Remote sensing of environment and disaster laboratory

Institute of Industrial Science, the University of Tokyo, Japan

Estimation of land price in Yangon, Myanmar based on
Empirical Model using Remotely Sensed dataphotoTanakorn Sritarapipat, Wataru Takeuchi
Institute of Industrial Science, The University of Tokyo

11.5 decrease
the economic
losses relative
to GDP causedby disasters

Abstract: This research proposed a methodology to estimate the land price in Yangon, Myanmar based on the empirical model by using Remote Sensing technology. In theestimation, we defined the land price is referred to four factors with (1) building types (2) land cover change (3) elevation (4) distance from railways. By using remotelysensed data, we employed stereo GeoEye images and multispectral Landsat images also VIIRS nighttime light data to provide building types with commercial, industrial, andresidential buildings. We used Landsat time series to obtain the land cover change. We extract the elevation from stereo GeoEye images. The empirical model as a linear
function was applied to link between the defined factors and in-situ land price. To estimate the coefficients in the estimator, the regression method was applied. In theexperimental results, our methodology estimated land price in Yangon with the efficiency at the township scale. In the validations of the remotely sensed data, the estimatedbuilding types map was compared with the land used map and also the elevation were compared with surveying elevation data.
3.2 The validation of the estimation of land price1. Introduction
Yangon, Myanmar- the largest city (> 5 million population)- the major economic area of the country- Floods have occurred every 1-2 years- An earthquake affected in 1930
- Land price map + Disaster vulnerable map Disaster risk in term of economic loss

Low land price

Low land priceHigh land price(Francisco-Anzola-flickr.com)

Downtown in Yangon
Table 1. Remotely sensed dataset)

2. Methodology
2.1 Factors to indicate land price(1) Building types, (2) land cover change,(3) elevation, (4) distance from railways

No.
1

Satellite and sensor
Landsat-3 MMSBands
4

Resolution
60 m.

Year
1978

2

Landsat-4 TM

730 m.

1990

3

Landsat-7 ETM

830 m.

2000

4Landsat-5 TM

730 m.

2009

5Landsat-8 OLI

1130 m.

2015

6GeoEye RGB

30.5 m.

2013

7VIIRS DNB

1460 m.

2012
2.2 Data preparation- Building types : Stereo GeoEye images, Landsat image, VIIRS nigthttime light building types with (1) commercial, (2) industrial, (3) residential buildings- Land cover change : Landsat time series  Land cover change from 1978 to 2009
with (1) urban and (2) non-urban- Elevation : Stereo GeoEye images  Elevation- Distance from railways : Railways (GIS data)  Distance from railways- Land price information : Land price information at township scale

Figure 3. The estimated land price map
in 2012 at the township scale

Figure 4. The actual land price map
in 2012 at the township scale

Figure 5. The comparison between estimatedFigure 6. The absolute error between the estimatedand actual land prices in 2012 at the township scale

and actual land price in 2012 at the township scale

Table 2. Parameter values)

2.3 Defining land price index
LPI = wB • B + wE • E + wR• R + wL• L
Where B = building types,
E = elevation,
R = distance from railways,
L = land cover change,
w = the weight of the factor

Parameters

Class

Index value

Building types

Commercial building

1.0(B)

Industrial building

0.5

Residential building

0.25

Urban from 1978

1.0

Urban from 1990

0.75

Urban from 2000

0.5

Urban from 20090.25

Elevation

17-68 m.

1.0(E)

8-17 m.

0.67

0-8 m.

0.33

0-684 m.

1.0

685-1,416 m.

0.8

1,417-2,358 m.

0.6

2,329-4,024 m.

0.4

4,025-8,228 m.

0.2

Land coverchanges(L)Distance fromrailways

LPI of 0  the lowest land price
LPI of 1  the highest land price(R)2.4 Estimating the weights of land price index
The least square regression  the estimated weights of land price index
wB = 0.213, wE = 0.219, wR = 0.029, wL = 0.108

3.1 The estimation of the land price map
Low land price

3.3 The validations of remotely sensed data
- Comparing with land use map in 2012
Accuracy of the estimated building types is 76%
Kappa coefficient of 0.58
- Comparing with the surveying elevation (98 locations)
RMSE of estimated elevation is 1.62 meter

3.4 Discussions- Building types, Elevation  high impact to land price- Land cover change medium impact to land price- Distance from railways  low impact to land price- Validations of the estimated building types and elevation
 the estimated data can be employed in the model with the reliability

4. Conclusions

3. Experimental Results

Figure 1. GeoEye RGB image
over Yangon, Myanmar

High land priceHigh land price

Downtown area Figure 2. The estimated land price map(High land price) based on the empirical model- Methodology to estimate land price mean absolute error of 0.175 at the township scale)- the validations of the estimated building types and elevation
 reliability of the estimation- To improve the accuracy,
 Assigning parameter values as an exponential function
 Using other factors (the road types, other transportations,and the remark structures)
Reference(1) Morley, I. 2013. Rangoon, Cities, 31, pp.601–614.
(2) Tsutsumi, M.; Shimada, A.; Murakami, D. 2011. Land price maps of Tokyo Metropolitan Area. Procedia - Socialand Behavioral Sciences, 21, pp.193-202.
(3) Xu, Z.; Li, Q. 2014. Integrating the empirical models of benchmark land price and GIS technology forsustainability analysis of urban residential development. Habitat International, 44, pp.79-92.
(4) Hu, S.; Cheng, Q.; Wang, L.; Xu, D. 2013. Modeling land price distribution using multifractal IDW interpolationand fractal filtering method. Landscape and Urban Planning, 110, pp.25-35.
(5) Lin R.; Zhu, D. 2014. A spatial and temporal analysis on land incremental values coupled with land rights in
China. Habitat International, 44, pp.168-176.

For further details, contact: Tanakorn Sritarapipat, Bw-601, 6-1, Komaba 4-chome, Meguro, Tokyo 153-8505 JAPAN (URL: http://wtlab.iis.u-tokyo.ac.jp/ E-mail: tanakorn@iis.u-tokyo.ac.jp)