GEOENGINE - MSc in Geomatics Engineering

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1 Master Thesis Neda Mohammadi Naghadeh Large-scale characterization of human settlement patterns using binary settlement masks derived from globally available TerraSAR-X data Duration of the Thesis: 6 months Completion: January 2014 Supervisor: Dipl.-Ing. René Pasternak Examiner: Prof. Dr.-Ing. Alfred Kleusberg ABSTRACT: The goal of master thesis is to demonstrate a method which facilitates quantitative and qualitative analysis of human settlements patterns on a national and global level. As at the beginning of 21th century the number and bigness of urban areas are going to increase, so it is vital challenge to distinguish where the urban patterns are going to develop, because urban land use change affects environmental changes. According to these facts, this study will utilize of Geographical Information Systems (GIS) and Landscape Metrics in order to find a way for this purpose and also to try enhance them. Landscape metrics enable to quantify a landscape (here: urban area or in general human settlements) with respect to spatial dimension, alignment and pattern at a specific scale and resolution. Introduction: Remote sensing techniques can be applied to the analysis of human and environmental dynamics within urban systems to aid in sustainable planning and management of these areas. Remote sensing has great potential for gaining comprehensive and accurate land-use information for analysis and planning of settlement areas. This research involves a methodology using information from Terra-SAR data to describe settlement patterns. The mission of thesis follows Remote sensing and Geospatial tools in order to classify large scale settlement patterns in Global area, therefore in this study we use GUF (Global Urban footprint) data, landscape metrics and Geospatial analysis to quantify and analysis the building density in two testsites. Result will be discussed in relation to the type of settlements distribution and be displayed in classified maps. 1

2 TerraSAR-X data Python and ArcGIS for landscape partition FRAGSTATS for spatial pattern analysis Analysis of data and classified map Figure 1: The procedure of task Study area and land cover data set: As study areas, two test sites were chosen, Munich which contains the city of Munich and its suburbs, and Emden which comprises the cities of Emden, Oldenburg, Wilhelmshaven, Aurich and Groningen (located in the Netherland) and also settlements around these cities. As it is clear from the figures below (Figure (2) & Figure (3)), the settlement patterns in the Munich and Emden have noticeable differences in terms of heterogeneity of populated areas. In Munich urban growth is concentrated in center, then settlements distribute as radial in suburb, while in Emden these dense patterns propagate in whole area in more homological situation, as the area of dense settlement patterns in Emden are not as large as agglomerated urban patterns in Munich. 2

3 Source: worldatlasbook.com Figure 2: Location of test site Munich in state of Bavaria and GUF (Global Urban Footprint layer) data with the resolution of 20m 3

4 Source: worldatlasbook.com Figure 3: Location of test site Emden in state of Lower Saxony and GUF (Global Urban Footprint layer) data with the resolution of 20m 4

5 Methodology: 1. Landscape Partition The methodology of this study is landscape partition, as landscape was subdivided in several sublandscapes in order to handle data more efficiently. As type of settlements differ entirely in the limited area. Therefore each test site was converted into square tiles to square tiles which are equal in terms of size and area in Munich and Emden. The landscape partition was done by ArcGIS and Python script programming language, it was started by making clip raster by the size of 900 pixels or meter (pixel size: 20m). In the following of this process, by tiles with 9000, 4500 and 2250 meters in row and column, however because of this way some parts of test sites must be left and will not be used for final results and analysis. 2. Choosing Geospatial Software for Analysis A wide range of geospatial software is available commercially, as freeware and as open source. Here we chose freely available software for this investigation, which is free in license and useful for landscape metrics and spatial analyst goals, additionally it is suitable for the data type in this study (raster data). FRAGSTATS is a computer software program designed to compute a wide variety of landscape metrics for categorical map patterns. Also in the recent years it was developed for variety types of Geographical data. FRAGSTATS is a public-domain GIS implementation of a set of spatial statistics that addresses a fundamental problem in GIS applications and it is a spatial pattern analysis program for categorical maps. FRAGSTATS computes three groups of metrics: Patch metrics, Class metrics and Landscape metrics. Patch metrics are computed for every patch (definition of patch: Surface area that differs from its surroundings in nature or appearance) in the landscape; the output file contains a row (observation vector) for every patch (defined as environmental units), where the columns (fields) represent the single metrics. Class metrics measure the aggregate properties of the patches belonging to a single class or patch type. Landscape metrics are computed for entire patch mosaic; the resulting landscape output file contains a single row (observation vector) for the landscape, where the columns (fields) represent the individual metrics. 5

6 3. Analysis of Landscape Patterns with FRAGSTATS metrics In urban remote sensing or in other words analysis of settlement patterns, a few special metrics of FRAGSTATS will be used. For Patch metrics, metrics such as Patch Area (AREA), Perimeter (PERIM), Radius of Gyration (GYRATE) could be suitable for this study, as Radius of Gyration (GYRATE) is a measure of patch extent, so this metrics displays the bigness of patches. It is clear that every landscape includes of several patches which the largest one is the sign of the most dense area in this task. In Class metrics, metrics like Total (Class) Area (CA), Percentage of Landscape (PLAND), Largest Patch Index (LPI), Total Edge (TE), Edge Density (ED), Number of Patches (NP), Patch Density (PD), Landscape Shape Index (LSI) and Euclidean Nearest-Neighbor Distance (ENN) are practical for analysis of residential area in this task. Percentage of Landscape (PLAND) and Class Area (CA) give information about the area of settlements. Number of Patches (NP) and Patch Density (PD) focus on the subdivision of aggregation, so NP and PD are considerable for number and density of settlements, whereas the sizes of patches (area of settlements) do not have equal bigness, they are not so practical for settlement pattern analysis through the large areas (not in equal tiles), but approximately an idea could be given in a limited area. The Largest Patch Index (LPI) gives information about the type and existence of a spatially dominant urban core. FRAGSTATS computes several statistics representing the amount of perimeter (or edge) at the patch, class, and landscape levels. Edge metrics usually are best considered as representing landscape configuration. At the patch level, edge is a function of patch perimeter (PERIM). At the class and landscape levels, total edge (TE) is an absolute measure of total edge length of a particular patch type or of all patch types. The edge density determines landscape configuration, with large values displaying a more organic, convoluted urban pattern. The nearest neighbor standard deviation valuates uniform or regular distribution of patches against a more irregular or uneven distribution in a landscape. Clearly low values of ENN reveals the dense settlement patterns, however FRAGSTATS metrics for ENN calculates the average amount, such as ENN- MN or ENN-SD. In the table below (Table (1)) some special metrics of FRAGSTATS which were used in this task are explained by formulas. 6

7 Table 1. Spatial metrics used in this study Subject Metric Formula Units Range 1 Patch Area (AREA) AREA = a ij ( 10, 000 ) hectares Area > 0, Patch metrics Perimeter (PERIM) PERIM = P ij meters PERIM > 0, Radius of gyration (GYRATE) GYRATE = h ijr z z r=1 meters GYRATE 0 Class metrics Class Area (CA) Percentage of Landscape (PLAND) Largest Patch Index (LPI) Total Edge (TE) Edge Density (ED) Number of Patches (NP) Patch Density (PD) Landscape Shape Index (LSI) Euclidean Nearest- Neighbor Distance (ENN) n 1 CA = a ij ( 10, 000 ) j=1 PLAND = P i = n j=1 a ij A hectares CA > 0, (100) percent 0<PLAND 100 LPI = max j=1 n (a ij ) (100) percent 0<LPI 100 A ED = m TE = e ik k=1 m k=1 e ik A (10, 000) meters meters per hectare TE > 0, ED > 0, NP=n_i none NP > 1, PD = n i (10, 000)(100) A LSI =. 25 e ik m k=1 A number per 100 hectares PD > 0, constrained by cell size none LSI > 1, ENN = h ij meters ENN> 0, 7

8 4. Final Result based on interpolation As it was explained before, in our study we divided the landscape to equal tiles in four steps. Therefore this master thesis must demonstrate that, partition will describe urbanization and structure of settlement patterns in an effective way. The figure (figure (4)) below reveals the interpolation for different size of tiles and how these partitions increase the accuracy of task. As it is clear, in the final step of partition with smaller tiles, we face with better results. In addition based on the goal of the classification in terms of settlement types, the suitable metrics could be chosen. Here because of type of data and classification which reveals density, was PLAND (percentage of landscape), clearly according to the purpose of study could be applied on other metrics. However the same result could be reached with LPI or NP/CA almost, as larger values for LPI reveal the dense area and vice versa less values of NP/CA display the dense settlements. Classification is done for three types of settlement patterns and contains the adjustment based on urban systems and structures. Nevertheless if interpolation could be applied for Global data, it would be better to use unique classification ranges. 5. Conclusion and future study Overall, Remote Sensing and GIS are useful tools for analysis of large scale settlement patterns, as the capability for landscape classification is one of the most important applications of Remote Sensing. Figure below (figure (4)) reveals how the methodology of partition could be useful to reach thigh accuracy with smaller tiles (not so tiny based on the resolution) and more steps of division. Another thing for high accuracy is the structural pattern of the settlements in terms of distribution, agglomeration and shape of build-up lands, which affects the classification of settlement patterns. As a future study for this task, it is useful to implement and develop a Cellular Automata (CA) Algorithm for urban patterns. Another option for future research could be the comparison of different resolutions of Terra-SAR data to reach classification with better accuracy. Test site: Munich 8

9 REFERENCES Figure 4: Classification of settlement types in Munich 9

10 McGarigal, K., SA Cushman, and E Ene., (2012), FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at the following web site: (last access: 11/10/2013). McGarigal, K., (2012), Fragstats.help.4.pdf. Available at the following web site: stupariu_i_g/docs/2013/noi/25_15_29_40fragstats.help.4.pdf (last access: 13/09/ 2013). Esch, T., Taubenböck, H., Roth, A., Heldens, W., Felbier, A., Thiel, M., Schmidt M, Müller, A., Dech, S., (2012), TanDEM-X mission new perspectives for the inventory and monitoring of global settlement patterns. In: Journal of Applied Remote Sensing. 6(1): 1-21, doi: /1.JRS Esch, T., (2010), Delineation of urban footprints from TerraSAR-X data by analyzing speckle characteristics and intensity information. In: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSIN. 48(2): Taubenböck, H., Wegman, M., Wurm, M., Ullmann,T., Dech, S., (2010), The global trend of urbanization-spatiotemporal analysis of mega cities using multi-temporal remote sensing, landscape metrics and gradient analysis. In: Proceeding of the conference SPIE Europe Remote Sensing, Earth Resources and Environmental Remote Sensing/GIS Applications, Toulouse, France, 20. September, edited by Michel, U., Civco, D.L., 7831:78310I 1-20, doi: /