Aleksandra Justyna Kocoń
Land use change detection in urban areas with Google Earth historical imagery: case study of Kinsealy-Drinan and Swords, Ireland
Msc thesis under the supervision of: dr hab. Jacek Kozak
MSc thesis submitted in the framework of, and according to the requirements of the UNIGIS Master of Science programme (Geographical Information Science & Systems).
Jagiellonian University, Kraków, Paris Lodron University of Salzburg
2011
I declare that all sources used in the thesis were properly acknowledged. The thesis is fully my work and it was not and will not be submitted as a thesis elsewhere.
Date ………... Signature ………
86
0
1. Introduction ...1
1.1. From Sputnik to Google Earth ...1
1.2. Satellite imagery and change detection ...3
1.2.1. Optical remote sensing ...6
1.2.2. Land use and land cover change detection methods ... 10
1.3. Google Earth ... 12
1.3.1. History... 13
1.3.2. Projection and datum ... 14
1.3.3. Historical Imagery ... 15
2. Aims of the thesis ... 22
3. Methods ... 23
3.1. Project feasibility in Google Earth ... 24
3.1.1. KML... 24
3.1.2. Google Earth imagery – origin, providers, spatial resolution and acquistion dates 26 3.1.3. Copyrights – ‘fair use’, attribution, using content for different purposes ... 28
3.2. The study area ... 30
3.2.1. Imagery coverage problem ... 30
3.2.2. Suburban towns Swords and Kinsealy-Drinan ... 33
3.2.3. Imagery description ... 38
3.2.4. Boundaries of Swords and Kinsealy-Drinan ... 39
3.3. Computation of grid ... 42
3.4. Land use change analysis ... 59
3.4.1. Analysis of land use with 250 m grid... 59
3.4.2. Visual inspection and data processing ... 60
3.5. Visualisation of results ... 66
4. Results ... 74
5. Discussion and conclusions ... 77
6. References... 79
7. List of figures ... 82 8. List of tables...
1 “The man went off into Universe,
to reach further,
to penetrate the distant worlds.
When he turned away, he saw how interesting is the Earth from the space.”1
Prof. Zbigniew Klos, Space Research Centre Polish Academy of Sciences, 2007
1. Introduction
1.1. From Sputnik to Google Earth
The first artificial satellite in the space was Sputnik 1 (fig. 1.1). It was launched in 1957 by Soviet Union and its aim was to “obtain data pertaining to the density of the upper layers of the atmosphere and the propagation of radio signals in the ionosphere”
(Journey through the Galaxy 2006).
Fig. 1.1. Satellite Sputnik 1, 58 cm diameter and weight about 84 kg (NASA2 2007) Source: left image - http://astroprofspage.com/archives/1035,
right image - http://www.astrographia.com/portfolio/10/sputnik-1
1 http://archiwum.polityka.pl/art/kosmiczne-byc-albo-nie-byc,359457.html
2 NASA – National Aeronautics and Space Administration
2 First satellites were focused on space exploration. For example Explorer 1 which was the first satellite launched by United States in 1958 was gathering “data about the radiation environment high above Earth's surface” (Jet Propulsion Laboratory 2010).
The Earth surface exploration and mapping from the space starts 15 years after Sputnik.
In 1972 the satellite initially called ERTS 1 (Earth Resources Technology Satellites), renamed later into Landsat 1, was launched. Example of Landsat image is presented below (fig. 1.2).
Fig. 1.2. Landsat 1 image of central part of East Timor, 19/19/1980 Source: http://www.yale.edu/gsp/east_timor/etimages.html
This satellite was equipped “with MSS (MultiSpectral Scanner) which imaged the Earth from a 900 km altitude with green, red and two infrared spectral bands at 80m resolution” (Hemphill 2010). “Landsat 1 was a test of the feasibility of an earth resources satellite system” (Hemphill 2010), and based on forestry and geologic applications the spectral wavelengths were determined. Nowadays Landsat is a very important source of historical imagery, especially when the aim of research is detection of land cover changes. The space and Earth exploration grew rapidly. Now (18/10/2010) there are 3,464 functioning satellites (CelesTrak 2010) in space orbiting Earth and gathering or sending data for many purposes – for example telecommunication satellites, navigation systems satellites, meteorological satellites (like EUMETSAT, NOAA) or satellites collecting images of the Earth (like GeoEye, IKONOS, SPOT). The amount of data delivered from satellites is enormous and this situation is described as ‘flood of data’. In many cases “the hard part of taking
3 advantage of this flood of geospatial information will be making sense of it - turning raw data into understandable information”(Gore 1998). All above this would not be possible without computers and developments of information technology and the internet. The data processing and storage required proper software, powerful computers and servers with memory, even up to 1000 terabytes (Longley et al. 2006). The internet is now one of the most popular ways of exchanging information and it was obvious that sooner or later satellite images will be available on the internet. Access to map via internet has started in 1993 (Longley et al. 2006). First commercial internet mapping service called MapQuest was established in 1996 (Longley et al. 2006). After that many geobrowsers were set up. One of the most popular is Google Earth which contains mostly satellite images for whole world. Although the spatial resolution of images in Google Earth varies, there are many high resolution images which normally would be relatively expensive to buy. For example one scene of SPOT colour image (60 km by 60 km) with resolution 2.5 m costs 8100 €, for smaller area like 20 km by 20 km one image costs about 3000 € (SPOT Image 2010).
1.2.Satellite imagery and change detection
The surface of the Earth is changing over time. Some changes are made by human like urban development or deforestation, some are caused by natural phenomena like hurricanes or earthquakes. The changes could be drastic when in very short time the surface is changed completely or subtle when the process is extended in time. There could be also changes depending on earth’s seasons, one area could be agriculture field in summer and snow cover area in winter. For detection of changes which occur on the Earth surface, especially over larger area, the satellite imagery is widely used. To detect any changes which occurred over time the multi-temporal image data are required. The satellites acquire data continuously and they have wide area of coverage. For example single SPOT 5 image covers area 60 km x 60 km and revisits the same area every 26 days (SPOT Image 2010). In comparison to aerial imagery where the acquisition is occasional – it is done on request and the coverage is limited, the satellite imagery is more convenient to use.
4 Below there are examples of changes. First example (fig. 1.3) shows Eastern Paraguay which was naturally covered by subtropical rain forest. Then the deforestation began.
The area covered by forest was converted into farmland. Until 2010 just few patches of the rain forest remained.
Fig. 1.3. Eastern Paraguay, images for 1975 and 2010 Source: http://na.unep.net/geas/hotspots/alert_2010_08.php
The next example (fig. 1.4) shows a new feature in the landscape. Al Wahda Dam in Morocco was built in 1996 in order to “reduce devastating flooding along the Ouergha River, provide water for irrigation, and generate hydroelectricity” (Atlas of Our Changing Environment 2010). On the image from 1987 there is no water feature visible and in the 2001 image there is huge area filled in water.
Fig. 1.4. Morocco, images for 1987 and 2001
Source: http://na.unep.net/atlas/webatlas.php?id=255
5 The third example presents an ecological catastrophe (fig. 1.5). The first image from October 2005 shows chemical waste reservoir in Kolontar, Hungary. In October 4th this reservoir ruptured and released toxic red mud. The image taken 4 days after the catastrophe shows the breakage in the wall and red sludge spilt over surrounding area.
Fig. 1.5. Kolontar, Hungary, images for 5th October 2005 and 8th October 2008 Source: http://www.digitalglobe.com/
The last example is related to urban sprawl and shows Kuala Lumpur, the capital city of Malaysia (fig. 1.6).
Fig. 1.6. Kuala Lumpur, images for 1974 and 2005 Source: http://na.unep.net/atlas/webatlas.php?id=263
6 Since 1974 the city grew significantly. Now the sprawl is encroaching on the coastline which is approximately 35 kilometers to the west form the city centre (Atlas of Our Changing Environment 2010).
Urban sprawl occurs in cities all over the world. The urban population is growing every year. The interesting fact is that from 2009 the number of people living in urban areas (3.42 billion) is greater than the number of people living in rural areas (3.41 billion) and the urban population is estimated to increase by 84 per cent by 2050 (United Nations 2010). Together with the growing urban population the number and size of cities is increasing. Usually, due to rapid urban growth and uncoordinated land management the phenomenon of sprawl occurs. Urban sprawl can be defined as “the spread of low- density, segregated-use, automobile-depended development that tends to be located on previously undeveloped land away from urban centers” (Soule 2006). In other words on the edges of the city highly segregated developments – single family residential and commercial – are consuming more and more space and “the rate of land loss to development is far more substantial than the rate of population increase” (Soule 2006).
Urban sprawl “separates where people live from where they work, shop, and pursue leisure or an education, thereby requiring them to use cars to move between these zones” (Macionis, Parrillo 2007) because of the distance and the lack of efficient public transport. The sprawl as “poorly planned urban development is threatening the environment, health, and quality of life” (Bhatta 2010). A few of numerous consequences are : traffic congestion, air pollution, water quality and quantity concerns, increase in temperature, high cost of providing and improving infrastructure, lost of agriculture land, forest, and wetlands (Bhatta 2010; Soule 2006). The antithesis of sprawl is called compact city or smart growth (Soule 2006). The smart growth model focuses on better quality of life and environment. It is “more town-centered, is transit and pedestrian oriented, and has a greater mix of housing, commercial and retail uses (...) preserves open space and many other environmental amenities” (Smart growth 2010).
1.2.1. Optical remote sensing
Based on presented examples it is clear that humans are able to detect changes by visual inspection, simply comparing one image to another. But if extracting more detailed information is required then the techniques of image processing need to be applied. In
7 order to understand basics of image processing, principles of optical remote sensing is given below.
Fig. 1.7. Sunlight, features and optical sensor - three main components of optical remote sensing system
Source: http://www.crisp.nus.edu.sg/~research/tutorial/optical.htm
In the optical remote sensing the Sun is the source of energy. The sensor mounted to the satellite or airborne measures reflected sunlight from features on the Earth’s surface (fig. 1.7). In this context the sunlight relates to whole electromagnetic spectrum, not just to visible part of light which is only small part of the electromagnetic spectrum (fig.
1.8).
Fig. 1.8. Electromagnetic spectrum
Source: http://www.dnr.sc.gov/ael/personals/pjpb/lecture/lecture.html
8 Different types of features, like water, vegetation or bare soil have different spectral characteristic. It means that the amount of reflected energy in respective range of electromagnetic spectrum is different. This is presented as spectral reflectance curves (fig. 1.9).
Fig. 1.9. Typical spectral reflectance curves of common earth surface materials in the visible and near to mid-infrared range
Source: http://www.cps-amu.org/sf/notes/m1r-1-8.htm
Most of sensors are multi-channel and “each channel is sensitive to radiation within a narrow wavelength band” (Liew 2007). The number of bands and their width should be related to peaks and valleys of spectral reflectance curves to allow proper detection of different types of features. For example, vegetation has very distinctive spectral signature and has the highest reflectance in near infrared (0.7-1.3µm) and that is why this band is used for vegetation detection.
The satellite imagery is an example of raster image. It means that the image consists of rows and columns of squares called pixels. Sensors record radiance for each pixel and for each band. The result is a multi-spectral image (fig. 1.10). It “consists of a few image layers, each layer represents an image acquired at a particular wavelength band”
(Liew 2007).
9 Fig. 1.10. Multi-spectral satellite image scheme
Source: http://www.ctahr.hawaii.edu/miuralab/projects/makaha/intro_RS.html
Using special software, for example IDRISI or ERDAS, the image can be displayed.
There are three channels with assigned primary colours – red, green and blue – and the user can create a colour composite image by assigning to each channel one of the bands.
Fig. 1.11. Landsat 7 true colour image and false colour image Source: http://landsat.gsfc.nasa.gov/education/compositor/
The effect could be a true colour image (fig. 1.11 left) or a false colour image (fig. 1.11 right). True colour image is when the red band is displayed in red, the green band in green and blue band in blue. For false colour image “display colour assignment for any
10 band of a multi-spectral image can be done in an entirely arbitrary manner” (Liew 2007). But it is very important to know which band is assigned to each primary colour in order to know what each colour on the image represents. For the right figure 1.11 the green band is displayed as green, blue band as blue and near infrared band is displayed as red. As mentioned above, vegetation has big reflectance in near infrared light and that is why all shades of red represent vegetation on that image.
When speaking about satellite imagery the resolution term is very important. There is spatial, temporal and spectral resolution. The temporal resolution means frequency of acquiring the imagery of the same area. Depends on satellites this resolution could be high when the revisit time is between 1 or 3 days, medium – 4-16 days or low – more than 16 days (Satellite Imaging Corporation 2010). The spectral resolution is related to the number and width of the sensor’s bands. The size of the pixel on the ground is called spatial resolution. The best spatial resolution possible to achieve at this moment is 0.5 m. The satellite imagery can be characterized as high, medium or low spatial resolution (table 1.1).
Table 1.1. Spatial resolution classification Spatial
resolution
Pixel size [m] Example of satellites
High 0.5-4 GeoEye-1,WorldView-2,WorldView-
1, QuickBird IKONOS ,SPOT-5
Medium 4-30 ASTER, LANDSAT 7
Low More than 30 NOAA AVHRR
Source: http://www.satimagingcorp.com/characterization-of-satellite-remote-sensing- systems.html, modified
1.2.2. Land use and land cover change detection methods
“There are many image analysis techniques available and the methods used depend on the requirements of the specific problem concerned” (Liew 2007). Advanced, non visual, techniques for change detection always rely on spectral properties. One of the most common change detection analyses is land use and land cover change. Land cover
11 is defined as “the observed physical cover, as seen from the ground or through remote sensing, including natural or planted vegetation and human constructions (buildings, roads, etc.), (…), water, ice, bare rock or sand surfaces (…)”(EPA 2010). Land use “is based upon function, the purpose for which the land is being used” (EPA 2010). For detection of land use and land cover change two classification algorithms are commonly used – unsupervised and supervised classification. The aim of classification is
“delineate different areas in an image into thematic classes” (Liew 2007) like vegetation, built up areas, water or bare soil. The classes are assigned based on spectral properties. The result of the classification is a thematic map. Then land use and land cover change is delineated by comparison of two or more thematic maps over time.
During an unsupervised classification the image is divided into spectral classes. Pixels are grouped automatically into “statistically separate clusters, depending on their reflectance values” (Remote sensing of Invasive Species in Makaha Valley 2010).
Before classification the analyst specifies the number of classes and after classification assign land cover classes. It is easy and quick process but accuracy is limited because classes selected by computer based on spectral segmentation may not be good representation of real land cover classes.
In supervised classification the analyst identifies sample areas for each land cover class.
Then software calculates “the statistical characterization of the reflectance for each class” (Environmental Remote Sensing Courseware 2010). Then during classification each pixel is compared to those training classes based on its reflectance and it is assigned to the most similar class. It is complex and time consuming process but the accuracy is high.
Fig. 1.12. Land use land cover classification for 1990 and 2006, Bamberg city
12 Fig. 1.13. Land cover change map for Bamberg city
Example above (fig. 1.12) shows land cover classes after supervised classification. Red colour is assigned to built up areas, green to forest, blue to water and yellow to agricultural land. Grey colour on the next image (fig. 1.13) represents land where land cover change was detected (this analysis was performed by ERDAS Imagine software by the author).
1.3. Google Earth
Google Earth is an Internet based, standalone application (downloadable from http://earth.google.com/download-earth.html). This geobrowser allows users to “fly anywhere on Earth to view satellite imagery, maps, terrain, 3D buildings (fig. 1.14) from galaxies in outer space to the canyons of the ocean”(Google Earth 2010).
13 Fig. 1.12. Functionality of Google Earth
Source: Google Earth, modified
1.3.1. History
The origin of Google Earth goes back to 2001, when digital mapping company Keyhole Inc. was founded. Keyhole was “the Internet 3D earth visualization pioneer”
(DigitalGlobe and Keyhole Deliver QuickBird Imagery to New Business Customers and Consumers with Internet 3D Earth Visualization Applications 2004). It was the first company who delivered “a 3D digital model of the entire earth via the Internet”
(Softpedia 2010). Keyhole developed EarthStream™ technology which “combined advanced 3D graphics and network streaming innovations to produce a high performance system that runs on standard PC's and commodity servers” (Softpedia 2010). This technology enabled “fast, fluid interaction with multi-terabyte network- resident databases of earth imagery and geospatial information” (DigitalGlobe and Keyhole Deliver QuickBird Imagery to New Business Customers and Consumers with Internet 3D Earth Visualization Applications 2004). The Keyhole Corporation was acquired by Google Inc. in October 2004 (Google 2010). In June 2005, based on
14 Keyhole’s technology, ”a satellite imagery-based mapping service combining 3D buildings and terrain with mapping capabilities and Google search” (Google 2010) was unveiled. This map service is now called Google Earth.
1.3.2. Projection and datum
Google Earth applies Simple Cylindrical projection (Plate Carree) with a WGS84 datum for its imagery base (fig. 1.15). This coordinate system is also known as Lat/Lon WGS 84 (Google Earth 2010). In ArcGIS this system is called GCS_WGS_1984 (fig. 1.16).
Fig. 1.13. Google Earth coordinate system Source:
http://earth.google.com/support/bin/static.py?page=guide.cs&guide=22373&topic=23750&ans wer=148111 , modified
Fig. 1.14. Geographic Coordinate System Properties for GCS_WGS_1984
15 1.3.3. Historical Imagery
A new feature called Historical Imagery (fig. 1.15) was added to Google Earth version 5 (Google Earth 2010). In February 2009 this version was released. Until February 2009 it was possible to display just one, best available image in the selected area. The term
‘best available image’ means that Google Earth could display older, higher resolution imagery if the newer image was unclear. Now for a given place the Historical Imagery feature allows users to flick through several satellite and aerial images, from a few decades old to the most recent.
Fig. 1.17. Google Earth with Historical Imagery time slider Source: Google Earth, modified
The Historical Imagery allows users to move back in time and explore changes which have occurred. Its potential is illustrated with a short visual analysis of a small area of Warsaw, outlining changes that occurred between December 1935 and July 2009. The first image which was captured in 1935 (fig. 1.18) clearly shows two bridges over the Vistula River. The left side of the river is a high density built up area, in comparison the
16 right side is largely green area. The second image (fig. 1.19) was taken in December 1945, three months after the Second World War was finished. From this image it is evident that the two bridges and buildings were destroyed. The third image (fig. 1.20) is from October 2000 and the two bridges that were destroyed have been reinstated and a third bridge built. The right side of river which was previously grass land is now covered by trees. A stadium called the Decade Stadium has also been constructed in this area. There is a large construction site visible on the images from April 2009 (fig. 1.21) and July 2009 (fig. 1.22). This construction site is located in the area previously occupied by the Decade Stadium.
Fig. 1.18. Part of Warsaw, December 1935 Source: Google Earth, modified
17 Fig. 1.19. Part of Warsaw, December 1945
Source: Google Earth, modified
Fig. 1.20. Part of Warsaw, October 2000 Source: Google Earth, modified
Image © 2010 GeoEye
18 Fig. 1.21. Part of Warsaw, April 2009
Source: Google Earth, modified
Fig. 1.22. Part of Warsaw, July 2009 Source: Google Earth, modified
On closer inspection of the stadium in the image from July 2003 (fig. 1.23) it is obvious that there is some strange activity there. The Stadium was opened in 1955 and hosted
Image © 2010 DigitalGlobe
Image © 2010 GeoEye
19 sports events until 1983. The Stadium then started to go into ruin and its renovation or rebuilding was not cost effective. The original purpose of the building changed from recreation to trade. From 1989 until 2007 there was a fair at the stadium which was one of the biggest markets in Europe (Stadion Narodowy w Warszawie 2010). This trading activity is visible on the imagery from 2003. Coincidentally, this site was then chosen as the location for the National Stadium and in October 2007 construction works began (Stadion Narodowy w Warszawie 2010). The image from April 2009 (fig. 1.24) shows construction works and for comparison there is a photo taken from the ground in March 2009 (fig. 1.25). The next satellite image, taken in July 2009 (fig. 1.26), shows the work progress in comparison to March 2009. The final image (fig. 1.27) presents a visual image of the stadium which is planned to be open in July 2011(Stadion Narodowy w Warszawie 2010).
Fig. 1.23. The Decade Stadium as a big market place, June 2003 Source: Google Earth, modified
20 Fig. 1.24. The National Stadium – construction works, April 2009
Source: Google Earth, modified
Fig. 1.25. The National Stadium - work progress March 2009 Source: http://stadionnarodowy.org.pl/gallery.html
21 Fig. 1.26. The National Stadium - construction works, July 2009
Source: Google Earth, modified
Fig. 1.27. The National Stadium – visualization Source: http://stadionnarodowy.org.pl/visual.html
22 2. Aims of the thesis
Satellite and aerial imagery are expensive to buy but there is also possibility to access them for free. Google Earth is one of the best known free geobrowsers which contains imagery for the whole world. Since 2009 it contains the Historical Imagery feature.
Historical Imagery, which is the key element for this thesis, allows users to display imagery from few decades back into the relatively current so it is possible to explore changes which occurred over time.
The images from Google Earth can be used for many purposes. One of the problems which occurs all over the world is urban growth. Urban development is consuming more and more land due to rising population in urban areas. So Google Earth with its Historical Imagery feature could be a proper source of information for estimation how much land was transformed into urban areas over certain period of time if images for such a period are available.
Google Earth displays satellite imagery without access to spectral properties. That is why common remote sensing methods for land use land cover change detection could not be applied here. So methodology of how to get useful information from the raw image has to be worked out.
Aim of this thesis is to establish methodology how to perform land use change detection for urban areas based on imagery in Google Earth. The result of the change detection should involve determination of quantitative and geospatial aspect of changes, in other words – how many land was transformed and where the changes occurred. Therefore, the research questions are:
1. Is it possible to perform land use change detection only in Google Earth?
2. What are limitations of Google Earth in reference to proposed methodology?
3. What are advantages of Google Earth?
4. How to visualize the result of the analysis?
23 3. Methods
The main idea of how to perform an estimation of the expansion of built up areas in Google Earth was quite simple and it was based on a raster concept. “In a raster representation geographic space is divided into an array of cells that are usually squares (…) All geographic variation is then expressed by assigning properties or attributes to these cells” (Longley et al. 2006). Based on that idea it was assumed that the imagery in Google Earth is overlaid with a transparent grid and then all grid cells are inspected to assess the respective land use for a given moment. The land use types are then based on visible and interpreted land cover (entirely built up, partially built up or non built up area) in a grid cell with a certain size. The interpretation could be applied for both images and then the results are compared to detect changes.
To perform land use change analysis based on Google Earth imagery following steps were carried out:
1. Checking the feasibility of the project in Google Earth. It contained issues of creating and storing features like metric grid, spatial resolutions and age of the imagery and copyrights
2. Choice of the study area and imagery 3. Computation of the grid
4. Visual inspection of grid cells and data processing 5. Map design in ArcGIS and visualization in Google Earth 6. Presentation of results.
24 3.1. Project feasibility in Google Earth
3.1.1. KML
KML is an abbreviation for 'Keyhole Markup Language'. This is “an XML grammar and file format for modeling and storing geographic features such as points, lines, images, polygons, and models for display in Google Earth, Google Maps and other applications” (Google Earth 2010). KML was developed by Keyhole Inc. Google Earth processes KML files in the same way as web browsers process XML and XTML files.
KML, like XML “has a tag-based structure with names and attributes used for specific display purposes“(Google Earth 2010). In April 2008, KML version 2.2 was approved as Open Standard and it was called OpenGIS® KML Encoding Standard by Open Geospatial Consortium (OGC®) (OGC 2010). This means that there is a standard way of using KML “to code and share visual geographic content in existing or future web- based online maps and 3D geospatial browsers (…)” (OGC 2010).
One of the most popular features added by users to Google Earth or Google Maps are points of interest called placemarks.
<?xml version="1.0" encoding="UTF-8"?>
<kml xmlns="http://www.opengis.net/kml/2.2" xmlns:gx="http://www.google.com/kml/ext/2.2"
xmlns:kml="http://www.opengis.net/kml/2.2" xmlns:atom="http://www.w3.org/2005/Atom">
<Placemark>
<name>Simple placemark</name>
<description>Attached to the ground. Intelligently places itself at the
height of the underlying terrain.</description> <gx:balloonVisibility>1</gx:balloonVisibility>
<Point>
<coordinates>-122.0822035425683,37.42228990140251,0</coordinates>
</Point>
</Placemark>
</kml>
Fig. 3.1. KML example for placemark
Source: http://code.google.com/apis/kml/documentation/kml_tut.html
25 Above (fig. 3.1) there is an example of KML file for placemark called “Simple placemark”. This placemark will be displayed in Google Earth at the location with the longitude -122.0822035425683 and latitude 37.42228990140251, with the altitude set to 0.
The use of KML file to display geographic features adds powerful capabilities to geobrowsers. Because of this, one can publish and share her/his own data. Data created in other software and different file formats once converted to KML can be displayed in Google Earth. An additional advantage within Google Earth is an ease at which single features can be created (fig. 3.2). A placemark can be added just in a few seconds by selecting the correct icon from the toolbar and filling in the relevant fields (i.e. name or description) (fig. 3.3). Appropriate styles and colours can also be selected. More complicated objects like metric grid lines have to be created in some external software and then brought back into Google Earth in KML file format. A zipped KML file is called KMZ.
Fig. 3.2. Icons for creation of geographic features Source: Google Earth, modified
Fig. 3.3. The placemark for the National Stadium Source: Google Earth, modified
Add placemark Add polygon Add path Add
26 3.1.2. Google Earth imagery – origin, providers, spatial resolution and acquisition
dates
There are many providers and many ways of acquiring imagery by Google Earth. Most of the imagery in Google Earth comes from satellites. Aerial photos taken by high resolution cameras attached to airplanes are also popular. Imagery can also be obtained from flying objects like balloons or kites (Taylor 2008).
Base imagery in Google Earth comes from satellite Landsat 7 ETM+ maintained by NASA. The spatial resolution for Landsat 7 ETM+ panchromatic imagery is 15 m and for multi-spectral imagery is 30 m (NASA 2010). Higher spatial resolution data are mainly provided by DigitalGlobe (fig. 3.4), SPOT Image and GeoEye. DigitalGlobe started providing images for GoogleEarth in 2002. There are three satellites now owned by DigitalGlobe which acquire imagery with sub-meter resolutions: QuickBird and WorldView-1 and WorldView-2 (DigitalGlobe 2010; WordView-2 2010). The French company SPOT Image has been providing 2.5 meter resolution imagery since 2007, acquired by the SPOT 5 satellite (SPOT Image 2010). GeoEye with its GeoEye-1 satellite has been providing 0.5 meter resolution images since 2008 (GeoEye 2010).
Sometimes city or state governments offer imagery to Google (Taylor 2008). One can find out who supplied the image to Google by checking the annotation on the middle bottom of the screen (fig. 3.4, fig. 3.5) Other companies mentioned there provide other data, for example Europa Technologies “supply many of the international borders, national borders, coastlines, airports and place name data (…)” (Europa Technologies 2010).
27 Fig. 3.4. Image provided by Digital Globe
Source: Google Earth, modified
Fig. 3.5. Image provided by Aerodata International Surveys Source: Google Earth, modified
28 The acquisition dates of the imagery vary, however most of the high resolution imagery is “approximately one to three years old” (Google Earth 2010). Usually the date of the image is shown on the bottom left corner (fig. 3.6), when this is not available it can be roughly determined from the historical imagery time slider.
Fig. 3.6. Image taken 1st April 2007 Source: Google Earth, modified
3.1.3. Copyrights – ‘fair use’, attribution, using content for different purposes The content of Google Earth “is owned either by Google or its suppliers” (Google 2010). Terms of service are described in Google Maps/Google Earth Terms and Conditions (Google 2010) and the Google Maps/Google Earth APIs Terms of Service (Google Code 2010). The use of content of Google Earth has to meet 'fair use' principles. “Fair use is a concept under copyright law in the United States that, (…), permits you to use a copyrighted work in certain ways without obtaining a license from the copyright holder”. To determine if a particular case fall in ‘fair use’ rules several factors have to be considered, such as:
“(1) the purpose and character of the use, including whether such use is of a commercial nature or is for nonprofit educational purposes;
(2) the nature of the copyrighted work;
29 (3) the amount and substantiality of the portion used in relation to the copyrighted work as a whole; and
(4) the effect of the use upon the potential market or value of the copyrighted work”
(Copyright 2010).
Everyone with no exception who uses Google Earth has to “provide attribution to Google (…)” (Google 2010). “Attribution is the line(s) shown on the bottom of the Content in the products along with copyright notices, such as "© 2009 Google, Map Data © 2009 Tele Atlas (the exact text of the attribution changes based on geography and Content type)” (Google 2010) (fig. 3.7). There are a few conditions with regard to placing attribution text:
(1) It must be visible to the average viewer or reader;
(2) Attribution text and Google logo which are automatically generated can be removed only if they will be placed elsewhere within the content of Google Earth. This text and logo should be visible.
In a printed form, if attribution cannot be provided within the imagery, then attribution has to be placed directly adjacent to the imagery (Google 2010).
Fig. 3.7. Correct way of providing attribution
Source: http://www.google.com/permissions/geoguidelines.html
Exporting content of Google Earth and using it within another application, like GIS software, is prohibited. This same rule applies for the offline use (Google 2010). The rules for publication of content in a thesis or other academic papers are general and
30 described above. As for contractors' or environmental consultants' reports “if the analysis of the scene in question has been created using Google Maps or Earth” (Google 2010) the content of Google Earth can be used in printed materials, but further modification of the content like “editing within another drafting, desktop publishing, or GIS application” (Google 2010) is prohibited.
3.2. The study area
3.2.1. Imagery coverage problem
At the beginning of the analysis the city of Dublin, Ireland was selected as a study area.
This is the area where the author is currently living. Due to problems with image coverage, the study area had to be changed. It was not possible to find two datasets for two periods of time covering the entire area. Figure 3.8 shows imagery acquired on October 2002 which covers just a part of the Dublin area. The image from September 2003 (fig. 3.9) covers a much bigger area, and the next available image from February 2004 covers just part of this area (fig. 3.10). When the slider is set to the last available date which is September 2008 the entire area is covered by images acquired on different dates (fig. 3.11).
Fig. 3.8. Dublin area - image from October 2002 Source: background image Google Earth
31 Fig. 3.9. Dublin area - image from September 2003
Source: background image Google Earth
Fig. 3.10. Dublin area - image from September 2003 with the image from February 2004 on the top
Source: background image Google Earth
32 Fig. 3.11. Best available images for Dublin area acquired up until September 2008
Source: background image Google Earth
Using the previous example of the Dublin area, some problems associated with land use and land cover change analysis based on Google Earth which are related to data spatial and temporal completeness and quality can be outlined:
1. for some areas, only one image is available;
2. lack of imagery with a sufficiently high resolution;
3. time gap between subsequent images is not large enough to detect land use changes;
4. coverage of the selected area is not complete for a given period of time.
33 3.2.2. Suburban towns Swords and Kinsealy-Drinan
The suburban towns of Swords and Kinsealy-Drinan Ireland were finally chosen as the study area. There is a full coverage for the years 2002 (fig. 3.12), 2003 (fig. 3.13), 2004 (fig. 3.14), 2005 (fig. 3.15), 2006 (fig.3.16) and 2008 (fig. 3.17). However, the image for year 2004 contains small amounts of clouds.
Fig. 3.12. Swords and Kinsealy-Drinan - imagery from October 2002 Source: background image Google Earth
34 Fig. 3.13. Swords and Kinsealy-Drinan - imagery from September 2003
Source: background image Google Earth
Fig. 3.14. Swords and Kinsealy-Drinan - imagery from February 2004 Source: background image Google Earth
35 Fig. 3.15. Swords and Kinsealy-Drinan - imagery from March 2005
Source: background image Google Earth
Fig. 3.16. Swords and Kinsealy-Drinan - imagery from June 2006 Source: background image Google Earth
36 Fig. 3.17. Swords and Kinsealy-Drinan - imagery from July 2008
Source: background image Google Earth
Swords and Kinsealy-Drinan provide an interesting example of an urban development.
The towns are located about 13 kilometers to the north of Dublin’s city centre, approximately 3 kilometers from Dublin Airport, and also close to the sea (fig. 3.18).
This convenient location was a huge boost for property development. Swords and Kinsealy-Drinan were transformed from small villages to towns which now fall within Dublin's commuter belt. Swords, for example, is one of the fastest growing towns in Ireland. Prior to 1961 the population of Swords was less than 2000 people (fig. 3.19).
The huge population explosion began in the 1970s and in 1981 the population exceeded 11 000. Over the next ten years the number of people increased to 17 705. This growth continued and in 1996 the population reached the level of 22 314 and increased steadily to almost 40 000 in 2006. In 2006, if including suburbs, Swords became the 8th biggest town in Ireland (Central Statistic Office Ireland 2006).
37 Fig. 3.18. Location of Swords and Kinsealy-Drinan in relation to Dublin and Dublin Airport Source: background image Google Earth
Fig. 3.19. Population of Swords from 1926 until 2006
Source: Based on data from the Central Statistic Office Ireland, data for 1926 -1991 on line at:
http://www.cso.ie/census/historical_reports.htm, data for 1996-2006 at http://census.cso.ie/census/ReportFolders/ReportFolders.aspx
38 3.2.3. Imagery description
The image from 2002 (fig. 3.20) is dated as of 19th October and is provided by GeoEye.
At that time GeoEye had one satellite called IKONOS (GeoEye 2010). IKONOS collects “black-and-white (panchromatic) images with 82-centimeter resolution and multi-spectral imagery with 4 m resolution. Imagery from both sensors can be merged to create 1 m colour imagery (pan-sharpened)” (GeoEye 2010). So the spatial resolution for the image in Google Earth is estimated as 1 m. The image from 2008 (fig. 3.21) is dated as of 4th July and is provided by DigitalGlobe. This company provides images for Google Earth with sub-meter spatial resolution.
Fig. 3.20. Image from 19th October 2002 provided by GeoEye Source: Google Earth
39 Fig. 3.21. Image from 14th July 2008 provided by DigitalGlobe
Source: Google Earth
3.2.4. Boundaries of Swords and Kinsealy-Drinan
The boundary polygons for Swords and Kinsealy-Drinan were derived from the shapefile from the Central Statistics Office Ireland website3. This shapefile layout was created for the purpose of the census. There was no metadata attached with the file and no coordinate system information and the shapefile was displayed in a wrong location in ArcGIS. The coordinate system for the project in ArcGIS was the same as for Google Earth (GCS_WGS_1984). The Irish National Grid called TM65_Irish_Grid (fig. 3.22) was finally set as the coordinate system for ‘city_towns’ and then the layer was displayed correctly in ArcGIS project (fig. 3.22).
3 http://beyond2020.cso.ie/censusasp/saps/boundaries/city-towns_bound.htm
40
Fig. 3.22. Choosing the correct Coordinate System along with parameters for TM65_Irish_Grid
Fig. 3.22. Polygons for Swords and Kinsealy-Drinan
Next, separate layers were created for Swords and Kinsealy-Drinan. The symbology for these layouts was set as red outlines with transparent inside. Then the layers were exported to KMZ file (fig. 3.23) and displayed in Google Earth (fig. 3.24). The
41 polygons were converted correctly because they fitted to natural and human made features on the imagery.
Fig. 3.23. Example of conversion of the shapefile for Swords to KMZ file
Fig. 3.24. Polygon for Swords displayed in Google Earth Source: background image Google Earth
42 3.3. Computation of grid
There is no way to make a metric grid directly in Google Earth because the tools are just for simple features like points, lines or polygons. For that reason there is a need to find other methods of bringing metric grid into Google Earth. One of such methods is a grid creation with GE-Path4. This is freeware software which can be used to “draw horizontal and/or vertical grids (equally spaced or not)” (GE-Path 2009). To create the grid, three parameters have to be determined: the start point, the end point and the number of lines. For the grid created by GE-Path the number of rows equals the number of columns, thus the grid is a square as well. In other words, from the starting point the horizontal and vertical distance should be equal and divisible by the side of the basic square.
Creation of grids with 250 meters grid cells involved 7 steps:
1. Choosing the position of the start point 2. Determination of the grid extent 3. Calculation of the end point
4. Grid creation (lines) in KML format in GE-Path 5. Conversion of KML grid into shapefile
6. Converting the polyline grid shapefile into polygons, removal of needless grid rows and label creation for every square
7. Conversion of polygon grid layout and labels into KML.
First four steps were the actual grid creation. The next three steps were performed to simplify visual analysis – the surplus rows were deleted, every square was labeled.
Additionally the conversion of polyline grid shapefile into polygons were needed for mapping the results of the analysis.
4 Sgrillo R (2009), GE-Path http://www.sgrillo.net/googleearth/gepath.htm
43 Step 1. Choosing position of the start point
The start point, which is the left lower corner of the grid, should be located in a place which is easy to identify. On the image from 2008 the start point is located on the intersection of the boundary hedges (fig. 3.25, fig. 3.26). The latitude for this point is 53°26’20.85’’N, longitude 6°16’14.00’’W.
Fig. 3.25. The start point on the hedge intersection for 2008 image Source: background image Google Earth
44 Fig. 3.26. Location of the start point in relation to the analyzed area
Source: background image Google Earth
When the image background was changed to 2002 the start point was off about 14 meters from the intersection (fig. 3.27). Here, there is a problem with the exact georeferencing of the imagery provided by Google Earth. Images from different points of time do not overlay each other exactly.
45 Fig. 3.27. Shift between images 2008 and 2002 based on start point location
Source: Google Earth and the author’s alterations
This problem can be avoided by creating a separate grid from the correct start point for every image. For the tested method based on 250 m grid this 14 m shift should not have significant influence on the results. For other study areas the shift could be much bigger so for that reason two grids were created from proper start points for each year to enable an accurate analysis.
For year 2002 the start point has a latitude 53°26’20.50’’N and a longitude 6°16’14.50’’W (fig. 3.28).
46 Fig. 3.28.The start point on the hedge intersection for 2002 image
Source: background image Google Earth
Step 2. Determination of the grid extent
The next step is to determine the grid extent. The length of the horizontal line should be about 5500 meters (fig. 3.29). The length of the vertical line could be 4500 meters (fig.
3.30), however there is a need to keep the horizontal and vertical lines the same because of the GE-Path limitation. Therefore the extent of the grid was 5500 meters to the north and west from the start point. This dimension is divisible by 250 meters.
47 Fig. 3.29. Grid’s horizontal dimension determination
Source: background image Google Earth
Fig. 3.30. Grid’s vertical dimension determination Source: background image Google Earth
48 Step 3. Calculation of the end point
The next step was to calculate coordinates for the right top corner of the grid (the end point). The distance between the start and end point (fig. 3.31) was calculated based on Phytagoras’ Theorem. Coordinates were calculated based on the starting point, and the bearing and the distance to the end point using the free online tool called Bearing and Distance Calculator by GeoMidpoint5 (fig. 3.33, fig. 3.34). This tool required coordinates in decimal degrees. Longitude and latitude of the start point were converted to decimal degrees using alternative online tool6 (fig. 3.32).
Fig. 3.31. Determining the distance between start and end point
Fig. 3.32. Coordinates conversion
Source: Federal Communications Commission (2010),
http://www.fcc.gov/mb/audio/bickel/DDDMMSS-decimal.html, modified
5 http://www.geomidpoint.com/destination/
6 http://www.fcc.gov/mb/audio/bickel/DDDMMSS-decimal.html 7.778174km
End point
5.5 km 45°
5.5 km Start point
49 Fig. 3.33. The end point calculation based on bearing and distance from the start point, for 2008 Source: GeoMidpoint (2010), http://www.geomidpoint.com/destination, modified
Fig. 3.34.The end point calculation based on bearing and distance from the start point, for 2002 Source: GeoMidpoint (2010), http://www.geomidpoint.com/destination, modified
Step 4. Grid creation (lines) in KML format in GE-Path
Creating a grid in GE-Path is relatively simple and requires the coordinates of the start and the end point and the number of lines (fig. 3.35). The numbers of lines are actually the number of grid cells in a row or a column, so e.g., 22 in the 250 m grid (5500m / 250m = 22). Because of inaccurate georeferencing of imagery, two grids were created:
one for 2002 and one for 2008 (fig. 3.36, fig.3.37).
50 Fig. 3.35. Grid creation, year 2008
Source: Sgrillo R (2009), GE-Path, http://www.sgrillo.net/googleearth/gepath.htm, modified Start and end point coordinates for 2008
Grid parameters
51 Fig. 3.36. 250 m grid for year 2008
Source: background image Google Earth
52 Fig. 3.37. 250 m grids for year 2008 (orange) and 2002 (blue). Background image - 2008 Source: background image Google Earth
Step 5. Conversion of KML grid into shapefile in DNRGarmin
Converting grid lines in KML format into ArcGIS shapefile was shown for 250 m grid for the year 2008 (fig 3.39, fig. 3.40). For this conversion DNR Garmin freeware software7 was used. DNR Garmin can be used as a single software or as an extension in ArcGIS (fig. 3.38).
7 http://www.dnr.state.mn.us/mis/gis/tools/arcview/extensions/DNRGarmin/DNRGarmin.html
53 Fig. 3.38. DNR Garmin as an ArcGIS extension
Fig. 3.39. Grid’s lines loaded in MN DNR and saved KML grid as shapefile layer
Fig. 3.40. 250 m grid with Kinsealy-Drinan and Swords boundaries
54 Step 6. Converting polyline grid shapefile into polygons, removal of unnecessary rows and label creation for every grid cell
Next, from the polyline layer the polygon layer was built and label points were added.
The new extension for ArcGIS called Tools for Graphics and Shapes v. 1.1.85 by Jeff Jenness8 was used (fig. 3.41).
Fig. 3.41. Functionality of Graphics and Shapes extension
Example of converting the 250 m grid for 2008 is shown on figure 3.42. After conversion the completion report was created (fig. 3.43). For the 250 meters grid 484 polygons were created which are presented on figure 3.44.
8 Jeness J (2010) Tools for Graphics and Shapes v. 1.1.85, on line at:
http://www.jennessent.com/arcgis/shapes_graphics.htm
55 .
Fig. 3.42. Conversion of line grid to polygon grid
Fig. 3.43. Report from conversion lines into polygons
56 Fig. 3.44. The grid polygons displayed in ArcGIS
Four upper rows were deleted to keep grid extension as close to Swords and Kinealy- Drinan boundary as possible (fig. 3.45). The number of squares in the grid were thus reduced to 396 and the vertical dimension to 4500 meters.
Fig. 3.45. Final grid (blue) displayed in ArcGIS
Next, labels were created for every square with Graphics and Shapes extension (fig.
3.41). The outcome of this step were grids made from independent grid cell polygons,
57 each with a unique number, and point layers with points located in the centre of every grid cell (fig. 3.46).
Fig. 3.46. The 250 m grid as polygon layout with point labels layout
All calculations of distances from the starting point to the end point were done assuming grids were constructed on a flat surface. Due to the Earth curvature, the sides of the squares were not exactly 250 meters. The measurement was taken in ArcGIS with snapping to the corners option. The horizontal dimension was about 0.13 m longer and the vertical dimension about 0.15 m shorter than 250 m (fig. 3.47). The problem could be solved using advanced geodetic calculations and an ellipsoid model. However, the error value is +/- 0.15 m and it would not have much influence on results of land use analysis. Therefore, an assumption was made that the area of 1 square in the 250 m grid is 6.25 hectares.
58
Fig. 3.47. Measurment of the square side
Step 7. Conversion of polygon grid layout and labels into KML
The last step was a conversion of grids and their labels to KML file. The Layer to KML converter tool within Conversion Tools in ArcGIS was used for this conversion. Finally, they could be displayed in Google Earth (fig.3.48) and actual analysis could be conducted.
Fig. 3.48. The 250 m grid with labels displayed in Google Earth Source: background image Google Earth
59 3.4.Land use change analysis
3.4.1. Analysis of land use with 250 m grid
Three categories of land use were delineated in the visual interpretation – non built up areas, partially built up areas and entirely built up areas. Coding scheme is given in table 3.1. Buildings, houses with gardens, green areas which were an integral part of residential development and roadways were considered as built up areas.
Table 3.1. Categories for 250 meters grid analysis
Category Description Code
non built up areas amount of built up areas within the square between
0 and 25%
1
partially built up areas amount of built up areas within the square between
25% and 75%
2
Entirely built up areas amount of built up areas within the square greater
than 75%
4
The visual inspection for the years 2002 and 2008 was performed using Google Earth interface. Codes were assigned to shapefile polygons (grid cells) in ArcGIS, and detection of changes was accomplished later by subtracting codes assigned for 2002 and 2008. Based on this subtraction the land use change map was created. The map could show total of 9 categories (table 3.2) depending on the use in 2002 and 2008. Six categories were ‘change’ categories while 3 represented ‘no change’ types.
60 Table 3.2. Possible land use change types
Code in 2002
Code in 2008
Land use change
Code in 2002 – Code in 2008 Interpretation
1 1 0 No change
1 2 -1 Change from non built up into
partially built up areas
1 4 -3 Change from non built up into
entirely built up areas
2 1 1 Change from partially built up
into non built up areas
2 2 0 No change
2 4 -2 Change from partially built up
into entirely built up areas
4 1 3 Change from built up into non
built up areas
4 2 2 Change from entirely built up
into partially built up areas
4 4 0 No change
The codes 1, 2 and 4 were chosen purposefully. The result of subtracting is either 0 which means no change or a value which is unique for every pair of codes, for example:
-1 means only the change from non built up areas into partially built up areas.
3.4.2. Visual inspection and data processing
The 250 meters grid for 2002 and 2008 and labels were analysed in Google Earth. To keep all layers in order two new folders (fig. 3.49) were created 250_2008 and 250_2002 and the corresponding layers were placed there.
61 Fig.3.49.Creating folder in Google Earth
It was easy to customize the layout of grids and labels, changing colour and width of the polygon outlines (fig. 3.50) or the size and colour of points and the scale for the labels (fig. 34).
Fig. 3.50. Style and colour settings for polygons (grid)
Fig. 3.51. Style and colour settings for points (labels)
62 Examples of three types of land use are provided below. Figure 3.52 shows non built up areas. There are no buildings within the square number 271, while the square 273 has some built up areas but the amount of it is less than 25%.
Fig. 3.52.Non built up areas
Source: background image Google Earth
Figure 3.53 shows partially built up areas. The amount of built up areas within the square 77 is just below 75%, including green urban areas, well maintained. Residential development, a roadway and a commercial centre are apparent in the square 78.
Fig. 3.53. Partially built up areas
Source: background image Google Earth
63 Figure 3.54 shows squares where built up areas are above 75%. Within those squares there are residential estates, roads and green urban areas. Square 275 contains a large green urban area which is not cultivated: the surface is very smooth and is completely within a residential development.
Fig. 3.54. Entirely built up areas
Source: background image Google Earth
64 Fig. 3.55. Greenhouses
Source: backround image Google Earth
It is evident from figure 3.55 that there are large areas of greenhouses present and these structures are unique to the Swords area. As greenhouses are temporary structures and used for agricultural activities they were considered as non built up areas.
To carry out the visual interpretation one copy of the grid was printed out. First, year 2002 was analysed, then 2008. Red colour was used to annotate 2002 codes, codes were placed on the left side of the square. Blue was assigned to the 2008 codes and placed on the right hand side of the square (fig. 3.56). In total it took 47 minutes to inspect the land use in 2008, and 50 minutes to inspect the 2002 image. At the beginning, the
65 inspection was carried out row by row, however it was discovered that it is more efficient to interpret 3 rows at a time.
Fig. 3.56. Visual inspection code result sheet
Results of coding were transferred to ArcGIS. A new field was added to the 250 m grid attribute table for 2002 and 2008 called ‘usage2002’ and ‘usage2008’ respectively (fig.3.57).
Fig. 3.57. Data transfer into ArcGIS
66 3.5.Visualisation of results
The new shapefile called ‘250_change’ was created to allow determination where the changes occurred and what was the origin usage in every square in 2002 in comparison to 2008. This shapefile contained codes for 2008 and 2002 and the new field ‘change’.
In the field ‘change’ subtracting codes procedure for every square were performed (fig.3.58).
Fig.3.58. Land use change
In the ‘change’ field there are four values 0, -1, -2 and -3. Table 3.2 contains the description for these codes, their origin and meaning.
Maps of land use and summary tables for 2002 and 2008 (fig. 3.59, fig.3.60) are presented below:
67 Fig. 3.59. Land use in 2002
Fig. 3.60. Land use in 2008
68 Finally, the last stage was the map of land use change creation and presentation it in Google Earth. Techniques to exhibit land use changes on the map had to be determined and the first strategy involved the use of transparent ‘Line fill symbols‘(fig. 3.61). The problem was however encountered during exportation the map to KML and opening it in Google Earth. It was discovered that Google Earth does not support such fill in type.
Fig.3.61. Land use change between 2002 and 2008
An alternative way to display map in Google Earth was proposed. The field ‘map’ was added to shapefile ‘250_change’. When in the field ‘change’ value 0 (no change) was given, thus in the field ‘map’ there was the same code as in ‘usage2002’ (which was the same as ‘usage2008’). For the rest of fields the ‘map’ value was taken from ‘change’
value (fig. 3.62).
69 Fig. 3.62. Coding field ‘map’
Based on the field ‘map’ the final land use change map was created in ArcGIS (fig.
3.63).
‘change’=0 then
‘map’=’usage2002’
=’usage2008
‘change’<>0 then
‘map’=’change’
70 Fig. 3.63. Swords and Kinsealy-Drinan land use change between 2002 and 2008
The next step was to display the map in Google Earth. Before converting this shapefile into KML file the transparency was set to 40% so that the background Google Earth image could also be visible. When the map was displayed in Google Earth after conversion, the outline of the squares was not clearly visible because of the transparency settings (fig. 3.64) which applied to the filling of the square and its outline.
For that reason the new layout containing outlined squares with transparency set to 0 was created. The rest of the squares in that file were set to be invisible (hollow). This layer was then overlaid on the map which has 40% transparency and as a result of this the map is more understandable (fig. 3.65).
71 Fig. 3.64. Displaying map in Google Earth - not clearly visible square outline
Source: background image Google Earth
72 Fig.3.65. Displaying map in Google Earth - more clearly visible square outline
Source: background image Google Earth
The last part was the legend creation. It was created and modified in ArcGIS and then saved as a jpg file and transferred to Google Earth through the ‘Add image overlay’
tool. Final map is presented on the figure 3.66.
73 Fig. 3.66. Land use change map with the legend
Source: background image Google Earth, 2008
