Figure 1. General location of Danang city, Vietnam 
(http://www.myvietnam.info/)(http://www.danang.gov.vn/) 
Climate of Danang city is in the zone of typical tropical 
monsoon. The average annual temperature is about 26°C, 
average rainfall is about 2,505mm per year and average 
humidity is 83.4%. There are two main seasons annually: the 
wet (August-December) and the dry (January-July). In 2009, the 
total population is about 887,070 and the population density is 
906.7 persons per km?. Danang city is known as one of the most 
densely populated and urbanized area in Vietnam. With the 
economy development and population increasing, the local 
LULC in Danang city has changed seriously. 
3. DATA AND METHOD 
3.1 Data sources and Image preprocessing 
Landsat and ASTER imagery were used for this study. The 
following criteria were considered for choosing proper data: (1) 
the images should be long time enough for detecting the land 
use change; (2) study area should not have cloud cover. 
Unfortunately, the study area is located near coastal. Due to the 
influence of weather, there are not many data satisfied both 
conditions. The images always have some thick cloud cover or 
haze. In addition, the study area is not entirely contained within 
one scene of Landsat either ASTER. Therefore, having 
acquisition images near anniversary dates for changing 
detection as Jensen mentioned (2005) was unavailable. In this 
study, three periods of satellite images were selected to classify 
study area: Landsat-3 MSS July 24, 1979; Landsat-7 ETM+ 
March 04 and April 14, 2003 (download free at 
http://earthexplorer.usgs.gov/ and http://glovis.usgs.gov/ ); and 
ASTER April 02, 2009. The details of data were described in 
Table 1. For this study, the reference data were also used, 
included: (1) topographic map 2001, at scale of 1/50.000; (2) 
land use maps in 1997, 2003 and 2010, at scale of 1/25.000. 
Because Landsat and ASTER imagery were collected at level 
IT and 1B respectively, geometric correction do not require. 
However, images were acquired at different spatial resolution 
and projections. Therefore, all images were first rectified to 
Universal Transverse Mercator (UTM) coordinate system, 
Datum WGS 84, Zone 48 North for matching the geographic 
projection of the reference data. Images were also co-registered 
together within 25 well distributed GCPs (ground control 
points) and polynomial 1sd by means of OrthoEngine provided 
by PCI Geomatica 10.3 software. RMS «0.5 was received. In 
addition, Nearest Neighbour resampling was set for not 
changing heavily the radiometric characteristic of image. In this 
study, the iteratively re-weighted multivariate alteration 
detection (IR-MAD) transformation was used for automatic 
radiometric 
normalization for all images by means of ENVI 4.3 software; 
see (Canty, 2010, A. A. Nielsen et al., 1998, Canty and Nielsen, 
2008). ASTER 02/04/2009 was chosen as reference image. 
However, this image does not cover all the region of study area, 
therefore a subset of 1800x1100 pixels with 30m spatial 
resolution including 968.17km? was created for all images for 
further studying. This territory was chosen to ensure the specific 
study area was in the analysis image. Besides the requirement of 
the same dimension, images must have the same spectral 
resolution. Hence, the composite of standard false colours was 
used for this study: Landsat MSS (754); Landsat TM/ETM+ 
(432); ASTER (321). 
3.2 LULC classification and Change detection 
Six land use/cover classes were defined for image classification 
based on the modified Anderson land use/cover scheme level I 
(Anderson eft al, 1976), included: (1) water, (2) forest, (3) 
shrub, (4) agriculture, (5) barren and (6) urban land. Anderson 
classification scheme was chosen because of the major land 
use/cover classes using images with differences in spatial 
resolution, which are Landsat MSS, Landsat ETM+ and 
ASTER. Supervised classification using maximum likelihood 
approach in ENVI 4.7 was individually applied for each image 
of study area to classify land use/cover. Maximum likelihood 
algorithm was preferred because this rule is considered to have 
accurate results because it has more accurate results than other 
algorithms (Mengistu D. A. and Salami A. T., 2007, Reis, 2008, 
Diallo Y. et al., 2009). 
Because of various image acquisition dates, training areas for 
the images of the years 1979, 2003 and 2009 were different 
during the classification. In addition, the training areas were 
verified by references data. As the next step, post-classification 
comparison change detection algorithm was selected to detect 
changes in LULC from 1979 to 2009 in study area in order to 
minimize the problem in radiometric calibration of imagery of 
two different dates. For comparison of the classification results 
of two dates, a change detection matrix was created based on 
pixel-by-pixel (Jensen, 2005). Thereby, each type of from-to 
LULC change is identified. 
3.3 Landscape fragmentation 
To quantify landscape structure of this study, FRAGSTATS was 
applied because this spatial statistic program offers a 
comprehensive choice of landscape metrics. This program was 
created by decision maker, forest manager and ecologists 
therefore it is appropriate for analyzing landscape fragmentation 
or describing characteristics of landscape, components of those 
landscapes (Keles et al, 2008). However, landscape patterns 
were complicated; hence, to clarify the relationship of spatial 
pattern and process it cannot use single metric alone (Ning ef 
al., 2010, Esbah et al., 2009). 
Based on the scale of study area (i.e. the district level) and its 
characteristic as well, eight related landscape metrics were 
selected: (1) total class area, (2) percentage of landscape, (3) 
number of patches, (4) largest patch index, (5) average size of 
patches, (6) mean patch shape, (7) proximity index, (8) 
Euclidean nearest distance, (9) Interspersion and juxtaposition 
index, (10) Shannon's diversity index, (11) Shannon's evenness 
index. A brief description of those landscape metrics used in 
study was given in Table 2. Those descriptions could be also 
found at user’s guide of FRAGSTATS™ (McGarigal, 2002). 
   
  
  
  
  
  
  
  
  
  
   
     
   
    
    
    
   
   
   
     
     
    
   
   
   
   
    
   
    
   
   
   
    
    
   
   
    
   
   
    
   
   
   
   
   
   
   
    
   
    
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