topography and land temperature investigation …1)(2).pdftopography and land temperature...

Topography and Land Temperature Investigation Using Arc GIS 10

Insan Akademika

Publications

INTERNATIONAL JOURNAL

OF BASIC AND APPLIED SCIENCE

P-ISSN: 2301-4458

E-ISSN: 2301-8038

Vol. 01, No. 02

Oct 2012 www.insikapub.com

390

Topography and Land Temperature Investigation Using Arc GIS 10

Babita Pal 1 and Sailesh Samanta 2

1Papua New Guinea University of Technology CAMPUS, Private Mail Bag, Lae- 411,

Morobe Province, Papua New Guinea

[email protected]

2 Department of Surveying and Land Studies,

Papua New Guinea University of Technology, Private Mail Bag, Lae- 411,

Morobe Province, Papua New Guinea

[email protected]

Key words Abstract

Geographic information

systems,

Remote Sensing,

Surface temperature,

3-D Modeling,

Spatial analysis

The paper seeks an empirical methodology for modeling and mapping of the

topography using remote sensing (RS) and geographic information systems (GIS)

techniques. Firstly this paper examines the mathematical approaches for different

surface analysis like contour, hill-shade, slope, aspect and cut/fill analysis. View-

shade analysis was also performed which have a large application on mobile

telecommunication in the hilly region. Secondly this paper examines statistical

approaches for monitoring surface using LANDSAT-7 ETM+ satellite data, which

has wide range of electromagnetic wavelength band, including visible, infrared

and thermal bands. Its thermal bands (band 6.1 and 6.2) can detect thermal

radiation released from objects on the earth surface. This study has been

conducted to develop surface temperature (ST) using 3 sets of algorithm from

LANDSAT-7 ETM+. Finally modeled land surface temperature data set was

overlaid on digital elevation model (DEM) to find out the relation of surface

temperature with variation of altitude of the study area.

© 2012 Insan Akademika All Rights Reserved

1 Introduction

Topography is the study of Earth's surface shape and features or those of planets, moons, and asteroids. It is

also the description of such surface shapes and features, especially their depiction in maps. In a broader

sense, topography is concerned with local detail in general, including not only relief but also vegetative and

human-made features, and even local history and culture. Topographic maps with elevation contours have

made "topography" synonymous with relief. Topography specifically involves the recording of relief or

terrain, the three-dimensional quality of the surface and the identification of specific landforms. This is also

known as geo-morphometry. In modern usage, this involves generation of elevation data in electronic form.

It is often considered to include the graphic representation of the landform on a map by a variety of

techniques, including contour lines, hypsometric tints, and relief shading. There are a variety of approaches

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mailto:[email protected]

mailto:[email protected]

http://en.wikipedia.org/wiki/Earth

http://en.wikipedia.org/wiki/Surface

http://en.wikipedia.org/wiki/Planet

http://en.wikipedia.org/wiki/Natural_satellite

http://en.wikipedia.org/wiki/Asteroids

http://en.wikipedia.org/wiki/Local_history

http://en.wikipedia.org/wiki/Culture

http://en.wikipedia.org/wiki/Elevation

http://en.wikipedia.org/wiki/Terrain

http://en.wikipedia.org/wiki/Landform

http://en.wikipedia.org/wiki/Map

Pal and Samanta International Journal of Basic and Applied Science,

Vol. 01, No. 02, Oct 2012, pp. 390-401

www.insikapub.com 391

to studying topography, which method(s) are used depend on the scale and size of the area under study, its

accessibility and the quality of existing surveying processes. Remote sensing has greatly speeded up the

process of gathering information, and has allowed greater accuracy control over long distances, the direct

survey still provides the basic control points and framework for all topographic work, whether manual or

GIS-based. A geographic information system (GIS) can recognize and analyze the spatial relationships that

exist within digitally stored spatial data. These topological relationships allow complex spatial modelling and

analysis to be performed. Topological relationships between geometric entities traditionally include

adjacency (what adjoins what), containment (what encloses what), and proximity (how close something is to

something else).

2 Materials and Methods

2.1 Data used

Different types of data were used for preparation of surface analytical maps of the area, like- shuttle radar

topography mission (SRTM) data, National atlas of Papua New Guinea and topographical map of the

corresponding area. One of the most widely used digital elevation model (DEM) data sources is the elevation

information provided by the shuttle radar topography mission (SRTM) (Coltelli, et. al., 1996), but as with

most other DEM sources, the SRTM data requires significant levels of pre-processing to ensure that there are

no spurious artifacts in the data that would cause problems in later analysis such as pits, spikes and patches

of no data (Dowding, et. al., 2004). In the case of the SRTM data, these patches of no data are filled,

preferably with auxiliary sources of DEM data, like-topographical maps. Both data sets were used for this

study. Optical bands with standard false color combination (SFCC) of LANDSAT 7 ETM+ satellite images

were used to find out the land use/ land cover classes in the study area. Thermal band were also used to

calculate the daily cold and hot surface. All other details of the variables are given in the Table 1.

Table 1. Topographical maps and other materials

Sl. No. Name of materials Scale/ Resolution Year of publication

1 Shuttle radar topography mission (SRTM) data 3-arc seconds 2003

2 Topographical maps

1:250000 1960

3 1:50000 1973 - 1980

4 LANDSAT-7, ETM+ Satellite Image 30 m 2001

2.2 Methodology for preparation of digital elevation model

Two methodologies were applied for preparation of digital elevation model of Papua New Guinea. The

original SRTM DEM was used to produce contours or points. Processing was made on a void by void basis.

In cases when a higher resolution auxiliary DEM is available, point coverage is produced of the elevation

values at the centre of each cell of the auxiliary DEM within void areas. When no high resolution auxiliary

DEM is available, the 30 second SRTM30 DEM was used as an auxiliary for large voids. For areas with a

high resolution auxiliary DEM, the contours and points surrounding the hole and inside the hole are

interpolated to produce a hydrologically sound DEM using the TOPOGRID algorithm in Arc/Info.

TOPOGRID is based upon the established algorithms of Hutchinson (1988; 1989), designed to use contour

data (and stream and point data if available) to produce hydrologically sound DEMs. This process

interpolates through the no-data holes, producing a smooth elevation surface where no data was originally

found.

http://en.wikipedia.org/wiki/GIS

http://en.wikipedia.org/wiki/Model_(abstract)

International Journal of Basic and Applied Science,

Vol. 01, No. 02, Oct 2012, pp. 390-401

Pal and Samanta

392 Insan Akademika Publications

On the other hand, DEM was also generated from topographical maps. Single map rectification was

performed using the geographical coordinate system and WGS 84 datum with a RMS error of 0.02. ERDAS

IMAGINE 9.0 was used to rectify topographical maps of the study area using the geographical Coordinate

system and WGS 84 datum with a RMS error of 0.25. The entire study area does not appear in single sheet.

All the geo-referenced topographical map sheets of the area were mosaicked to get the entire study area.

Using the similar projection as well as datum for all the soil maps were mosaicked in the environment.

Prepared data sets in this process were compared with the SRTM data set to find out the accuracy of both

data sets.

After obtaining satisfied level of accuracy, we have proceeded with the generation of the final output. The

entire data range was stretched with distinct color. The final map was generated to represent the digital

elevation model with 90m spatial resolution for total Papua New Guinea land area (Fig. 1). The detail map

was generated with the help of 90m digital elevation model data and 30m LANDSAT-7, ETM+ image after

overlaying them in the virtual GIS viewer of ERDAS IMAGINE (Fig. 2). The part of Morobe province, Lae

coast and surrounding area was cropped for this purpose from digital elevation model data as well as from

the LANDSAT-7, ETM+ Satellite image. We processed 15 vertical exaggerations to present the 90 m spatial

resolution Digital Elevation Model.

Fig. 1: DEM of the study area (90 m spatial resolution)


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Fig. 2: Overlay of DEM and ETM+ Image (Mouth of Markham River, Lae, Morobe)

2.3 Surface analysis using digital elevation model

Surfaces represent phenomena that have values at every point across their extent. The values at the infinite

number of points across the surface are derived from a limited set of sample values. These may be based on

direct measurement, such as height values for an elevation surface, or temperature values for a temperature

surface; between these measured locations values are assigned to the surface by interpolation. Surfaces can

also be mathematically derived from other data, such as slope and aspect surfaces derived from an elevation

surface, a surface of distance from bus stops in a city, or surfaces showing concentration of criminal activity

or probability of lightning strikes. Surfaces can be represented using contour, arrays of points, Triangulated

Irregular Network (TIN) and raster. However most surface analysis in GIS is done based on raster or TIN

data. TINs are constructed from a set of known values, sometimes called "spot heights" that are used as

initial nodes in the triangulation.

2.3.1 Contour

Contours are poly-lines that connect points of equal value such as elevation, temperature, precipitation and

pollution or atmospheric pressure. The distribution of the Polyline shows, how values change across a

surface. If, there is little change in a value, the Polyline are spaced farther apart. Where the values rise or fall

rapidly, the Polyline are closer together. EARDAS IMAGINE and ArcGIS were used to generate the

contours according to user specified interval (Fig. 3) from DEM data. The contour attribute table contains an

elevation value for each contour.


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Pal and Samanta


Fig. 3: Contour characteristics of the, based on DEM data


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2.3.2 Slope

The slope tool calculates the maximum rate of change between each cell and its neighbors, for example, the

steepest downhill descent for the cell (the maximum change in elevation over the distance between the cell

and its eight neighbors). The lower the slope value indicates the flatter the terrain and the higher the slope

value the steeper the terrain. The output slope raster can be calculated as percent of slope or degree of slope.

EARDAS IMAGINE and ArcGIS were used to generate slope map of the study area using the DEM data

(Fig. 4).

2.3.3 Aspect

Aspect identifies the steepest down slope direction from each cell to its neighbors. It can be thought of as

slope direction or the compass direction a hill faces. Aspect is measured clockwise in degrees from zero, due

north to 360, again due north, coming full circle. The value of each cell in an aspect dataset indicates the

direction the cell's slope faces. Flat areas having no down slope direction were given a value of -1. EARDAS

IMAGINE and ArcGIS were used to generate aspect map using the DEM data (Fig. 4).

2.3.4 Hill-shade

The hill-shade function obtains the hypothetical illumination of a surface by determining illumination values

for each cell in a raster. It does this by setting a position for a hypothetical light source and calculating the

illumination values of each cell in relation to neighboring cells. It can greatly enhance the visualization of a

surface for analysis or graphical display, especially when using transparency. The hill-shade (Fig. 4) below

has an azimuth of 315 and an altitude of 45 degrees. EARDAS IMAGINE and ArcGIS were used to generate

hill-shade map using the DEM data.

2.3.5 View-shade

View-shade (Fig.5) identifies the cells in an input raster that can be seen from one or more observation points

or lines. Each cell in the output raster receives a value that indicates how many observer points can be seen

from each location. If we have only one observer point, each cell that can see that observer point is given a

value of one. All cells that cannot see the observer point were given a value of zero. The observer points

feature class can contain points or lines. The nodes and vertices of lines will be used as observation points.

EARDAS IMAGINE and ArcGIS were used to generate view-shade map using the DEM data.


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Fig. 4: Slope, Aspect & Hill-shade of Goroka, based on DEM


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Fig. 5: View-shade, relief & land use characteristics of the area


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Pal and Samanta


2.4 Surface temperature assessment

A surface temperature model was used to calculate the surface temperature from 7th band of LANDSAT

ETM+ satellite image. ERDAS IMAGINE 8.5 was used for this purpose. Unlike the published TM-

temperature model that would consider the min-max digital count for every image, to get the spectral

radiance, L, the following algorithms (Equation1 to 3) were applied to compute the surface temperature

from band7th band of LANDSAT ETM+ satellite image.

ETM+ bands are quantized as 8 bit data; all information was stored in digital number (DN) with range

between 0 and 255 (8 bit). The data was converted to radiance using a linear equation (Sobrino 2004) as

shown below:

CVR = G (CVDN) + B ...(1)

Where : CVR = the cell value as radiance,

CVDN = the cell value digital number, G is the gain (0.05518 for ETM+ 7), and

B = the offset (1.2378 for ETM+ 7)

The thermal band’s radiance values were converted to brightness temperature values by applying the inverse

of the Planck function. The formula (2) used (Sobrino 2004) to convert radiance to brightness temperature

was:

T = K2/{(k1/CVR) + 1} ...(2)

Where : T = degrees Kelvin, CVR

is the cell value as radiance,

K1 = calibration constant 1{666.09 (for ETM+)}, and

K2 = calibration constant 2{1282.71 (for ETM+)}

Conversion brightness temperature from degrees Kelvin to degree Celsius was converted using the following

equation:

TC = TK – 273 ...(3)

Where : TC = temperature in degrees Celsius, and

TK = temperature in degrees Kelvin

3 Results and Discussion

The absolute relief of PNG is approximately 3621 meter derived by 3D analysis process from digital

elevation model data set (Fig. 1). The relative relief of Goroka town and its surroundings is 846m (max

altitude-2233m and minimum-1387m). As a whole if we consider PNG, the maximum slope is 89.74 degree,

whereas Goroka town and its surroundings contains maximum slope of 36.28 degree, is shown in Fig.4.

More than 70% of the area comes under 15 degree of slope. This moderate slope is good for agriculture

activity, not for landslide. The airport is located in the plane land area with 0 degree slope, easily identified

by the slope and aspect map.

In the atmosphere we discovered that air temperature usually decreases with an increase in elevation through

the troposphere. The normal lapse rate of temperature is the average lapse rate of temperature of 0.65oC/100

meters. The decrease in temperature with height is caused by increasing distance from the source of energy

that heats the air, the Earth's surface. Air is warmer near the surface because it's closer to its source of heat


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(Fig. 6 & 7). The further away from the surface, the cooler the air will be. It's like standing next to a fire; the

closer you are the warmer you'll feel.

The calculated land surface temperature shows (Fig. 6) a range of temperature 14 to 34 (represented by

graduated color-blue to red) degree centigrade in the Goroka region. The hot temperate areas were clearly

identified (Red color pixels) from the resulted dataset. The Goroka city region and the barren lands

(sand/boulders) areas were showing the high temperature value (red) for their high thermal influencing

properties. The anthropogenic activity like roads, buildings and other construction are the causes of more

thermal radiation, easily identified by temperature modeling. Temperature is less (blue) in the north-east and

south-west hilly area where dense vegetation is present. Due to increase of the height hilly region shows less

temperature (Fig. 7) then the plane land and water body.

Fig. 6: Surface temperature characteristics of the study area, based on ETM+ data


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Pal and Samanta


Fig. 7: Surface temperature characteristics in relation to DEM of the area

4 Conclusions

Digital elevation data set is very useful to find out the slope, aspect, hill-shade and relief, which are very

important for agricultural activity. The view-shade also derived from DEM, which is using to find the

minimum number of stations to give the communication coverage by mobile communication tower for a

certain region/area. With the change of elevation/vertical height the weather or climate also changed. DEM

can be used to model the air temperature of a particular area along with other independent variables. Surface

temperature also depends on different variable of the Earth surface or atmosphere. For temperature modeling

we used ETM+ satellite image and compared with digital elevation model data set. There are many different

processes to model the temperature using some dependent and independent climate variables. If we bring

into play the entire variables like- land use/land cover, soil texture, digital elevation model, distance from

sea, latitudinal location, continentality, solar radiation and cloudiness factor, aspect then the model may

predict accurate result. This temperature model is applicable for Papua New Guinea. We can run this model

for calculating the mean monthly temperature of any part of the world. It is only possible, if all data sets like

elevation, soil texture and land use/land cover data of the area are available.

Acknowledgement

One of the authors (SS) expresses sincere gratitude to Papua New Guinea University of Technology &

Department of Surveying and Land studies for providing digital image interpretation laboratory facility to

carry out this work. The authors are also grateful to the all the academic staff of GIS section for their

valuable comments and suggestions.

References

Coltelli, M., Fornaro, G., Franceschetti, G., Lanari, R., Migiaccio, M., Moreira, J. R., Papathanassaou, K. P.,

Puglisi, G., Riccio, D. and Schwabisch, M. (1996) SIR-C/X-SAR multifrequency multipass

interferometry: A new tool for geological interpretation, Journal of Geophysical Research, vol. 101,

pp. 127-148.


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Dowding, S., Kuuskivi, T. and LI, X. (2004) Void fill of SRTM elevation data – Principles, Processes and

Performance, In: Images to Decisions: Remote Sensing Foundations for GIS Applications, ASPRS,

Fall Conference, September 12-16, Kansas City, MO, USA.

Hutchinson, M. (1988) Calculation of hydrologically sound digital elevation models. Third International

Symposium on Spatial Data Handling, Columbus, Ohio, International Geographical Union.

Hutchinson, M. (1989) A new procedure for gridding elevation and stream line data with automatic removal

of spurious pits, Journal of Hydrology vol. 106, pp.211-232.

Sobrino, J. A., Jimenez-Munoz, J. C. and Paolini, L. (2004) Land surface temperature retrieval from

LANDSAT TM 5, Remote Sensing of Environment, vol. 90, pp. 434-440.

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