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Quality analysis in land cover change studies

Quality analysis in land cover change studies. February, the 17th, 2011 Barcelona. Joan Pino CREAF. A case study: the Barcelona Region. 1956. 2000. Changes in four land cover categories. 2000. 1956. Increase 2000/1956. 113253 ha (35%). 127072 ha (39.3%). Forest. 112%. 35814 ha

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Quality analysis in land cover change studies

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  1. Quality analysis in land cover change studies February, the 17th, 2011 Barcelona QUAlity aware VIsualisation for the Global Earth Observation system of systems Joan Pino CREAF

  2. A case study: the Barcelona Region 1956 2000 Kick off meeting. February 17th, 2011

  3. Changes in four land cover categories 2000 1956 Increase 2000/1956 113253 ha (35%) 127072 ha (39.3%) Forest 112% 35814 ha (11.1%) 62203 ha (19.2%) Scrubland 174% 152815 ha (47.2%) 67018 ha (20.7%) Cropland 44% 12068 ha (3.7%) 56797 ha (17.6%) Urban 471% Kick off meeting. February 17th, 2011

  4. Change in forests 2000 1956 Forest 78.5 % Scrubland 15.6 % Cropland 1.3 % Urban 4.6 % Kick off meeting. February 17th, 2011

  5. Change in scrubland 2000 1956 Forest 36.4 % Scrubland 51.6 % Cropland 3.1 % Urban 8.8 % Kick off meeting. February 17th, 2011

  6. Change in cropland 2000 1956 Forest 17.5 % Scrubland 17.4 % Cropland 43.8 % Urban 21.4 % Kick off meeting. February 17th, 2011

  7. Change in urban 2000 1956 Forest 2.5 % Scrubland 3.5% Cropland 2.3 % Urban 91.7 % Kick off meeting. February 17th, 2011

  8. But….how (un)certain are these changes? A first approach to the problem in classified satellite images Kick off meeting. February 17th, 2011

  9. Key points by Serra et al (2003) • A significant proportion of boundary errors are expected when change detection from remote sensing data is often done by simple overlay of classified maps. • A specific post-classification is proposed that considers the overall accuracy of the overlay (as the product of the acuracies of the overlayed classifications) • A method is proposed to increase accuracy, by Eroding the boundaries of the polygons to avoid comparing areas with locational inaccuracy Resampling the two layers accounting for the different pixel size and grid origin Kick off meeting. February 17th, 2011

  10. What about photo-interpreted maps? Kick off meeting. February 17th, 2011

  11. Some definitions • Accuracy: the deviation around the true population mean. The standard deviation can be taken for accuracy estimation under the assumption of infinite population. • Bias: The difference between the estimated mean and true mean of the population. • Precision: depends on the deviation and the number of samples, obtained by a repeated sampling procedure. The standard error, sampling error or the confidence interval can be taken to quantify the precision. Kick off meeting. February 17th, 2011

  12. Estimating spatial accuracy • Validation of polygon borderlines: • A distance threshold error has to be defined first. • One interpretation with borderlines has to be selected as accurate and all other interpretations are compared with this reference data. • The accurate and validation lines are buffered with the acceptable distance error. • With overlay a 2x2 cross table can be produced containing the area proportion or number of pixel for the individual combination. Accuracy (pearson correlation) Kick off meeting. February 17th, 2011

  13. Estimating thematic accuracy A data source has to be defined as reference data (ground truth) Changes between the reference data and the validation data are summarised in a confusion matrix, from which overall, omission and commission errors can be estimated. Kick off meeting. February 17th, 2011

  14. A first experience (2003-2006) Kick off meeting. February 17th, 2011

  15. Aim: Detecting changes in Natura 2000 areas (1950’s- 2000’s) Stratification: Biogeographical Regions Map of Europe (BRME) 75 Windows: 30 x 30 km (black) 59 Transects: 2 x 15 km (red) Focussing on 4 Annex-I habitats which are found in main bio-geographical regions: (i) Freshwater habitats, (ii) Natural and semi-natural grassland formations, (iii) Raised bogs and mires and fens and (iv) Forests. Kick off meeting. February 17th, 2011

  16. Transects Kick off meeting. February 17th, 2011

  17. Quality assessment of photo-interpretation:Transect re-interpretation Points to be re-interpreted by a set of teams • Selected across a 500-m grid • Interpretation of: • Land cover category (common manual) • Distance to the category border • Compared with reference data (local photo-interpreter) Kick off meeting. February 17th, 2011

  18. Thematic accuracy Differences between the local interpretation and the validation Reference Data (CLC-Classes) Total class consistency in percent for all interpreters, with: L = number of validations Xij=consistent observation between observers Xvj=observations of local interpreter in class j Kick off meeting. February 17th, 2011

  19. Total achieved accuracy for each class The percentage values are referring to the mean number of observations by the local interpreters. Kick off meeting. February 17th, 2011

  20. Spatial accuracy Distance between the validation point and the next borderline to another class, compared with that of the reference Kick off meeting. February 17th, 2011

  21. Example of distance measurements for 7 interpreters (A to G) Relative distances with their mean, standard deviation and error Reference interpreter Quality of the individual interpreters can be measured from mean/median values with SD Kick off meeting. February 17th, 2011

  22. Quality of the individual interpreter (figures in m) Kick off meeting. February 17th, 2011

  23. Geometric accuracy for one transect Per LC class Overall Kick off meeting. February 17th, 2011

  24. A first step Kick off meeting. February 17th, 2011

  25. Thanks ! QUAlity aware VIsualisation for the Global Earth Observation system of systems

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