International Journal of Remote Sensing
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An integrated soft and hard classification approach for evaluating urban expansion from multisource remote sensing data: a case study of the Beijing–Tianjin–Tangshan metropolitan region, China Shisong Cao, Deyong Hu, Zhuowei Hu, Wenji Zhao, You Mo & Kun Qiao To cite this article: Shisong Cao, Deyong Hu, Zhuowei Hu, Wenji Zhao, You Mo & Kun Qiao (2018) An integrated soft and hard classification approach for evaluating urban expansion from multisource remote sensing data: a case study of the Beijing–Tianjin–Tangshan metropolitan region, China, International Journal of Remote Sensing, 39:11, 3556-3579, DOI: 10.1080/01431161.2018.1444291 To link to this article: https://doi.org/10.1080/01431161.2018.1444291
Published online: 28 Feb 2018.
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INTERNATIONAL JOURNAL OF REMOTE SENSING, 2018 VOL. 39, NO. 11, 3556–3579 https://doi.org/10.1080/01431161.2018.1444291
An integrated soft and hard classification approach for evaluating urban expansion from multisource remote sensing data: a case study of the Beijing–Tianjin–Tangshan metropolitan region, China Shisong Cao
a
, Deyong Hua, Zhuowei Hua, Wenji Zhaoa, You Moa and Kun Qiaob
a College of Resource Environment and Tourism, Capital Normal University, Beijing, China; bInstitute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, China
ARTICLE HISTORY
ABSTRACT
Integrating soft and hard classification to monitor urban expansion can effectively provide comprehensive urban growth information to urban planners. In this study, both the impervious surface coverage (as a soft classification result) and land cover (as a hard classification result) in the Beijing–Tianjin–Tangshan metropolitan region (BTTMR), China, were extracted from multisource remote sensing data from 1990 to 2015. Then, we evaluated urban expansion based on centre migration, standard deviation ellipse, and spatial autocorrelation metrics. Furthermore, the differences between the soft and hard classification results were analysed at the landscape scale. The results showed that (1) the impervious surface area increased considerably over the past 25 years. Notably, the areas of urban built-up land and industrial production land increased rapidly, while those of ecological land and agricultural production land seriously decreased. (2) The distribution of impervious surfaces was closely related to the regional economic development plan of ‘One Axis, Two Wing, and Multi-Node’ in the BTTMR. (3) The contributions of different land use types to impervious surface growth ranked from high to low as follows: urban built-up land, rural residential land, industrial production land, agricultural production land, and ecological land. (4) The landscape metrics varied considerably based on the hard and soft classification results and were sensitive to different factors.
Received 30 June 2017 Accepted 7 February 2018
1. Introduction Over the past 30 years, urbanization and industrialization have become global phenomena (United Nations 2012; Taubenböck et al. 2009). As Chinese State Council (2014) points out, ‘142 cities with population levels of 1 million, six cities with population levels of 10 million, existed in China in 2014.’ China experienced rapid urbanization and industrialization during the past decades, and these processes are expected to continue in the 21st century. As a result, many natural surfaces have been replaced by impervious surfaces such as roads,
CONTACT Zhuowei Hu University, Beijing, China
[email protected]
© 2018 Informa UK Limited, trading as Taylor & Francis Group
College of Resource Environment and Tourism, Capital Normal
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parks, and buildings (Peng et al. 2016; Peng 2008; Sun 2013). Additionally, the urbanization process has contributed to land use/land cover (LULC) changes, and many plantation and ecological lands have been converted to urban built-up land. Depending on their definition of urban built-up land, previous researchers estimated that the global built-up area accounts for about 1–3% of the world’s total land surface (Liu et al. 2014). Overall, the rapid expansion of urban built-up land and the growth of impervious surface areas significantly influence regional environments and urban radiation energy budgets. Numerous studies of urban expansion have been performed (e.g. Herold, Goldstein, and Clarke 2003; Sun, Zhao, and Qu 2015; He et al. 2008; Xiao et al. 2006). Remote sensing data with improved spatial accuracy and free availability coupled with a geographical information system (GIS) can be used to accurately quantify the patterns and variability of urban expansion (Epstein, Payne, and Kramer 2002). Previous studies reported that several remote sensing classification methods, including supervised classification (e.g. Bhatta 2009; Mundia and Murayama 2010; Tewolde and Cabral 2011; Sharma, Pandey, and Nathawat 2012), unsupervised classification (e.g. Schneider and Woodcock 2008), image differencing (e.g. Lu et al. 2010), and normalized difference vegetation index (e.g. Banzhaf, Grescho, and Kindler 2009; Soffianian et al. 2010) can be effectively applied to monitor spatial-temporal patterns and rates of urban expansion. Ground surfaces constituting individual pixels of remote sensing data often consist of more than one land cover type. Thus, methods of remote sensing data classification can be divided into two different categories: hard classification and soft classification (Karnieli 1996). Hard classification methods perform well with pure pixels, which often occur at the transition of different land cover types, but these methods fail in the regions of mixed pixels; in contrast, soft classification methods can give good results for mixed pixels but fail in the regions of pure pixels. Both classification types have been widely applied in urban expansion research. From a soft classification perspective, most researchers have analysed urban expansion based on the impervious surface coverage. For example, Xiao et al. (2007) and Fan (2014) retrieved the spatial distribution of impervious surfaces in Beijing based on Classification and Regression Tree (CART) and Vegetation-Impervious-Soil model (V-I-S), respectively, and found that the impervious surface coverage gradually decreased from the city centre to the suburbs, but the rate of expansion increased over time. Hao et al. (2016) extracted the impervious surface coverage from multisource remote sensing data and found that the impervious surface area increased with the number of ring roads. From a hard classification perspective, most researchers have focused on the LULC changes caused by urbanization, such as those associated with agricultural lands (e.g. Pauchard et al. 2006; Soffianian et al. 2013; Cofie 2011; Tewolde and Cabral 2011; Haregeweynabbaaa 2012), grass lands (e.g. Mundia and Murayama 2010; Banzhaf, Grescho, and Kindler 2009), forest and bush lands (e.g. Pauchard et al. 2006), and water bodies and wetlands (e.g. Fazal and Amin 2011; Haregeweynabbaaa 2012). There are many differences between hard and soft classification results. Hard classification results can reflect the overall trends of LULC changes, while soft classification can provide more details regarding urban growth. An integrated soft and hard classification approach could provide urban planners with comprehensive urban growth information; however, most previous studies have analysed urban expansion based on only soft or hard classification and not an integrated method.
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Based on the discussion above, we comprehensively evaluated urban expansion in the Beijing–Tianjin–Tangshan metropolitan region (BTTMR) based on an integrated soft and hard classification method from 1990 to 2015. The specific objectives of this study are as follows. (1) Evaluate the impervious surface (soft classification) growth in BTTMR from 1990 to 2015. (2) Monitor the LULC (hard classification) changes in BTTMR from 1990 to 2015. (3) Analyse the proportion of the impervious surface area (soft classification) associated with different land cover (hard classification) types. (4) Quantify the differences between soft and hard classification results.
2. Materials 2.1. Description of the study area Our study area (Figure 1) was the BTTMR, which includes two municipalities, Beijing and Tianjin, and two cities in Hebei Province, Tangshan and Langfang. The BTTMR, as the economic, political and cultural centre of northern China, is the third economic growth pole among Chinese metropolitan regions after the Yangtze River Delta and the Pearl River Delta. Since 1990, the area has experienced unprecedented urbanization and rapid industrialization. As a result, many natural surfaces have been replaced by urban surfaces, such as roads, parks, and buildings. The urban expansion in the BTTMR has been characterized by ‘spreading’ and ‘aggressive’ changes in past decades.
2.2. Data sources and pre-processing Table 1 shows the summary of satellite images used in our research. To map and analyse urban expansion in the BTTMR, 30.0 m cloud-free Landsat Thematic Mapper (TM)/Operational Land Imager (OLI) images of the study area were acquired from the U.S. Geological Survey (USGS) at decadal intervals (1990, 1995, 2000, 2005, 2010 and 2015) (USGS 2017). The images included summer and winter images for mapping the impervious surface coverage and LULC. Atmospheric correction was performed on those images. The digital number values of Landsat images were converted to at-satellite reflectance using the dark-object subtraction method (Chander, Markham, and Helder 2009; Chavez 1988; Modica et al. 2016; Roy et al. 2014). Multi-Spectral QuickBird high-resolution images (with a 2.4 m spatial resolution) from 2005 covering the Beijing urban core area were obtained to assist in impervious surface mapping and accuracy assessment. Defense Meteorological Satellite Program (DMSP)/ Operational Linescan System (OLS) stable light data (NTL) were downloaded from the National Centers for Environmental information at decadal intervals (1992, 1995, 2000, 2005, and 2010) (NOAA 2017a). National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer (VIIRS) NTL for 2015 was downloaded from the National Centers for Environmental information (NOAA 2017b). DMSP/OLS and NPP/VIIRS NTL data were resampled to 30.0 m for matching the spatial resolution of Landsat TM/OLI images. All images were geo-referenced to World Geodetic
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Figure 1. Overview of the study area (note: DEM represents Digital Elevation Model).
System 1984 (WGS-84)/ Universal Transverse Mercator Projection (UTM) with root mean square errors of less than 0.5 pixel.
3. Methods 3.1. Soft classification: impervious surface coverage extraction The soft classification scheme in this research was impervious surface coverage extraction. We extracted the impervious surface coverage using the CART method, which is the main method of impervious surface coverage extraction used by the US National Land Cover Database (NLCD) (Yang et al. 2003). The general CART algorithm is a method of spatial data
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Table 1. A summary of satellite images used in research. Dataset Landsat5-TM
Path/row 123/32 123/33 122/32 122/33
Landsat8-OLI
DMSP/OLS VIIRS/NPP Multi-Spectral QuickBird data
123/32 123/33 122/32 122/33 – – –
Acquisition date 18 September 1990, 11 April 1990, 16 September 1995, 5 December 1995, 13 September 2000, 31 October 2000, 6 May 2005,14 November 2005, 20 May 2010, and 28 November 2010 18 September 1990, 7 December 1990, 16 September 1995, 5 December 1995, 13 September 2000, 6 April 2000, 22 May 2005,14 November 2005, 20 May 2010, and 28 November 2010 10 August 1990, 29 October 1990, 7 July 1995, 30 December 1995, 6 September 2000,11 December 2000, 4 September 2005, 23 November 2005, 4 October 2010, and 5 November 2010 11 September 1990, 29 October 1990, 4 May 1995, 30 December 1995, 5 August 2000, 11 December 2000, 4 September 2005, 23 November 2005, 4 October 2010, and 5 November 2010 18 May 2015 and 10 January 2015 22 August 2015 and 16 April 2015 15 August 2015 and 3 November 2015 2 October 2015 and 3 November 2015 NTL in 1992, 1995, 2000, 2005, and 2010 NTL in 2015 2005
Spatial resolution (m) 30.0 30.0 30.0 30.0 30.0 30.0 30.0 30.0 1000.0 500.0 2.4
mining put forward by Breiman in 1984. The CART algorithm conducts a binary recursive partitioning process. Each parent node is split into two child nodes and each child node is treated as a potential parent node in the process. The regression tree algorithm produces rules based on training data. Each rule set defines the conditions under which a multivariate linear regression model is established. Regression tree models can account for the nonlinear relationship between the input and target variables and allow for both continuous and discrete variables as input variables. It has been proved that the accuracy and predictability of the regression tree models were better than those of the simple linear regression models. (Huang and Townshend 2003; Liu, Niu, and Wang 2005; Hu et al. 2017). Three main steps were involved in extracting the impervious surface coverage. (1) First, the binary classification results for Beijing, including impervious surfaces and non-impervious surfaces, were extracted from QuickBird high-resolution images in 2005. Then, based on a statistical neighbourhood analysis, we obtained the impervious surface coverage (from 1% to 100% at a 30.0 m resolution) from the binary classification results. The extraction results were divided into the training and testing samples for the CART model. (2) Second, we estimated impervious surface coverage in the BTTMR in 2005 based on the CART model. The predict independent variables of the CART model, including the reflectance values of bands 1, 2, 3, 4, 5, and 7 of Landsat 5 TM data and digital numbers (DNs) of DMSP/OLS stable NTL data; The target variable was the impervious surface coverage determined in step (1). (3) Third, we estimated the impervious surface coverage in the BTTMR in 1990, 1995, 2000, and 2010 based on the reflectance values of bands 1, 2, 3, 4, 5, 7 of TM and the DNs of DMSP/OLS stable NTL data. The impervious surface coverage in 2015 was estimated based on the reflectance values of bands 2, 3, 4, 5, 6, 7 of OLI data
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and radiance values of NPP/VIIRS NTL data. We collected the impervious surface coverage data of the regions with high impervious surface coverage in the BTTMR in 2005 as the training and testing samples (the assumption here is that there are no changes or very few changes of the impervious surface coverage in the regions with high impervious surface coverage from 1990 to 2015). Based on steps 1–3, we mapped the impervious surface coverage from 1990 to 2015 (Figure 2).
3.2. Hard classification: LULC classification The hard classification scheme in this research was LULC extraction. We used supervised image classification and artificial visual interpretation for LULC mapping. For clear identification of the LULC types from the spectral response, we selected TM and OLI images in dry season. The band combinations of 4–3-2 for TM imagery and 5–4-3 for OLI imagery were chosen in the supervised image classification (Jensen 2004). The sufficient training and testing samples were extracted directly from TM and OLI images based on prior knowledge and field observation. Based on the spectral response of features on the Landsat images and field observation, seven LULC types, including forestlands, shrub land and grasslands, water bodies, industrial production land, agricultural production land, urban built-up land and rural residential land, were identified. We used the Maximum Likelihood classifier algorithm, which is one of the most popular and widely used types of image classification algorithm, for supervised image classification (Stow and Chen 2002). Two-thirds of the training samples were randomly selected and used for classification, and the others were used for accuracy assessment using confusion matrix. To facilitate the analysis herein, we combined seven LULC types into five categories, including ecological land, industrial production land, agricultural production land, urban built-up land and rural residential land (Table 2). Based on the step above, we mapped the LULC of the BTTMR from 1990 to 2015 (Figure 3).
3.3. Accuracy assessment In hard classification, we used a confusion matrix in the accuracy assessment. In soft classification, we selected three variables, the average error (AE), relative error (RE), and Pearson correlation coefficient (r) between actual and predicted values to perform the accuracy assessment (Yang et al. 2003).The quality of the regression tree can be measured by AE of a tree T, expressed by AE ðTÞ ¼
N 1X jyi gðX i Þj N i¼1
(1)
where the function gðX i Þ represents the regression plane through the example set, X represents a set of m predict independent variables X = {x1, x2, . . ., xm}, N is the number of samples used to establish the tree, and yi is the actual value of the predicted variable.
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Figure 2. Results of soft classification in the BTTMR: (a) 1990, (b) 1995, (c) 2000, (d) 2005, (e) 2010, and (f) 2015 (note: ISC represents impervious surface coverage).
To compare the quality of several regression trees, RE is often used and is defined as ðREÞ ðTÞ ¼
ðAEÞðTÞ ðAEÞðuÞ
(2)
where AE (u) is the average error that would result from always predicting the mean value, and is used to standardize the average error AE (T).
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Table 2. Descriptions of land use/land cover types. LULC type Ecological land Industrial production land Agricultural production land Urban built-up land Rural residential land
Description A region that provides important ecological functions such as, climate regulation, water conservation, etc.; such regions include forestlands, grasslands, and water bodies A region that provides industrial products, including factories, quarries, mining, and oil-field waste, outside cities, as well as land for special uses, such as roads and airports A region that provides agricultural products and their services, including paddy lands, and arid lands A region that provides functions of non-agricultural life and public activities for the population; such regions include the built-up areas of large, medium and small cities and counties A region that provides functions of agricultural life and public activities for the population; such regions include rural residential areas outside cities and counties
In addition to the AE and RE, r is also used and is defined as N N N P P P N Xi Yi Xi Yi i¼1 i¼1 i¼1 ffis ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi r ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 N N N N P P P P 2 2 N Xi ð Xi Þ N Yi ð Yi Þ i¼1
i¼1
i¼1
(3)
i¼1
where X is the actual value, Y is the predictive value, and N is the number of samples. All three statistical measures were used in the study to evaluate model performance. The initial predictive models were developed based on CART algorithm by using the Cubist software which is produced by the Rulequest for data mining. In addition, the predictive accuracy can be estimated by n-fold cross-validation in Cubist. Therefore, the training data set can be divided into n blocks of roughly equal size. For each block in turn, a model is built from the data in the remaining blocks and tested using the holdout block (Michie et al., 1994).
3.4. Spatial metrics 3.4.1. Centre migration In this research, the centre migration model was used to calculate the spatial distribution of impervious surface cover in different periods. By comparing the trajectories of the centre of impervious surface coverage in different periods, we analysed the overall trend and spatial distribution of the impervious surface coverage in the BTTMR at different times. The centre of impervious surface coverage can be calculated with following formula (Xu and Li 2005): m P
Xt ¼
m P
xi
i¼1
m
; Yt ¼
yi
i¼1
m
(4)
where Xt and Yt represent the latitude and longitude, respectively, of the centroid of impervious surface coverage in year t; m represents the number of impervious surface patches; and xi and yi represent the latitude and longitude, respectively, of the centre of patch i. We divided impervious surface coverage into five different types: high-density impervious surfaces, medium high-density impervious surfaces, medium-density impervious
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Figure 3. Results of hard classification in the BTTMR: (a) 1990, (b) 1995, (c) 2000, (d) 2005, (e) 2010, and (f) 2015.
surfaces, medium low-density impervious surfaces, and low-density impervious surfaces. The associated impervious surface percentages were (80%, 100%], (60%, 80%], (40%, 60%], (20%, 40%], and (0%, 20%], respectively. Thus, we calculated the centre coordinates of these different types of impervious surface coverages.
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3.4.2. Standard deviation ellipse Based on standard deviation ellipse model, we analysed the spatial patterns of impervious surfaces. The standard deviation ellipse model is one of the classical methods used to analyse a directional distribution and can reflect the overall and dominant distribution directions of spatial elements. The method involves three variables: the standard deviations of the principal and auxiliary axes and the deflection angle. The standard deviations of the principal and auxiliary axes represent the concentration density of a spatial element, and the deflection angle represents the leading direction of a spatial element. These variables were calculated according to David (1999) and Scott and Janikas (2010). 3.4.3. Local gi statistic We selected the local Gi statistic to analyse the spatial autocorrelation of the impervious surface coverage (Chen 2009): 8 n P > Wi;j xj > > > j¼1 > > ¼ G n i > P ; "jÞi > > xj > > > j¼1 < n P Wi;j (5) > j¼1 > > Þ ¼ ; "jÞi EðG i > nðn1Þ > > > > VðG Þ ¼ EðGi 2 Þ ðEðGi ÞÞ2 i > > > Gi EðGi Þ > : ZðGi Þ ¼ pffiffiffiffiffiffiffiffi VðGi Þ
where xj represents impervious surface coverage for patch j, and Wi,j represents the distance weighting between patches i and j. n is the number of patches in the BTTMR and "jÞi indicates that patches i and j cannot be the same patch. A significant positive value of Z (Gi) suggests that the impervious surface coverage of patches adjacent i is high, while a significant negative value of Z (Gi) reflects the opposite trend.
3.4.4. Landscape metrics We compared hard classification results with soft classification results using various landscape metrics, including the number of patches, largest patch index, landscape shape index, and cohesion at the landscape scale. These landscape metrics were calculated in Fragstats 4.1 (Mcgarigal and Marks 1995).
3.5. Contribution index We calculated the contribution index values of different land covers to analyse the contribution of each cover to the growth of impervious surfaces (Qiao, Tian, and Lin 2013): CI ¼ ððISPÞF ðISPÞÞ S SF
(6)
where CI represents the contribution index; ISPF and ISP represent the mean values of impervious surface coverage for different land covers and the entire BTTMR, respectively; and SF and S represent the areas of different land covers and the entire BTTMR, respectively.
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4. Results 4.1. Accuracy assessment 4.1.1. Soft classification Table 3 presents the accuracy assessment of soft classification from 1990 to 2015. The AE, RE, and r values between model estimates and the extraction results from QuickBird high-resolution images in 2005 were 8.30%, 0.35, and 0.83, respectively. The AE, RE, and r values in other years were within 11.50%, below 0.44, and higher than 0.73, respectively. 4.1.2. Hard classification Table 4 presents the accuracy assessment of hard classification from 1990 to 2015. The producer and user accuracies of all land use types were higher than 84%. The overall accuracy in 1990, 1995, 2000, 2005, 2010, and 2015 was 91%, 89%, 89%, 90%, 90%, and 91%, respectively. Additionally, the Kappa coefficient was 0.86, 0.80, 0.80, 0.84, 0.84, and 0.86 in 1990, 1995, 2000, 2005, 2010, and 2015, respectively. Based on the above accuracy assessment and compared with previous studies (Yang et al. 2003; Hu et al. 2017; Wang et al. 2015), we confirmed that the soft and hard classification results meet the analytical needs of our research. 4.2. Spatiotemporal changes in impervious surfaces 4.2.1. Variations in impervious surfaces Figure 4 presents the impervious surface variations from 1990 to 2015. Notably, the following findings are illustrated in Figure 4. (1) Both the impervious surface area and coverage in the BTTMR and different cities increased from 1980 to 2015. The total impervious surface area exhibited a gradual increase from 1637.02 km2 in 1990 to 4468.93 km2 in 2015, an increase of about 2831.91 km2. The annual rate of change was about 113.28 km2 year–1. The most rapid period of expansion was between 2010 and 2015. The growth rates of impervious surfaces in different cities ranked from high to low as follows: Beijing about 39.35 km2 year–1), Tianjin (about 30.75 km2 year–1), Tangshan (about 26.56 km2 year–1), and Langfang (about 16.62 km2 year–1). The associated increases in the impervious surface area were 983.79 km2, 768.73 km2, 663.98 km2, and 415.42 km2, respectively. (2) The impervious surface coverage in the BTTMR exhibited a gradual increase from 3.45% in 1990 to 9.42% in 2015, an increase by a factor of about 1.7. In addition, the coverage in Beijing, Tianjin, Tangshan, and Langfang increased from 4.67%, 4.51%, 1.45%, and 2.51% in 1990 to 10.66%, 11.14%, 6.55%, and 8.99% in 2015, respectively. Table 3. Accuracy assessment of soft classification. Year
AE (%)
RE
r
1990 1995 2000 2005 2010 2015
10.40 9.30 8.90 8.30 8.60 11.50
0.43 0.40 0.36 0.35 0.44 0.41
0.73 0.77 0.80 0.83 0.76 0.78
LULC type EL IPL APL UBL RLL Overall accuracy (%) Kappa coefficient
Producer’s accuracy (%) 87 95 87 94 92 91 0.86
1990
User’s accuracy (%) 86 94 87 93 91 Producer’s accuracy (%) 84 93 84 93 91 89 0.80
1995 User’s accuracy (%) 85 92 85 92 90 Producer’s accuracy (%) 87 91 85 94 90 89 0.80
2000 User’s accuracy (%) 88 92 85 93 91
2005 Producer’s accuracy (%) 89 92 87 92 93 90 0.84
Accuracy User’s accuracy (%) 88 90 87 91 91
Producer’s accuracy (%) 88 93 85 94 91 90 0.84
2010 User’s accuracy (%) 89 92 84 95 90
Producer’s accuracy (%) 87 93 87 92 93 91 0.86
2015 User’s accuracy (%) 86 94 86 93 94
Table 4. Accuracy assessment of hard classification (note: EL, IPL, APL, UBL, and RLL represent ecological land, industrial production land, agricultural production land, urban built-up land and rural residential land, respectively).
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Figure 4. Impervious surface variations from 1990 to 2015: (a) impervious surface coverage, and (b) impervious surface area.
4.2.2. Spatial distribution of impervious surfaces According to the national urban agglomeration plan released by the Chinese government, the economic spatial structure of the BTTMR exhibits a ‘One Axis, Two Wing, and Multi-Node’ structure (Figure 5). ‘One Axis’ refers to the logistical corridor between Beijing and Tianjin, dual-core cities in the BTTMR; ‘Two Wing’ refers to the construction of logistical operation support on both sides of the ‘One axis’, and ‘Multi-Node’ refers to the different cities in the BTTMR. Based on centre migration and the standard deviation ellipse model, we analysed the directional distribution of impervious surfaces and the associated spatial relationship with the economic structure in the BTTMR from 1990 to 2015. The following findings can be observed in Figure 5. (1) The extent of centre migration decreased with increasing impervious surface coverage. High-density impervious surfaces exhibited the smallest extent of centre migration, while low-density impervious surfaces exhibited the largest extent. (2) As the impervious surface coverage increased, the distance between the mass centre of impervious surfaces and the ‘One Axis’ decreased. The mass centre of high-density impervious surfaces was closest distance to the ‘One Axis’, while that of low-density impervious surfaces was farthest from the axis. (3) The same directions were observed for the ‘One Axis’ and the principal axes of standard deviation ellipses from 1990 to 2015. This finding indicates that impervious surfaces in the BTTMR are concentrated in the logistical corridor between Beijing and Tianjin. Table 5 presents the standard deviation ellipses parameters of impervious surface coverage. The following conclusions can be drawn from Table 5. (1) The ratios of the principal and auxiliary axis lengths increased from 1990 to 2000, and decreased from 2000 to 2015. The effect of impervious surface accumulation associated with the ‘One Axis’ was higher from 1990 to 2000, while that of ‘Two Wing’ was higher from 2000 to 2015, respectively.
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Figure 5. Variations in the standard deviation ellipse and mass centre of impervious surface coverage from 1990 to 2015 and the relationship with the spatial economic structure: (a) mass centre of low-density impervious surfaces, (b) mass centre of medium low-density impervious surfaces, (c) mass centre of medium-density impervious surfaces, (d) mass centre of medium high-density impervious surfaces, and (e) mass centre of high-density impervious surfaces (note: CM represents centre of mass, SDE represents standard deviation ellipse, and the green circles represent the different cities in the BTTMR and the circle sizes are proportional to the sizes of those cities).
(2) The angle (from east to north) increased and then decreased from 1990 to 2000, and 2000 to 2015, respectively. The expansion direction of impervious surfaces in these different periods was originally to the south, and then to the north in the BTTMR. (3) To explore the characteristics of spatial variations in impervious surfaces, we calculated the local Gi statistic of impervious surface coverage in the BTTMR (Figure 6). As shown in Figure 6, the extent of high-high assembling regions expanded in the BTTMR from 1990 to 2015 due to rapid economic and social development. From 1990 to 2000, high-high assembling regions were mainly located in the dual-core cities of Beijing and Tianjin. Over time, the extent of high-high assembling regions continued to expand. From 1990 to 2000, high-high assembling regions gradually appeared in the non-core cities of Tangshan and Langfang.
4.3. Variations in LULC changes Table 6 presents the hard classification results of LULC changes between 1990 and 2015. As shown in Table 6, the areas of urban built-up land and industrial production land increased rapidly, while the areas of ecological land and agricultural land decreased considerably.
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Figure 6. Spatial hotpot maps of impervious surfaces in the BTTMR: (a) 1990, (b) 1995, (c) 2000, (d) 2005, (e) 2010, and (f) 2015 (note: Categories used to describe the spatial clustering of the impervious surface coverage are high-high assembling region, high-high moderately assembling region, non-assembling region, low-low moderately assembling region, low-low assembling region. These categories correspond to different Z (Gi) values, which are higher than 2.58, between 1.96 and 2.58, between – 1.96 and 1.96, between – 2.57 and – 1.96, and lower than – 2.58).
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Table 5. Standard deviation ellipses parameters of impervious surface coverage. Parameter Principal axis (km2) Auxiliary axis (km2) Angle (°)
1990
2000
2010
2015
85,443 58,067 121
85,347 56,599 121
86,119 59,523 123
93,298 65,398 105
(1) Urban built-up land exhibited the largest growth of any LULC type. The area of urban built-up land increased from 1188 km2 in 1990 to 2879 km2 in 2015, an increase of about 1691 km2 at an annual rate of increase of 67.64 km2 year–1. Additionally, over the past 25 years, agricultural production land exhibited a gradual decrease from 26,350 km2 in 1990 to 23,765 km2 in 2015, a decrease of about 2585 km2 at an annual rate of decrease of 103.40 km2 year–1. (2) The ranks of the areas of different LULC types in the BTTMR ranged from high to low as follows (in 2015): agricultural production land (50.12%), ecological land (30.50%), rural residential land (8.31%), urban built-up land (6.07%), and industrial production land (4.12%).
4.4. Impervious surface proportions of different LULC types According to the above results of soft and hard classification, the changes in the impervious surface proportions of different land use types were analysed, as shown in Table 7: (1) All of the impervious surface proportions of different land cover types exhibited increasing trends. Industrial production land displayed the largest growth, with an increase in the impervious surface proportion from 5.53% in 1990 to 16.61% in 2015, an increase of a factor of about 2.0. The impervious surface proportions of urban built-up land and rural residential land increased from 50.36 and 7.27% in 1990 to 54.72 and 18.03% in 2015, respectively. (2) The ranks of impervious surface proportions from high to low as follows (in 2015): urban built-up land (54.72%), rural residential land (18.03%), industrial production land (16.61%), agricultural production land (6.58%), and ecological land (1.94%).
5. Discussion 5.1. The contributions of different functional lands to the impervious surface area growth Over the past 25 years, the BTTMR has experienced rapid urbanization and industrialization. The soft classification results showed that impervious surfaces rapidly increased. Additionally, the hard classification results showed that the areas of urban built-up land and industrial production land rapidly increased, while the areas of ecological land and agricultural production land considerably decreased. Our conclusions are largely consistent with those of previous research (Li, Gong, and Liang 2015; Wang et al. 2015). Both hard and soft classification methods are widely used in urban expansion studies. Most previous research focused only on a single analysis of soft (impervious surface
Area (km2)
14,658 1296 26,350 1188 3401
LULC type
EL IPL APL ULL RLL
30.91 2.73 55.56 2.50 7.17
Proportion (%)
1990
16,016 1632 23,769 2068 3597
Area (km2) 33.76 3.44 50.16 4.36 7.58
Proportion (%)
1995
14,789 1443 25,071 1939 3690
Area (km2) 31.20 3.04 52.88 4.09 7.78
Proportion (%)
2000
14,570 1652 24,358 2588 3803
Area (km2) 30.73 3.48 51.38 5.46 8.02
Proportion (%)
2005
14,529 1763 24,099 2789 3806
Area (km2)
30.64 3.72 50.83 5.88 8.03
Proportion (%)
2010
14,463 1954 23,765 2879 3941
Area (km2)
30.50 4.12 50.12 6.07 8.31
Proportion (%)
2015
Table 6. LULC changes between 1990 and 2015 (note: EL, IPL, APL, UBL, and RLL represent ecological land, industrial production land, agricultural production land, urban built-up land and rural residential land, respectively).
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Table 7. Impervious surface proportions of different LULC types (note: EL, IPL, APL, UBL, and RLL represent ecological land, industrial production land, agricultural production land, urban built-up land and rural residential land, respectively). LULC type EL IPL APL UBL RLL
Proportion in 1990 (%) 0.53 5.53 2.44 50.36 7.27
Proportion in 1995 (%) 0.86 4.89 2.16 45.69 7.08
Proportion in 2000 (%) 0.89 6.78 2.72 52.54 8.84
Proportion in 2005 (%) 0.94 7.98 2.97 48.39 10.34
Proportion in 2010 (%) 1.29 10.67 4.03 49.68 11.76
Proportion in 2015 (%) 1.94 16.61 6.58 54.72 18.03
coverage) or hard (LULC) classification results but ignored the integration of both schemes. Hard classification methods show good results in large homogenous regions where pure pixels are dominant, but they fail in fragmented regions where mixed pixels are dominant. Conversely, soft classification methods are thought to have greater accuracy in fragmented areas than in regions with pure pixels. Thus, hard classification results can reflect the overall trends of LULC changes, while soft classification can provide more details regarding urban growth. An integrated soft and hard classification approach for evaluating urban expansion could provide urban planners with more comprehensive urban growth information. Additionally, different land covers have different land use functions. Our study integrated both schemes to analyse the contributions of different land covers to the growth of impervious surfaces (Cao et al. 2017). According to the above contribution index method, the contributions of different land covers to impervious surfaces were calculated (Figure 7). (1) The contribution index values of different land use types rank from high to low as follows: urban built-up land, rural residential land, industrial production land, agricultural production land, and ecological land.
Figure 7. Contribution index values of different LULCs to impervious surface area growth from 1990 to 2015 (note: EL, IPL, APL, UBL, and RLL represent ecological land, industrial production land, agricultural production land, urban built-up land and rural residential land, respectively).
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(2) The contribution index values of urban built-up land, rural residential land and industrial production land were positive, while those of agricultural production land and ecological land were negative. Additionally, urban built-up land exhibited the largest contribution to impervious surface growth, and this contribution rapidly increased over time.
5.2. The differences between hard and soft classification results Based on the above results, urban built-up land and rural residential land exhibited the largest impervious surface proportions of 54.72% and 18.03%, respectively. Thus, we combined the urban built-up land and rural residential land as a hard classification result, and compared it with the soft classification result (Figure 8). As shown in Figure 8, the landscape heterogeneity in soft classification was higher than that in hard classification because the small patches were preserved in the soft classification result. Furthermore, we analysed the differences in landscape metrics, including the number of patches, largest patch index, landscape shape index, and cohesion between the hard classification and soft classification results (Figure 9). (1) The number of patches in soft classification was significantly higher than that in hard classification. Over time, the number of patches increased rapidly in both sets of results. (2) The largest patch index represents the proportion of the largest patch area to the total landscape area. From 1990 to 2015, the largest patch indexes of both soft and
Figure 8. Differences between the soft and hard classification results in 2015: (a) soft classification and (b) hard classification.
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Figure 9. Differences in landscape metrics between the soft and hard classification results: (a) number of patches, (b) largest patch index, (c) landscape shape index, and (d) cohesion.
hard classification exhibited decreasing trends. This trend suggests that schemes yielded increases in the total area, while the largest patch area decreased. Moreover, the rate of decrease of the largest patch index of hard classification was significantly higher than that of soft classification. Thus, the hard classification results were more sensitive to the largest patch index than the soft classification results. (3) The landscape shape index represents landscape fragmentation. The landscape shape index of the soft classification results was higher than that of the hard classification results. Thus, the soft classification results exhibited a higher degree of fragmentation than the hard classification results. Over time, both the soft and hard classification exhibited increasing degrees of fragmentation. (4) Cohesion represents landscape contraction or expansion. The soft classification results were more sensitive to cohesion than were the hard classification results. Furthermore, the soft classification results reflected a trend of landscape contraction from 2010 to 2015, while those of hard classification reflected extension over the same period.
6. Conclusions An integrated soft and hard classification approach for evaluating urban expansion could provide urban planners with more comprehensive urban growth information. To take advantage of both methods, we used an integrating soft and hard classification to
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evaluate urban expansion. Both the impervious surface coverage and the LULC in the BTTMR from 1990 to 2015 were retrieved from multisource remote sensing data. In addition, we analysed the spatiotemporal dynamics and variations in the retrieval results based on centre migration, standard deviation ellipse, contribution index and landscape metrics. The main conclusions of the study are as follows. (1) The impervious surface area in the BTTMR considerably increased over the past 25 years. Notably, the areas of urban built-up land and industrial production land rapidly increased, while those of ecological land and agricultural production land decreased. (2) The distribution of impervious surfaces was closely related to the regional economic development plan of ‘One Axis, Two Wing, and Multi-Node’ in the BTTMR. (3) The contributions of urban built-up land, rural residential land, and industrial production land to the growth of impervious surfaces were positive, while those of other LULC types were negative. (4) The landscape metrics varied considerably based on the hard and soft classification results and were sensitive to different factors.
Disclosure statement No potential conflict of interest was reported by the authors.
Funding This research is supported by the National Natural Science Foundation of China (Parameterizing urban surface radiation and energy budget based on three-dimension modelling and sky view factor, No. 41671339)
ORCID Shisong Cao
http://orcid.org/0000-0001-9164-5805
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