D01

Research Abstracts on Spatial Information ScienceCSIS DAYS 2007

Estimating Land Use Change in the Kathmandu Metropolitan Area Using a Markov Chain Model
Rajesh Bahadur Thapa, Yuji Murayama
Division of Spatial Information Science, University of Tsukuba
Contact address: <thaparb@yahoo.com> Web: <http://giswin.geo.tsukuba.ac.jp/sis/>

Land use change(1) Introduction: Since the 1960s, the agriculture (5) Results: Fig. 1 (A and B) presents the land use patterns
in 1989 and 1999. Fig. 1 (C) shows the locations of land
landscape of the Kathmandu metropolitan region
use change between the years 1989 and 1999. Four landtransformed dramatically into an urban form driven byuse classes, namely Built-up (consisting of HMG
the vehicular arteries and migration into the city. High
Secretariat, Industrial area, Institutional area, Open field
population influx and rapid land use change may cause aand Urban built-up), Agriculture, Water and Forest wereserious pressure to the limited resources withused to compute Markov transition probabilities and toundesirable impact to the environmental conditions andpredict the land use change statistics for the years 2009
livelihoods of inhabitants in the city area.
and 2019 (Fig. 2). The quantitative result (Fig. 2) shows(2) Objective: The main objective of this paper is tovery low existence of water (<5%) and forest covermonitor and estimate the land use change in the(<10%). The water cover appears to be almost constant
Kathmandu metropolitan area using Landsat satellite
in the past and in future. The forest cover shows slightly
data and a Markov chain model.
decreasing pattern in the past but it will be constant in(3) Data: Two images of Landsat satellite acquired on 31stnext decade. The trend of built-up land is being rapidly
October, 1989 and 4th November, 1999 covering theincreased since 1989 which will be doubled by the year
Kathmandu metropolitan area were classified using the2019 (>60%) where agriculture land will be half during
supervised maximum likelihood method. The accuracy
the same period. The overall result showed the trend ofof classification results achieved were 91% and 92% for
rapid expansion of urban built environment andthe year 1989 and 1999. The land use change matrix
shrinkage of the cultivated land.
between the years was prepared for analyzing major
land use changes in the city. The matrix was limited to (6) Conclusions: This paper is able to identify the urban
land use change thematically from remote sensing data.
four categories of land use change (built-up, agriculture,
The Markov chain model has shown the capabilities ofwater and forest).
descriptive power and simple trend estimation for land(4) Methodology: Markov chains models have been widely
use and land cover change in the Kathmandu
used to model land use changes in urban areas, since themetropolitan region. The integration of satellite remote
1960s. This paper assumed land use change from time
sensing, GIS and Markov chains modeling provided aT1 to T2 is a Markov chain with stationary transitionmeans to study changes in urban land use.
probabilities, and different categories of land uses were
the state of a chain. Furthermore, the current process of70
land use change can affect the results of land change in60
future. A Markov chain equation (Pij x Lj = Lj+1) was5040used. Pij is transition probabilities where land is changed
30
from state i to state j at given time period. A probability
20
10vector of Pij with row components entries are0
non-negative and sum to 1. Lj is a vector of land use at19891999
2009
2019
state j. Lj+1 represents the projection of land use
Yearproperties for the next time period. In this study, we
Built up
Agricult ure
Wat erForestprojected the land use change in the metropolitan area
for the year 2009 and 2019 by computing first andFig. 2: Land use change estimates for next decadesecond orders of Markov chain.

Fig. 1: Land use changes in Kathmandu metropolitan area

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