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Jean-Louis WEBER Consultant European Environment Agency Scientific Committee

The Natural Capital/Ecosystem Capital Accounting (ECA) project for Mauritius Production of the urban areas land cover layer from high resolution data on buildings, using smoothing ( Gaussian filter ) techniques & Land cover change account 2000 – 2010 / Urban sprawl.

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Jean-Louis WEBER Consultant European Environment Agency Scientific Committee

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  1. The Natural Capital/Ecosystem Capital Accounting (ECA) project for Mauritius Production of the urban areas land cover layer fromhighresolution data on buildings, usingsmoothing(Gaussianfilter) techniques & Land cover change account 2000 – 2010 / Urbansprawl Jean-Louis WEBER Consultant European Environment Agency Scientific Committee Honorary Professor, School of Geography, University of Nottingham jlweber45@gmail.com

  2. Introduction • The land coverlayers for urban areas have been producedusing the geo-database of buildings of StatisticsMauritius. It includes data of 2010 and circa 2000. • The processingconsists in data rasterisationat 10 metersfollowed by smoothing (SAGA Gis Gaussianfilter) in order to agglomerate buildings into « urban areas », thusassimilatingsmallholes and streets. Deanse and dispersedurban areas (e,g, in the countryside) canbemapped. • Accounts compare the stocks and change betweentwo dates.

  3. The buildings Shapefile

  4. The buildings raster (tif) 10 meters x 10 meters

  5. The buildings Shp and Raster 10 m

  6. Smoothing (blurring) with SAGA Gis/ GridFilters/ User DefinedFilterInput: raster 10 m, values 1 to 101FilterMatrix (for gaussianblurat 10 pixels radius or 100 m, using a kernel of 21 x 21 cells): here Kernel_21_10

  7. Smoothed (Gaussianblur) raster, radius of 100 meters (kernel = 21) Sequence of treatmentswith SAGA GIS: Input: shapefile, scalecirca 1/5000 or finer Raster (tif) at 10 meters

  8. The buildings raster smoothedat 100m (values in the neighbourhood)

  9. Building raster, 10 m and smoothedat 100m (values in the neighbourhood)

  10. Building Shp and smoothed tif (values in the neighbourhood)

  11. Agglomeration/generalisation: cells > 20% of the smoothed value NB: cells are of 10 x 10 meters

  12. Agglomeration/generalisation: shp and cells > 25% of the smoothed value NB: cells are of 10 x 10 meters – here, the threshold captures dispersedurban

  13. Agglomeration/generalisation: shp and cells > 50% of the smoothed value NB: cells are of 10 x 10 meters – here, the thresholdeliminatesdispersedurban…

  14. Provisional conclusion • The 20% thresholdseems a priori more appropriate for urban areas mapping. The same or differentthresholdscanbechosen for different classes (e.g. forêts, wetlands…) and in differntgeographicalcontexts. • The urban layer willbeoverlaid and combinedwith the otherlayers on agriculture, forêts, natural zones. • Smallerthemeswillbegivenpriority to the largerones in order to minimise the relative errors. Adjustmentswillbedoneaccordingly. • The methodis to someextent a simulation of visualphoto-interpretation.

  15. Land cover change account 2000 – 2010 / Urbansprawl • Sources: the databases of buildup areas 2010 (LAVIMS) and ~2000

  16. Land Cover / M01 Urban 2000

  17. Land Cover / M01 Urban 2010

  18. Land Cover change / M01 Urban 2000 - 2010

  19. Land Cover stock and change / M01 Urban

  20. Urbandensity (%) by Districts 2000

  21. Urbandensity (%) by Districts 2010

  22. Urbandensity (%) by Districts / Increase 2000-2010

  23. A first account of Land Cover change/ Urbansprawl 2000-2010 by districts

  24. A firsaccount of Land Cover change/ Urbansprawl 2000-2010

  25. M01-Urban 2010 by river catchments

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