Change Detection Methods Recommended by Euroland

geoland2, FP7-SPACE-2007-1 Date Issued: 18.03.2011 Issue: I2.00

geoland

2 Towards an Operational GMES Land Monitoring Core Service

CMS EUROLAND Technical Note – Change Detection Status Report

g2_EL-RP-D_TN-01 Issue I2.00

EC Proposal Reference No. FP-7-218795

Due date of deliverable: April 2010 Actual submission date: April 2010 Actual submission date of update: March 2011

Start date of project: 01.09.2008 Duration: 50 months EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

Organisation name of lead contractor for this deliverable: Infoterra GmbH

Book Captain: S. Kuntz (ITD) Contributing Authors: M. Braun (EARSeL) C. Ernst (UCL) H. Gallaun (JR) C. Santos (Indra) T. Häme (VTT) P. Jacob, V. Herrera-Cruz (ITD) T. Katagis (AUTh) M. Keil (DLR) A. Lamb (ITUK) K. Larsson, M. Rosengren (Metria) T. Markus (SYKE) G. Smith (Spekto Natura) J. Weichselbaum (GeoVille) A. Kotarba (SRC)

Project co-funded by the European Commission within the Seventh Framework Programme Dissemination Level PU Public x PP Restricted to other programme participants (including the Commission Services) RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (including the Commission Services)

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 Document Release Sheet

Book captain: S. Kuntz (ITD) Sign Date

Approval S. Kuntz (ITD) Sign Date

Review: J. Janoth (ITD) Sign Date

Endorsement: Co-ordinator (AST) Sign Date All Geoland Distribution: consortium

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 Change Record

Issue/Rev Date Page(s) Description of Change Release

23.04.10 All Release of template D1.00

D1.00 29.04.10 All Integration of contributions from DLR, D2.00 Geoville, Metria, Spekto Natura, VTT, Indra, ITUK, Joanneum D2.00 04.05.10 All Final review & reformatting to g2 standard D3.00 document

D3.00 06.05.10 All Release of first Issue I1.00

I1.00 28.01.11 p. 23 ff. New Chapter 4; operational change detection D4.00 methods overview template D4.00 02.02.11 23 ff Integration of Enhanced MAD (eMAD) Change D4.01 Detection method, CD method in support of CLC updates, Update methods for HR Layers, CD methods in support of VHR data updates, overview D4.01 22.02.11 23ff Method for Impervious Layer update D5.00 integrated

D5.00 24.02.11 37ff Minor changes D5.01

D5.00 04.03.11 29 ff Integration of AutoChange Method D6.00

D6.00 18.03.2011 All Release of Issue 2 I2.00

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 TABLE OF CONTENTS

1 Executive Summary ...... 7 2 State of the art ...... 8 3 Methods tested and benchmarked in previous projects ...... 13 4 Change detection methods Recommended by Euroland...... 21 4.1 Generic Change detection methods ...... 21

4.1.1 Enhanced MAD (eMAD) Change Detection method ...... 21 4.2 Change Detection methods in support of CLC updates...... 23

4.2.1 CLC update based on FTS sealing / Imperviousness layer ...... 23 4.2.2 CLC update supported by Iterative Change Vector Approach ...... 26 4.3 Update methods FOR HR Layers ...... 29

4.3.1 AutoChange ...... 29 4.3.2 Semi-automated Change Detection of Built-up-area and Sealing degree ...... 32 4.3.3 Supervised Change Detection for Forest Monitoring ...... 37 4.4 Change detection methods in support of VHR resolution data updates ...... 41

4.4.1 Object-based multi-criteria Change Detection Method (Urban Atlas Full Update) ....41 4.4.2 Object-based Change Detection (Urban Atlas Automatic Update) ...... 44 4.5 Change detection methods Overview ...... 48 5 Annex: Technology Readiness Levels...... 50

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

List of Tables

Table 2-1: Comparison of supervised and unsupervised change detection approaches...... 10 Table 2-2: Summary of methods for change detection ...... 10 Table 3-1: Methods benchmarked in previous projects...... 14 Table 3-2: Methods benchmarked in geoland2 ...... 18 Table 4-1: Major processing steps. WU refers to a bi-temporal Image 2006 data set, WR to larger areas (mosaiced dats sets) up to > 100.000 km²...... 33

List of Figures

Figure 4-1: Schematic flow of AutoChange...... 30 Figure 4-2: Schematic workflow for the supervised change detection method for Forest Monitoring ...... 38 Figure 4-3: Automatic part of the production chain for the Urban Atlas Update...... 41 Figure 4-4: Generic work flow for the Automatic Update of the Urban Atlas...... 44

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 1 EXECUTIVE SUMMARY

In January 2010 the Euroland team has been approached by the project reviewers and the

GMES Bureau to

 provide a status report on change detection methods developed, benchmarked and applied in previous projects, and  to explain the rationale of currently selected change detection processes to be applied in geoland2 tests and implementation work. In this technical note first a short review of the state of the art is given (based on a comprehensive document of FP6 geoland 1 and the findings of FP6 BOSS4GMES 2) and the combined operational experiences of change detection methods implemented and used by the partners of Euroland.

1 ) Line Eikvil, Anne H.S. Solberg & Steffen Kuntz (2006): Land Cover Findings – Multi-sensor, multi- scale and multi-temporal classification approaches; Technical Note, © geoland consortium 2 ) BOSS4GMES (2009): Report on GMES Land Services - FTS Service Specifications, Data Requirements, Production Infrastructure, and Production Network Management; Document D12-5; Issue I1.1; © BOSS4GMES consortium

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 2 STATE OF THE ART

For change detection in general there are two main approaches:

 temporal trajectory analysis including time series of images over different seasons and

 bi-temporal analysis for two points in time. Bi-temporal change detection is by far the most common approach when using HR satellite data. The reason for this is the operational limitation. It can be performed at decision level or at data level. For change detection at decision level (sometimes called “supervised approach”) an independent classification of each of the two images is first performed and the results are then compared (therefore also called “post-classification comparison” or “post classification change detection”). Change detection at data level (also called unsupervised approach) is performed directly on two satellite images in order to extract the land cover changes directly from the spectral information. The most common methods include image differencing/ratioing, linear transforms, change vector analysis and image regression. The advantage of the decision level approach is that it provides insight in the change relationships, but misclassifications present in either classification will directly result in a false land cover change (LCC) and large errors in change area estimates. Factors that could also affect the results of the supervised approach, but to a lesser extent are: i) Differential MMUs – that is when the spatial resolution of the data used in the generation of each LC map differs, ii) misregistration in each LC map, iii) processing errors introduced to the boundaries of all land cover parcels due to vectorization/rasterization of the LC maps. Therefore, it is important, and challenging at the same time, to perform accuracy assessment for change analysis so as to provide quality assurance in the final products. Direct measurements of accuracy are usually performed but tend to be expensive, subjective, error prone, and impractical. An indirect change detection assessment, based on the fact that illogical changes mapped (e.g., from urban industrial to rural) haven’t really occurred, could introduce the possibility to use the ratio of illogical change to total detected change as an indicator of change detection accuracy (Gao 2009 3). The data level approach is often simpler for specific change mapping purposes, but in order to separate LCC from other changes (spectral changes) detected in the images it requires identification of a threshold for defining change/no change. Also a rigorous and consistent pre-processing is required to minimize technical changes. A widely used approach is to assume that only a few changes have occurred so that pixels in the difference image which are significantly different from the mean of the density function can be labeled as changed. Other approaches tried to minimize statistically the overall

3 ) Gao, J., 2009. Digital Analysis of Remotely Sensed Imagery. McGraw Hill

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 change detection error probability, or considered the spatial-contextual information in the pixel neighborhood. A third approach is to determine the threshold manually while displaying both images (before and after) together with the difference image. A new approach based on objects is gaining importance in the last years, along with specific software applications especially built for object-based analysis (e.g. eCognition, Feature Analyst, etc). In the frame of the local component of the LMCS, different methodologies based on objects are being tested. The advantages of this approach are:

o Slight geometric errors in the input images do not introduce significant change errors.

o Other than spectral and radiometric information can be considered (e.g. contextual information)

o Multi-parameter classification can be implemented. o Urban Atlas is an object classification. The existing objects can be used as starting point for segmentation and classification. Basically, object-based methods allow comparisons of objects at different dates based on user-defined parameters. To achieve useful results it is necessary to have solid expertise on relevant features to detect changes. In turn, change characterization is highly dependent on the class and the type of change. Thus, effective approaches must address changes in a type-by-type basis (i.e. change “construction site to built-up can not be analyzed using the same parameters as “land without current use to construction site”. At the moment, given the potential small size of changes and the high complexity of labeling the type of change, research is focusing on “hotspot” detection in order to i) improve completeness of change detection and ii) guide interpreters to avoid missing changes. There are two major sources of possible error sources inherent in satellite imagery which are related to the quality of the pre-processing level; i.e. the geometry and radiometry. Automated change detection requires that images are geometrically registered to each other with accuracy better than one pixel and that the radiometry is normalized or well calibrated so that the changes being studied can be separated from changes that may appear as a result of geometric and radiometric errors. Another type of confusion is caused by natural behavior of the surface relative to the changes which are attempting to be mapped. For instance, an arable field will change from bare to fully vegetated during a growing season. If this change is detected then it will not signify a change from the arable land cover class. With respect to the EO data sources available for pan-European approaches, this issue is of special importance, as Image2006 and Image2009 consist of a heterogeneous mixture of different sensors, from different seasons and even from different years. Here, the challenge is to differentiate between seasonal changes introduced by vegetation growth and agriculture practice and the “real” changes of land cover and/or intensity (e.g. increase of imperviousness or management intensity in agriculture). Sensor calibration differences and Document-No. g2-EL-TN-EL01 © geoland2 consortium Issue: I2.00 Date: 18.03.2011 geoland2 Page: 9 of 50

EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 illumination effects due to varying solar elevation and topography can be overcome by transforming all the data to Top of Atmosphere reflectance or further to perform atmospheric and illumination correction into ground reflectance. Seasonal changes of vegetation will still have to be separated from thematic land use changes. Table 2-1and Table 2-2 4 summarize the review of methods for change detection.

Table 2-1: Comparison of supervised and unsupervised change detection approaches.

Supervised Unsupervised Object-based Level of change Change Change detection at Change detection at both detection detection at data level decision and data level decision level

Change information Provides explicit Separates ‘change’ Hot-spot location and/or labelling of from ‘no change’. change probability change and class transitions.

Change Obtained directly Obtained through Changes are treated in type- computation from the classified interpretation of the by-type basis. images. difference image.

Ground reference Required. Not required. data

Spectral Multispectral. Many methods work on Multispectral information one spectral band, however multi-band change based on multidate composite images is applied, as well,

Data requirements Both approaches are sensitive to atmospheric Radiometry in both dates conditions and sensor differences; but in different must be as close as ways possible. Radiometric balancing methods application are advisable.

Table 2-2: Summary of methods for change detection

Method Pros Cons Multitemporal Avoids the problem of selecting the High-resolution images will in optimal points in time. general not give the necessary temporal resolution to obtain the

4 ) from Eikvil et al., 2006; modified and updated by geoland2; see footnote No. 1; for details regarding each of the methods mentioned in the tables please refer to the geoland TN.

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

Method Pros Cons time series required. Can cope with variable seasonality Required knowledge of the temporal behaviour of the land cover types being considered. Absolute calibrated data is needed. Bi-temporal unsupervised techniques Image math Simple and fast. Sensitive to radiometric differences and sensor and environmental differences between images. Image regression Less sensitive to differences caused by Requires development of accurate other factors than image math. regression functions. Vegetation index Emphasizes differences in the spectral Enhances random noise or differencing response of different features and reduces coherence noise impacts of topographic effects and illumination

Background Easy to implement Low accuracy subtraction

Change vector Enables processing of the full spectral Sensitive to radiometric differences. analysis dimension and provides the change direction. Transformations Can be applied to multispectral data. Results can be difficult to interpret without a good knowledge of the study area. Multi-date Quick and easy. Can be applied to multi- Results can be difficult to interpret clustering spectral data without a good knowledge of the study area.

Bi-temporal supervised techniques Post-classification Minimizes the problem of radiometric Sensitive to mis-classification and comparison correction between dates. No threshold mis-registration. Error rate is selection necessary. Produces the new cumulative. labeling directly. Can be used for multisensor images. Spectral mixture The results are stable, accurate It is considered a complex method model and repeatable. and requires advanced image processing analysis

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

Method Pros Cons Expectation – Reported to provide higher change Requires estimating the a priori joint Maximization (EM) detection accuracy than other change class probability. detection detection methods

Hybrid change This method excludes unchanged pixels Requires selection of thresholds to detection from classification to reduce classification implement classification, which is errors somewhat complicated to identify change trajectories Artificial neural A nonparametric method that has the Long training time is required. ANN networks (ANN) ability to estimate the properties of data is often sensitive to the amount of based on the training samples training data used. ANN functions are not common in image processing software Biophysical This method can accurately detect Requires great effort to develop the parameter method Vegetation change based on vegetation model and achieve accurate image calinration. Requires a large Physical structures number of field data. The method is only suitable for vegetation studies. Cross correlation Eliminates the problems from radiometric Cannot always detect subpixel analysis. and phenological differences. changes unless the new and old landcover spectral reflectances are highly contrasting Multidate direct The method has the same advantages as Requires ground reference data not classification the post-classification comparison. In only for all classes, but also for all addition, it exploits the multitemporal the class transitions. information and the error rate will not be cumulative. Bi-temporal – Stepwise combination of unsupervised and supervised techniques Step 1. Can be run automatically. Must be followed by an interactive Unsupervised for step delineation of Changes includes both seasonal changes and thematic. Step 2A. Multi-date If an unsupervised method is used for Requires ground truth for changed direct classification grouping of spectral changes, ground areas or must rely on manual within changed truth is needed only for labeling the interpretation of spectral classes. area different types of class transitions. Using the map from the first date simplifies the labeling The method has the same advantages as the post- classification comparison. In addition, it exploits the multi-temporal information and the error rate will not be cumulative. Step 2B. Multi-date Normal classification procedure for or Single date Document-No. g2-EL-TN-EL01 © geoland2 consortium Issue: I2.00 Date: 18.03.2011 geoland2 Page: 12 of 50

EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

Method Pros Cons classification single/multi-date classification. outside change area Method Pros Cons Bi-temporal object-based techniques Post-classification Minimizes the problem of radiometric Sensitive to mis-classification. Error comparison correction between dates. No threshold rate is cumulative. selection necessary. Produces the new labeling directly. Can be used for multisensor images. Multi-criteria Different types of change can be Requires expert knowledge of both change approached in different ways complex software and land identification dynamics in order to identify the most appropriate parameters to identify changes.

3 METHODS TESTED AND BENCHMARKED IN PREVIOUS PROJECTS

The following tables provide a short overview which change detection methods have been tested and benchmarked in previous projects and in geoland2.

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

Table 3-1: Methods benchmarked in previous projects Project Application Method tested Results Publication Comments CORINE 2000 / Pan-European LC/LU Visual comparison of Consistent pan-European EEA Approaches differ 2006 data base; 25 ha MMU, Image2000 / Image change layers (2000 – 2006) Büttner 2010 (EIONET EWG) significantly among MS changes in 5 ha MMU 2006 Most MS still apply visual change detection

CLC2000 New water Thresholding new New small water bodies Metria (internal report) Implemented and used image with old map under production CLC2006 Update Forest change ENFORMA Afforestation Metria (internal report) Implemented and used under production Finnish CLC2006 general LU/LC change Changed areas: Land use and cover changes: European version: Finnish Implemented and used difference images of Corine Land Cover 2006 Final under production. 5 ha MMU in European red/NIR channels, Report, EEA version Requires satellite images Type of change: with red and NIR channels 0.5 ha MMU in national combination of and similar pixel size, good version CLC2000 and quality Land Use GIS data CLC2006

BOSS4GMES Urban change detection Comparison of  Very good accuracy in TerraSAR-X should be using SAR imagery raster/vector dense urban areas tested in sparse areas in classification (t0) vs.  Accuracy drops in sparse order to test if change SAR classification (t1) urban areas detection accuracy improves. BOSS4GMES Land Cover Change Statistical-semantic  The method proved Comber et al. (2004a,b Requires the removal of Product for the UK from method reasonably easy to automate 2005a,b inconsistencies arising from LCM2007  The vast majority of the BOSS4GMES, D12-5 ; I.1.1 changing methods between inconsistencies appear to previous UK land cover data relate to either inappropriate sets. LUT’s values and to misclassifications in one or other of the input maps  Recommendation to use the approach for UK LC updates

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

BOSS4GMES Land cover change Change indicator  Change indicators (derived BOSS4GMES, D222-1; Method needs some further product assessment for bases vs. visual using local image attribute Infoterra GmbH refinement and development LMCS. Assessment ingestion of changes changes) as support to towards operationalisation, area: parts of visual ingestion of changes especially concerning Luxembourg in urban areas. reduction of false alarms for  Indicator-based change sealing updates detection proved potential for further development  GSE Land / GSE LC/LU maps 2000, 2005 Automated image - Large degree of automation GSE Land S5 Service Effort highly depends on SAGE (M2.4/M2.6); 0,25 ha classification of for land cover changes specifications quality of derived LC change MMU for settlements; 1 change candidates - Visual interpretation and candidates; LU changes ha MMU outside of and visual ancillary data needed to need to be added visually; settlements interpretation capture land use changes

GSEFM Clear cut mapping ENFORMA Operational clear cut GSEFM_T3_Ph3_PM11_S6_ Runs on large area mosaics. mapping and monitoring over METRIA_accuracy_clear_cuts Only changes within forest Sweden. Tested in UK, Latvia mask. and Ireland. Euroland Forest Forest clear cut and AutoChange, First quantitative tests Häme, Tuomas; Heiler, Istvan; The method uses bi- Monitoring regeneration mapping software available indicate good performance on Miguel-Ayanz, Jesus San. temporal data. It is an free of charge to the Finnish test site. 1998. An unsupervised unsupervised approach but Europand partners for change detection and has features on a supervised Geoland2 research recognition system for forestry. method through the project. VTT International Journal of automatic intelligent Remote Sensing, vol. 19, 6, p. selection of the training set 1079 - 1099 for the unsupervised analysis. The method conducts change analysis and general land cover classification during the same run. In the multi-layer output the change magnitude is given as continuous value and change type as four classes. Land cover classification is another layer. The final change and land cover maps are formed by combining the three layers.

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ENFORMA FP4 Clear cut mapping Single band scen to Software implementation for Rosengren, M. G., and Willén, scene calibration and clear cut mapping and E., 2004. Multiresolution SPOT difference image monitoring over Sweden. 5 data for boreal forest Tested in UK, Latvia and monitoring. ISPRS 2004, Ireland Istanbul, Turkey. “The Future of Land cover change Use of OBIA Operational use of the model Presentation at the Object The model was based on Forests”. AUTh & mapping between 1987 classification and Ground truth data collected based landscape analysis fuzzy logic using object WWF GREECE and 2007 in Greece Landsat imagery – from extensive field surveys (OBLA) (7-8/04, 2009), features such as mean and Development of semi- were used to assess the RSPSoc Land Cover/Land standard deviation. automated accuracy of the product. Use SIG : Thresholds of the classification Overall accuracy was high. “Development of a transferable membership functions of the algorithms object-based classification features used in order to model for nationwide land classify the different classes cover mapping in Greece” were found using field data Polychronaki A, Gitas I.Z., and by visual interpretation Galidaki G. & Dimitrakopoulos of high resolution images. K. Use of more accurate thematic agricultural maps could increase the accuracy of the classification of those areas. Post-classification detection approach was implemented.

Pilot Study in the Clear cut mapping Change Vector Clear cut mapping applied in Gallaun, H., Schardt, M. & An automatic thresholding Field of Analysis boreal, temperate, alpine and Häusler, T. 1999. Pilot Study procedure was implemented. Monitoring Mediterranean test sites. on Monitoring of European Forested Areas Forests. Tagungsband der Jahrestagung des Arbeitskreises„ Interpretation von Fernerkundungsdaten der DGPF, Halle 28.–29.4.1999: 157–161.

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

OFAC National and regional - Multi-date - Fully automated method Duveiller, G., Defourny, P., Method used for moist forest cover and forest segmentation - 4 forest cover dynamics : Desclée, B. & Mayaux, P., tropical forest in Central cover change estimate - Automated deforestation, degradation, 2008. Deforestation in Central Africa in Central Africa classification reforestation and Africa : Estimates at regional, Landsat imagery - change detected by regeneration national and landscape levels a statistical method by advanced processing of systematically-distributed Landsat extracts. Remote Sensing of Environment, 112, 1969-1981 JRC TREES3 Producing estimates on - Multi-date Achard, F., Beuchle, R., tree cover change over segmentation Bodart, C., Brink, A., Carboni, the tropics - spectral library S., Eva, H., Mayaux, P., Simonetti, D., Stibig, H.J. (2009). Monitoring Forest Cover at global scale: The JRC approach”, Proceedings of the 33rd International Symposium on Remote Sensing of Environment, Stressa-Italy, 4-8 May 2009

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Table 3-2: Methods benchmarked in geoland2 Task Application Method Results Publication Comments tested Euroland – local “Hotspot” detection Object-based  Very reliable for detection of Joeri Van Wolvelaer, César LMCS approaches hotspots Santos González, Antonio  It assists photointerpreter’s Garzón López, Anna Maria work avoiding in-depth search Deflorio (2010). “Urban in empty areas Atlas updating by semi-  It saves change-searching automatic change time mapping”. '30th EARSeL  Detection of some particular Symposium: Remote types of changes need further Sensing for Science, research Education and Culture' Euroland – local Change detection and change Visual  Good results when preformed LMCS labelling interpretation by experienced interpreters  Very time consuming  Some small changes are missed Euroland – local Automatic Change detection Object-based.  Very high accuracy LMCS Combination of  Detection of changes. No UA data + FTS labelling Sealing layer  Small changes are missed due to FTS sealing layer resolution  Very good results for urban growth/sprawl estimation  Fast performance Euroland – Impervious areas update Automated High degree of automation Upcoming paper for Effort needed and quality continental Update of built-up and sealing derivation of enables application at low budget; EARSEL Conference 2010 of results depends on LMCS layer from FTSP2006; changes Sealing 09 data Automated results are good, but quality of Image09 and (built-up and sealing) in 20m, and of new built- post-editing is obligatory also highly on the quality updated built-up and sealing up candidates of the 2006 built- layer in 20m and 1ha plus visual post- up/sealing layer European layer editing Euroland – Update of German national LC Object based  Improvement of throughput Method implemented in continental data base based on model approach by provision of change DLM-DE project LMCS Imperviousness layer and EO indication layer data  significant reduction of “false alarms” Document-No. g2-EL-TN-EL01 © geoland2 consortium Issue: I2.00 Date: 18.03.2011 geoland2 Page: 18 of 50

EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 Task Application Method Results Publication Comments tested Forest changes ENFORMA Clear cut mapping Euroland- Forest Area Change Mapping Supervised Tests show a high degree of Only image data acquired continental Change automation in areas with in the vegetation period LMCS Detection for homogeneous forests. Areas with can be used. Forest small, fragmented forests require Monitoring increased effort for visual refinement. Euroland- CLC-Update Iterative Change Increased performance for the The method is insensitive continental Vector Approach identification and delineation of to phenological changes. LMCS changes in the course of the CLC update. Tested in selected regions in central Europe show reliable results. Further testing is ongoing. SATChMo Generic, to support a 5-class Multivariate Algorithm functioning in both per- Multivariate Alteration Run-time software European AFS change matrix Alteration pixel and object-based Detection (MAD) and MAF available in Public Domain Detection (MAD) implementations. No further testing post processing in as SATChMo still awaits the first multispectral and bi- temporal tranche of VHR data, as temporal image data: New T1. T2 is only expected for 2011 approaches to change detection studies (Nielsen, A; Conradsen, K; Simpson, J), Remote Sensing of Environment, 1998, vol 64, no. 1, pp 1-19 SATChMo Generic, to support a 5-class Object-based Algorithm functioning but no further Object-based change Custom implementation European AFS change matrix change through testing as SATChMo still awaits detection using correlation correlation the first temporal tranche of VHR image analysis and image analysis data, as T1. T2 is only expected segmentation (Im, J; for 2011 Jensen, J; Tullis, J), IJRS January 2008, vol 29, number 2, pp 399-423.

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

Satchmo – SM07 Generic LC and LCC maps OFAC Stratification of the regions: WP 3250: LCC maps on African AFS samples TREES3 1.humid forest areas => UCL Review: Europe & Africa GISAT method automated chain (demonstration (UCL) over Central Africa) 2.Dryer areas => the following approach (demonstration for the whole IGADD) Land cover map interactive object- based (multitemporal segmentation) approach for the best single date : 1.JRC provided the 400+ land cover maps for the 2 dates 2.GISAT further classify the other vgt and barren classes into the proposed legend by fine tuning using GUI in e-Cognition 3.UCL develop and apply an automated chain to label the second date : backdating (currently 1990 or 2000, later on 2010) 4.UCL develop and apply an automated land cover change detection algo 5.JRC-GISAT : interactive final check with some UCL input for automated quality control (visualisation based on JRC or Euroland existing tools) 6.ALL : statistics extraction (land cover accounting) and comments

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 4 CHANGE DETECTION METHODS RECOMMENDED BY EUROLAND

4.1 GENERIC CHANGE DETECTION METHODS

4.1.1 Enhanced MAD (eMAD) Change Detection method

4.1.1.1 Brief method description

The enhanced MAD (eMAD) is an object-based, bi-temporal change detection method, focused on the very high resolution imagery and applicable for high resolution data with very high resolution panchromatic channel. In order to detect a change, bi-temporal image segmentation is performed and each image object is described with its spectral and textural characteristics. Then, the Multivariate Alteration Detection (MAD) transformation is applied, and results with n components, where n in the number of input layers (pairs of T1 and T2 layers). MAD transformation assumes multivariate normal distribution of X and Y random vectors, which represent T1 and T2 multispectral images of a scene. The difference D between the two images is calculated by D= aTX - bTY. The vectors a and b are sought subject to the condition that the variance of D is maximized and subject to the constraints that var(aTX)= var(bTY)=1. Determining the vectors a and b in this way is a standard statistical procedure which considers a generalized eigenvalue problem. For a given number of bands N, the procedure returns N orthogonal difference images, referred to as to the MAD components (Nielsen et al., 1998, Rem. Sens. Environ., 64; Nielsen, 2002, IEEE Trans. Image Process., 11; Canty and Nielsen, 2008, Rem. Sens. Environ., 112). The sum of squares of the standardized MAD components approximates the chi-square distribution. Base on statistical properties of chi-square distribution, the confidence level of change is determined and used for an easy „change“/„no change“ mask generation. Object characteristics are used for refinement of the change mask and results with final change mask. The characteristics allow also for classification of change (indication of change vector).

4.1.1.2 Key features to be monitored

In the present shape, the method is dedicated to detect changes within all thematic classes, without discriminating between them. However, the method gives a possibility for classifying the vector of change, and tests done so far are promising. The considered classes are: urbane impervious, bare ground, water, snow/ice, forest, non-forest vegetation.

4.1.1.3 Technical requirements

The complete workflow is implemented within the Definiens eCognition environment and make use of the software-dedicated MAD plugin. The method can be run as a fully automatic (with default parameters) and as semi-automatic, allowing the operator for interactive tuning of parameters.

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 4.1.1.4 Possible constraints

The eMAD method was developed with focus on the VHR imagery and is expected to indicate change incorrectly as a pixel size increases. However, HR imagery can be analyzed as long as VHR panchromatic band is available. Despite the data resolution, operator interaction may be necessary to decide whether a cultivated bare soil or the urban bare soil (construction sites) is observed and if it should be considered as a change.

4.1.1.5 Expected benefits

Object-oriented approach allows for and easy application of minimum mapping unit. Changes can be determined at different confidence level to reflect the data user needs. The method is insensitive for seasonal changes of vegetation.

4.1.1.6 Technical change detection methods sheet

Information Needs

Method Applicability Comments

eMAD European -

Method Application Requirements

Change Detection Method

enchaced MAD (eMAD) change detection

Input data sources

Very High Resolution multispectral imagery or High Resolution multispectral imagery with Very High Resolution panchromatic channel (spatial resolution of 1-2 m/pixel or higher).

Ancillary data required for the tasks

No ancillary data required

Ancillary data optional for the task

No optional ancillary data required

Methodology

Multi-technique approach: MAD transformation with differencing of spectral and textural characteristics

Change classes addressed

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

Changes detection addressed to all classes as one

Expected Thematic accuracy for the change classes

Proportion of correct change/no change detections ~90%; probability of detection depends on definition of minimal mapping unit.

Constraints (e.g. omission / commission errors expected)

Commission errors possible – bare soil

Benefits

Fast change detection with objects as a base image segments

Access / ownership

Method developed at Space Research Centre of Polish Academy of Science

Service offer

Licence and/or service available from the Space Research Centre PAS

Comments

AOB

4.2 CHANGE DETECTION METHODS IN SUPPORT OF CLC UPDATES

4.2.1 CLC update based on FTS sealing / Imperviousness layer

4.2.1.1 Brief method description

This method intersects a vectorised and size-filtered binary version of the FTS sealing layer with the urban polygons of the CLC layer to be updated. The outcome is used as an indicator layer for potential areas of change within urban (built-up) classes. The potential changes need to be visually inspected by an interpreter with the usage of satellite data. True or technical changes compliant with the CLC change rules are then implemented by manual interaction to match the CLC look&feel principles.

4.2.1.2 Key features to be monitored

Changes within urban areas and other built-up areas such as roads, paved places etc.

4.2.1.3 Technical requirements

Standard GIS environment with ArcGIS and ERDAS Imagine

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4.2.1.4 Possible constraints

Usage rights concerning FTS Sealing / Imperviousness layer, Possible commission errors within FTS sealing layer (may happen with bare soil, sands, rocks), Possible omission errors within FTS sealing layer where low-density built-up areas coincide with predominant vegetation, such as in certain (mostly older) suburbs with large trees obscuring sealed areas.

4.2.1.5 Expected benefits

Increased performance with object identification and delineation. Better reliability and quality in object delineation and generalisation as indicator polygons allow quick visual assessment of the regional context of urban and non-urban features.

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

4.2.1.6 Technical change detection methods sheet

Information Needs

Method Applicability Comments

CORINE European

Method Application Requirements

Change Detection Method

Indicator-based change detection for built-up areas

Input data sources

Image2006 – Image2009 – Image2012 etc

Ancillary data required for the tasks

FTS Sealing (2006) or Imperviousness layer 2009

Ancillary data optional for the task

Topographic maps

Methodology

Intersection of vectorised binary sealing layer with CLC urban classes

Change classes addressed

Sealing, built-up

Expected Thematic accuracy for the change classes

> 85 %

Constraints (e.g. omission / commission errors expected)

Omission errors possible in: low-density built-up areas dominated by dense vegetation, e.g. large trees in suburban areas.

Commission errors possible in: soil areas such as bare fields, beaches, bare rocks.

Benefits

Increased performance and reliability in updating urban features in CLC

Access / ownership

Astrium GEO / Infoterra GmbH

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

Service offer

Customised production offer

Comments

AOB

4.2.2 CLC update supported by Iterative Change Vector Approach

4.2.2.1 Brief method description

First, a cloud and cloud shadow mask is generated with a semiautomatic tool, in case that no appropriate cloud and cloud shadow mask is available. In hilly to mountainous terrain, illumination effects which are caused by the sun-terrain-sensor geometry are then reduced by application of the automatic “Self Calibrating Topographic Normalisation Method”. Based on the pre-processed satellite data, automatic, unsupervised clustering is performed which subdivides the CLC categories into spectrally consisted sub-categories. For each sub-category, change vectors are calculated by excluding vigorous changes iteratively. Areas which show significant deviations from the “mean spectral behaviour” are then identified. These areas are generalised, e.g. according minimum size and show as the result “potentially changed areas”. They need to be visually inspected for final definition of the changed areas according to the CLC nomenclature definitions.

4.2.2.2 Key features to be monitored

All CLC categories, except “Arable land” and “Heterogeneous agricultural areas”.

4.2.2.3 Technical requirements

The pre-processing tools are implemented within the IMPACT software of Joanneum Research, the change vector approach is implemented in the ArcGIS software of ESRI. The cloud and cloud shadow generation tool works semi-automatically, the other tools automatically.

4.2.2.4 Possible constraints

In case that the majority of the area is affected by changes, the results are not appropriate.

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 4.2.2.5 Expected benefits

Increased performance for the identification and delineation of changes in the course of the CLC update. The method is insensitive to phenological changes.

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

4.2.2.6 Technical change detection methods sheet

Information Needs

Method Applicability Comments

Iterative Change European Vector Approach

Method Application Requirements

Change Detection Method

Iterative Change Vector Approach

Input data sources

Image2006 – Image2009 – Image2012 etc

Ancillary data required for the tasks

Corine Landcover data for one date.

Ancillary data optional for the task

For mountainous terrain, a DEM is required if the topographic normalisation tool is applied.

Methodology

Unsupervised clustering is performed to subdivide the CLC categories into spectrally consisted sub-categories. For each sub-category, change vectors and confidence intervals are calculated by excluding vigorous changes iteratively.

Change classes addressed All CLC categories, except “Arable land” and “Heterogeneous agricultural areas”.

Expected Thematic accuracy for the change classes

> 85 %

Constraints (e.g. omission / commission errors expected)

Changes should not affect the majority of the area.

Benefits

Increased performance and reliability in CLC updating

Access / ownership

Joanneum Research Forschungsgesellschaft mbH.

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Service offer

Customised production offer

Comments

AOB

4.3 UPDATE METHODS FOR HR LAYERS

4.3.1 AutoChange

4.3.1.1 Brief method description

The AutoChange method and software is meant to detect and identify changes that have occurred between the acquisition dates of two satellite images. The changes of interest should cover only a fraction of the total area of interest. AutoChange is not applicable to monitor change that comprises the majority of the area. The algorithm is described in Häme et al. (1998). It outputs a multi-band raster file in which the output bands represent land cover, change magnitude and change type. The change magnitude is output as a continuous variable whereas the land cover class and change type are category variables. It is possible to output in all nine output bands depending on the user-given parameters. In addition to the raster image output, a text file is produced. This file includes important information on class means and number of observations in the classes, for instance. The text file is also an input to a separate change classification program. The AutoChange is also an effective tool to make an unsupervised classification to mono-temporal data. In change analysis the mono-temporal classification can be use to make a mask of clouds and agricultural lands, for instance.

4.3.1.2 Key features to be monitored

AutoChange is applicable to detect rapid changes such as forest cutting, land cover type change, construction, coastal line movement. When combining the change magnitude and change outputs with the unsupervised land cover classification that also is an output of AutoChange, changes on the agricultural land can be separated from forest changes for instance. The final change map is produced by combining the change magnitude, change type and land cover classification using simple logical operations.

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 4.3.1.3 Technical requirements

AutoChange takes as input two satellite images, an optional mask image, and a parameter file (Fehler! Verweisquelle konnte nicht gefunden werden.). It is executed as a batch run from a command prompt. In addition to the actual AutoChange software the user needs a display program and a formula editor to combine and threshold the output bands. The parameter file can be made with any text editor. Spreadsheet software such as Microsoft Excel is useful to visualize class means. Additionally, a format converter and a separate classifier may me needed. The complete software compilation to run AutoChange smoothly consists of

 AutoChange.exe (actual AutoChange)  gdal_translate.exe (to make conversion to an from ERMapper format)  Display software & formula editor (off the shelf)  Spreadsheet software (to visualize class spectral means)  stats_clusters.exe (separate change classifier) The AutoChange pack includes AutoChange, gdal_translate and stats_clusters.

INPUT

Earlier image Later image Parameters (reflectance) (reflectance)

Mask image (optional)

CHANGE ANALYSIS USING A SAMPLE

Homogeneous Homogeneous sample for sample for clustering clustering

Clustering to Adding class primary cl. labels from primary clust.

Secondary cl..

Computation of change statistics • Primary classes • Change magnitude • Change type

OUTPUT FOR EACH PIXEL

Primary Change Change classes magnitude type

Clustering Biomass sample index

Figure 4-1: Schematic flow of AutoChange

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 4.3.1.4 Possible constraints

Although AutoChange has features of a supervised classifier through the intelligent selection of the observations to the clustering it is using an unsupervised approach. This means that it attempts the extract all the changes whether they are relevant to the user or not. For instance the seasonal changes can cause much larger intensity changes than clear cuts. Particularly the use of the near infrared band can be problematic and for instance in forest clear cut mapping this band may be excluded. Another fact to remember is that AutoChange uses the Euclidean distance. Thus a band’s weight is proportional to its dynamical range. Using the default parameter values the bands are internally normalized to the same standard deviation to avoid the dominance of the NIR band. Image normalization does not lead to a successful result if the clouds are major seasonal variation in the agricultural land, for instance, is not taken into consideration. It is important to exclude clouds and other high variability but low interest areas using user defined parameter values or making a specific mask file as a pre-processing step. It can also be done with AutoChange. With very high resolution data an image segmentation should be done to extract homogeneous objects before applying AutoChange.

4.3.1.5 Expected benefits

AutoChange has proven to be an effective and fast tool to map rapid changes that have occurred between the acquisition dates of two images. Its special benefit is combination of the change and land cover classification which makes it possible to separate the changes of interest from irrelevant changes thus enabling to prepare a final maps of changes with one tool.

Information Needs

Method Applicability Comments

AutoChange Land areas -

Method Application Requirements

Change Detection Method

AutoChange hierarchical change estimation method

Input data sources

Mainly optical data but also applied to SAR imagery successfully. Very high resolution data (0.5 – 5 m) requires image segmentation as pre-processing step

Ancillary data required for the tasks

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No ancillary data required

Ancillary data optional for the task

Ground reference data on change

Methodology

Hierarchical unsupervised clustering with intelligent selection of training sample, computing change magnitude as a continuous value and land cover type and change type as category variable.

Change classes addressed

Informative change classes are defined by combining change type, change magnitude and change type

Expected Thematic accuracy for the change classes

Depends completely on the change classes targeted and images used. In forest clearing, for instance the accuracy is very high.

Constraints (e.g. omission / commission errors expected)

Commission errors due to poor image match, seasonal changes. Omission when the user has not considered the influence of the near infrared band particularly when image seasons are different.

Benefits

Combines change detection and land cover mapping which enables extracting the change of interest

Access / ownership

VTT Technical Research Centre of Finland

Service offer

License available free of charge for Geoland2 from VTT

Comments

AOB

4.3.2 Semi-automated Change Detection of Built-up-area and Sealing degree

The method is composed of fully automated steps and a visual interpretation part. It is fully developed and has been operationally applied to the update of the European High-resolution Layer of Built-up Areas and Soil Sealing 2006 with Image2009 data.

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 4.3.2.1 Brief method description

The HR built-up and sealing layer update method is a change detection method based on bi- seasonal Vegetation Index (NDVI) comparison, with major components being NDVI calibration, automatic derivation and subsequent visual post-editing of built-up-area changes, and fully automatic derivation of sealing-degree changes. It is based on Image 2006 and Image 2009. The automatic steps of the change detection procedure have been implemented in tailor-made ERDAS models that are optimised towards minimisation of human interaction and application at the largest-possible spatial units. The procedure is covered by a total of seven automatic models plus the visual interpretation, which is an intermediate step, followed by the two last automatic steps. The following table contains a summary of the major processing steps:

Table 4-1: Major processing steps. WU refers to a bi-temporal Image 2006 data set, WR to larger areas (mosaiced dats sets) up to > 100.000 km²

Major Steps of Built-up Area and Soil Sealing Processing Tools/Methods, Key Update Unit approaches

1. Data preparation: Quality control (geometry, Individual Visual check, image acquisition time…) images 2009 interpretation, mosaicing Generation of cloud/shadow/haze masks Mosaicing of bi-temporal 2006 cloud mask

2. NDVI Calibration using the Sealing Degrees of 2006 Individual ERDAS Modeler, Batch as reference images 2006 Iterative NDVI calibration to (WU – Derivation of NDVI Max for Image 2006 (outside sealing 2006. built-up areas) Working Units) and Calculation of differences images between sealing 2009 degrees 2006 and the new calibrated NDVIs within built-up areas

3. Mosaicing of the calibrated NDVIs (Max of 2006) Output: WR ERDAS Modeler and difference images to sealing 2006 (Working Derivation of sealing MIN and Region) Mosaicing of the calibrated NDVIs (Min and Max ) of MAX per pixel for all Image images Image 2009, along with difference layers (cal. NDVI 2009 datasets and thus 2009 – sealing 2006) and meta data w.r.t. input reduction of the variety of images used individual input data to two coherent layers

4. Identify 2009 Built-Up candidates and commission WR ERDAS Modeler errors 2006 by means of Probability zoning (using thresholds for the differences of sealing degree, Applying a bundle of sealing degree 2009, various buffers, various object radiometric, spatial and size thresholds) context based thresholds

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

5. Visually post-editing of the change candidates Tiles Visual Interpretation 6. Rule-based assignment of the built-up changes to WR ERDAS Modeler true changes and omission errors 2006 Taking into account possible Derivation of all meta information, especially related errors of the 2006 built-up to clouds 2006 and 2009 layer and technical changes 7. Drive sealing levels (imperviousness) for 2009 and WR ERDAS Modeler sealing changes between 2006 and 2009 Derive only radiometrically and spatially significant sealing changes

4.3.2.2 Key features to be monitored

The key features to me monitored are the built-up area and the degrees of soil sealing (imperviousness). As the built-up area of 2006 was derived with a classical mapping approach as opposed to the update method, technical changes have to be taken into account among the monitoring features. In addition, corrections of the 2006 built-up and sealing map were performed and these were used for rendering a corrected built-up map for 2006. This way, change figures of the highest possible accuracy were derived, and error propagation was minimised.

4.3.2.3 Technical requirements

The changes of the built-up surfaces and sealing degrees were derived with COTS software and standard PCs. Besides ArcGIS, ERDAS IMAGINE was used as the major software system, where the processing was based on models specifically tailor-made for this project. These models were developed with the ERDAS Model Maker, a graphic flow chart model building enhancement to the ERDAS IMAGINE Spatial Modeler. The processes can be translated in any other image processing software, this does however cause substantial extra effort. Within the consortium it was done by one company that did not use ERDAS.

4.3.2.4 Possible constraints

Possible constraints arise mainly in cases where the data quality of Image 2006 and especially of Image 2009 is not sufficient. This may be due to cloud cover and haze, and to less suitable acquisition times, such as March, April (in large parts of Europe), or October, not mentioning November. Also the availability of only one acquisition time poses problems, as the bi-temporal image coverage is important for excluding certain seasonal vegetation changes in croplands. Even though the input EO data specifications exclude unsuitable data, in some cases no better data have been available. Another constraint is the necessity of applying visual post-editing to the automatically derived results. Without, the required accuracy and credibility of the results could not be reached. Mitigation in this respect would be additional EO data, in order to further exclude false change alarms and better map true changes automatically.

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 4.3.2.5 Expected benefits

The major benefits of the change detection method applied to built-up and sealing changes are the following:

 The method is fully repeatable for the generation of a further update. Some improvements and adaptations would be made, but basically the method could be re-applied and deliver comparable results.

 The costs are moderate in spite of the visual interpretation element, as the other steps are highly automated.

 The method or parts of it could be expanded and adapted to other change subjects.  Due to this developed method it was possible to produce the requested built-up and sealing update services and make them available to the customers, e.g. for the CLC update in a rather short period. Thus it shows the potential of HR (!) remote sensing based change detection methods for such large-scale applications requiring the highest possible degree of automation.

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4.3.2.6 Technical change detection methods sheet

Information Needs

Method Applicability Comments

Fully operational method that has been Euroland – European applied to the update of the European built- continental LMCS up area and soil sealing layer

Method Application Requirements

Change Detection Method

Based on automatic bi-seasonal Vegetation Index (NDVI) comparison, with major components being NDVI calibration, automatic derivation and subsequent visual post-editing of built-up-area changes, and fully automatic derivation of sealing-degree changes.

Input data sources

Image2006 – Image2009

Ancillary data required for the tasks

In some cases Google Earth (often constrained by topicality)

Ancillary data optional for the task

Other LC data, such as national LC data (often constrained by topicality)

Methodology

Hybrid methodology, using both the original map (HR built-up and sealing layer 2006) and EO data (calibrated NDVI) comparison.

Change classes addressed

Changes of built-up surfaces and changes of sealing (imperviousness) degrees (plus addressing mapping errors of 2006 and technical chnages)

Expected Thematic accuracy for the change classes

> 85 %

Constraints (e.g. omission / commission errors expected)

Quality (clouds, haze) , missing or bad acquisition dates of EO data

Visual editing necessary to achieve required accuracy

Benefits  The method is fully repeatable for the generation of a further update. Some improvements

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 and adaptations would be made, but basically the method could be re-applied and deliver comparable results.

 The costs are moderate in spite of the visual interpretation element, as the other steps are highly automated.

Access / ownership

Euroland HR sealing layer consortium / mainly GeoVille Austria

Service offer

Service offer of this and downstream services

Comments

The method includes certain monitoring aspects, which could be enhanced with additional input data (e.g. AWIFS) and then more broadly applied to more change detection subjects.

AOB

Even though this method is based on and works well with relative calibration methods of the NDVI, the issue of radiometric alignment of the input EO data remains to be solved for general change detection and practical solutions at the European scale.

4.3.3 Supervised Change Detection for Forest Monitoring

4.3.3.1 Brief method description

The method is based on bi-temporal HR satellite imagery, e.g. Image 2006 and Image 2009, and reference data. The general workflow is shown in the following figure.

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EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

Figure 4-2: Schematic workflow for the supervised change detection method for Forest Monitoring The processing steps are: 1. Optional: automatic image co-registration is performed 2. Optional, for mountainous areas: automatic “Self Calibrating Topographic Normalisation” 3. Relative calibration: Only forested areas are used to derive the calibration parameters (the JRC-Forest Layer or other available forest area map is used as input to exclude non-forest areas). 4. Radiometric change detection is performed by a change vector approach to delineate areas which show significant radiometric changes between the image acquisition dates. 5. Forest probability maps for the different dates are derived by logistic regression. 6. Change interpretation: Only areas showing significant changes in radiometry (resulting from radiometric change detection step) are treated for the change labelling, which results from the forest probability maps for the dates t1 and t2. 7. The change interpretation results are aggregated according to the nomenclature definitions and the forest maps for dates t1 and t2 as well as the forest change map is generated.

Optional, refinement of the results is performed by visual interpretation to increase the accuracy.

EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 4.3.3.2 Key features to be monitored

Forests

4.3.3.3 Technical requirements

The tools are implemented within the IMPACT software of Joanneum Research.

4.3.3.4 Possible constraints

High geometric accuracy is a main factor for successful monitoring of changes by multi-temporal imagery. Only image data acquired in the vegetation period can be used for monitoring of the forests by this approach. Areas with cloud or cloud shadow or haze at one of the image acquisition dates cannot be monitored, which leads to gaps e.g. in areas with frequent cloud cover. To achieve a high thematic accuracy, visual refinement is necessary, especially in areas with heterogeneous land cover.

4.3.3.5 Expected benefits

Increased performance for the identification and labelling of changes in the forest area compared to visual interpretation.

EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

4.3.3.6 Technical change detection methods sheet

Information Needs

Method Applicability Comments

Supervised Change Method has been applied to update HR Detection for Forest European forest layer in selected test sites. Monitoring Method Application Requirements

Change Detection Method

Supervised Change Detection for Forest Monitoring

Input data sources

Image2006 – Image2009

Ancillary data required for the tasks

VHR imagery, partly Google Earth

Ancillary data optional for the task

Other LC data, such as national LC data (often constrained by topicality)

Methodology

Relative calibration is performed for identification of areas which show significant radiometric changes between the image acquisition dates. Labelling of the change type is performed by supervised classification of the calibrated imagery. Optional, visual refinement is applied to increase accuracy.

Change classes addressed

Forest / Non-Forest

Expected Thematic accuracy for the change classes

> 85 %

Constraints (e.g. omission / commission errors expected)

Image quality (clouds, cloud shadows, haze)

Benefits  Compared to visual interpretation, increased performance for the identification and labelling of changes in forests.

Access / ownership

EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

Joanneum Research Forschungsgesellschaft mbH

Service offer

Customised production offer

Comments

AOB

4.4 CHANGE DETECTION METHODS IN SUPPORT OF VHR RESOLUTION DATA UPDATES

4.4.1 Object-based multi-criteria Change Detection Method (Urban Atlas Full Update)

4.4.1.1 Brief method description

This object-based approach can be considered as a vector-to-image-to-image change detection approach. For the particular case in which this method is being applied (i.e. Urban Atlas (UA) update), the current UA polygons act as objects and provide the starting information about classes to set up the detection algorithms. Changes are approached in a different way depending on the original class at t0 and the possible changes that each particular class can experience. Thus, objects are compared image to image but applying different detection algorithms based on the class to which a polygon belongs.

Figure 4-3: Automatic part of the production chain for the Urban Atlas Update.

EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 4.4.1.2 Key features to be monitored

The output of this method, is a layer (for internal use of operators while monitoring changes) indicating any possible change, regardless the type, occurred for any of the considered classes in the Urban Atlas for the full extent of the AoIs (LUZs). This is not labelled, as at this stage land use identification requires human interpretation. However, since different algorithms are applied to detect different types of changes, depending on their nature, guiding labelling could be added in the future.

4.4.1.3 Technical requirements

Software for object-based image analysis (OBIA) is required. Currently there are different options in the market. In geoland2, for the update of the Urban Atlas, eCognition 8.0 is the software package used. Regarding input imagery, acquisition angles and dates should ideally be as similar as possible for both datasets, in order to avoid unwanted seasonal variation and shadow problems.

4.4.1.4 Possible constraints

Geographic transferability is still under investigation in order to make algorithms as transferable as possible. Due to the wide environmental biodiversity across Europe, some algorithms may have to go through some fine tuning, which would imply further research. This approach has been specifically developed for the update of the Urban Atlas. Transferability for other purposes (e.g. CLC update) should be further analysed,

4.4.1.5 Expected benefits

Visual change detection in HR imagery is time-consuming, tedious and tiring, which have a direct impact in the quality of the results. This methodological approach will assist operators to improve completeness of the dataset by reducing omission errors, as well as lead them to concrete areas and points where change candidates exist. Thus, the operators will count on an additional tool to improve the final output and time-efficiency.

EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

4.4.1.6 Technical change detection methods sheet

Information Needs

Method Applicability Comments

Object-based multi- European Urban Atlas criteria change update detection

Method Application Requirements

Change Detection Method

Object-based multi-criteria change detection

Input data sources

European Urban Atlas SPOT-5 2006, European Urban Atlas SPOT-5 2009 and Urban Atlas 2006 Vector data.

Ancillary data required for the tasks

Recent VHR imagery (e.g. Google Earth)

Ancillary data optional for the task

N/A

Methodology

Object-based multi-criteria change detection

Change classes addressed

Urban Atlas classes

Expected Thematic accuracy for the change classes

N/A.

Constraints (e.g. omission / commission errors expected)

Small change polygons (close to MMU) may be overlooked.

Benefits  Improve completeness reducing omission errors.

 Lead them to concrete areas and points where change candidates exist.

 Reduce production time.

EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

Access / ownership

Indra Espacio, Planetek, Eurosense

Service offer

Change candidates layer.

Comments

AOB

4.4.2 Object-based Change Detection (Urban Atlas Automatic Update)

4.4.2.1 Brief method description

This approach aims at detecting changes related to artificial surfaces, based on the imperviousness information stored in the FTS layer and the Urban Atlas Land Use information. Using IMAGE2009 as reference, meaningful objects derived from the combination of Urban Atlas and the imagery are extracted. Based on some assumptions for the different LU classes, these objects are then assessed in terms of the amount of artificial surface inside them. Thus, the output provides a layer of objects classified as changes (or change candidates). This is a fully automatic method that only requires the operator to specify the input data. Given that this approach is highly automatic and straightforward, and the input data is guaranteed for the whole of Europe (FTS Imperviousness layer and Image2009-2012), it could be interesting to transfer it to the update of CLC. It would provide a change layer related to artificial surfaces that would help the operator identify such type of change, even providing the shape. Furthermore, the MMU considered for CLC is more suitable for this in order to create a very complete change layer.

UA data Identification of Sealing calculation Sealing data New “sealed” Object generation of new objects Data integration objects Image 2009

UA UPDATE

Figure 4-4: Generic work flow for the Automatic Update of the Urban Atlas.

EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2 4.4.2.2 Key features to be monitored

This method highlights changes implying new artificial-surfaced features occurred between two dates, at a MMU of 1 ha. No change labelling is considered.

4.4.2.3 Technical requirements

Software for object-based image analysis (OBIA) is required. Currently, there are different options in the market. In geoland2, for the update of the Urban Atlas, eCognition 8.0 is the software package used.

4.4.2.4 Possible constraints

Since the resolution of both IMAGE2009 and the Imperviousness layer is coarser than the one used for the production of the original Urban Atlas, small changes (under 1 ha) are not detected.

4.4.2.5 Expected benefits

This approach provides a very quick way to assess the magnitude of changes in the cities and their urban fringe, providing figures for statistics in a very efficient manner. This information can also be used to set priority of production for the full update of cities, if the most changing cities should be updated first or more urgently. As above-mentioned, a similar approach could be used for the update of CORINE, regarding changes towards artificial land cover classes.

EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

4.4.2.6 Technical change detection methods sheet

Information Needs

Method Applicability Comments

Object-based European Urban Atlas change detection update

Method Application Requirements

Change Detection Method

Object-based change detection

Input data sources

European Urban Atlas, Image2009, FTS Imperviousness layer.

Ancillary data required for the tasks

N/A

Ancillary data optional for the task

N/A

Methodology

Comparison of Urban Atlas objects (and sub-objects) vs. imperviousness data.

Change classes addressed

Non-artificial LU classes towards artificial LU classes

Expected Thematic accuracy for the change classes

Not defined yet.

Constraints (e.g. omission / commission errors expected)

Small change objects (< 1 ha) can not be detected.

Benefits  Straightforward method to get a quick picture and figures about changes occurred in urban areas and surrounding areas.

 Input data readily available.

 Tool easy to use.

 Transferable to CLC update for some types of changes (to be tested)

EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

Access / ownership

Indra Espacio, Planetek, Eurosense

Service offer

Change candidates layer.

Comments

AOB

EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

4.5 CHANGE DETECTION METHODS OVERVIEW

Method Custom Description Technology Provider / Offer Recommenda name er Readiness Owner; tions /

Level where to Comments

(see Annex) access

enhanced Member The enhanced MAD (eMAD) is an object-based, 4 Polish Space Licence and/or geoland2 MAD states; bi-temporal change detection method, focused Research service available development service on the very high resolution imagery and Centre PAS from the Space providers applicable for high resolution data with very high Research resolution panchromatic channel. Centre PAS

AutoChange Service The AutoChange method and software is 6 VTT Technical Licence and/or providers detecting and identifying changes that have Research service available occurred between the acquisition dates of two Centre of from VTT, satellite images. Finland License free of charge for It outputs a multi-band raster file in which the Geoland2 output bands represent land cover, change magnitude and change type.

Corine Land Member This method intersects the Imperviousness layer 6 Astrium GEO / Service Operationally Cover states with the urban polygons of the CLC layer to be Infoterra applied in Update updated. The outcome is used as an indicator GmbH Germany´s CLC Support layer for potential areas of change within urban update 2006 using the (built-up) classes. Impervious- ness layer

EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

Impervious- EEA / Semi-automated Change Detection of Built-up- 6 Geoville / Service Operationality ness layer user DGs area and Sealing degree Infoterra / demonstrated update / member Metria / GISAT for EEA38 in

states / Planetek 2010 in the framework of geoland2

Urban Atlas DG Object-based multi-criteria vector-to-image-to- 4 Indra Espacio, Service Full Update REGIO image change detection approach. Planetek, Eurosense

Urban Atlas DG Approach aims at detecting changes related to 5 Indra Espacio, Service Automatic REGIO artificial surfaces, based on the imperviousness Planetek, Update information stored in the FTS layer and the Eurosense Urban Atlas Land Use information. Using IMAGE2009 as reference, meaningful objects derived from the combination of Urban Atlas and the imagery are extracted.

EC Proposal Reference No.: FP-7-218795 Change Detection Status Report geoland 2

5 ANNEX: TECHNOLOGY READINESS LEVELS

ID Level Description Phase 1 Idea Basic principles observed and reported; technology identified as scientifically sound R&D 2 Concept Technology is feasible; concept developed; requirements defined R&D 3 Design Proven concept + detailed requirements and detailed design & specification; interface R&D definition 4 Prototype Prototype implemented and tested/validated positively according to the respective R&D requirements 5 Preliminary operations Successful implementation under operational conditions, but further Implementation upgrade/improvement needed 6 Operations Successful operations of well implemented service/system (cost & quality); all user Operations requirements fulfilled