A landscape characterization method for the Uusimaa region + identification of potential use for GIS

Size: px

Start display at page:

Download "A landscape characterization method for the Uusimaa region + identification of potential use for GIS"

Ann Harrell
5 years ago
Views:

1 A landscape characterization method for the Uusimaa region + identification of potential use for GIS

2 Methodology: Updating the Finnish method The key finding The scope of characterization methods are limited to the non-urban realm The Uusimaa region includes the only metropolitan area of the nation The border between the urban and non-urban realms is becoming diffused Identifying territories where urban and rural areas merge Methodological approach Ultimate goal of characterization method: Degree of heterogeneity and discontinuity of city and countryside To identify so-called hybrid landscapes Methodology based on research by Tisma, A., van der Velde, R., Nijhuis, S., & Pouderoijen, M. (2013). A method for metropolitan landscape characterization; case study Rotterdam

3 Defining the scope Step I Scale Purpose Data Regional Scale Desktop study Step II Data sources Classifying data GIS Analysis Step III Classification Combining data Landscape Character Types Result Maps Definitions Visualizations

4 Selection of layers Describing the process Characterizations were based on the combination of following layers: Layer 1: DEM (Digital Elevation Model) Layer 2: Corine land cover Layer 3: Visibility (based on Corine Level 4) Layer 4a: Slope (flat or hilly, based on DEM) Layer 4b: Landform (based on DEM) Layer 5: Urban / Hybrid / Non-urban

5 Layer 1: DEM Describing the process Reclassified to identify regional differences in elevation 4 levels: Low, Medium, Medium high, High Starting point Reclassified raster layer and assigned values, cell size 500 m

combined Values 1000-4000 Starting point

6 Layer 2: Corine land cover Describing the process Reclassification: 4 categories; water and wetlands combined Values Starting point Reclassified raster layer and assigned values, cell size 500 m

7 Layer 3: Visibility (Corine land cover) Describing the process Corine Level 4 classes reclassified: Open, Semi-open, Closed For example: residential areas are closed, agricultural areas open, different between forest (closed) and sparse forest areas (semi-open) Starting point Reclassified raster layer and assigned values, cell size 500 m

8 Layer 4a: Slope (flat or hilly) Describing the process Based on DEM Slope Two categories: Flat, Hilly Slopes > 4% classified as hilly Starting point Reclassified raster layer and assigned values, cell size 500 m

9 Layer 4b: Landform Describing the process A layer based on Elevation model (raster, 25 x 25 m) SAGA GIS: TPI (The Topographic Position Index) Based Landform Classification Starting point Reclassified raster layer and assigned values, cell size 500 m

(TPI) compares the elevation of each cell in a DEM to the mean elevation of a specified

10 Topographic Position Slope Position Layer 4b: Landform Describing the process SAGA GIS: TPI (The Topographic Position Index) Based Landform Classification The Topographic Position Index (TPI) compares the elevation of each cell in a DEM to the mean elevation of a specified neighborhood around that cell ArcMap: Int: Change linear value to integer value Cell size 500 x 500 m Landform

11 Layer 5: Urban / Hybrid / Non-urban Describing the process A layer based on Corine land cover to identify the degree of urbanity per 500 m cell Traffic infrastructure is excluded, emphasis on residential areas Identification of hybrid cells, ie. cells that are not urban or entirely non-urban Reclassified raster layer and assigned values, cell size 500 m

Converting resulting raster to polygons 4. Merging all polygons into one 5. Union with fishnet 6.

12 Layer 5: Urban / Hybrid / Non-urban Describing the process 1. Fishnet with cell size 500 x 500 m 2. Extracting built surfaces (excluding roads and traffic infrastructure) from Corine 20 x 20 m data 3. Converting resulting raster to polygons 4. Merging all polygons into one 5. Union with fishnet 6. Now percentage of built surfaces within a cell could be calculated Percentage of urbanity per 500 m cell showing Helsinki Zoomed in example: blue represents cells with low amount of built surfaces

Result 2 Changing the classification to count areas over 30% of built surfaces as urban produces better results.

13 Layer 5: Urban / Hybrid / Non-urban Describing the process Result 1 Cells with over 50% built surfaces are so uncommon that there s no clear distinction between hybrid and urban categories. Result 2 Changing the classification to count areas over 30% of built surfaces as urban produces better results. Hybrid cells still seem randomly scattered. Final result Final classification: continuous patterns of hybrid cells are now visible. Areas with 0-15% of built surfaces are counted as non-urban.

Combining the layers Layers and value ranges used for calculations DEM 10 00-40 00

14 Combining the layers Layers and value ranges used for calculations DEM SLOPE CORINE LANDFORM VISIBILITY DEGREE OF URBANITY 1-3

15 Analysis steps Description of the GIS process Reclassifying layers and resampling to 500 x 500 m cell size Using Raster Calculator to sum up values for all the layers Using Focal Statistics -> Majority to detect the most common value within the given radius Converting the resultant raster layer to polygons Cleaning up with buffer Assigning colors with symbology options

16 Combining the layers First results Raster calculator used to summarize all values of different layers Result: Way too many categories Next step: testing focal majority with different settings

17 Combining the layers Focal Statistics: different radiuses 24 types Focal Statistics Rectangle, 35 cells Majority 11 types Focal Statistics Circle, 20 km radius Majority

18 Combining the layers - Option 1 DEM + Corine + Visibility + Flat or hilly + Urbanity Focal Majority, Circle, 10 km radius

19 Additional visualizations Zonal statistics for defined landscape units Degree of urbanity Zonal Statistics Mean value for % of built surfaces per unit Forest dominance Zonal Statistics Majority value calculated from Corine layer containing only forests and rest classified as zero

Combining the layers - Option 2 DEM + Corine + Visibility + Landform + Urbanity Urban Coast Agricultural Valley Agricultural Plain Semi-open Forest Plain Forested Coast

20 Combining the layers - Option 2 DEM + Corine + Visibility + Landform + Urbanity Urban Coast Agricultural Valley Agricultural Plain Semi-open Forest Plain Forested Coast Marine Slope Lake Region Lake Region Urban Slope Upper Agricultural Valley Forested Slope Semi-open Forested Slope Upper Semi-open Forest Slope Focal Majority, Circle, 15 km radius

21 Combining the layers Findings and what to fix 1. Lakes and coastal areas are detected quite well 2. Still probably too many classes - radius for focal majority could be increased a. Getting rid of too small units 3. Areas with hybrid character not represented in the final classification due to their small physical footprint 4. Better implementation of landform analysis; giving more emphasis to landform instead of elevation

22 Combining the layers Findings and what to fix 1. Check Lakes Region Lakes with 1,5 km buffers

23 Data sources Elevation model / National Land Survey of Finland Topographic Database / National Land Survey of Finland Agriculture areas, water areas, buildings... Kiinteiät muinaisjäännökset (INSPIRE-ainesto) / Museovirasto RKY - valtakunnallisesti merkittävät rakennetut kulttuuriympäristöt (INSPIRE-aineisto) / Museovirasto Suojeltu rakennusperintö ( INSPIRE-aineisto) / Museovirasto YKR / Suomen ympäristökeskus Valuma-aluejako ja uomaverkosto / Suomen ympäristökeskus Metsäkasvillisuusvyöhykkeet ja suokasvillisuusvyöhykkeet / Suomen ympäristökeskus? Kallioperä / GTK Maaperä / GTK Zonation luontotiedot / Uudenmaan liitto Maakunnalliset kulttuuriympäristöt / Uudenmaan liitto Peltolohkorekisteri / Maaseutuvirasto viljelyksessä olevat peltoalueet - ajantasaisin tieto

Tutorial 8 Raster Data Analysis

Objectives Tutorial 8 Raster Data Analysis This tutorial is designed to introduce you to a basic set of raster-based analyses including: 1. Displaying Digital Elevation Model (DEM) 2. Slope calculations