# Measuring Regional Difference in Urban Growth of Taipei City (Taiwan, China) by Means of ENVI GIS and Remote Sensing Data

Polina Lemenkova

► To cite this version:

Polina Lemenkova. Measuring Regional Difference in Urban Growth of Taipei City (Taiwan, China) by Means of ENVI GIS and Remote Sensing Data. CzechGlobe Post Graduate Students (PGS) Conference, CzechGlobe Research Institute Czech Academy of Science (Ústav výzkumu globální změny AV ČR, v. v. i.), Feb 2014, Brno, Czech Republic. pp.50, 10.13140/RG.2.2.12140.62082. cel-01991208

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<figure><img src="image_1.png" /></figure>
# Measuring regional difference in urban growth of Taipei city (Taiwan, China) by means of ENVI GIS and remote sensing data
Structure of Presentation

Current presentation consists in 2 parts:

1) Overview of the **environmental** research problem, urbanization and characteristics of Taipei. Consequences of urban sprawl for the global cities, such as Taipei.

2) Detailed **technical** description of the GIS part: remote sensing data capture, pre-processing, algorithm processing, image classification and spatial analysis.
# Part I

## Part I:

### Environmental Analysis
Summary

* Research area: Taipei urban landscapes, Taiwan R.O.C.

* Research aim: spatio-temporal analysis of urban dynamics in study area during 15 years (1990-2005)

* Research objective: application of geoinformatic tools, remote sensing data and application of methodology to spatial analysis for urban studies, a case study of Taipei

* Current work has been supported by the Taiwan Ministry of Education Short Term Research Award (STRA) for research stay at National Taiwan University (NTU), Department of Geography during April-May 2013.
Introduction: study area

• **Study area** is located in Taiwan, Taipei city capital area.

• **Location:** Taipei is located on the north of the Taiwan island

• **Significance:** Taipei is the Taiwan's core urban political & economic center

• **Characteristics:** the fundamental features of the Taipei city include its favorable topographic location and climatic- geographic settings. This provided advantageous conditions for habitation here during centuries.

<figure><img src="image_2.png" /><figcaption>Fig.1. Study area: overlay of the Landsat TM image with vector map of Taiwan Island and elevation contours. ArcGIS visualization.</figcaption></figure>

**Physical geography:** the geomorphology of Taipei is characterized by mostly flat relief within the city and several rivers of Tamsui basin with alluvial soils. Natural borders of the city are created by surrounding mountains and hills (Tsou & Cheng, 2013). Rich sediments from the upstream area maintain soils fertility, creating favorable resources for agricultural activities (Huang et al., 2001).
How anthropogenic activities affect landscapes

Urban growth and city sprawl affects ecosystems.

Consequences of human impacts include:

- landscape degradation,

- changes in land cover and land use types,

- decrease in biodiversity richness within the city,

- deforestation, urbanization, and wetlands destruction (Chen and Lin, 2013)

- decreased in species; losses of rare and extinct species

(McKinney, 2006; Shochat et al., 2004; Ramachandra et al., 2012)

In turn, modified land use types...

- affect hydrological components in the surrounding watersheds and

- cause changes of the land use **patterns**, namely, landscape **composition**, **configuration**, **connectivity**, **variety** and **abundance** of patch types within a landscape, which are the primary descriptors of the landscape patterns (Lin et al., 2007).
“Land cover” vs “land use” types...

1. **Land cover** data documents **how much of a region is covered by** forests, wetlands, impervious surfaces, agriculture, and other land and water types. Water types include wetlands or open water.

while...

2. **Land use** shows just how **people** use the landscape – whether for development, conservation, or mixed uses. The different types of land cover can be managed or used quite differently.

**Land cover** _can be_ determined by analyzing satellite and aerial imagery.

**Land use** _cannot be_ determined from satellite imagery.

Land cover maps provide information to help best understand the current landscape.

This research focused on study of **land cover types in Taipei** using GIS & RS.

To see changes in land cover types over time, images (RS data) for several different years are needed. With this information, I evaluated past situation as well as gained insight into the effects of urban sprawl in Taipei area.
# Taipei's urban area

A Taipei population living within an area of 271.8 square kilometers has a density of 9566 persons / square km, which is higher than the corresponding figures for most of Asia's other major cities (Tsou & Cheng, 2013).

Located on the north of the Taiwan Island, Taipei is the country's core urban political and economic center with population reaching over 2.6 M and continuing to expand within the metropolitan region.

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>
Urbanization of Taipei

Global cities, such as Taipei, have to face specific environmental problems,
due to the **concentrated human density** and **environmental pressure** within
the **limited geographic space and resources**.

Consequences of urbanization (selected, the most major ones):

1) transformation in **structure of natural landscapes**: e.g. simplification of species, changes in landscape composition, disconnected and disrupted components within the hydrological systems, fragmentation of natural habitats.

2) changes in **land cover types**: e.g., from forests, river deltas, meadows, native grasslands, wetlands, etc towards anthropogenic affected **agricultural** areas or **built-up areas** which consist of large amounts of **impervious surfaces**, covered by concrete blocks, asphalt pavement, bricks, buildings...

3) significantly altered **aboveground net primary productivity**

4) serious changes in **soil respiration rates**, compared to natural ecosystems (Kaye et al., 2005),

5) **air pollution** and

6) decreased **water quality** (Huang et al., 2001)
Urban areas in Taipei: regional differences

Uncontrolled urbanization is gradual limitation of
the land and water resources, which leads
insufficient infrastructure within the city and
consequently, contributes to the development
of densely concentrated mixed types of land
use.

According to Tsou and Cheng (2013), about 45% of the Taipei city is categorized as restricted with regard to availability and usage due to its topography. Only 14% (46.30 km2) is available for further development (residential and commercial).

Nowadays, new Taipei city face consists of built-up
new apartments, buildings, office centers and
residential areas, re-structured districts. This
process is reinforced by the average structure
age of the buildings and modernization (Lin and
Jhen, 2009).

<figure><img src="image_5.png" /></figure>

Regional differences in Taipei city: these
areas have been undergone urbanization
at different rates and in different years.
Urban ecosystems: structure & functions

The urban ecosystems have highly complex composing structure and functioning. In the variety of Earth landscapes, urban landscapes have the highest level of ecosystems hierarchy.

The energy flows and density reach the most intensity in the cities (comparing to natural and semi-natural ecosystems).

From the geographic point of view, city landscapes are composed of the green areas o various shape and size, water bodies, human built-up quarters and hilly mountainous areas.

Typical functions of urban ecosystem include: **cultural, supporting** and **regulating** services which indicate core criteria for the measurement of the environmental quality of urban areas (*Flores et al., 1998*).

*Bonaiuto et al. (2003)* developed the complexity of these functions and suggested four aspects of the urban landscapes: 1) **spatial** (architecture and planning space, 2) **organization** and access to green spaces within the city), 3) **human** (i.e. people and social interrelations), 4) **functional** (recreational, commercial, transport services and welfare) and 5) **contextual** aspects (life style and environmental health).
<figure><img src="image_6.png" /><figcaption>Urban structure: a simplified flowchart</figcaption></figure>
Urbanization

In the past decades, the process of urbanization became more and more
notable in the world. About half of the world's population will reside in urban
areas and cities by 2000 (Huang et al., 1998).

World's global megapolises (e.g. New York, London, Tokyo, Taipei, Paris, Seoul, Hong Kong, Singapore, cites of Silicon Valley) are the key engines of the human development and global cooperation.

The importance of these cities cannot be underestimated: such cities are the
drivers of these economies and play more important role than national and
regional economy in their countries (Scott, 2000; Hsu, 2005; Sassen, 2001).

Capitals and megapolises:...

- control resources and information,

- attract major material and economic forces,

- trigger human activities,

- attract financial investments for further development

- involve informational knowledge centers and strategies, etc
Urbanization of Taipei

Global cities, such as Taipei, have to face specific environmental problems,
due to the **concentrated human density** and **environmental pressure** within
the **limited geographic space and resources**.

<u>Consequences of urbanization:</u>

1) transformation in **structure of natural landscapes**: e.g. simplification of species, changes in landscape composition, disconnected and disrupted components within the hydrological systems, fragmentation of natural habitats.

2) changes in **land cover types**: e.g., from forests, river deltas, meadows, native grasslands, wetlands, or agricultural areas, towards anthropogenic affected **built-up areas** which consist of large amounts of **impervious surfaces**, covered by concrete blocks, asphalt pavement, bricks, buildings...

3) significantly altered **aboveground net primary productivity**

4) serious changes in **soil respiration rates**, compared to natural ecosystems (Kaye et al., 2005),

5) **air pollution** and decreased **water quality** (Huang et al., 2001)
Taipei comparing to largest urban areas in the world

<figure><img src="image_7.png" /></figure>

Taipei's population = 2,618,772 people

Taipei metropolitan area (Taipei, New Taipei & Keelung)= 6,900,273 people

Taiwan' population (for the whole country) according to the last two censuses:
year 1990: 20.393,628; year 2000: 22.226,879.; year 2012: 23,315,822.

Source: "Number of Villages, Neighborhoods, Households and Resident Population". MOI Statistical Information Service. Retrieved 2 January 2013.
Environmental problems of Taipei

Human-driven effects on the environment and landscapes of Taipei agglomeration consist in **high industrialization and urbanization**.

The spatiotemporal pattern of landscape diversity changed within Taipei metropolitan region between 1971 and 2005.

Rapid urbanization affects **interrelations between natural and urban ecosystems**, changes their **structure, size and shape**, which gradually became a serious environmental problem.

Thus, in Taipei, natural, typical land cover types are being gradually transformed into human-affected artificial surfaces (Hung et al., 2010).

Analysis of the landscape biogeographic characteristics of the urban forests shows that species diversity, composition and richness, spatial variability is done by *Jim and Chen, 2008*. According to their findings, the **Taipei's urban the built-up areas with space limitations the poorest biodiversity richness**, while parks accommodate the highest one, as represented by native species.
## Part II: GIS - Processing
Techniques

* The research was performed using ENVI GIS software using Landsat TM images for years 1990 and 2005.

* The landscapes in study area at both Landsat TM images were classified using "K-means" algorithm into different land use types

* The area covered by each land cover class is compared and dynamics is analyzed for respecting years.

* The changes in selected land cover types were analyzed and the human impacts on natural landscapes detected.

* Finally, classified land cover types across study area were compared at both maps of land cover types for the years 1990 and 2005, respectively.
# Methods

Methods used in the current work include following steps:

* Data capture

* Color composition from 3 bands

* Defining Region of Interest (ROI)

* Selection of 3 different regions within the city area

* Image classification

* Combining classes and re-classification

* Post-processing

* Spatial analysis for 3 different areas of Taipei city
Data capture

The research data include Landsat TM imagery taken from the U.S.G.S. website covering world with satellite images.

The importance of the GIS and remote sensing (RS) data were successfully used for environmental monitoring since 1970s (rapid development of Earth observation satellite systems and launch of the Landsat TM program).

However, with regards to selected territory in Taipei, given study period (1999-2005), and focused on 3 different areas within Taipei, the effective application of the RS data, such as Landsat TM, have received scant attention. Therefore, current study applies Landsat TM imagery for geospatial analysis of the study area in the period of 1990-2005 with comparison of three districts of Taipei.

<figure><img src="image_8.png" /></figure>
# Data

* Data used in the research include 2 Landsat TM images.

* Images cover the same area of southern surroundings of Taipei, Taiwan (along the Tamsui river).

* The data taken in summer periods, on 1990 and 2005.

<figure><img src="image_9.png" /><figcaption>Landsat TM Image 1990</figcaption></figure>

<caption>Landsat TM Image 2005</caption>
Preliminary data processing.

The preliminary data processing includes image
contrast stretching, which is useful as by default,
ENVI displays images with a 2% linear contrast
stretch.

For better contrast
the histogram
equalization
contrast stretch
was applied to the
image in order to
enhance the visual
quality

<figure><img src="image_10.png" /></figure>
Landsat TM image (1990). Bands 1-7
 fragmen (fragment). Color composites (ENVI)

<figure><img src="image_11.png" /></figure>
Landsat TM image (2005). Bands 1-7 (fragment).
Color composites (ENVI)

<figure><img src="image_12.png" /></figure>
# K-means algorithm: a logical procedure

K-means algorithm procedure has 3 steps:

1) Choosing the initial centroids for defined number of land classes

Looping between the other two major steps:

2) Assignation of each pixel to the nearest centroid which represent certain land cover class

3) Creating new centroids by taking the mean value of all pixels assigned to each previous land cover class (i.e. centroid).

The difference between the old and the new centroids is the "inertia". The algorithm repeats these last two steps until this value (the "inertia") is less than a threshold.

In other words, it <u>smoothes</u> iteratively the allocation of pixels until
the centroids do not move significantly.
## K-Means classification: a general idea.

K-means is a flat clustering algorithm which is often used in classification techniques.

The objective of K-means: to minimize the average squared Euclidean distance between the cluster centers (the means).

<figure><img src="image_13.png" /></figure>
# K-Means clustering: a mathematical approach

The K-means analysis separates pixels into various clusters by defining the mathematical centroids of all clusters - groups of pixel with similar values of spectral reflectance, or digital number (DNs):

$$J(X, C) = \sum_{i=0}^{n} \min_{\mu_j \in C} (||x_j - \mu_i||^2)$$

The K-means separates raster pixels in *n* clusters (groups of equal variance) by minimizing the 'inertia' criterion.
Classification *K-means* by means of ENVI GIS.

<figure><img src="image_14.png" /></figure>
Land cover types

Yeh and Huang (2009) developed classification of the land cover
types in the city. Their studies were adopted and adjusted to the
current studies suggesting following types:

1) Forests;

2) Urban areas – 2 (roads);

3) Grasslands;

4) Open fields (little or no vegetation);

5) Water areas;

6) Urban vegetation – 1 (bushes);

7) Cultivated lands

8) Agricultural facilities;

9) Urban vegetation –2 (parks and squares);

10) Urban areas -2 (built-up surfaces).
Re-classifying

<figure><img src="image_15.png" /><figcaption>Raw image</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>Classification results</figcaption></figure>

Class
"water"
Classification using *K-means* method (I)

1990 Landsat TM scene classification (fragment).
Random color visualization

<figure><img src="image_17.png" /></figure>
Classification using *K-means* method (II)

2005 Landsat TM scene classification (fragment).
Random color visualization

<figure><img src="image_18.png" /></figure>
Combining classes

<figure><img src="image_19.png" /></figure>
# Creating Region Of Interest (ROI)

<figure><img src="image_20.png" /></figure>
Subset Data via ROI

<figure><img src="image_21.png" /></figure>
Regional division of Taipei city

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>
# Change detection statistics: process

1.

<figure><img src="image_24.png" /></figure>

2.

<figure><img src="image_25.png" /></figure>

5.

<figure><img src="image_26.png" /></figure>

6.

3.

<figure><img src="image_27.png" /></figure>

4.

<figure><img src="image_28.png" /></figure>

7.

<figure><img src="image_29.png" /></figure>

## Process workflow:

1). Change detection statistics (selection the tool from the menu)

2). Setting options of process

3). Choice of 'Initial Stage' image

4). Choice of 'Final Stage' image

5). Defining equivalent classes

6). Defining pixel size for area statistics

7). Change detection statistics output
Post-classification data processing

<figure><img src="image_30.png" /></figure>
Statistical processing

<figure><img src="image_31.png" /><figcaption>Getis Ord G statistics</figcaption></figure>

Measures the concentration of
the emergency values (highest or
lowest) for the land cover classes

<figure><img src="image_32.png" /><figcaption>Geary statistics</figcaption></figure>

Measures spatial dependence
and autocorrelation of pixels.

<figure><img src="image_33.png" /><figcaption>Moran statistics</figcaption></figure>

Measures spatial features
similarity values based on
pixels location and values
Computed areas of land cover types

<figure><img src="image_34.png" /></figure>

According to results, the **whole study area** has changes in the selected land cover classes:

1) area of land cover class “urban areas” increased from 16.9% in 1990 to 21.8% in 2005

2) area of land cover class “urban vegetation” decreased from 3.1% in 1990 to 2.9% in 2005

3) area of land cover class “forests” decreased from 62.4% in 1990 to 60.8% in 2005

4) area of land cover class “grasslands” decreased from 15.6% in 1990 to 10.3% in 2005
# Results: regions I, II and III.

However, three different regions of Taipei have these differences in the land cover types:

<table><thead><tr><th></th><th>1990, %</th><th>2005, %</th></tr></thead><tbody><tr><td>Region I: "urban areas"</td><td>17,1</td><td>21,9</td></tr><tr><td>Region I: "urban vegetation"</td><td>5,7</td><td>4,1</td></tr><tr><td>Region I: "forests"</td><td>19,8</td><td>18,6</td></tr><tr><td>Region I: "grasslands"</td><td>21,4</td><td>18,3</td></tr><tr><td>Region II: "urban areas"</td><td>79,3</td><td>83,2</td></tr><tr><td>Region II: "urban vegetation"</td><td>4,3</td><td>5,2</td></tr><tr><td>Region II: "forests"</td><td>32,7</td><td>31,8</td></tr><tr><td>Region II: "grasslands"</td><td>15,9</td><td>14,7</td></tr><tr><td>Region III: "urban areas"</td><td>24,3</td><td>38,2</td></tr><tr><td>Region III: "urban vegetation"</td><td>3,7</td><td>4,3</td></tr><tr><td>Region III: "forests"</td><td>42,4</td><td>41,8</td></tr><tr><td>Region III: "grasslands"</td><td>13,8</td><td>12,1</td></tr></tbody></table>
Conclusions (I)

To resume a table at the previous slide, following conclusion can be drawn.

Comparing regions I, II and III, the conclusion is the following:

**Region I** has changes in the land cover types since 1990s. It has much more changes comparing to the core, old city area (region II)

**Region II** (the core city) is the most stable region. It has the least changes, because this area has been already industrialized for a long time.

**Region III**, has undergone notable changes. This is caused by the intensive relocation of the population to the Taipei area after 1980s. This is because regions in Taiwan are dynamically developing within the Taipei since 1990s.
Conclusions (II)

<figure><img src="image_35.png" /></figure>

1) Region I = located on the left bank of Tamsui river, “agricultural” area

2) Region II = the core, old city of Taipei

3) Region III = the region located southwards from the core city
Discussion

The spatial analysis performed by means of GIS ENVI enabled to use satellite images for social and urban studies. The spatio-temporal analysis was applied to Landsat TM images taken at 1990 and 2005. Built-in functions of the mathematical algorithms (K-means) enabled to process raster Landsat TM images and to derive information from them.

The classification of the images was used to analyze changes in the Taipei's landscapes that consist in different land cover types, including built-up areas and natural land cover types.

According to the results of image processing and spatial analysis, the structure, shape and configuration of urban landscapes in Taipei changed since 1990: the areas occupied by human activities increased, while natural landscapes undergone modifications.

Changes in urban landscapes of Taipei metropolis area are caused by the increased anthropogenic pressure, intensified urbanization and occupied lands for the urban needs.
Acknowledgements

Current research was funded by the Taiwan Ministry of Education Short Term Research Award (STRA).

The research has been performed during two-month research stay at National Taiwan University (NTU), Department of Geography, Taipei, in April-May 2013.

The software ENVI GIS used in this work was provided by the GIS laboratory, Department of Geography, NTU.
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Webpage "Scikit-learn: machine learning in Python: http://scikit-learn.org/stable/index.html#"
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