospheres-extended AVHRR climate dataset, Bulletin of the American Meteorological Society, 95, 909–922.  [5] Khlopenkov, K.V., A.P. Trishchenko, Y. Luo (2010): Achieving subpixel georeferencing accuracy in the Canadian AVHRR processing system. IEEE Transactions on Geoscience and Remote Sensing, 48 (4) (2010), pp. 2150-2161. Analysing the Temporal DimensionProc. of the 2017 conference onBig Data from Space (BiDS’17) doi: 10.2760/383579156 Toulouse, France28–30 November 2017

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