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Assessment of changes in urban green spaces of Mashad city using satellite data
International Journal of Applied Earth Observation and Geoinformation 11 (2009) 431–438
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Assessment of changes in urban green spaces of Mashad city using satellite data Reza Rafiee a,*, Abdolrassoul Salman Mahiny b, Nematolah Khorasani c a
Department of Fishery, Faculty of Marine Science, Chabahar Maritime University, Chabahar, Iran Department of Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran c Department of Environmental Sciences, Faculty of Natural Resources, University of Tehran, Karaj, Iran b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 26 October 2008 Received in revised form 22 May 2009 Accepted 20 August 2009
Green spaces play important functions in urban environments. Reducing air pollution, providing shade and habitat for arboreal birds, producing oxygen, providing shelter against winds, recreational and aesthetic qualities and architectural applications are the main functions of urban green spaces. With the rapid change of urban area in Mashad city during the past decades, green spaces have been fragmented and dispersed causing impairment and dysfunction of these important urban elements. The objective of this study was to detect changes in extent and pattern of green areas of Mashad city and to analyze the results in terms of landscape ecology principles and functioning of the green spaces. In this research, we classified a Landsat TM and an IRS LISS-III image belonging to the years 1987 and 2006, respectively. We then used a post-classification comparison to determine the changes in green space areas of Mashad city during the 19 years covered by the images. Then, we applied landscape ecology calculations to derive metrics that quantified pattern of the changes in the green areas. The results showed that during 19 years from 1987, a significant decrease had occurred in the extent of urban green spaces with a concomitant fragmentation resulting in downgrading and destruction of the functions and services these areas provide. We conclude that the general quality of life in the central parts of the city has been diminished. We also state that a combination of remote sensing image classification, landscape metrics assessment and vegetation indices can provide a tool for assessing life quality and its trend for urban areas. ß 2009 Elsevier B.V. All rights reserved.
Keywords: Urban green space Change detection Landscape metrics Post-classification comparison Mashad city
1. Introduction Urbanization both in population and spatial extent, transforms the landscape from the natural cover types to impervious urban lands (Xian et al., 2005). This phenomenon is one of the most important factors that changes land surface leading to modification of receiving environments which are usually composed of natural cover. The pressure for additional housing and business demands in towns and urban areas alters existing urban green spaces even more in the route to development (The World Resources Institute, 1996). Urban green spaces provide a variety of functions which can be grouped in three classes including architectural application and aesthetics, climatic and engineering functions (Miller, 1997). Also, urban green spaces provide the opportunity for recreation and experiencing nature. These functions are essential for improving the quality of citizen life. Therefore, allocation of urban land to green spaces as a class of land use is an important policy issue in almost all cities. However, due to physical expansion of Mashad
* Corresponding author. Tel.: +98 545 2224264; fax: +98 545 2221025. E-mail address: rzarafi[email protected] (R. Rafiee). 0303-2434/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jag.2009.08.005
city, extensive destruction of green spaces has occurred that conflicts with an environmentally sound developing paradigm. Until now, little research has been carried out to explore the change in the green spaces and other urban land use/land cover types in the study area. The purpose of this study is to assess changes in the green spaces of Mashad city and to monitor the trend of changes through a change detection procedure. The information on these changes is of great use to land managers and policy makers in order to make informed decisions that effectively balance the positive aspect of development and its negative impact on the receiving environment. The information will also help to preserve the urban green space resources and enhance urban life quality. As humans arbitrarily modify the environment, the ultimate juxtaposition and configuration of the landscape elements are mostly determined by people inhabiting the landscape. Hence, spatial pattern analysis can reveal the influence of humans on land use and provides necessary information for guiding these changes in an environmentally wise manner. As such, to achieve a comprehensive assessment of changes, landscape metrics have also been applied in the present study. These metrics make quantification and interpretation of the spatial pattern available to land use and land cover evaluators (McGarigal et al., 2002). The objective of this study are: (1) generation of a change map of green spaces in the extent of Mashad city using the NDVI image
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differencing, which makes detection of subtle changes possible and helps in discerning ‘‘from-to’’ changes which is achieved using a post-classification comparison. In this way, it is possible to provide a spatial dynamic and change detection of green spaces in the Mashad city (2) to enable a comprehensive analysis of change of green spaces through application of a set of landscape metrics. 1.1. Land use/land cover change detection using satellite data Satellite remote sensing provides an important source of land use/land cover data and can be utilized to monitor the changes in these data efficiently. Remote sensing change detection is a process used to identify difference of state of objects or phenomena on images observed at different times (Singh, 1989). This technique has been applied widely for the study of environmental changes (Mass, 1999; Li and Yang, 2004; Salman Mahiny, 2004; Yuan et al., 2005). Until now, an array of digital algorithms has been developed to detect land cover changes from remote sensing data (Jensen, 1996; Coppin and Bauer, 1996; Lu et al., 2004). However, despite the wide range of these methods, they can basically be summarized in two broad categories, those that detect change and then assign classes (pre-classification) and those that first assigns classes and then detect change such as post-classification comparison (Van Oort, 2007). In the pre-classification methods, the images of two dates are transformed into a new single image which contains the spectral change of the surface. The most important issue in these methods is to distinguish change and no change pixels. A common method for this is to use the statistical threshold (Yuan et al., 1999). While the pre-classification methods are able to detect areas of changes (Van Oort, 2007) they do not provide a confusion matrix indicating their nature, which on the other hand is delivered by the post-classification approaches (Jensen, 1996). Through application of this technique, a detail matrix with ‘‘fromto’’ information on changes is generated. Also, post-classification comparison removes the need to atmospheric correction in the two images. However, accuracy of this technique is dependent on the accuracy of each classified map and the poor results of classification lead to generation of uncertainty in the change map (Daryaei, 2003). In addition, this method works well in large homogeneous areas with major changes but not in highly heterogeneous regions and small changes (Yang and Lo, 2002). 1.2. Application of landscape metrics to analyze land use/land cover change Spatial characteristics such as location, direction and distance are the bases to analyze the spatial structure of landscape. Spatial metrics which are also known as landscape metrics are commonly used to quantify the spatial structure including pattern and shape of the focused elements in the environment. There has been an increasing interest in application of the landscape metrics to analyze the urban environment (e.g. Alberti and Waddell, 2000; Herold et al., 2005; Tang et al., 2008; Lin et al., 2008). These metrics have been developed in the late 1980s based on information theory and fractal geometry in the categorical, patch based representation landscape (Herold et al., 2005). As investigated by Parker et al. (2001), landscape metrics capture inherent spatial structure of the environment, and are used to enhance interpretation of spatial pattern and characteristics of the landscape. The spatial pattern of the land use reflects underlying human processes that influence the urban environment (Bastian, 2000). Until now, a great number of the landscape metrics has been formulated to quantify the landscape (Forman and Godron, 1986; Romme, 1982; O’Neill et al., 1988; Stauffer and Aharony, 1992; Imbernon and Branthomme, 2001). The landscape metrics fall into two general categories, those that quantify the composition of landscape and those that quantify
the spatial configuration of the landscape (McGarigal and Marks, 1995). Composition indices, such as number of patches, patch density and patch size relate to the non-spatial feature of the landscape associated with the variety and abundance of patch types. Configuration metrics, on the other hand, explicitly interpret the spatial structure of landscape such as neighborhood relationships, alignment of borders and contagion. These two groups reflect two aspects of landscape pattern and complete each other for a thorough interpretation of changes in the landscape (Tang et al., 2008). However, most of the metrics are correlated that can lead to redundant information and making the interpretation difficult. Hence, choosing suitable metrics is a first step towards useful interpretation of the landscape and depends largely on the objective of research (Turner et al., 2001). 2. Study area Mashad is the capital of Khorasan Razavi Province of Iran (Fig. 1). It is one of the most important cities because of its religious, historical and economic values that attract a large number of people each year. In 1986, its population was 668,000 whereas its current population is about 2.4 millions. Since 1987, built-up areas in the city have expanded significantly (Rafiee, 2007); the city has witnessed a rapid growth in construction which has caused destruction of green spaces areas. This trend in the green spaces is in sharp contrast with the rules governing improvement and establishment of new green spaces within the current boundary and the projected future of the city. In fact, municipality closely attends to the green areas and scrutinizes even single tree uprooting. On the other hand, there are nongovernmental organizations and the general public who watch the trend carefully and exert controlling effects on green space removal. In addition, the provinces of Iran are all under extensive land use evaluation and planning, the results of which will be available in near future. The application is mostly environmentally oriented giving high value to green spaces and aims to upgrade the per capita green areas in the newly built regions. However, there are other players in the filed including major private stakeholders who have influence in deciding the physical and biological properties of built-up area development plans. The end result of confrontation between these groups with conflicting objectives is normally compensatory and in recent years, the municipality has been forced to replace any removed green space area as a necessity when developing housing complexes. 3. Data and preprocessing A set of cloud-free imagery of Landsat TM belonging to the year 1987 and IRS LISS-III dated 2006 were acquired whose scenes covered around 220 km2 including Mashad city and its outskirts. The images were acquired in the summer months which represent same vegetation condition of green areas in the city (Table 1). The IRS image was registered to a topographic map of Mashad city corresponding UTM projection at 30-m resolution. The 1987 image was then co-registered to the IRS image using 13 GCPs by scene-to-scene resampling corresponding to UTM projection and 30-m resolution for which the total RMSE of 0.21 pixels was achieved. The correction was conducted using the first order polynomial model and the nearest neighborhood method for resampling. Without radiometric calibration of multi-temporal dataset, false changes can occur in the classified maps. Therefore, for quantitative analysis based on radiometric information such as NDVI differencing, the images should be corrected radiometrically to compensate for radiometric divergence (Mass, 1999). Due to a lack of information for absolute radiometric correction of the
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Fig. 1. Location of Khorasan Razavi Province which is in the north east of Iran (left) and Mashad city extent (right).
remote sensing data, we performed a relative radiometric correction using dark and bright pixel control set (Hall et al., 1991). The correction was performed on each of the red and near infra red (NIR) bands of IRS image with the reference to TM scenes. Afterwards, using on-screen interpretation and digitization, a vector layer was created to extract the boundary of the study area from all images which included Mashad city and its environs. We applied a supervised classification to TM and IRS images to map an aggregate land cover of the study area. Based on our target in this research, an aggregate level of map was deemed sufficient; hence the images were classified into three classes of urban, barren land and green spaces. A false color composite image for each date was created to help visualize the various land cover and land use of the area. Then, by visual interpretation of the false color composite images and by reference to a 1:25,000 topographic map of the year 1994 training data were selected for each class. Selected pixels were randomized to minimize autocorrelation. Afterwards, through unsupervised classification of the image of the years 2006 and 1987 into 100 classes and crossing them with the original training pixels, sampled pixels were purified (Salman Mahiny, 2004). Purification in this context meant removal of those pixels which were a combination of different land uses and land covers mistakenly selected for different categories. Without the purification, classification accuracy is normally lower. After purification of training data, we used maximum likelihood classifier to classify the
Table 1 Description of the satellite imagery used in this study. Characteristics/sensor
TM
LISS-III
Image size Pixel size Number of bands Path and raw used in this Research Date of acquisition
185 km 185 km 28.5 m 7 P: 159; R: 35 1987/09/04
140 km 140 km 23.5 m 4 P: 76; R: 45 2006/07/07
raw imagery for each date. Then, we chose another set of training samples to assess the accuracy of each classified map. The detailed results of accuracy assessment are depicted in Table 4. 4. Methodology 4.1. NDVI differencing and post-classification Remote sensing data can provide information on vegetation structure and amount of biomass and leaf area as vegetation cover can be estimated through various indices such as Normalized Difference Vegetation Index (NDVI) (Purevdorj et al., 1998). NDVI image for each date was calculated separately and the results were subtracted. In the resultant image, zero values indicated no change areas and the changed areas between the two dates had a negative or positive value, with the negative values representing decrease in vegetation and positive values indicating increase in vegetation. However, slight changes in the brightness values between the two dates occurred due to noises even after radiometric normalization and thus it was necessary to develop a set of threshold values to discriminate the real changes (Cakir et al., 2006). To find the optimum high-end and low-end thresholds we used the accuracy assessment curve. Accuracy assessment curve provides a graphic and quantitative representation of the relationship between the threshold levels used to classify the change areas and different accuracy assessment figures (Morisette and Khorram, 2000). In this research, thresholds at low-end and high-end were set independently based on cumulative producer’s and user’s accuracy. First, based on NDVI differencing image histogram, threshold values were set up equidistant to the mean of Brightness Values (BVs) of NDVI differencing image in each tail. Then, NDVI differencing image values were classified into three classes entitled: (1) vegetation decrease if a pixel value was lower than the low-end threshold, (2) vegetation increase if a pixel value was higher than the high-end threshold and (3) no
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change class values between the two thresholds. Then, producer and user accuracy of both change classes were extracted from the error matrix and the optimum threshold was determined based on the resultant curve. Post-classification, obviously involves comparison of independently classified maps of each date of interest followed by a pixelby-pixel or segment comparison to detect change area in the cover types (Coppin et al., 2004). We applied post-classification comparison to produce a detail matrix of change in the Mashad city and determine those land use classes that the green spaces class has been changed into. The method provided a quantitative map of land use/land cover change. 4.2. Analysis of urban green space change pattern using landscape metrics An attempt was made to assess the fragmentation and landscape pattern in the study area. In doing so, several landscape metrics were calculated using FRAGSTATS 3.3 software (McGarigal et al., 2002). As investigated by O’Neill et al. (1988), because there are correlation and overlap between landscape metrics, it is not necessary to calculate all landscape metrics. Hence, in order to reduce redundancy, we tried to choose a small set of metrics which were sensitive to landscape change including composition and configuration metrics. In the present study, the chosen metrics included: class area (CA), percent of landscape (PLAND), number of patches (NP), mean patch size (MPS), area weighted mean shape index (AWMSI), mean nearest neighbor distance (MNND) and largest patch index (LPI). Landscape metrics that were used in the present study are described in Table 2. Also, to derive the location of most significant changes and their pattern in the city, two transects were considered from north to south and the west to east. The center of transects were located in the center of city in the year 1987. The west–east and north-east transects consisted of nine and six, 2 km 2 km zones, respectively. 5. Results and discussion 5.1. Change detection map Image differencing was applied to NDVI images of the 1987 and 2006. Using the cumulative producer and user accuracy, high-end and low-end threshold values were detected for the difference image. A low-end threshold value of 11% decrease and a high-end threshold value of 41% increase in BVs were chosen for the
Fig. 2. Cumulative producer’s and user’s accuracy curve for NDVI differencing change detection map.
Fig. 3. Green space change detection map where the decreased areas are represented in red and the increased areas in green. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
optimum threshold values. The low-end threshold corresponded to the pixel values of 0.11841 and the high-end threshold corresponded to 0.2522 (Fig. 2). Using the optimum threshold values, the NDVI map was classified into tree classes as described in the previous section. The change detection map is depicted in Fig. 3 and the accuracy matrix is presented in Table 3. The result of the NDVI change detection revealed that during the 1987–2006, the urban green spaces have decreased signifi-
Table 2 Definition of landscape metrics used to analyze pattern of change. Landscape metrics
Abbreviation
Description
Compositional metrics Percent of landscape Largest patch index Number of patches Mean patch size Class area
PLAND LPI NP MPS CA
The proportion of total area occupied by a particular patch type; a measurement of dominance of patch types. The ratio of the area of the largest patch to the total area of the landscape. Total number of patches in the landscape or corresponding patches type. The area occupied by a particular patch type divided by the number of patches of corresponding type. Equal the sum of the areas (m2) of all the patches corresponding to a class divided by 10,000 to convert to hectares; that is equal the total area of that class in the landscape.
Configuration metric Area weighted mean shape index Landscape shape index
AWMSI LSI
Mean nearest neighbor distance
MNND
Mean shape index
MSI
Mean patch shape index weighted by relative patch size. The total length of patch edges within the landscape divided by the total area, adjusted by a constant for a square standard. Equal the distance (m) mean value over all patches to the nearest neighbor of corresponding patch, based on shortest edge to edge distance from cell center to cell center. A Patch-level shape index averaged over all patches in the landscape equal to the sum of the patches perimeter (m) divided by the square root of patches area (m2) for each patch of the corresponding patch type, divided by the number of patches of the same type.
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Table 3 Accuracy assessment of the change detection map produced through NDVI differencing method. Change map
No change Decrease Increase Reference totals Producer accuracy (%)
Reference data No change
Decrease
Increase
1236 34 27 1297 95.3
32 672 0 704 95.45
29 0 204 233 87.55
Classified totals
User accuracy (%)
1297 706 231 2234
95.3 95.18 88.31
Classified totals
User accuracy (%)
Overall accuracy assessment = 94.54% Overall kappa = 0.9012
Table 4 The Accuracy assessment of landscape map with maximum likelihood classifier. Classified map
Reference map Urban
Green space
Classified map of 1987 year Urban Green spaces Barren land Reference totals Producer accuracy (%)
660 2 109 771 85.60
2 56 373 2 1 411 376 469 99.20 87.63 Overall accuracy assessment = 89.35% Overall kappa = 0.8337
718 377 521 1616
91.92 98.93 78.88
Classified map of 2006 year Urban Green spaces Barren land Reference totals Producer accuracy (%)
563 3 19 585 96.23
18 41 590 0 0 489 608 530 97.04 92.26 Overall accuracy assessment = 95.29% Overall kappa = 0.9223
622 593 508 1723
90.51 99.49 96.26
cantly while in some areas they underwent some increase. During the years 1987–2006, around 34.37 km2 decrease was detected in the green spaces area and in the same period, an increase of around 3.57 km2 was spotted. Totally, a reduction of 30.8 km2 of green spaces was recorded for the area. A supervised classification with maximum likelihood classifier was implemented on the two images. Landscape map of Mashad city in 1987 and 2006 is shown in Fig. 4 and accuracy of the maps are presented in Table 4. As can be seen in Fig. 4, in 1987, large patches of the green spaces were well distributed over the city and occupied 45 km2 corresponding to 23% of the study area. In 2006, the urban areas increased significantly and became a dominant class occupying 147 km2 corresponding to 76% of the study area (Table 5). The expansion of the urban areas occupied a large area of barren land and green spaces. Land cover classification result in
Barren land
1987 was compared with that of the year 2006. The result of direct post-classification comparison showed that the most important reason for the decrease in urban green spaces was conversion to urbanized areas (Table 6) and it was so for the barren lands. These results revealed that in the study area, urbanization is the most important phenomenon that has affected the other land uses and consumed vacant areas. Also, it showed that part of the process entails conversion of green spaces to barren lands to give way to further development into urban areas. 5.2. Landscape metrics analysis 5.2.1. Synoptic characteristics of landscape A study of synoptic characteristics of landscape metrics over the study area provides general information on urban green spaces and
Fig. 4. The landscape map of Mashad city.
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Table 5 The analysis of area change in the Mashad urban area. Class
Urban Barren lands Green area
Area (km2)
Table 6 Green space conversion matrix from 1987 to 2006 (decrease). %Area
To urban (%)
1987
2006
1987
2006
74.16 74.42 45.79
147.42 31.96 15.00
38.15 38.27 23.56
75.84 16.44 7.72
other land uses. In 1987, there were 1462 green patches covering 45.80 km2 and in the year 2006, totally there were 1390 green patches covering 15.00 km2. Comparison of the 2 years showed there was totally a 30.8 km2 reduction in the green spaces.
From green spaces
To green spaces
71.37
To barren land (%) 28.63
From urban (%)
From barren land (%)
1.40
98.60
Decrease in the CA and NP and increase in the MNND of the green space class meant that a large number of patches of green spaces were totally vanished leading to increase of distances between the green space patches and also decrease of area and number of
Fig. 5. West to East gradient graphs of the selected metrics.
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Table 7 The value of each landscape metric in the study area. Year/metrics Green spaces class 1987 2006 Urban class 1987 2006
CA (km2)
PLAND%
NP
45.80 15.00
23.56 7.72
1462 1390
1 0.52
39.45 37.86
3.13 1.07
4.40 2.80
97.17 124.84
74.16 147.42
38.15 75.74
621 457
12.24 24.81
31.78 37.41
11.94 32.25
19.05 27.22
81.75 71.63
patches over the years 1987–2006. Comparing two indices of LPI and MPS indicated a significant decrease which meant the remaining patches of green spaces in the 2006 are smaller than those in the 1987. However, comparing the shape indices of the 2 years revealed that the AWMSI and LSI decreased from 1987 to 2006. This meant the complexity of the green spaces in the landscape decreased in the study period. Referring to Fig. 4, from 1987 to 2006 a large amount of green space area and barren lands has been converted to the urban class leading to a decrease in the landscape shape complexity and simplification of landscape over much of the study area. For the urban class, the CA increased from 74.16 km2 in the 1987 to 147.42 km2 in the 2006. Conversely, NP decreased from 621 in 1987 to 457 in 2006. MNND for the urban class decreased from 1987 to 2006. These findings along with the fact that PLAND metric for urban class increased from 38% to 75% suggest that the urban sprawl process is occurring strongly and dominates the study area and the city is becoming more compact. The increase in the score of LPI and MSP indices reinforces this conclusion. The change in the synoptic characteristics revealed that since 1987, a large amount of landscape has changed and simplified because of its conversion to the densely built-up area (Table 7). 5.2.2. Gradient analysis of the study area using landscape metrics The west–east and north–south transects showed similar patterns in terms of the gradient analysis, so in this section we present only the results pertaining to the west–east transect. Gradient analyzes of the landscape level metrics of green space patches are shown in Fig. 5. NP metric decreased significantly along much of the transect length but also a decrease has happened in the west area from the center of city. As can be seen in Fig. 4, most of the urban sprawl has occurred in the west of the city and decrease of this metric implies the relationship between dynamics of this variation and the urbanization. Referring to the MPS and LPI gradient graphs, the above judgment is confirmed and indicates that most of the changes have occurred around 8 km from the center of the city. Decrease in MPS and LPI scores between 6 and 10 km towards west and the other 4 km towards east from the city center indicated that the green spaces at this distance were modified and became smaller in comparison with other distances. As shown in Fig. 5d, the LSI metric decreased from 1987 to 2006 and there are two peaks. LSI metric showed complexity of the landscape and its decrease means a reduction of the complexity of the landscape. As can be seen in Fig. 4, from the year 1987, urban land use dominates the landscape and in 2006, the landscape complexity reduces because of urbanization and conversion of the landscape into a single land use. Most of this conversion has occurred 2–6 km towards west and around 2 km towards east from the center. The gradient analysis of the study area for green spaces proved very helpful in giving the decision makers norms of qualitative and quantitative changes in the green spaces of the city. Using this information, the influential figures and organizations can take correct measures for reversing the downgrading trend in the green spaces by not only quantitative measures but also
LPI%
LSI
MPS (ha)
AWMSI
MNND (m)
qualitative restoration attempts. This information was not available when only post-classification comparison was used. 6. Conclusion Green space areas in the densely populated cities of today are valued more than before while at the same time are suffering shrinkage due to pressures for more open lands for housing development. The results of this study revealed that green space areas in the Mashad city during the years 1987–2006 have become isolated and decreased. In this study, we integrated postclassification comparison and landscape metrics analyzes to detect changes in the quantity and quality of the green spaces and the corresponding modifications in the urban patches in the Mashad city. Post-classification is a useful and easy to implement procedure, however, errors in classification may occur that decrease the accuracy of the results by a power of the error in the separate classified maps. The result of the post-classification comparison can be summarized in useful tables and also change maps can be easily produced. In this research, we were able to produce the comparison table and the corresponding map which were very helpful in interpreting the results and locating the changed areas in the city. We also used landscape metrics to measure some of the aspects of green space quantity and quality and their changes through time. This was also very much informing in terms of the modifications brought about to the green space patches by the developing city, where we were able to make judgments as to the changes in the composition and configuration of the green space patches. Patch metrics in terms of composition and configuration have been shown to have many effects on the functions of green spaces composed of trees and lawns. The analysis of the change in the landscape patterns provided insight into the nature of the changes that had taken place in the Mashad city. We conclude that urbanization in the Mashad city has had important effects upon the urban environment through which green spaces have been converted into built-up areas with corresponding loss of functions of the green areas. Players with some roles in shaping the city and its expansion are now reaching some consensus as to the balance between built-up and natural areas. This is especially backed by the ongoing land use planning project in the Province which together with the activities taken by NGO’s and the Foundation of Holy Imam Reza Shrine contributes to development of a new trend in protecting green areas. In summer time, the population of the city doubles by the pilgrims who are happy to have even a single tree to set up their tent and stay in the city. With the rapid downgrading quality and quantity of the green spaces in the Mashad city, managers are required to take a timely measure to reverse the trend of changes that were partially shown in this study. Otherwise, soon we will face a totally artificial and unpleasing urban environment with lost functions and services of the green areas. This study can be improved with more investigation on deriving important factors which have caused the changes in urban green
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land use/land cover in the Mashad city and also detailed effects of the changes on values of the green space areas. Acknowledgments We would like to offer our great thanks to the editor and two anonymous reviewers for their useful and constructive comments that improved the first version of the manuscript. We appreciate help from the part of Akram Hossiennia, S. Reza Asvad, Saeedeh Moradi, Abbas Zamani and S. Shahram Naghibzadeh and are grateful for their cooperation with us during this research. And here, we express our special thanks to Mr. Sardarzehi for his help and our colleagues in the faculty of Marine Sciences of Chabahar Maritime University. References Alberti, M., Waddell, P., 2000. An integrated urban development and ecological simulation model. Integrated Assessment 1, 215–227. Bastian, O., 2000. Landscape classification in Saxony (Germany) a tool for holistic regional planning. Landscape and Urban Planning 50, 145–155. Cakir, H.I., Khorram, S., Nelson, S.A.C., 2006. Correspondence analyses for detecting land cover change. Remote Sensing of Environment 102, 306–317. Coppin, P.L., Bauer, M.E., 1996. Change detection in forest ecosystems with remote sensing digital imagery. Remote Sensing Reviews 13, 207–234. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., Lambin, E., 2004. Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing 25 (9), 1565–1596. Daryaei, J., 2003. Digital Change Detection Using Multi-scale Wavelet Transformation & Neural Network. M.Sc. Thesis. Submitted to ITC. Netherland. Forman, R.T.T., Godron, M., 1986. Landscape Ecology. John Wiley & Sons, New York, New York, USA. Hall, F.G., Stiebel, D.E., Nickson, J.E., Goetz, S.J., 1991. Radiometric rectification: towards a common radiometric response among multitude, multisensor images. Remote Sensing of Environment 35 (1), 11–27. Herold, M., Couclelis, H., Clarke, K.C., 2005. The role of spatial metrics in the analysis and modeling of urban land use change. Computers, Environment and Urban Systems 29 (2005), 369–399. Jensen, J.R., 1996. Introductory Digital Image Processing: A Remote Sensing Perspective, second edition. Prentice Hall, p. 316. Imbernon, J., Branthomme, A., 2001. Characterization of landscape patterns of deforestation in tropical rain forest. International Journal of Remote Sensing 22 (9), 1753–1765. Li, C.H., Yang, Z.F., 2004. Spatio-temporal changes of NDVI and their relations with precipitation and Run off in the Yellow River Basin. Geographical Research 23, 753–759. Lin, Y.P., Lin, Y.B., Wang, Y.T., Hong, N.M., 2008. Monitoring and prediction land-use changes and the hydrology of urbanized Paochiao Watershed in Taiwan using remote sensing data urban growth models and a hydrological model. Sensors 8, 680–685. Lu, D., Mausel, P., Brondizio, E., Moran, E., 2004. Change detection techniques. International Journal of Remote Sensing 25 (12), 2365–2407.
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Contents lists available at ScienceDirect
International Journal of Applied Earth Observation and Geoinformation journal homepage: www.elsevier.com/locate/jag
Assessment of changes in urban green spaces of Mashad city using satellite data Reza Rafiee a,*, Abdolrassoul Salman Mahiny b, Nematolah Khorasani c a
Department of Fishery, Faculty of Marine Science, Chabahar Maritime University, Chabahar, Iran Department of Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran c Department of Environmental Sciences, Faculty of Natural Resources, University of Tehran, Karaj, Iran b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 26 October 2008 Received in revised form 22 May 2009 Accepted 20 August 2009
Green spaces play important functions in urban environments. Reducing air pollution, providing shade and habitat for arboreal birds, producing oxygen, providing shelter against winds, recreational and aesthetic qualities and architectural applications are the main functions of urban green spaces. With the rapid change of urban area in Mashad city during the past decades, green spaces have been fragmented and dispersed causing impairment and dysfunction of these important urban elements. The objective of this study was to detect changes in extent and pattern of green areas of Mashad city and to analyze the results in terms of landscape ecology principles and functioning of the green spaces. In this research, we classified a Landsat TM and an IRS LISS-III image belonging to the years 1987 and 2006, respectively. We then used a post-classification comparison to determine the changes in green space areas of Mashad city during the 19 years covered by the images. Then, we applied landscape ecology calculations to derive metrics that quantified pattern of the changes in the green areas. The results showed that during 19 years from 1987, a significant decrease had occurred in the extent of urban green spaces with a concomitant fragmentation resulting in downgrading and destruction of the functions and services these areas provide. We conclude that the general quality of life in the central parts of the city has been diminished. We also state that a combination of remote sensing image classification, landscape metrics assessment and vegetation indices can provide a tool for assessing life quality and its trend for urban areas. ß 2009 Elsevier B.V. All rights reserved.
Keywords: Urban green space Change detection Landscape metrics Post-classification comparison Mashad city
1. Introduction Urbanization both in population and spatial extent, transforms the landscape from the natural cover types to impervious urban lands (Xian et al., 2005). This phenomenon is one of the most important factors that changes land surface leading to modification of receiving environments which are usually composed of natural cover. The pressure for additional housing and business demands in towns and urban areas alters existing urban green spaces even more in the route to development (The World Resources Institute, 1996). Urban green spaces provide a variety of functions which can be grouped in three classes including architectural application and aesthetics, climatic and engineering functions (Miller, 1997). Also, urban green spaces provide the opportunity for recreation and experiencing nature. These functions are essential for improving the quality of citizen life. Therefore, allocation of urban land to green spaces as a class of land use is an important policy issue in almost all cities. However, due to physical expansion of Mashad
* Corresponding author. Tel.: +98 545 2224264; fax: +98 545 2221025. E-mail address: rzarafi[email protected] (R. Rafiee). 0303-2434/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jag.2009.08.005
city, extensive destruction of green spaces has occurred that conflicts with an environmentally sound developing paradigm. Until now, little research has been carried out to explore the change in the green spaces and other urban land use/land cover types in the study area. The purpose of this study is to assess changes in the green spaces of Mashad city and to monitor the trend of changes through a change detection procedure. The information on these changes is of great use to land managers and policy makers in order to make informed decisions that effectively balance the positive aspect of development and its negative impact on the receiving environment. The information will also help to preserve the urban green space resources and enhance urban life quality. As humans arbitrarily modify the environment, the ultimate juxtaposition and configuration of the landscape elements are mostly determined by people inhabiting the landscape. Hence, spatial pattern analysis can reveal the influence of humans on land use and provides necessary information for guiding these changes in an environmentally wise manner. As such, to achieve a comprehensive assessment of changes, landscape metrics have also been applied in the present study. These metrics make quantification and interpretation of the spatial pattern available to land use and land cover evaluators (McGarigal et al., 2002). The objective of this study are: (1) generation of a change map of green spaces in the extent of Mashad city using the NDVI image
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differencing, which makes detection of subtle changes possible and helps in discerning ‘‘from-to’’ changes which is achieved using a post-classification comparison. In this way, it is possible to provide a spatial dynamic and change detection of green spaces in the Mashad city (2) to enable a comprehensive analysis of change of green spaces through application of a set of landscape metrics. 1.1. Land use/land cover change detection using satellite data Satellite remote sensing provides an important source of land use/land cover data and can be utilized to monitor the changes in these data efficiently. Remote sensing change detection is a process used to identify difference of state of objects or phenomena on images observed at different times (Singh, 1989). This technique has been applied widely for the study of environmental changes (Mass, 1999; Li and Yang, 2004; Salman Mahiny, 2004; Yuan et al., 2005). Until now, an array of digital algorithms has been developed to detect land cover changes from remote sensing data (Jensen, 1996; Coppin and Bauer, 1996; Lu et al., 2004). However, despite the wide range of these methods, they can basically be summarized in two broad categories, those that detect change and then assign classes (pre-classification) and those that first assigns classes and then detect change such as post-classification comparison (Van Oort, 2007). In the pre-classification methods, the images of two dates are transformed into a new single image which contains the spectral change of the surface. The most important issue in these methods is to distinguish change and no change pixels. A common method for this is to use the statistical threshold (Yuan et al., 1999). While the pre-classification methods are able to detect areas of changes (Van Oort, 2007) they do not provide a confusion matrix indicating their nature, which on the other hand is delivered by the post-classification approaches (Jensen, 1996). Through application of this technique, a detail matrix with ‘‘fromto’’ information on changes is generated. Also, post-classification comparison removes the need to atmospheric correction in the two images. However, accuracy of this technique is dependent on the accuracy of each classified map and the poor results of classification lead to generation of uncertainty in the change map (Daryaei, 2003). In addition, this method works well in large homogeneous areas with major changes but not in highly heterogeneous regions and small changes (Yang and Lo, 2002). 1.2. Application of landscape metrics to analyze land use/land cover change Spatial characteristics such as location, direction and distance are the bases to analyze the spatial structure of landscape. Spatial metrics which are also known as landscape metrics are commonly used to quantify the spatial structure including pattern and shape of the focused elements in the environment. There has been an increasing interest in application of the landscape metrics to analyze the urban environment (e.g. Alberti and Waddell, 2000; Herold et al., 2005; Tang et al., 2008; Lin et al., 2008). These metrics have been developed in the late 1980s based on information theory and fractal geometry in the categorical, patch based representation landscape (Herold et al., 2005). As investigated by Parker et al. (2001), landscape metrics capture inherent spatial structure of the environment, and are used to enhance interpretation of spatial pattern and characteristics of the landscape. The spatial pattern of the land use reflects underlying human processes that influence the urban environment (Bastian, 2000). Until now, a great number of the landscape metrics has been formulated to quantify the landscape (Forman and Godron, 1986; Romme, 1982; O’Neill et al., 1988; Stauffer and Aharony, 1992; Imbernon and Branthomme, 2001). The landscape metrics fall into two general categories, those that quantify the composition of landscape and those that quantify
the spatial configuration of the landscape (McGarigal and Marks, 1995). Composition indices, such as number of patches, patch density and patch size relate to the non-spatial feature of the landscape associated with the variety and abundance of patch types. Configuration metrics, on the other hand, explicitly interpret the spatial structure of landscape such as neighborhood relationships, alignment of borders and contagion. These two groups reflect two aspects of landscape pattern and complete each other for a thorough interpretation of changes in the landscape (Tang et al., 2008). However, most of the metrics are correlated that can lead to redundant information and making the interpretation difficult. Hence, choosing suitable metrics is a first step towards useful interpretation of the landscape and depends largely on the objective of research (Turner et al., 2001). 2. Study area Mashad is the capital of Khorasan Razavi Province of Iran (Fig. 1). It is one of the most important cities because of its religious, historical and economic values that attract a large number of people each year. In 1986, its population was 668,000 whereas its current population is about 2.4 millions. Since 1987, built-up areas in the city have expanded significantly (Rafiee, 2007); the city has witnessed a rapid growth in construction which has caused destruction of green spaces areas. This trend in the green spaces is in sharp contrast with the rules governing improvement and establishment of new green spaces within the current boundary and the projected future of the city. In fact, municipality closely attends to the green areas and scrutinizes even single tree uprooting. On the other hand, there are nongovernmental organizations and the general public who watch the trend carefully and exert controlling effects on green space removal. In addition, the provinces of Iran are all under extensive land use evaluation and planning, the results of which will be available in near future. The application is mostly environmentally oriented giving high value to green spaces and aims to upgrade the per capita green areas in the newly built regions. However, there are other players in the filed including major private stakeholders who have influence in deciding the physical and biological properties of built-up area development plans. The end result of confrontation between these groups with conflicting objectives is normally compensatory and in recent years, the municipality has been forced to replace any removed green space area as a necessity when developing housing complexes. 3. Data and preprocessing A set of cloud-free imagery of Landsat TM belonging to the year 1987 and IRS LISS-III dated 2006 were acquired whose scenes covered around 220 km2 including Mashad city and its outskirts. The images were acquired in the summer months which represent same vegetation condition of green areas in the city (Table 1). The IRS image was registered to a topographic map of Mashad city corresponding UTM projection at 30-m resolution. The 1987 image was then co-registered to the IRS image using 13 GCPs by scene-to-scene resampling corresponding to UTM projection and 30-m resolution for which the total RMSE of 0.21 pixels was achieved. The correction was conducted using the first order polynomial model and the nearest neighborhood method for resampling. Without radiometric calibration of multi-temporal dataset, false changes can occur in the classified maps. Therefore, for quantitative analysis based on radiometric information such as NDVI differencing, the images should be corrected radiometrically to compensate for radiometric divergence (Mass, 1999). Due to a lack of information for absolute radiometric correction of the
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Fig. 1. Location of Khorasan Razavi Province which is in the north east of Iran (left) and Mashad city extent (right).
remote sensing data, we performed a relative radiometric correction using dark and bright pixel control set (Hall et al., 1991). The correction was performed on each of the red and near infra red (NIR) bands of IRS image with the reference to TM scenes. Afterwards, using on-screen interpretation and digitization, a vector layer was created to extract the boundary of the study area from all images which included Mashad city and its environs. We applied a supervised classification to TM and IRS images to map an aggregate land cover of the study area. Based on our target in this research, an aggregate level of map was deemed sufficient; hence the images were classified into three classes of urban, barren land and green spaces. A false color composite image for each date was created to help visualize the various land cover and land use of the area. Then, by visual interpretation of the false color composite images and by reference to a 1:25,000 topographic map of the year 1994 training data were selected for each class. Selected pixels were randomized to minimize autocorrelation. Afterwards, through unsupervised classification of the image of the years 2006 and 1987 into 100 classes and crossing them with the original training pixels, sampled pixels were purified (Salman Mahiny, 2004). Purification in this context meant removal of those pixels which were a combination of different land uses and land covers mistakenly selected for different categories. Without the purification, classification accuracy is normally lower. After purification of training data, we used maximum likelihood classifier to classify the
Table 1 Description of the satellite imagery used in this study. Characteristics/sensor
TM
LISS-III
Image size Pixel size Number of bands Path and raw used in this Research Date of acquisition
185 km 185 km 28.5 m 7 P: 159; R: 35 1987/09/04
140 km 140 km 23.5 m 4 P: 76; R: 45 2006/07/07
raw imagery for each date. Then, we chose another set of training samples to assess the accuracy of each classified map. The detailed results of accuracy assessment are depicted in Table 4. 4. Methodology 4.1. NDVI differencing and post-classification Remote sensing data can provide information on vegetation structure and amount of biomass and leaf area as vegetation cover can be estimated through various indices such as Normalized Difference Vegetation Index (NDVI) (Purevdorj et al., 1998). NDVI image for each date was calculated separately and the results were subtracted. In the resultant image, zero values indicated no change areas and the changed areas between the two dates had a negative or positive value, with the negative values representing decrease in vegetation and positive values indicating increase in vegetation. However, slight changes in the brightness values between the two dates occurred due to noises even after radiometric normalization and thus it was necessary to develop a set of threshold values to discriminate the real changes (Cakir et al., 2006). To find the optimum high-end and low-end thresholds we used the accuracy assessment curve. Accuracy assessment curve provides a graphic and quantitative representation of the relationship between the threshold levels used to classify the change areas and different accuracy assessment figures (Morisette and Khorram, 2000). In this research, thresholds at low-end and high-end were set independently based on cumulative producer’s and user’s accuracy. First, based on NDVI differencing image histogram, threshold values were set up equidistant to the mean of Brightness Values (BVs) of NDVI differencing image in each tail. Then, NDVI differencing image values were classified into three classes entitled: (1) vegetation decrease if a pixel value was lower than the low-end threshold, (2) vegetation increase if a pixel value was higher than the high-end threshold and (3) no
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change class values between the two thresholds. Then, producer and user accuracy of both change classes were extracted from the error matrix and the optimum threshold was determined based on the resultant curve. Post-classification, obviously involves comparison of independently classified maps of each date of interest followed by a pixelby-pixel or segment comparison to detect change area in the cover types (Coppin et al., 2004). We applied post-classification comparison to produce a detail matrix of change in the Mashad city and determine those land use classes that the green spaces class has been changed into. The method provided a quantitative map of land use/land cover change. 4.2. Analysis of urban green space change pattern using landscape metrics An attempt was made to assess the fragmentation and landscape pattern in the study area. In doing so, several landscape metrics were calculated using FRAGSTATS 3.3 software (McGarigal et al., 2002). As investigated by O’Neill et al. (1988), because there are correlation and overlap between landscape metrics, it is not necessary to calculate all landscape metrics. Hence, in order to reduce redundancy, we tried to choose a small set of metrics which were sensitive to landscape change including composition and configuration metrics. In the present study, the chosen metrics included: class area (CA), percent of landscape (PLAND), number of patches (NP), mean patch size (MPS), area weighted mean shape index (AWMSI), mean nearest neighbor distance (MNND) and largest patch index (LPI). Landscape metrics that were used in the present study are described in Table 2. Also, to derive the location of most significant changes and their pattern in the city, two transects were considered from north to south and the west to east. The center of transects were located in the center of city in the year 1987. The west–east and north-east transects consisted of nine and six, 2 km 2 km zones, respectively. 5. Results and discussion 5.1. Change detection map Image differencing was applied to NDVI images of the 1987 and 2006. Using the cumulative producer and user accuracy, high-end and low-end threshold values were detected for the difference image. A low-end threshold value of 11% decrease and a high-end threshold value of 41% increase in BVs were chosen for the
Fig. 2. Cumulative producer’s and user’s accuracy curve for NDVI differencing change detection map.
Fig. 3. Green space change detection map where the decreased areas are represented in red and the increased areas in green. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
optimum threshold values. The low-end threshold corresponded to the pixel values of 0.11841 and the high-end threshold corresponded to 0.2522 (Fig. 2). Using the optimum threshold values, the NDVI map was classified into tree classes as described in the previous section. The change detection map is depicted in Fig. 3 and the accuracy matrix is presented in Table 3. The result of the NDVI change detection revealed that during the 1987–2006, the urban green spaces have decreased signifi-
Table 2 Definition of landscape metrics used to analyze pattern of change. Landscape metrics
Abbreviation
Description
Compositional metrics Percent of landscape Largest patch index Number of patches Mean patch size Class area
PLAND LPI NP MPS CA
The proportion of total area occupied by a particular patch type; a measurement of dominance of patch types. The ratio of the area of the largest patch to the total area of the landscape. Total number of patches in the landscape or corresponding patches type. The area occupied by a particular patch type divided by the number of patches of corresponding type. Equal the sum of the areas (m2) of all the patches corresponding to a class divided by 10,000 to convert to hectares; that is equal the total area of that class in the landscape.
Configuration metric Area weighted mean shape index Landscape shape index
AWMSI LSI
Mean nearest neighbor distance
MNND
Mean shape index
MSI
Mean patch shape index weighted by relative patch size. The total length of patch edges within the landscape divided by the total area, adjusted by a constant for a square standard. Equal the distance (m) mean value over all patches to the nearest neighbor of corresponding patch, based on shortest edge to edge distance from cell center to cell center. A Patch-level shape index averaged over all patches in the landscape equal to the sum of the patches perimeter (m) divided by the square root of patches area (m2) for each patch of the corresponding patch type, divided by the number of patches of the same type.
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Table 3 Accuracy assessment of the change detection map produced through NDVI differencing method. Change map
No change Decrease Increase Reference totals Producer accuracy (%)
Reference data No change
Decrease
Increase
1236 34 27 1297 95.3
32 672 0 704 95.45
29 0 204 233 87.55
Classified totals
User accuracy (%)
1297 706 231 2234
95.3 95.18 88.31
Classified totals
User accuracy (%)
Overall accuracy assessment = 94.54% Overall kappa = 0.9012
Table 4 The Accuracy assessment of landscape map with maximum likelihood classifier. Classified map
Reference map Urban
Green space
Classified map of 1987 year Urban Green spaces Barren land Reference totals Producer accuracy (%)
660 2 109 771 85.60
2 56 373 2 1 411 376 469 99.20 87.63 Overall accuracy assessment = 89.35% Overall kappa = 0.8337
718 377 521 1616
91.92 98.93 78.88
Classified map of 2006 year Urban Green spaces Barren land Reference totals Producer accuracy (%)
563 3 19 585 96.23
18 41 590 0 0 489 608 530 97.04 92.26 Overall accuracy assessment = 95.29% Overall kappa = 0.9223
622 593 508 1723
90.51 99.49 96.26
cantly while in some areas they underwent some increase. During the years 1987–2006, around 34.37 km2 decrease was detected in the green spaces area and in the same period, an increase of around 3.57 km2 was spotted. Totally, a reduction of 30.8 km2 of green spaces was recorded for the area. A supervised classification with maximum likelihood classifier was implemented on the two images. Landscape map of Mashad city in 1987 and 2006 is shown in Fig. 4 and accuracy of the maps are presented in Table 4. As can be seen in Fig. 4, in 1987, large patches of the green spaces were well distributed over the city and occupied 45 km2 corresponding to 23% of the study area. In 2006, the urban areas increased significantly and became a dominant class occupying 147 km2 corresponding to 76% of the study area (Table 5). The expansion of the urban areas occupied a large area of barren land and green spaces. Land cover classification result in
Barren land
1987 was compared with that of the year 2006. The result of direct post-classification comparison showed that the most important reason for the decrease in urban green spaces was conversion to urbanized areas (Table 6) and it was so for the barren lands. These results revealed that in the study area, urbanization is the most important phenomenon that has affected the other land uses and consumed vacant areas. Also, it showed that part of the process entails conversion of green spaces to barren lands to give way to further development into urban areas. 5.2. Landscape metrics analysis 5.2.1. Synoptic characteristics of landscape A study of synoptic characteristics of landscape metrics over the study area provides general information on urban green spaces and
Fig. 4. The landscape map of Mashad city.
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Table 5 The analysis of area change in the Mashad urban area. Class
Urban Barren lands Green area
Area (km2)
Table 6 Green space conversion matrix from 1987 to 2006 (decrease). %Area
To urban (%)
1987
2006
1987
2006
74.16 74.42 45.79
147.42 31.96 15.00
38.15 38.27 23.56
75.84 16.44 7.72
other land uses. In 1987, there were 1462 green patches covering 45.80 km2 and in the year 2006, totally there were 1390 green patches covering 15.00 km2. Comparison of the 2 years showed there was totally a 30.8 km2 reduction in the green spaces.
From green spaces
To green spaces
71.37
To barren land (%) 28.63
From urban (%)
From barren land (%)
1.40
98.60
Decrease in the CA and NP and increase in the MNND of the green space class meant that a large number of patches of green spaces were totally vanished leading to increase of distances between the green space patches and also decrease of area and number of
Fig. 5. West to East gradient graphs of the selected metrics.
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Table 7 The value of each landscape metric in the study area. Year/metrics Green spaces class 1987 2006 Urban class 1987 2006
CA (km2)
PLAND%
NP
45.80 15.00
23.56 7.72
1462 1390
1 0.52
39.45 37.86
3.13 1.07
4.40 2.80
97.17 124.84
74.16 147.42
38.15 75.74
621 457
12.24 24.81
31.78 37.41
11.94 32.25
19.05 27.22
81.75 71.63
patches over the years 1987–2006. Comparing two indices of LPI and MPS indicated a significant decrease which meant the remaining patches of green spaces in the 2006 are smaller than those in the 1987. However, comparing the shape indices of the 2 years revealed that the AWMSI and LSI decreased from 1987 to 2006. This meant the complexity of the green spaces in the landscape decreased in the study period. Referring to Fig. 4, from 1987 to 2006 a large amount of green space area and barren lands has been converted to the urban class leading to a decrease in the landscape shape complexity and simplification of landscape over much of the study area. For the urban class, the CA increased from 74.16 km2 in the 1987 to 147.42 km2 in the 2006. Conversely, NP decreased from 621 in 1987 to 457 in 2006. MNND for the urban class decreased from 1987 to 2006. These findings along with the fact that PLAND metric for urban class increased from 38% to 75% suggest that the urban sprawl process is occurring strongly and dominates the study area and the city is becoming more compact. The increase in the score of LPI and MSP indices reinforces this conclusion. The change in the synoptic characteristics revealed that since 1987, a large amount of landscape has changed and simplified because of its conversion to the densely built-up area (Table 7). 5.2.2. Gradient analysis of the study area using landscape metrics The west–east and north–south transects showed similar patterns in terms of the gradient analysis, so in this section we present only the results pertaining to the west–east transect. Gradient analyzes of the landscape level metrics of green space patches are shown in Fig. 5. NP metric decreased significantly along much of the transect length but also a decrease has happened in the west area from the center of city. As can be seen in Fig. 4, most of the urban sprawl has occurred in the west of the city and decrease of this metric implies the relationship between dynamics of this variation and the urbanization. Referring to the MPS and LPI gradient graphs, the above judgment is confirmed and indicates that most of the changes have occurred around 8 km from the center of the city. Decrease in MPS and LPI scores between 6 and 10 km towards west and the other 4 km towards east from the city center indicated that the green spaces at this distance were modified and became smaller in comparison with other distances. As shown in Fig. 5d, the LSI metric decreased from 1987 to 2006 and there are two peaks. LSI metric showed complexity of the landscape and its decrease means a reduction of the complexity of the landscape. As can be seen in Fig. 4, from the year 1987, urban land use dominates the landscape and in 2006, the landscape complexity reduces because of urbanization and conversion of the landscape into a single land use. Most of this conversion has occurred 2–6 km towards west and around 2 km towards east from the center. The gradient analysis of the study area for green spaces proved very helpful in giving the decision makers norms of qualitative and quantitative changes in the green spaces of the city. Using this information, the influential figures and organizations can take correct measures for reversing the downgrading trend in the green spaces by not only quantitative measures but also
LPI%
LSI
MPS (ha)
AWMSI
MNND (m)
qualitative restoration attempts. This information was not available when only post-classification comparison was used. 6. Conclusion Green space areas in the densely populated cities of today are valued more than before while at the same time are suffering shrinkage due to pressures for more open lands for housing development. The results of this study revealed that green space areas in the Mashad city during the years 1987–2006 have become isolated and decreased. In this study, we integrated postclassification comparison and landscape metrics analyzes to detect changes in the quantity and quality of the green spaces and the corresponding modifications in the urban patches in the Mashad city. Post-classification is a useful and easy to implement procedure, however, errors in classification may occur that decrease the accuracy of the results by a power of the error in the separate classified maps. The result of the post-classification comparison can be summarized in useful tables and also change maps can be easily produced. In this research, we were able to produce the comparison table and the corresponding map which were very helpful in interpreting the results and locating the changed areas in the city. We also used landscape metrics to measure some of the aspects of green space quantity and quality and their changes through time. This was also very much informing in terms of the modifications brought about to the green space patches by the developing city, where we were able to make judgments as to the changes in the composition and configuration of the green space patches. Patch metrics in terms of composition and configuration have been shown to have many effects on the functions of green spaces composed of trees and lawns. The analysis of the change in the landscape patterns provided insight into the nature of the changes that had taken place in the Mashad city. We conclude that urbanization in the Mashad city has had important effects upon the urban environment through which green spaces have been converted into built-up areas with corresponding loss of functions of the green areas. Players with some roles in shaping the city and its expansion are now reaching some consensus as to the balance between built-up and natural areas. This is especially backed by the ongoing land use planning project in the Province which together with the activities taken by NGO’s and the Foundation of Holy Imam Reza Shrine contributes to development of a new trend in protecting green areas. In summer time, the population of the city doubles by the pilgrims who are happy to have even a single tree to set up their tent and stay in the city. With the rapid downgrading quality and quantity of the green spaces in the Mashad city, managers are required to take a timely measure to reverse the trend of changes that were partially shown in this study. Otherwise, soon we will face a totally artificial and unpleasing urban environment with lost functions and services of the green areas. This study can be improved with more investigation on deriving important factors which have caused the changes in urban green
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