Possible links between a sample of VHR images and LUCAS
|
|
- Susan Gilmore
- 5 years ago
- Views:
Transcription
1 EUROPEAN COMMISSION EUROSTAT Directorate E: Sectoral and regional statistics Unit E-1: Farms, agro-environment and rural development CPSA/LCU/08 Original: EN (available in EN) WORKING PARTY "LAND COVER/USE STATISTICS" OF THE STANDING COMMITTEE FOR AGRICULTURAL STATISTICS Meeting on 0 October 009, 9:30 a.m. in Luxembourg, BECH Building, Room: Quetelet Point 3.3. of the agenda Possible links between a sample of VHR images and LUCAS Presented by J.Gallego (JRC)
2 The potential use of a sample of Very High Resolution satellite images for the estimation of land cover area change in the EU F.J. Gallego JRC-IPSC. MARS unit SUMMARY This document reports the results of simulations performed to assess the feasibility and potential cost-efficiency of a sample of very high resolution (VHR) images for two similar but different problems in the European Union (EU): Land cover area estimation and land cover area change estimation. The results suggest that three are few chances to reach cost-efficiency for land cover area estimation, but that there is a good potential of use for land cover area change estimation combining VHR images with ground observation from LUCAS or other national area frame surveys. The simulations have been performed using CORINE Land Cover 000 (CLC000) and the layer of changes CLC90-CLC000. The impact of the geographic smoothing of CLC, compared with reality, needs to be studied more in detail, but is unlikely to modify the conclusions of the simulations. 1 DATA AND STUDY AREA CLC000 has been produced by photo-interpretation with common rules of Image000, a coverage of Landsat ETM+ images (Multispectral+Panchromatic) resampled with 1.5 m resolution (JRC-EEA, 005). The nomenclature of CLC000 has 44 classes. The minimum mapping unit is 5 ha; smaller units are included in the dominant land cover type around or grouped in an area coded as heterogeneous. The class heterogeneous is important due to the relatively coarse scale of CLC. The nominal location accuracy is 100 m, but the reached accuracy is much better; in fact the location accuracy of Image000 is generally under 0 m (Gallego, 005). A raster layer has been produced to facilitate certain operations of spatial analysis. The results presented in this paper were obtained using a raster version of CLC000 with a resolution of 100m. The study area is the set of countries for which the change layer CLC000-CLC90 is available Figure 1: study area (countries for which CLC change is available
3 SAMPLING 10X10 KM SITES FOR LAND COVER AREA ESTIMATION. Using CLC-000 as pseudo-truth in the CLC change area we have simulated the potential sampling error of estimates from simple random samples (srs) of 350 sites of 10x10 km. Since we are using a pseudo-truth for which we know the whole population the variance of the mean can be computed exactly without simulations: V y = V Y Where ( Y ) ( ) ( ) n V is the population variance. We have compared the coefficients of variation of srs with n=350 sample sites with the estimated coefficients of variation of LUCAS 006 (table 1). Table 1: Sampling errors of LUCAS and expected sampling errors of a sample of VHR images Coefficient of Variation (CV) Sample of 350 images 10 x 10 km LUCAS 006 (11 countries) artificial arable perm crops pastures heterogeneous 5.7 n.a. total agriculture forest and woodland bare other vegetation glaciers water The sampling errors of the first column can be improved with stratification and co-variables (Euroland, CLC )., but it is not realistic to reach Coefficients of Variation similar to those of LUCAS. For example in agricultural surveys on area frame a relative efficiency of (dividing the CV by ) is considered a good result (Taylor et al., 1997, Carfagna and Gallego, 005) and only exceptionally the CV can be divided by 3 (relative efficiency = 9). The non-sampling errors are mainly linked with the identification mistakes (commission-omission errors) and need some further information from confusion matrices (using the validation survey for LUCAS and crossing LUCAS data with classified images)..1 Equivalent number of points The previous paragraph does not take into account the cost issue. One way to compare costs is computing what we can call the equivalent number of points of a site. The concept can be defined in the following way: Let us consider a single-stage sampling plan Ψ of n elementary units (points in this case); let Φ be a sampling plan of m clusters and let estimated variances for land cover class c with both sampling plans; standard binomial formula: s c = p ( 1 p) ψ and of CLC. The equivalent number of points of a cluster can be defined as n s and c,ψ s be the c,φ s c,ψ is computed with the s c,φ is computed from the set of 10x10 km tiles n s ξ c = m s that, for land cover c, a sample of m sites gives the same sampling error as a sample of non-clustered points cψ cφ. This means m ξ c
4 Table : Estimated equivalent number of points of a site of 10x10 km for some land cover types % area cv 350 points (%) cv 350 sites 10 km (%) equivalent number of points/site artificial arable perm crops pastures heterogeneous total agriculture forest and woodland bare other vegetation glaciers water Table shows that the equivalent number of points of sites of 10 x 10 km, using CLC000 as pseudo-truth, ranges roughly between 3 and 7. This number is probably underestimated because the spatial auto-correlation of CLC000 is overestimated due to the smoothing effect. We can conjecture that the equivalent number of points may reach an indicative value of Taking into account that the average cost of ground survey per point in LUCAS was 3 (Eurostat, 007), the cost per site, including image purchase and analysis should be less than , depending on which land cover classes are given priority. This level of cost is not realistic at the moment and therefore the target of land cover area estimation should be excluded with the cost structure in the EU. 3 LAND COVER AREA change ESTIMATION. The conclusions of the previous paragraph change if we consider the CLC land cover change as pseudo-truth. For this purpose changes have been regrouped as described in table 3: Table 3: Groups of land cover change categories CLC 000 artificial crops pasture heterog forest & wood Natural artificial CLC crops pasture heterog forest &wood natural No change 1 New artificial Agricultural expansion 3 Agricultural abandonment 4 Other changes
5 3.1 Equivalent number of points The lower spatial autocorrelation for land cover change leads to a much higher number of equivalent points (table 3), often around 100. This means that there may be room for costefficiency of remote sensing if the cost per site can be kept below 000 or Table 4: Estimated equivalent number of points of a 10x10 km site for main land cover change types new artificial new agriculture agricultural abandonment other changes % area cv points n= cv (n=00) sites 10 km Number of points for the same CV equiv n points per site CV (n=00) sites 30 km Number of points for the same CV equiv n points Comparing identification errors. The above considerations on sampling errors from ground surveys per point and from remote sensing surveys per site implicitly assume that the non-sampling errors (mainly identification mistakes) are similar. For land cover change we can reasonably expect that the identification errors due to mislocation are smaller on satellite images because of the better overview of the context, although this needs to be confirmed, 4 THE CLC CHANGE LAYER AS PSEUDO-TRUTH We have also implicitly assumed that the spatial correlation structure of CLC change is close to the spatial correlation structure of the real changes. Actually a visual inspection of a map of abundance of changes raises some doubts. We have aggregated the changes reported in CLC change with two criteria: thematically with the grouping described in Table 3 and geographically with a grid of 10x10 km. This leads to the maps reported in Figure to Figure 5.
6 Figure : Rate of artificialisation in CLCchange per cell of 10 x 10 km. Figure 3: Rate of change to agriculture in CLCchange per cell of 10 x 10 km.
7 Figure 4: Rate of agricultural abandonment in CLCchange per cell of 10 x 10 km. Figure 5: Rate of other changes in CLCchange per cell of 10 x 10 km.
8 5 SIZE OF SAMPLING SITES We have tested the impact of larger sites (this would correspond to SPOT, Rapid Eye) on the potential sampling error. The optimal size of the site strongly depends on the cost function, i.e. the cost per site as a function of its size. In a first analysis we have tested cost functions of the type: C = α + β n + nγ s = α + n( β + γ s) We try three different assumptions: for the cost per unit β + γ s, assuming β = 1 and s expressed in number of 10x10 km. Simple random samples on CLC change (as pseudo-truth) has been used at this stage. The coefficients of variation reported correspond to samples of 350 sites of 10x10 km and a number of sites of other dimensions with the same cost. 5.1 Comparison with two types of cost functions A parameter γ=0.5 corresponds to an intensive manual input (labour intensive photointerpretation). The cost of a site of 50x50 km is 9 times the cost of a site of 10x10 km Table 5: Estimated coefficients of variation assuming a labour intensive image interpretation. Size of sites LC change 10 km 0 km 30 km 40 km 50 km 60 km New artificial New agriculture Agricultural Abandonment Other changes Assuming a value γ=0.01 corresponds to a mainly automatic processing. The cost of a site 50x50 km is only 5% higher than the cost of a site of 10x10 km. Table 6: Estimated coefficients of variation assuming a mainly automatic image processing. Size of sites LC change 10 km 0 km 30 km 40 km 50 km 60 km New artificial New agriculture Agricultural Abandonment Other changes Assuming a highly automated approach is more realistic with the set up of SA. However the cost function is difficult to determine because of the links with pricing policies for different image types. For example the cost of a 30x30 or 50x50 km site will be very different if we assume the acquisition of SPOT images, IRS LISS-IV or RapidEye images.
9 6 CONCLUSION: POSSIBLE LINKS BETWEEN A SAMPLE OF VHR IMAGES AND LUCAS. 6.1 For land cover area estimation. There seems to be little room for a cost-efficient use of a sample of VHR images for land cover area estimation. In the current situation, a sample of VHR images is more expensive and less reliable than data acquired on the ground. Exceptions appear when access to sampled points or areas is problematic: mountain areas, very large forests with few roads, large private properties in which the entrance permits are difficult to obtain, military areas, etc. The definition of a stratum areas difficult to access needs to be studied more in depth on the basis of LUCAS 006 and 009 observation mode per point. We should also assess the possible cost of a complementary survey on a sample of VHR images to cover non-accessible areas. 6. For land cover change area estimation. Consistent estimates of LULC change matrices in the EU are not available at the moment. CLC change gives a valuable idea of the location of the main changes, but its direct use to derive change matrices gives heavily biased estimates. LUCAS made an attempt of more consistent estimates between 001 and 003, but the results were unrealistic because of the wrongly designed ground survey scheme: surveyors in 003 did not have the information on the 001 observation and co-location inaccuracy resulted into a large amount of fake changes. LUCAS 009 seems to have been designed to avoid past mistakes, but its suitability remains to be checked. A combined use of ground observations (LUCAS ) and photo-interpretation might be a more suitable solution, but further work is still needed to define the precise procedure. REFERENCES Carfagna E., Gallego F. J. (005) Using remote sensing for agricultural statistics. International Statistical Review, 73(3), Eurostat (007) LUCAS 006 Quality Report. Standing Committee for Agricultural Statistics, - 3 November 007. Document ESTAT/CPSA/5a, Luxemburg Gallego F.J., 005, Stratified sampling of satellite images with a systematic grid of points, ISPRS Journal of Photogrammetry and Remote Sensing, 59, JRC-EEA, 005, CORINE Land Cover updating for the year 000: Image000 and CLC000; Products and methods; ed. Vanda Lima, Report EUR 1757 EN. JRC-Ispra Taylor J., Sannier C., Delincé J, Gallego F.J., (1997), Regional Crop Inventories in Europe Assisted by Remote Sensing: Synthesis Report. EUR EN, JRC Ispra, 71pp.
Directorate E: Sectoral and regional statistics Unit E-4: Regional statistics and geographical information LUCAS 2018.
EUROPEAN COMMISSION EUROSTAT Directorate E: Sectoral and regional statistics Unit E-4: Regional statistics and geographical information Doc. WG/LCU 52 LUCAS 2018 Eurostat Unit E4 Working Group for Land
More informationComparing CORINE Land Cover with a more detailed database in Arezzo (Italy).
Comparing CORINE Land Cover with a more detailed database in Arezzo (Italy). Javier Gallego JRC, I-21020 Ispra (Varese) ITALY e-mail: javier.gallego@jrc.it Keywords: land cover, accuracy assessment, area
More informationUse of auxiliary information in the sampling strategy of a European area frame agro-environmental survey
Use of auxiliary information in the sampling strategy of a European area frame agro-environmental survey Laura Martino 1, Alessandra Palmieri 1 & Javier Gallego 2 (1) European Commission: DG-ESTAT (2)
More informationEVALUATING THE COST- EFFICIENCY OF REMOTE SENSING IN DEVELOPING COUNTRIES
EVALUATING THE COST- EFFICIENCY OF REMOTE SENSING IN DEVELOPING COUNTRIES Elisabetta Carfagna Research Coordinator of the Global Strategy to Improve Agricultural and Rural Statistics - FAO Statistics Division
More informationUK Contribution to the European CORINE Land Cover
Centre for Landscape andwww.le.ac.uk/clcr Climate Research CENTRE FOR Landscape and Climate Research UK Contribution to the European CORINE Land Cover Dr Beth Cole Corine Coordination of Information on
More informationLUCAS: A possible scheme for a master sampling frame. J. Gallego, MARS AGRI4CAST
LUCAS: A possible scheme for a master sampling frame. J. Gallego, MARS AGRI4CAST Area frames of square segments Square segments on a classified image 2/16 Sampling farms through points farm a farm b farm
More informationCORINE LAND COVER CROATIA
CORINE LAND COVER CROATIA INTRO Primary condition in making decisions directed to land cover and natural resources management is presence of knowledge and high quality information about biosphere and its
More informationSpatial Disaggregation of Land Cover and Cropping Information: Current Results and Further steps
CAPRI CAPRI Spatial Disaggregation of Land Cover and Cropping Information: Current Results and Further steps Renate Koeble, Adrian Leip (Joint Research Centre) Markus Kempen (Universitaet Bonn) JRC-AL
More informationValidation and verification of land cover data Selected challenges from European and national environmental land monitoring
Validation and verification of land cover data Selected challenges from European and national environmental land monitoring Gergely Maucha head, Environmental Applications of Remote Sensing Institute of
More informationObject Oriented Classification Using High-Resolution Satellite Images for HNV Farmland Identification. Shafique Matin and Stuart Green
Object Oriented Classification Using High-Resolution Satellite Images for HNV Farmland Identification Shafique Matin and Stuart Green REDP, Teagasc Ashtown, Dublin, Ireland Correspondence: shafique.matin@teagasc.ie
More informationSampling scheme for LUCAS 2015 J. Gallego (JRC) A. Palmieri (DG ESTAT) H. Ramos (DG ESTAT)
Sampling scheme for LUCAS 2015 J. Gallego (JRC) A. Palmieri (DG ESTAT) H. Ramos (DG ESTAT) Abstract The sampling design of LUCAS 2015 took into account experience from previous campaigns. While remaining
More informationLUCAS: current product and its evolutions
LUCAS: current product and its evolutions Workshop Land Use and Land Cover products: challenges and opportunities Brussels 15 Nov 2017 Eurostat E4: estat-dl-lucas@ec.europa.eu Contents 1) The context 2)
More informationLUCAS 2009 (Land Use / Cover Area Frame Survey)
EUROPEAN COMMISSION EUROSTAT Directorate E: Sectoral and regional statistics Unit E-1: Farms, agro-environment and rural development LUCAS 2009 (Land Use / Cover Area Frame Survey) M3 - Non sampling error
More informationSpanish national plan for land observation: new collaborative production system in Europe
ADVANCE UNEDITED VERSION UNITED NATIONS E/CONF.103/5/Add.1 Economic and Social Affairs 9 July 2013 Tenth United Nations Regional Cartographic Conference for the Americas New York, 19-23, August 2013 Item
More informationCopernicus Land Services to improve EU statistics
Copernicus Land Services to improve EU statistics Gallego J. 2017 EUR 29027 EN This publication is a Technical report by the Joint Research Centre (JRC), the European Commission s science and knowledge
More informationLand Cover and Land Use Diversity Indicators in LUCAS 2009 data
Land Cover and Land Use Diversity Indicators in LUCAS 2009 data A. Palmieri, L. Martino, P. Dominici and M. Kasanko Abstract Landscape diversity and changes are connected to land cover and land use. The
More informationLAND USE, LAND COVER AND SOIL SCIENCES - Vol. I - Land Use and Land Cover, Including their Classification - Duhamel C.
LAND USE AND LAND COVER, INCLUDING THEIR CLASSIFICATION Duhamel Landsis Groupement Européen d Intérêt Economique, Luxembourg Keywords: land cover, land use, classification systems, nomenclature, information
More informationCOMBINING ENUMERATION AREA MAPS AND SATELITE IMAGES (LAND COVER) FOR THE DEVELOPMENT OF AREA FRAME (MULTIPLE FRAMES) IN AN AFRICAN COUNTRY:
COMBINING ENUMERATION AREA MAPS AND SATELITE IMAGES (LAND COVER) FOR THE DEVELOPMENT OF AREA FRAME (MULTIPLE FRAMES) IN AN AFRICAN COUNTRY: PRELIMINARY LESSONS FROM THE EXPERIENCE OF ETHIOPIA BY ABERASH
More informationLAND COVER CHANGES IN ROMANIA BASED ON CORINE LAND COVER INVENTORY
LAND COVER CHANGES IN ROMANIA BASED ON CORINE LAND COVER INVENTORY 1990 2012 JENICĂ HANGANU, ADRIAN CONSTANTINESCU * Key-words: CORINE Land Cover inventory, Land cover changes, GIS. Abstract. From 1990
More informationLAND USE MAPPING AND MONITORING IN THE NETHERLANDS (LGN5)
LAND USE MAPPING AND MONITORING IN THE NETHERLANDS (LGN5) Hazeu, Gerard W. Wageningen University and Research Centre - Alterra, Centre for Geo-Information, The Netherlands; gerard.hazeu@wur.nl ABSTRACT
More informationLand accounting perspective on water resources management
European Water 60: 161-166, 2017. 2017 E.W. Publications Land accounting perspective on water resources management G.T. Paschos, G.E. Bariamis * and E.A. Baltas Department of Water Resources and Environmental
More informationIDENTIFICATION OF TRENDS IN LAND USE/LAND COVER CHANGES IN THE MOUNT CAMEROON FOREST REGION
IDENTIFICATION OF TRENDS IN LAND USE/LAND COVER CHANGES IN THE MOUNT CAMEROON FOREST REGION By Nsorfon Innocent F. April 2008 Content Introduction Problem Statement Research questions/objectives Methodology
More informationLUCAS Technical reference document U1 LUCAS Survey data user guide. (Land Use / Cover Area Frame Survey)
Regional statistics and Geographic Information Author: E4.LUCAS (ESTAT) TechnicalDocuments 2015 LUCAS 2015 (Land Use / Cover Area Frame Survey) Technical reference document U1 LUCAS Survey data user guide
More informationCURRENT AND FUTURE ACTIVITIES TO IMPROVE STRATIFICATION FOR SEASONAL AGRICULTURE SURVEYS IN RWANDA
CURRENT AND FUTURE ACTIVITIES TO IMPROVE STRATIFICATION FOR SEASONAL AGRICULTURE SURVEYS IN RWANDA Roselyne Ishimwe 1 *, Sebastian Manzi 1 National Institute of Statistics of Rwanda (NISR) *Email: Corresponding
More informationLand Use and Land cover statistics (LUCAS)
EUROPEAN COMMISSION EUROSTAT Directorate E: Sectoral and regional statistics Doc. ENV/DIMESA/7.1/2012 Original in EN Agenda point 7.1 Land Use and Land cover statistics (LUCAS) DIMESA Directors' Meeting
More informationMANUAL ON THE BSES: LAND USE/LAND COVER
6. Environment Protection, Management and Engagement 2. Environmental Resources and their Use 5. Human Habitat and Environmental Health 1. Environmental Conditions and Quality 4. Disasters and Extreme
More informationLand Monitoring Core Service Implementation Group (LMCS IG) - Results and Outlook
Land Monitoring Core Service Implementation Group (LMCS IG) - Results and Outlook Pr. Dietmar Grünreich, President of BKG, Germany Chairman of the GMES LMCS IG Outline 1 Introduction 2 Preparatory Projects
More informationHow proximity to a city influences the performance of rural regions by Lewis Dijkstra and Hugo Poelman
n 01/2008 Regional Focus A series of short papers on regional research and indicators produced by the Directorate-General for Regional Policy Remote Rural Regions How proximity to a city influences the
More informationSéminaire de l'umr Economie Publique. Spatial Disaggregation of Agricultural. Raja Chakir. February 21th Spatial Disaggregation.
Séminaire de l'umr Economie Publique : An : An February 21th 2006 Outline : An 1 2 3 4 : An The latest reform the Common Policy (CAP) aims to encourage environmentally friendly farming practices in order
More informationMAPPING LAND COVER OF EUROPE FOR 2006 UNDER GMES
Proceedings of the 2 nd Workshop of the EARSeL SIG on Land Use and Land Cover MAPPING LAND COVER OF EUROPE FOR 2006 UNDER GMES Chris Steenmans 1 and George Büttner 2 1. European Environment Agency, Kongens
More informationPilot studies on the provision of harmonized land use/land cover statistics: Synergies between LUCAS and the national systems
1 Pilot studies on the provision of harmonized land use/land cover statistics: Synergies between LUCAS and the national systems Norway Erik Engelien Division for Natural resources and Environmental Statistics,
More informationOpen call for tenders No EEA/MDI/14/001
1 April 2014 Clarification No 1 Reference: Title: Open call for tenders No EEA/MDI/14/001 Copernicus Initial Operations 2011-2013 - Land Monitoring Service Local Component: riparian zones Question 1 Section
More informationFOREST CHANGE DETECTION BY MEANS OF REMOTE SENSING TECHNIQUES FROM THE EU PROJECT CORINE LAND COVER
FORESTRY IDEAS, 2010, vol. 16, 1 (39) FOREST CHANGE DETECTION BY MEANS OF REMOTE SENSING TECHNIQUES FROM THE EU PROJECT CORINE LAND COVER Youlin Tepeliev and Radka Koleva* University of Forestry, Faculty
More informationSIF_7.1_v2. Indicator. Measurement. What should the measurement tell us?
Indicator 7 Area of natural and semi-natural habitat Measurement 7.1 Area of natural and semi-natural habitat What should the measurement tell us? Natural habitats are considered the land and water areas
More informationSystem of Environmental-Economic Accounting. Advancing the SEEA Experimental Ecosystem Accounting. Extent Account (Levels 1 and 2)
Advancing the SEEA Experimental Ecosystem Accounting Extent Account (Levels 1 and 2) Overview: The Extent Account 1. Learning objectives 2. Review of Level 0 (5m) What is it? Why do we need it? What does
More informationCompact guides GISCO. Geographic information system of the Commission
Compact guides GISCO Geographic information system of the Commission What is GISCO? GISCO, the Geographic Information System of the COmmission, is a permanent service of Eurostat that fulfils the requirements
More information2.1.2 Land cover data
2.1.2 Land cover data Land cover data was used as an approximate measure of the different habitat groupings throughout Britain. Land cover data was obtained from three sources The European Environment
More informationC N E S, U M R I R I S A
M O N I T O R I N G U R B A N A R E A S W I T H S E N T I N E L - 2. APPLICATION TO THE UPDATE OF THE COPERNICUS HIGH RESOLUTION LAYER IMPERVIOUSNESS DEGREE O c t o b e r 2 5 th 2016, Brussels A n t o
More informationChange detection for Finnish CORINE land cover classification
Change detection for Finnish CORINE land cover classification Markus Törmä, Pekka Härmä, Suvi Hatunen, Riitta Teiniranta, Minna Kallio, Elise Järvenpää Finnish Environment Institute SYKE, Mechelininkatu
More informationSwedish examples on , and
Swedish examples on 11.2.1, 11.3.1 and 11.7.1 Marie Haldorson, Director Seminar in Nairobi 7 Dec 2018 SDG Indicator Tests by Countries in Europe GEOSTAT 3: ESS Project with a purpose to guide countries
More informationORTHORECTIFICATION AND GEOMETRIC QUALITY ASSESSMENT OF CARTOSAT-1 FOR COMMON AGRICULTURAL POLICY MONITORING
ORTHORECTIFICATION AND GEOMETRIC QUALITY ASSESSMENT OF CARTOSAT-1 FOR COMMON AGRICULTURAL POLICY MONITORING S. Kay a, R. Zielinski European Commission DG-JRC, IPSC, Agriculture and Fisheries Unit, TP266,
More informationComparability of landscape diversity indicators in the European Union
Comparability of landscape diversity indicators in the European Union F. Javier Gallego, Paula Escribano, Susan Christensen Space Applications Institute, JRC Ispra. SUMMARY: CORINE Land Cover allows landscape
More informationRedesign sample for Land Use/Cover Area frame Survey (LUCAS) 2018
Redesign sample for Land Use/Cover Area frame Survey (LUCAS) 2018 MARCO BALLIN, GIULIO BARCAROLI, MAURO MASSELLI, MARCO SCARNÓ S TAT I S T I C A L W O R K I N G PA P E R S 2018 edition Redesign sample
More informationProblems arising during the implementation of CLC2006
Problems arising during the implementation of CLC2006 George Büttner, Barbara Kosztra ETC-LUSI / FÖMI (HU) EIONET WG meeting on Land Monitoring IGN Portugal, 10-12 March 2010 Contents of presentation Present
More informationSimple areal weighting: illustration
1 Using land over information to map population density Geographial disaggregation of statistial data Tallin, September 25-28, 2001 Spae Appliations Institute Diretorate General Joint Researh Centre European
More informationAUTOMATIC GENERALIZATION OF LAND COVER DATA
POSTER SESSIONS 377 AUTOMATIC GENERALIZATION OF LAND COVER DATA OIliJaakkola Finnish Geodetic Institute Geodeetinrinne 2 FIN-02430 Masala, Finland Abstract The study is related to the production of a European
More informationValidation of CESBIO OSO 2016 products CESBIO OSO 2016 FINAL VALIDATION REPORT
Validation of CESBIO OSO 2016 products CESBIO OSO 2016 FINAL VALIDATION REPORT Document: CESBIO_OSO_Validation_Report Date: 09/02/2018 Document Preparation and Release Affiliation Name(s) Date Signature
More informationCopernicus for Statistics
Copernicus for Statistics Stephan Arnold Areal statistics, Federal Statistical Office Thomas Wiatr Remote sensing, Federal Agency for Cartography and Geodesy Content Requirements and Setting Eurostat s
More informationEvaluating Urban Vegetation Cover Using LiDAR and High Resolution Imagery
Evaluating Urban Vegetation Cover Using LiDAR and High Resolution Imagery Y.A. Ayad and D. C. Mendez Clarion University of Pennsylvania Abstract One of the key planning factors in urban and built up environments
More informationThe Attribute Accuracy Assessment of Land Cover Data in the National Geographic Conditions Survey
The Attribute Accuracy Assessment of Land Cover Data in the National Geographic Conditions Survey Xiaole Ji a, *, Xiao Niu a Shandong Provincial Institute of Land Surveying and Mapping Jinan, Shandong
More informationKey Words: geospatial ontologies, formal concept analysis, semantic integration, multi-scale, multi-context.
Marinos Kavouras & Margarita Kokla Department of Rural and Surveying Engineering National Technical University of Athens 9, H. Polytechniou Str., 157 80 Zografos Campus, Athens - Greece Tel: 30+1+772-2731/2637,
More informationModule 2.1 Monitoring activity data for forests using remote sensing
Module 2.1 Monitoring activity data for forests using remote sensing Module developers: Frédéric Achard, European Commission (EC) Joint Research Centre (JRC) Jukka Miettinen, EC JRC Brice Mora, Wageningen
More informationThe Added Value of Geospatial Data in a Statistical Office. Pedro Diaz Munoz Director Sectoral and Regional Statistics EUROSTAT European Commission
The Added Value of Geospatial Data in a Statistical Office Pedro Diaz Munoz Director Sectoral and Regional Statistics EUROSTAT European Commission Why integrate Responsibility of all the information we
More informationThe Icelandic geographic Land Use database (IGLUD)
Page 1 of 7 The Icelandic geographic Land Use database (IGLUD) Jón Kilde: Norsk institutt for skog og landskap Adresse: http://skogoglandskap.pdc.no/utskrift.php? seks_id=21176&sid=19698&t=v Guðmundsson
More informationTHE OVERALL EAGLE CONCEPT
Sentinel Hub THE OVERALL EAGLE CONCEPT GEBHARD BANKO, 30. MAY 2018, COPENHAGEN ISO TC 211, STANDARDS IN ACTION SEMINAR CONTENT Background and Motivation Criteria and Structure of Data Model Semantic decomposition
More informationPresentation of the different land cover mapping activities in the French Guiana
Presentation of the different land cover mapping activities in the French Guiana LCCS Land Cover Classification System 9 to 13 March 2015 Paramaribo - Suriname INTRODUCTION French Guiana : 8 046 427 ha
More informationGISCO Working Party Meeting. 8 March 2012 Luxembourg. The ESPON 2013 Programme: State of Affairs. Marjan van Herwijnen project expert in the ESPON CU
GISCO Working Party Meeting 8 March 2012 Luxembourg The ESPON 2013 Programme: State of Affairs Marjan van Herwijnen project expert in the ESPON CU The ESPON 2013 Programme Role in Structural Funds 2007-2013:
More informationStandardization of the land cover classes using FAO Land Cover Classification System (LCCS)
Sofia, 17-18 September 2008, LPIS Workshop LPIS applications and quality 1 Standardization of the land cover classes using FAO Land Cover Classification System (LCCS) Pavel MILENOV Agriculture Unit, JRC
More informationDeveloping a global, people-based definition of cities and settlements
Developing a global, people-based definition of cities and settlements Cooperation between: Directorate General for Regional and, Joint Research Centre, EUROSTAT (European Commission, European Union) OECD,
More informationComparison of PROBA-V, METOP and emodis NDVI anomalies over the Horn of Africa Third Main Title Line Third Line
Comparison of PROBA-V, METOP and emodis NDVI anomalies over the Horn of Africa Third Main Title Line Third Line Evaluation of PROBA-V satellite data quality Michele Meroni Felix Rembold 0 5 Report EUR
More informationCell-based Model For GIS Generalization
Cell-based Model For GIS Generalization Bo Li, Graeme G. Wilkinson & Souheil Khaddaj School of Computing & Information Systems Kingston University Penrhyn Road, Kingston upon Thames Surrey, KT1 2EE UK
More information1. Introduction. S.S. Patil 1, Sachidananda 1, U.B. Angadi 2, and D.K. Prabhuraj 3
Cloud Publications International Journal of Advanced Remote Sensing and GIS 2014, Volume 3, Issue 1, pp. 525-531, Article ID Tech-249 ISSN 2320-0243 Research Article Open Access Machine Learning Technique
More informationVegetation Change Detection of Central part of Nepal using Landsat TM
Vegetation Change Detection of Central part of Nepal using Landsat TM Kalpana G. Bastakoti Department of Geography, University of Calgary, kalpanagb@gmail.com Abstract This paper presents a study of detecting
More informationEnvironmental Impact Assessment Land Use and Land Cover CISMHE 7.1 INTRODUCTION
7 LAND USE AND LAND COVER 7.1 INTRODUCTION The knowledge of land use and land cover is important for many planning and management activities as it is considered an essential element for modeling and understanding
More informationSEASONAL AGRICULTURE SURVEY (SAS) The Overview of the Multiple Frame Sample Survey in Rwanda
SEASONAL AGRICULTURE SURVEY (SAS) The Overview of the Multiple Frame Sample Survey in Rwanda Sébastien MANZI Director of Economic Statistics December 16, 2013 National Institute of Statistics of Rwanda
More informationGeographically weighted methods for examining the spatial variation in land cover accuracy
Geographically weighted methods for examining the spatial variation in land cover accuracy Alexis Comber 1, Peter Fisher 1, Chris Brunsdon 2, Abdulhakim Khmag 1 1 Department of Geography, University of
More informationUSE OF SATELLITE IMAGES FOR AGRICULTURAL STATISTICS
USE OF SATELLITE IMAGES FOR AGRICULTURAL STATISTICS National Administrative Department of Statistics DANE Colombia Geostatistical Department September 2014 Colombian land and maritime borders COLOMBIAN
More informationContents Introduction... 3 Get the data... 4 Workflow... 7 Test 1: Urban fabric (all)... 8 Test 2: Urban fabric (industrial and commercial)...
AAXY tutorial Contents Introduction... 3 Get the data... 4 Workflow... 7 Test 1: Urban fabric (all)... 8 Test 2: Urban fabric (industrial and commercial)... 9 Test 3: Urban fabric (residential)... 10 Test
More informationApplied spatial data for sustainable development strategy in Germany
Applied spatial data for sustainable development strategy in Germany Stephan Arnold Areal Statistics, Federal Statistical Office, Germany Federal Statistical Office Headquarter: Wiesbaden Branch Office:
More informationThe German GMES extension to support land cover data systems: Status and outlook
The German GMES extension to support land cover data systems: Status and outlook Bergen, 2nd July 2010 Oliver Buck, EFTAS GmbH Co-Funded by the Federal Ministry of Economics and Technology (BMWi) via the
More informationSpatial Accuracy Evaluation of Population Density Grid disaggregations with Corine Landcover
Spatial Accuracy Evaluation of Population Density Grid disaggregations with Corine Landcover Johannes Scholz, Michael Andorfer and Manfred Mittlboeck Abstract The article elaborates on the spatial disaggregation
More informationAre EU Rural Areas still Lagging behind Urban Regions? An Analysis through Fuzzy Logic
Are EU Rural Areas still Lagging behind Urban Regions? An Analysis through Fuzzy Logic Francesco Pagliacci Department of Economics and Social Sciences Università Politecnica delle Marche Ancona (Italy)
More informationSIGMA thematic map validation
LPVE 2018 1 LPVE 2018 2 SIGMA GIS validation tools Thematic map validation plugin with 3 tools: Estimate sample size tool Estimate sample size using map class accuracies tool Build confusion matrix - tool
More informationNew LPIS data and their quality control in Macedonia. Pavel TROJACEK & Adam ZLOTY EKOTOXA s.r.o.
New LPIS data and their quality control 15th GeoCAP Conference, Taormina, Italy 18 20 November 2009 Outline of the presentation Intro: About Macedonia 1. Design of LPIS 2. LPIS methodology 3. New spatial
More informationGeometric and Landcover Signatures of Local Authorities in Peloponnesus
Geometric and Landcover Signatures of Local Authorities in Peloponnesus GEORGE CH. MILIARESIS Department of Geology University of Patras Geology Department, University of Patras, Rion 265-04 GREECE gmiliar@upatras.gr
More informationCopernicus Land HRL Imperviousness: 2012 dataset, indicator Title
Copernicus Land HRL Imperviousness: 2012 dataset, 06-09 indicator and outlook Title 2015+ Tobias LANGANKE First name SURNAME Project manager, Copernicus Position land services Name European of the Environment
More informationUrban settlements delimitation using a gridded spatial support
Urban settlements delimitation using a gridded spatial support Rita Nicolau 1, Elisa Vilares 1, Cristina Cavaco 1, Ana Santos 2, Mário Lucas 2 1 - General Directorate for Territory Development DGT, Portugal
More information7.1 INTRODUCTION 7.2 OBJECTIVE
7 LAND USE AND LAND COVER 7.1 INTRODUCTION The knowledge of land use and land cover is important for many planning and management activities as it is considered as an essential element for modeling and
More informationUsability of the SLICES land-use database
Usability of the SLICES land-use database Olli Jaakkola * & Ville Helminen** * Finnish Geodetic Institute, P.O.Box 15 (Geodeetinrinne 2), FIN-02431 MASALA, FINLAND, e-mail: olli.jaakkola@fgi.fi ** Finnish
More informationM.C.PALIWAL. Department of Civil Engineering NATIONAL INSTITUTE OF TECHNICAL TEACHERS TRAINING & RESEARCH, BHOPAL (M.P.), INDIA
INVESTIGATIONS ON THE ACCURACY ASPECTS IN THE LAND USE/LAND COVER MAPPING USING REMOTE SENSING SATELLITE IMAGERY By M.C.PALIWAL Department of Civil Engineering NATIONAL INSTITUTE OF TECHNICAL TEACHERS
More informationFig. 1: Test area the Municipality of Mali Idjos, Cadastral Municipality Feketic
Remote Sensing Application for Agricultural Land Value Classification Integrated in the Land Consolidation Survey Stojanka Brankovic, Ljiljana Parezanovic Republic Geodetic Authority, Belgrade, Serbia
More informationExploring the Potential of Integrated Cadastral and Building Data for Evaluation of Remote-Sensing based Multi-Temporal Built-up Land Layers
Exploring the Potential of Integrated Cadastral and Building Data for Evaluation of Remote-Sensing based Multi-Temporal Built-up Land Layers Johannes H. Uhl, Stefan Leyk, Aneta J. Florczyk, Martino Pesaresi,
More informationo 3000 Hannover, Fed. Rep. of Germany
1. Abstract The use of SPOT and CIR aerial photography for urban planning P. Lohmann, G. Altrogge Institute for Photogrammetry and Engineering Surveys University of Hannover, Nienburger Strasse 1 o 3000
More informationSupplementary material: Methodological annex
1 Supplementary material: Methodological annex Correcting the spatial representation bias: the grid sample approach Our land-use time series used non-ideal data sources, which differed in spatial and thematic
More informationNR402 GIS Applications in Natural Resources. Lesson 9: Scale and Accuracy
NR402 GIS Applications in Natural Resources Lesson 9: Scale and Accuracy 1 Map scale Map scale specifies the amount of reduction between the real world and the map The map scale specifies how much the
More informationKNOWLEDGE-BASED CLASSIFICATION OF LAND COVER FOR THE QUALITY ASSESSEMENT OF GIS DATABASE. Israel -
KNOWLEDGE-BASED CLASSIFICATION OF LAND COVER FOR THE QUALITY ASSESSEMENT OF GIS DATABASE Ammatzia Peled a,*, Michael Gilichinsky b a University of Haifa, Department of Geography and Environmental Studies,
More informationManaging uncertainty when aggregating from pixels to parcels: context sensitive mapping and possibility theory
Department of Geography Managing uncertainty when aggregating from pixels to parcels: context sensitive mapping and possibility theory Dr Lex Comber ajc36@le.ac.uk www.le.ac.uk/gg/staff/academic_comber.htm
More informationASSESSING THEMATIC MAP USING SAMPLING TECHNIQUE
1 ASSESSING THEMATIC MAP USING SAMPLING TECHNIQUE University of Tehran, Faculty of Natural Resources, Karaj-IRAN E-Mail: adarvish@chamran.ut.ac.ir, Fax: +98 21 8007988 ABSTRACT The estimation of accuracy
More informationInfrastructure for Spatial Information in Europe (INSPIRE)
Infrastructure for Spatial Information in Europe (INSPIRE) 2011 GISCO Working Party 8-9.3.2012 INSPIRE is about improving access to spatial information: the environment doesn t stop at borders EU Geoportal
More informationAccuracy Assessment of Land Use & Land Cover Classification (LU/LC) Case study of Shomadi area- Renk County-Upper Nile State, South Sudan
International Journal of Scientific and Research Publications, Volume 3, Issue 5, May 2013 1 Accuracy Assessment of Land Use & Land Cover Classification (LU/LC) Case study of Shomadi area- Renk County-Upper
More informationEO Information Services. Assessing Vulnerability in the metropolitan area of Rio de Janeiro (Floods & Landslides) Project
EO Information Services in support of Assessing Vulnerability in the metropolitan area of Rio de Janeiro (Floods & Landslides) Project Ricardo Armas, Critical Software SA Haris Kontoes, ISARS NOA World
More informationTHE USE OF REMOTE SENSING WITHIN THE MARS CROP YIELD MONITORING SYSTEM OF THE EUROPEAN COMMISSION
THE USE OF REMOTE SENSING WITHIN THE MARS CROP YIELD MONITORING SYSTEM OF THE EUROPEAN COMMISSION B. Baruth * a, A. Royer a, A. Klisch a, G. Genovese b a EC - Joint Research Centre, IPSC, Agriculture Unit,
More informationC o p e r n i c u s L a n d M o n i t o r i n g S e r v i c e
C o p e r n i c u s L a n d M o n i t o r i n g S e r v i c e Submodule D: stability of protected areas & related pressures: Natura2000 sites Copernicus EU Copernicus EU Copernicus EU www.copernicus.eu
More informationRating of soil heterogeneity using by satellite images
Rating of soil heterogeneity using by satellite images JAROSLAV NOVAK, VOJTECH LUKAS, JAN KREN Department of Agrosystems and Bioclimatology Mendel University in Brno Zemedelska 1, 613 00 Brno CZECH REPUBLIC
More informationClassifications of the Rural Areas in Bulgaria
Centre for Research on Settlements and Urbanism Journal of Settlements and Spatial Planning J o u r n a l h o m e p a g e: http://jssp.reviste.ubbcluj.ro Classifications of the Rural Areas in Bulgaria
More informationDiscussion paper on spatial units
Discussion paper on spatial units for the Forum of Experts in SEEA Experimental Ecosystem Accounting 2018 Version: 8 June 2018 Prepared by: SEEA EEA Revision Working Group 1 on spatial units (led by Sjoerd
More informationField data acquisition
Lesson : Primary sources Unit 3: Field data B-DC Lesson / Unit 3 Claude Collet D Department of Geosciences - Geography Content of Lesson Unit 1: Unit : Unit 3: Unit 4: Digital sources Remote sensing Field
More informationESBN. Working Group on INSPIRE
ESBN Working Group on INSPIRE by Marc Van Liedekerke, Endre Dobos and Paul Smits behalf of the WG members WG participants Marc Van Liedekerke Panos Panagos Borut Vrščaj Ivana Kovacikova Erik Obersteiner
More information79 International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-2013 ISSN
79 International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-2013 Approach to Assessment tor RS Image Classification Techniques Pravada S. Bharatkar1 and Rahila Patel1 ABSTRACT
More informationDeriving Uncertainty of Area Estimates from Satellite Imagery using Fuzzy Land-cover Classification
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 10 (2013), pp. 1059-1066 International Research Publications House http://www. irphouse.com /ijict.htm Deriving
More information