ANALYSIS OF LANDSCAPE DYNAMICS IN SHANGHAI USING LANDSCAPE 
METRICS: EFFECTS OF SPATIAL RESOLUTIONS 
Qian Zhang 3, b ' *, Yifang Ban 3 , Jiyuan Liu b , Quanqin Sha b , Yunfeng Hu 3 ' b 
Dept, of Urban Planning and Environment, KTH-Royal Institute of Technology, Drottning Kristinas vag30, 
Stockholm, Sweden - (qianzh, yifang)@infra.kth.se 
b Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, datum road, 
anwai, chaoyang district, Beijing, P.R.China - liujy@igsnrr.ac.cn, (shaoqq, huyf)@lreis.ac.cn 
KEY WORDS: Remote Sensing, Urban, Ecology, Analysis, Change, SPOT 
ABSTRACT: 
It is increasingly evident that urban sprawl leads to dramatic changes in landscape patterns and thus changes in ecosystem 
functioning. Analysis of the landscape patterns and their dynamic under urbanization is of great importance for sustainable 
development, especially in cities with significant changes like Shanghai. The objective of this research is to illustrate the landscape 
dynamic under the urbanization process in a selected test area of Shanghai in 1991, 1998 and 2007 using multitemtopal remote 
sensing and landscape metrics; and to determine the optimal resolution suitable for this case study. Preliminary results show that it is 
a quick and executable way to assessing the impact of urban sprawl on landscape dynamic using remote sensing data and landscape 
matrices; and the optimal resolution for the case study is 10-30 meters. 
1. INTRODUCTION 
Humanity today is experiencing a dramatic shift to urban living. 
More than 95% of the net increase in the global population will 
be in cities of the developing world, which will approach the 
80% urbanization level of most industrialized nations today 
(Grimm et al., 2008). Indubitably, urban sprawl is one of the 
severe land use/cover change types especially for the 
developing countries just like China (Chen et al., 2000), 
Increasing evidence indicates that urban sprawl leads to 
dramatic changes in landscape pattern and thus changes in 
ecosystem functions (Turner, 1989; Herold et al., 2003), for 
examples, the rapid reduce of biodiversity, the shortage of 
water resources and the heavy deterioration of air quality 
(Wilson, et al., 2003). Therefore, analyzing the landscape 
patterns and their dynamic under urbanization is of great 
importance for sustainable development, especially in cities 
with significant changes like Shanghai. 
Landscape metrics is one of imperative methods for 
understanding the structure, function and dynamics of 
landscapes and has a pivotal role to play in finding those 
solutions and navigating a sustainable urban future (Wu,2006; 
Jelinski et al., 2000). At the same time, development of remote 
sensing and geographic information techniques provides rich 
data source and powerful spatial analysis methods for the 
research on landscape metrics. 
Past studies focused on the development of different indexes 
portraying different aspects of landscape, numerous indexes 
emerged at a time. Software such as Fragstats (McGarigal and 
Marks, 1995) and APACK (Mladenoff and DeZonia, 2001) is 
available to researchers world-wide. Since then, more and more 
research were conducted applying these metrics (Wu & David, 
2002; Luck & Wu, 2002; Li et al., 2004; Guo et al., 2007; Zhou 
et al., 2007; Li et al. , 2005) and discussion were made on the 
do’s and don’ts during the application (Li et al.,2004; Li & Wu, 
2004), as well as, the effects of grain (Buyantuyev & Wu, 2007; 
Meng et al., 2007; Zhang et al., 2007; Tan et al., 2005; Wu, 
2004; Qi & Wu, 1996), extent(2fw et al., 2004; Zhao et al., 2007) 
and land use (Peng et al., 2006) on the landscape metrics 
analysis(<S7jao & Wu, 2008). Specific landscape metrics and 
scale should be selected after careful consideration when it 
comes to the specific landscape analysis task. 
2. STUDY AREA AND DATA 
Shanghai is undergoing rapid urban sprawl because of the 
unprecedented economic and population growth recently (Meng 
et al, 2007). A 5km*5km study area located in central zone of 
shanghai is chosen in this case study (Fig. 1). The major 
landuse classes are residential area, industrial area, commercial 
area, transportation, infrastructural area (i.e., public facilities), 
greenbelt, water and unclassified areas. The SPOT images used 
for land use type interpretation were acquired on 2007-12- 
31 (pan+xs), 1998-04-16(pan), 1998-05-27(xs) and 1991-09- 
20(pan). 
Figure 1. Location (Left) and SPOT XS 
Images (R: 1, G: 2, B: 3) in 2007(Right) of study area 
* Corresponding author. Tel.: +46 8 790 7334; Fax: +46 8 790 7343; E-mail: qian.zhang@infra.kth.se 
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