geoengine - msc in geomatics engineering · geoengine - msc in geomatics engineering 5 methodology:...

GEOENGINE - MSc in Geomatics Engineering

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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.


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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.

TerraSAR-X data

Python and ArcGIS for

landscape partition

FRAGSTATS for

spatial pattern analysis

Analysis of data and

classified map


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9999999

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


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Figure 3: Location of test site “Emden” in state of Lower Saxony and GUF (Global Urban

Footprint layer) data with the resolution of 20m

Source: worldatlasbook.com


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Methodology:

1. Landscape Partition The methodology of this study is landscape partition, as landscape was subdivided in several sub-

landscapes 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 18000 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.


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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.


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Table 1. Spatial metrics used in this study

Subject Metric Formula Units Range

Patch metrics

Patch Area (AREA) 𝑨𝑹𝑬𝑨 = 𝒂𝒊𝒋 (𝟏

𝟏𝟎, 𝟎𝟎𝟎) hectares

Area > 0,

without limit

Perimeter (PERIM) 𝑷𝑬𝑹𝑰𝑴 = 𝑷𝒊𝒋 meters PERIM > 0,

without limit

Radius of gyration

(GYRATE) 𝐆𝐘𝐑𝐀𝐓𝐄 = ∑

𝐡𝐢𝐣𝐫

𝐳

𝐳

𝐫=𝟏

meters GYRATE ≥ 0

Class metrics

Class Area (CA) 𝑪𝑨 = ∑ 𝒂𝒊𝒋

𝒏

𝒋=𝟏

(𝟏

𝟏𝟎, 𝟎𝟎𝟎) hectares

CA > 0,

without limit

Percentage of

Landscape

(PLAND)

𝑷𝑳𝑨𝑵𝑫 = 𝑷𝒊 =∑ 𝒂𝒊𝒋

𝒏𝒋=𝟏

𝑨(𝟏𝟎𝟎) percent 0<PLAND≤100

Largest Patch Index

(LPI) 𝑳𝑷𝑰 =

𝒎𝒂𝒙𝒋=𝟏𝒏 (𝒂𝒊𝒋)

𝑨(𝟏𝟎𝟎) percent 0<LPI≤100

Total Edge (TE) 𝑻𝑬 = ∑ 𝒆𝒊𝒌

𝒎

𝒌=𝟏

meters TE > 0,

without limit

Edge Density (ED) 𝑬𝑫 =

∑ 𝒆𝒊𝒌𝒎𝒌=𝟏

𝑨(𝟏𝟎, 𝟎𝟎𝟎)

meters

per

hectare

ED > 0,

without limit

Number of Patches

(NP) NP=n_i none NP > 1,

without limit

Patch Density (PD) 𝑷𝑫 =𝒏𝒊

𝑨(𝟏𝟎, 𝟎𝟎𝟎)(𝟏𝟎𝟎)

number

per 100

hectares

PD > 0,

constrained by cell

size

Landscape Shape

Index (LSI) 𝐋𝐒𝐈 =

. 𝟐𝟓 ∑ 𝐞𝐢𝐤∗𝐦

𝐤=𝟏

√𝐀 none LSI > 1,

without limit

Euclidean Nearest-

Neighbor Distance

(ENN)

𝑬𝑵𝑵 = 𝒉𝒊𝒋 meters ENN> 0,

without limit


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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”


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Figure 4: Classification of settlement types in “Munich“

REFERENCES


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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:

http://www.umass.edu/landeco/research/fragstats/fragstats.html. (last access: 11/10/2013).

McGarigal, K., (2012), Fragstats .help.4.pdf. Available at the following

web site:

http://www.unibuc.ro/prof/patru 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:

10.1117/1.JRS.6.061702.

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): 905–916.

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: 10.1117/12.864917.

http://www.umass.edu/landeco/research/fragstats/fragstats.html

http://www.bibsonomy.org/brokenurl#doi:%2010.1117/1.JRS.6.061702

http://www.bibsonomy.org/brokenurl#doi:%2010.1117/1.JRS.6.061702

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