Detecting urban ecological land-cover structure using remotely sensed imagery: A multi-area study focusing on metropolitan inner cities

Int J Appl  Earth Obs Geoinformation 75 (2019) 106–117

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Detecting urban ecological land-cover structure using remotely sensed imagery: A multi-area study focusing on metropolitan inner cities

T

Xin Luoa, , Xiaohua Tonga, , Zhi Qianb, Haiyan Pana, Sicong Liua ⁎

a b

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College of Surveying and Geo-informatics, Tongji University, 1239 Siping Road, Shanghai 200092, China Shanghai Development Research Center, Shanghai 200092, China

ARTICLE INFO

ABSTRACT

Keywords: Surface water Green space Sentinel-2 Google earth Landscape metric Metropolitan cities

With increasing attention being paid to sustainable urban development and human habitation improvement, urban ecological land cover (UELC), i.e., surface water and green space, has played an important role of the highly compact inner urban regions. In this study, we developed an efficient approach for UELC mapping by coupling Sentinel-2 multi-spectral imagery and Google Earth high-resolution imagery. In contrast with the conventional single-source and multi-source imagery-based classification methods, the proposed method respectively achieved the highest overall accuracies of 91.50% and 94.05% in the UELC mapping for two test sites (i.e. Shanghai and Seoul). The proposed method is used for urban surface mapping among six world-class cities. For an in-depth analysis of the landscape structures for inner urban regions, seven landscape metrics are introduced for the quantification of the UELC structure based on the obtained high-precision UELC maps. The result shows that London appears to have the best UELC-induced ecological quality, that is, with high percentage of landscape, area-weighted mean fractal dimension, edge density, Shannon’s evenness index values and a low contagion index value, while Tokyo is exactly the opposite. Several common characteristics found through the statistical analysis are: 1) all the inner-city regions have small UELC coverage (< 50%) and low shape complexity; 2) green space generally contributes more to urban eco-environment than the urban surface water; and 3) all cities show high landscape consistency in the inner urban region.

1. Introduction From the perspective of urbanization history, the earliest urban model was designed to achieve the highest real estate production with little consideration of landscaping or ecological effects (Gaja, 2008), and thus led to a series of urban environment problems (e.g. Wu, 2014; Roy et al., 2012; Wolch et al., 2014; Huang et al., 2016). In the new era, urban sustainable development and ecological construction which bring multiple environmental, economic and social benefits have played an increasingly important role in urban planning, construction and management (Wu et al., 2014; Zhou and Wang, 2011). Urban ecological land cover (UELC), i.e., surface water and green space is the basic element in the urban environment. It benefits for human beings in terms of body health and the comfort of city living, particularly in the densely populated inner-city areas. The UELC structure, with respect to composition and configuration, is associated with the urban ecological processes (Uuemaa et al., 2009; Du et al., 2016). To enrich the knowledge in understanding the urban landscape issues and determine the interaction between UELC structure

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and the underlying ecological processes, the crucial step of UELC landscape quantification should be carried out (Gergel and Turner, 2017; Buyantuyev et al., 2010; Chefaoui, 2014). In the landscape ecology literature, many landscape indicators have been proposed and used in a wide variety of fields and they perform well in characterizing the relevant landscape patterns (Peng et al., 2010; Sofia et al., 2014; Lausch et al., 2015). For example, the Area-Weighted Mean Patch Size (Area_AM) and Patch Size Standard Deviation (AREA_SD) are used to reflect the central tendency through computing the distribution of patch area (McGarigal and Marks, 1995). Patch Density (PD), Largest Patch Index (LPI) and Edge Density (ED) are used to determine the fragmentation degree of the different land cover categories. And the Landscape Shape Index (LSI) and Patch Size Coefficient of the Variation (AREA_CV) are used to indicate the Connectivity, insularity and spatial heterogeneity in the landscape (McGarigal and Marks, 1995). The landscape quantification facilitates the study on ecology due to the significant influence of landscape on many ecological processes (Song et al., 2014; Maimaitiyiming et al., 2014; Tian et al., 2014). Buyantuyev and Wu (2010) studied urban heat island and landscape heterogeneity

Corresponding author at: Tongji University, College of Surveying and Geo-informatics, 1239 Siping Road, Shanghai, 200092, China. E-mail address: [email protected] (X. Luo).

https://doi.org/10.1016/j.jag.2018.10.014 Received 22 December 2017; Received in revised form 14 October 2018; Accepted 17 October 2018 0303-2434/ © 2018 Elsevier B.V. All rights reserved.

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by linking spatiotemporal variations in surface temperatures to landcover patterns and found that vegetation and pavements are the main factors for the spatial variations of surface temperature. By measuring the spatial characteristic with landscape indicators, Kong et al. (2007) discovered the landscape attributes such as size, shape, and distribution of urban green spaces play decisive role in defining their ecological and landscape functions. However, some issues still exist in the UELC structure quantification and the related ecological effect studies. When implementing urban landscape analysis, the primary and fundamental step is the land cover map preparation. Recent studies have found that the accuracy of landscape quantification is strongly affected by the land-cover maps (Lechner et al., 2013; Liu and Yang, 2015). One such reason is that errors in the land cover map may propagate to the landscape quantification and further lead to erroneous scientific results (Fan and Myint, 2014). With the increasing applications of remote sensing technique, recent studies have provided different frameworks to improve the landcover mapping based on remotely sensed imagery (Fan and Myint, 2014; Song et al., 2014). However, many factors such as the properties of remotely sensed imagery (e.g., the spatial resolution, signal-to-noise ratio), preprocessing details (e.g., geographical registration, radiance correction), and the classification methods still could bring errors into the land-cover mapping, and thus affect the landscape study. The priority issue is the availability and accessibility of the remotely sensed data. Nowadays, an increasing volume of remotely sensed data have become freely accessible, for example, the high-resolution Google Earth imagery, which is composed of various types of high-resolution satellite images (e.g., QuickBird, IKONOS, and SPOT), it provides precise geographical and abundant spatial information of the land surface. And the Sentinel-2 and Landsat imagery, which are acquired by the medium-resolution imaging system, they provide large-scale, multispectral and temporal information of the land surface. Due to the properties, the high-resolution and medium-resolution images have been extensively applied for the urban surface mapping (Liu and Yang, 2013; Singh et al., 2015; Ghaffarian and Ghaffarian, 2014; Lu et al., 2015; Belgiu and Csillik, 2017; Yang et al., 2017). Nevertheless, as the urban surface mapping is commonly based on the single-source satellite imagery alone and not make the most use of the information provided by multi-source images, the classification accuracy is always limited. The limitations of the traditional pixel-based classification methods are also universal and make it difficult for researchers to realistically and accurately represent landscape structures (Lechner et al., 2013). The pixel-based classifiers focus on the spectral dimension of the satellite imagery and ignore the use of the spatial information provided by the imagery. While the object-based classifiers which first segment the imagery into objects, hence not only spectral response, but also spatial features of the object can be used for the land cover classification. To take full advantage of the spatial structure information provided by the remotely sensed imagery, the object-based classifier has become a preferred method particularly in the high-resolution imagery

classification (Blaschke et al., 2014; Novack et al., 2011; Pu et al., 2011). In this study, to fully exploit the spectral and spatial information of the multi-source images, we developed a new object-based UELC mapping framework by using multi-source satellite images. Unlike the traditional multi-source imagery-based method first fuse the multisource images into one improved image, then the image classification is carried out accordingly (Gao et al., 2017; Kumar et al., 2014; Sukawattanavijit and Chen, 2015). The proposed framework makes the most use of the spectral and spatial information through an efficient cooperation of the multi-source images. Specifically, it consists of three steps: 1) the elimination of the spatial displacement and the radiometric differences among multi-source satellite images, 2) the extraction of distinctive spectral and spatial features by using the multi-source images, and 3) the multi-feature cooperation for the per-object classification. With the new method implementation, the fine spatial information of the high-resolution images and the fine spectral information of the multi-spectral images are effectively cooperated for the UELC mapping. Moreover, for in-depth eco-landscapes understanding of the world-class cities, seven landscape metrics are applied for the UELC structure analysis among the six representative metropolitan inner cities. The materials used in our study are presented in Section 2. Next, the framework for the UELC mapping and landscape analysis are presented in Section 3. And the results, discussion and conclusion are presented in the last three Sections, respectively. 2. Materials 2.1. Study areas Urban ecological construction is unfolding at a global scale. A key factor in measuring the degree of comfort of city living is opportunity to be immersed in nature. In our study, six of the world’s leading cities— London, New York, Tokyo, Paris, Seoul, and Shanghai are selected for the comparison and analysis. London, New York, Tokyo, and Paris are ranked as the top-four cities between 2008 and 2016 in the Global Power City Index, an annual report compiled by The Mori Memorial Foundation’s Institute for Urban Strategies (Foundation, The Mori Memorial, 2016). Seoul and Shanghai represent the fast-developing cities, and they have risen in rank from 13 to 6 and 25 to 12, respectively, in this period. We determined the city centers according to the geographical space from the Google Map and the basic data corresponding to land area, population and population density (Foundation, The Mori Memorial, 2016). Thus, the city centers of Shanghai people’s square, Metropolitian Government Building, Trafalgar Squre, City Hall Park, Hotel de Ville and Seoul City Hall, those correspond to the cities of Shanghai, Tokyo, London, New York, Paris and Seoul are determined in our study. The core inner-city area of each city that within a radius of 5-km around the city center was then taken as the target area in this study. In the study areas, the surface water and green space areas are

Table 1 Description of the six test sites and the corresponding acquisition dates of the collected data. Test site

Major type of surface water

Major type of green space

Major type of non-UELC area

Acquisition date Sentinel-2 scene

Google Earth scene

Shanghai

Dispersed street trees, lawns, and bushes

Buildings with different colors; Bright commercial buildings

July 20, 2016

July 21, 2016

Tokyo

Rivers vary in size and shape; Small pond Spindly river

Large parks

Dark residential buildings

Aug. 7, 2015

London New York Paris

Turbid river Clear and wide river Spindly river

Sparse and degraded grass Parks on the islands Dispersed parks

Sept. 11, 2016 Aug. 22, 2015 July 16, 2015

Seoul

Clear river

Mountain forest

Bright buildings; Bare land Dense buildings Bright commercial buildings; Colorful residential buildings Shadows of buildings

May 27, 2015 June 2, 2014 June 4, 2015 May 7, 2015 Aug 7, 2016

Oct. 15, 2016

June 12, 2015

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Fig. 1. The six inner-city scenes illustrated using Sentinel-2 images (RGB = 432).

characterized by various sizes, shapes and distributions, and the highly heterogenous non-UELC surface is easily confused with the UELC surface. Therefore, all these factors make the high-precision UELC mapping a challenging task. The six inner cities are summarized in Table 1, and the extent of each study site is shown in Fig. 1.

obtained from the Copernicus Open Access Hub (https://scihub. copernicus.eu/). These images were then collaboratively used for each inner-city study (Fig. 2). Finally, six remotely sensed imagery pairs acquired in the growing season from May to October, mostly in 2015 were obtained, representing the current land cover in 2015 (Table 1).

2.2. Data sets

3. Methods

Two kinds of satellite images are adopted in our study: Google Earth high-resolution imagery and Sentinel-2 multi-spectral imagery. The Sentinel-2 data contain 13 spectral bands: four bands at a 10-m resolution, six bands at a 20-m resolution, and three bands at a 60-m spatial resolution. Considering the suitability for our study, high-resolution Google Earth images at level 16 (nearly 2-m spatial resolution) were captured using 91 Weitu software (http://www.91weitu.com/ index.html) and the 10–20 m multi-spectral Sentinel-2 images were

The framework of UELC mapping and analysis contains three main procedures: 1) multi-source satellite image standardization, which includes image-to-image registration for the Sentinel-2 images and relative radiometric correction for the Google Earth images; 2) objectoriented UELC classification, where surface water and green space are extracted in sequence; and 3) landscape characteristics quantification and analysis by using landscape metrics.

Fig. 2. Characteristics of the Google Earth imagery and Sentinel-2 imagery. The colors represent the spectral response of the image bands. 108

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3.1. Multi-source satellite imagery standardization

interpolation and then stacked with the Google Earth imagery for multiscale segmentation (Fig. 4). In this procedure, a multi-resolution segmentation approach which takes the heterogeneity and size of the image objects into account is used for the initial segmentation at the pixel level (Benz et al., 2004). This segmentation approach adopts a “bottom-up” image segmentation algorithm that begins with singlepixel objects which are iteratively grown through pair-wise merging of neighboring objects (Benz et al., 2004). Previous studies have shown that a higher weight of 0.9 should be given to the spectral feature for a better segmentation (Pu et al., 2011), therefore, we set the spectral weight and compactness weight to constant values of respective 0.9 and 0.5 in our study. Additionally, through testing different values by visually interpreting the image segmentation results, a very fine scale parameter of 50 is determined to ensure all the segments are internally homogenous (Zhou and Troy, 2008). Since the initial segmentation inevitably suffers over-segmentation problem, therefore, a region merging process by using spectral difference segmentation method is carried out based on the initial over-segmentation result (eCognition Developer T. 9.0 User Guide, 2014). In the specific implementation, the parameter is determined by experiments and the maximum spectral difference of the gray values are finally set to 150 in all the test sites during this segmentation.

All data acquired by the Sentinel-2 MSI have been systematically processed to top-of-atmosphere (TOA) Level-1C orthoimage products by the Payload Data Ground Segment (PDGS). Atmospheric correction was also applied for the bottom-of-atmosphere (BOA) corrected reflectance product generation through the Sentinel-2 Toolbox. In addition, all the images were projected into the GCS.WGS.1984 coordinate system in our study. And at least 15 control points in each study site were selected manually from the Google Earth high-resolution images for the Sentinel-2 image co-registration (Feyisa et al., 2014; Storey et al., 2016). As a result, image-to-image co-registration was performed with a root mean square error (RMSE) of less than 2.93 m for each of the test site. As little ancillary information associated with radiometric correction was contained in the Google Earth images, to minimize the radiometric variability between the Sentinel-2 and Google Earth images, a no-change stratified random sample (NCSRS) based linear regression method was applied to the Google Earth imagery for false reflectance image generation (Rahman et al., 2015). Specifically, to reduce the geometric error between these scenes, we resampled the Google Earth images from 2 m to 10 m, accordingly, image difference histograms from the Google Earth images and Sentinel-2 images were created, which is aiming for the no-change area determination according to a range threshold within mean ± 3SD (standard deviation) (Rahman et al., 2015). The collected no-change samples were used to develop a linear regression model for the transformation coefficient determination. The linear regression can be shown as follows:

GECorrected = gain × GEDN + bias

3.2.2. UELC mapping with the spatial-spectral features As mentioned before, the high landscape heterogeneity of the urban environment makes the urban surface mapping a challenging task. In this study, we carried out a simple hierarchical object-based UELC classification strategy (Fig. 4). Specifically, surface water, which is generally regarded as the easily recognized land-cover type, was first extracted using the remotely sensed imagery. The urban green space classification then followed based on the rest image. For the spectral features extraction, we analyzed the spectral characteristics of respective water surface and green space, and then a series of spectral indices were tested including the normalized difference water index (NDWI) (McFeeters, 1996), normalized difference water index (NDWI) (Rogers and Kearney, 2004), modified normalized difference water index (MNDWI) (Xu, 2016) and automated water extraction index (AWEI) (Feyisa et al., 2014) for the water surface extraction, and the ratio vegetation index (RVI) (Jordan, 1969), normalized difference vegetation index (NDVI) (Rouse et al., 1974), soil-adjusted vegetation index (SAVI) (Huete, 1988) and enhanced vegetation index (EVI) (Huete et al., 1997) for the green space extraction. Through a comprehensive evaluation, the two spectral indices of AWEI and NDVI were selected to extract the spectral features of water surface and green space

(1)

where GE represents the Google Earth images. Gain and bias are the transformation coefficients used for the relatively radiometric correction of the Google Earth images, which can be derived from a linear regression analysis between the Sentinel-2 BOA reflectance imagery and Google Earth imagery (Fig. 3). 3.2. Object-oriented UELC classification scheme 3.2.1. Image segmentation High-resolution Google Earth imagery which provides great spatial context is valuable for a better identification of the landscape object. Because of the excellent water and vegetation recognition property of the near-infrared (NIR) band (McFeeters, 2013; Li et al., 2013a), the Sentinel-2 10-m NIR band is up-sampled to 2 m by bilinear

Fig. 3. Overview of the preprocessing procedure. 109

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Fig. 4. Illustration of the multi-scale segmentation and object-based UELC mapping2.

respectively. For the spatial feature extraction, a lot of object-based spatial features were tested in our study, including the shape, textural and proximity features, with a consideration of the efficiency and accuracy in our object-based urban surface mapping, we selected the spatial homogeneity feature which is defined by the standard deviation of the reflectance values for the water extraction in our study. Through the cooperation of the spatial-spectral features and a visual interpretation of the image classification results, the UELC maps could be fast produced.

For the quantitative accuracy analysis, the ground truth maps of Shanghai and Seoul are digitized manually on-screen from the higher spatial resolution Google Earth images (> 1 m). The producer’s accuracy and user’s accuracy are used for the accuracy evaluation for each ecological land-cover type. And the Kappa coefficient and overall accuracy are selected for the overall classification accuracy assessment. Moreover, to demonstrate the superiority of our proposed method, three conventional supervised classification algorithms including kNearest Neighbors (KNN), Random Forest (RF), and the support vector machine (SVM) are used in the comparison tests based on single-source Sentinel-2 imagery and multi-source imagery respectively. For the implementation of conventional multi-source imagery-based method, the Gram-Schmidt pan Sharpening technique is firstly used for image fusion between the Google Earth panchromatic image (obtained by the integration of the three true-color channels) and the Sentinel-2 image in our study (Laben and Brower, 2000). Then the fused imagery that with improved spatial and spectral resolution is used for the multi-source imagery-based comparative classifications. Beyond that, the training samples of water surface, green space, and non-UELC land cover are obtained by random and extensive sampling based on the Google Earth images and Sentinel-2 images through visual interpretation. To validate the performance of methods, the classification maps generated by the proposed method and the comparative methods are quantitatively compared with the manually created reference maps. Table 3 shows the results of the Shanghai and Seoul test cases, in general, the highest accordance to the reference map is obtained by the proposed method in both test areas of Shanghai and Seoul, indicated by overall accuracies of 91.50% and 94.05%, and Kappa coefficients of 0.7668 and 0.8772, respectively. Generally, the multi-source imagerybased classification performs better than the single-source imagerybased classification. For the Shanghai test areas, the multi-source imagery-based KNN classifier and SVM classifier achieved the highest overall accuracy of 89.23% and 86.98% correspond to the pixel-based and object-based classifications respectively. While for the Seoul test areas, the multi-source imagery-based SVM classifier achieved the highest overall accuracies of 92.66% and 86.98% correspond to the pixel-based and object-based classifications. From the classification errors of the proposed method and the pixel-based and object-based method shown visually in Fig. 7, we can see that the proposed method is able to balance the errors of omission and commission, resulting in a higher accuracy of UELC mapping. Accounting for the characteristics of the two validation areas, the UELC of the inner area of Shanghai is made up mostly of small patches

3.3. Collective landscape metrics for UELC structure quantification UELC which consists of the water surface and green space, that together with the non-UELC land cover to make up the whole urban ground. The landscape structure is analyzed using FRAGSTATS 4.2 (Wu et al., 2002) on the two sides of composition and configuration in our study. By considering the landscape metric type and its applicability for the landscape analysis level, we select six widespread and well-documented landscape metrics for the eco-landscape analysis (Torras et al., 2009; Peng et al., 2010). The compositional metrics are the three indices of percentage of landscape (PLAND), largest patch index (LPI), and the area-weighted mean fractal dimension (FRAC_AM). And the three-configurational metrics are edge density (ED), shannon’s evenness index (SHEI), contagion index (CONTAG) (Table 2). Moreover, to examine whether the landscape structure is consistent across spatial regions, in this study, on basis of the landscape metrics calculated for different regions, we introduced a normalized difference landscape metric (NDLM) for the region difference evaluation:

NDLM =

Metricscale1 Metricscale2 Metricscale1 + Metricscale2

(2)

where Metricscale1 and Metricscale2 correspond to the landscape metrics those are applied for the different regions. 4. Results 4.1. Mapping accuracy and methods comparison With the implementation of the proposed method, the multi-source satellite images are standardized in terms of geographical location and radiation firstly (Fig.5), then the object-oriented UELC classification is used for the UELC mapping based on the standardized images. As shown in Fig.6, UELC maps of the six inner urban areas are produced. 110

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Table 2 Description of landscape metrics used in the study (from McGarigal and Ene, 2012; Wu et al., 2002). Landscape metric

Class

Level

Compositional Percentage of landscape (PLAND)

Area

Class

Largest patch index (LPI) Area-weighted mean patch fractal dimension index (FRAC_AM) Configurational Edge density (ED)

Description n a j = 1 ij A

PLAND = Pi =

× 100%

Pi is the percentage of patch type (class) i, aij is the area (m2 ) of patch ij, n is the number of patches corresponding to Area

Shape

Class

Class

patch type (class) i, and A is the total area of the landscape (m2 ).

LPI =

max (aij ) j =1 n A

× 100%

aij is the area (m2 ) of patch ij, and A is the total area of the landscape (m2 ).

FRAC _AM =

aij n j = 1 ai

×

2 × ln (0.25 × Pij) lnaij

aij represents the area (m2 ) of patch ij. ai is the area (m2 ) corresponding to patch type (class) i, n represents the patches number of corresponding to patch type (class) i, and Pij is the perimeter (m) of patch ij.

Edge

Class

Shannon’s evenness index (SHEI)

Diversity

Landscape

Contagion index (CONTAG)

Aggregation

Landscape

ED =

m e k = 1 ik A

× 10000

m represents the patch types (classes) number of the landscape; eik is the total edge length (m) of patch type (class) i of the landscape SHEI =

n (P × lnP ) i i=1 i ln n

n is the patch types (classes) number of the landscape; and Pi is the perimeter (n) of patch type (class) i. n i=1

n k = 1 Pi ×

CONTAG = 1 +

gik × ln Pi × m g k = 1 ik 2ln (n )

gik n g k = 1 ik

n is the patch types (classes) number of the landscape; Pi presents the percentage of patch type (class) i in the landscape; and gik is the adjacencies number of pixels that between patch types (classes) i and k.

of surface water and green space, thus lead to massive mixed pixels in the image. Meanwhile, the buildings of this area, which are made of various materials, also cause serious confusion and bring errors in the UELC mapping. As Fig. 7 (d, e, f) shows, surface water is easily confused with the asphalt; however, because of the effective use of the spatialspectral information of the Sentinel-2 and Google Earth images, the proposed method performs the best in the UELC classification in the Shanghai test area. Compared to the Seoul test area, the UELC mapping in Shanghai is more challenging due to the scattered UELC and the heterogenous non-UELC. Nevertheless, the proposed method achieves a high UELC mapping accuracy in both test regions according to the measures of Kappa coefficient and overall accuracy. It is worth noting that even though the comparative methods perform well in the easily recognized ground surfaces, a notable weakness appears when they are applied to small-object recognition. As the Fig. 7 (k, l, m) shows, an

elongated road within the UELC can be distinguished only by the proposed method. In conclusion, for the Seoul test area, the proposed method performs a little better than the other methods, whereas in the highly complex Shanghai area, the proposed method obtains significant improvement in terms of Kappa coefficient and overall accuracy. 4.2. Compositional and configurational characteristics of the inner-city areas PLAND reflects the total amount of UELC in the inner urban region. Among the six study cities, New York possesses the highest UELC percentage but Tokyo has the lowest. The largest proportions of green space and surface water occur in London and New York, respectively. As the Fig. 8(a) shows, green space takes up a greater proportion than surface water in the inner cities, except for New York. The LPI indicates

Fig. 5. A visualization of multi-source satellite imagery standardization in terms of geographic location and radiation, by taking the Shanghai test area for example. 111

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Fig. 6. UELC maps derived by the proposed method.

that the ecological land-cover type of the largest patch differs across the six inner cities. New York and Shanghai have the largest surface water patches and Seoul possesses the largest green space patch of the six inner cities. Meanwhile, the other three cities, which do not feature a prominent UELC patch, have more fragmented UELC landscape in their inner regions (Fan and Myint, 2014). FRAC_AM is straightforwardly used to measure overall shape complexity. The gentle FRAC_AM trend for the UELC among the six inner cities suggests that the shape complexity of the UELC in the different cities is similar. Their shape is mainly regular and monotonous as the FRAC_AM values are all less than 1.35 (Fig. 8 (b)). The inner urban region of Tokyo has the lowest FRAC_AM value, to some extent, indicating that the inner region of Tokyo is somewhat divorced from nature, and the UELC in Tokyo is of exceptionally low ecological quality. Compared to surface water, green space features more complicated shapes in all the study regions. The ED, SHEI and CONTAG indices are used to quantify the configurational characteristics among the six urban core regions. As Fig. 8 (c) shows, London achieves the highest ED as it contains smaller and more fragmented UELC patches, and that result in a better edge effect in the urban core region. The lowest ED occurs in the inner city of Tokyo, which could be caused by the low proportion of UELC and the low shape complexity of the UELC patches. By considering the EDs for the respective components of the UELC, it is noteworthy that green space obtains far higher EDs than surface water in all the inner cities, which demonstrates that the edge-related ecological quality is mainly

contributed by the green space. The Evenness (SHEI) index measures the degree of evenness among the land-cover types. The attributes of evenness are analyzed in two landscapes: the UELC landscape composed of surface water and green space, and the whole landscape composed of UELC and non-UELC land cover. As shown in Fig.9 (a), Shanghai has the highest SHEI value in the UELC landscape, which suggests a trend of equalization of green space and surface water in the inner city. However, the areas of green space and surface water are seriously disproportionate in Tokyo, which scores the lowest SHEI value in the UELC landscape. For the whole landscape, New York, London, and Seoul all score high SHEI value of above 0.92, indicating a proportional distribution of UELC and non-UELC land cover in these inner cities. The CONTAG index measures the spatial aggregation of patch types at the landscape level, where a higher CONTAG value means that a dominant land-cover type may exist in the landscape, and a lower CONTAG value indicates a high degree of fragmentation of the landscape (Fan and Myint, 2014). As Fig.9 (b) shows, the patch types in the inner city of Tokyo are highly aggregated, that is, with the highest CONTAG value both in the UELC landscape and the whole landscape. In contrast, Shanghai has the lowest CONTAG value in the UELC landscape, indicating that there are many small surface water or green space patches dispersed in the landscape. For the whole landscape, London, New York, and Seoul all have low CONTAG values, demonstrating a more interspersed distribution of the UELC and non-UELC land cover in the inner cities. 112

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Table 3 Accuracy assessment for the UELC maps. Shanghai

Single-source imagery

Methods

Pixel-based Object-based

Multi-source imagery

Pixel-based Object-based

Seoul

Single-source imagery

Pixel-based Object-based

Multi-source imagery

Pixel-based Object-based

Surface water

Green space

Prod. acc

User acc.

Prod. acc

User acc.

SVM RF KNN SVM RF KNN SVM RF KNN SVM RF KNN Proposed method

92.24% 82.26% 91.98% 92.33% 91.38% 88.57% 96.61% 65.08% 87.54% 95.15% 94.10% 90.97% 93.40%

80.56% 96.81% 77.92% 87.33% 86.80% 87.12% 28.40% 65.26% 85.98% 69.33% 25.39% 74.07% 96.88%

87.86% 86.02% 86.48% 79.78% 85.60% 72.68% 52.76% 57.37% 69.55% 84.07% 80.79% 82.78% 73.40%

52.78% 37.91% 55.31% 54.15% 32.41% 48.45% 76.68% 71.59% 69.28% 62.33% 51.09% 53.25% 73.20%

Methods

Surface water

SVM RF KNN SVM RF KNN SVM RF KNN SVM RF KNN Proposed method

Green space

Prod. acc

User acc.

Prod. acc

User acc.

96.44% 95.99% 96.69% 96.72% 96.27% 96.28% 96.49% 92.96% 95.31% 98.87% 93.20% 97.92% 97.31%

90.95% 93.71% 90.39% 88.20% 89.59% 89.85% 90.54% 96.51% 95.26% 80.37% 95.94% 91.24% 99.69%

93.78% 94.27% 93.05% 87.50% 90.19% 90.15% 93.84% 93.94% 94.06% 92.86% 93.73% 93.83% 89.32%

67.56% 62.31% 66.36% 85.33% 80.82% 81.80% 83.67% 82.25% 80.56% 86.86% 83.43% 82.01% 89.04%

4.3. Region difference of the UELC landscape

Kappa

Overall accuracy

0.6469 0.5112 0.6587 0.6543 0.4334 0.5898 0.4924 0.6014 0.7066 0.6882 0.4034 0.6296 0.7668

84.64% 76.73% 85.41% 85.73% 70.47% 83.05% 76.59% 86.17% 89.23% 86.98% 66.59% 84.10% 91.50%

Kappa

Overall accuracy

0.7312 0.6841 0.7163 0.8309 0.818 0.8247 0.8536 0.8461 0.8364 0.8564 0.8536 0.8457 0.8772

85.79% 82.93% 84.95% 91.68% 90.89% 91.25% 92.66% 92.32% 91.77% 92.78% 92.72% 92.23% 94.05%

understanding by effectively quantifying the percent tree canopy cover using the open-source Google Street View images. Being rid of lacking data, future developments in understanding eco-landscape may benefit much from the effective collaboration of the multi-source and multiview data. Urban landscape quantification facilitates a better analysis between complicated landscape structures and a specific ecological issue. Recent study revealed the environment temperature is significantly influenced by both the area and shape of the water body (Sun and Chen, 2012). Generally, the water body area with complex shapes has the greater positive effects on urban cooling. Likewise, the urban green space also plays a significant role in mitigating the urban heat intensity. Previous research has extensively explored the effects of the configuration and composition of green space on land surface temperatures (Peng et al., 2016; Maimaitiyiming et al., 2014). These studies indicate that the urban heat intensity is associated with both landscape configuration and composition, and generally the landscape composition affects thermal environment more. As urban microclimate is closely correlated with the landscape patterns in urban environment, urban landscaping inevitably results in various effects on the eco-environment, including biodiversity, human health as well as the regional urban thermal environment (Li et al., 2013b; Peng et al., 2012). The main eco-landscape features of the six-current metropolitan inner cities have been revealed in our study, accordingly, London which characterized with a high percentage, edge density and shape complexity of UELC seems have the best ecological quality induced by eco-landscape (Maimaitiyiming et al., 2014; Tian et al., 2014). It is assuredly that with serious ecological environment problems occur in the process of urbanization, the indepth interaction analysis between UELC landscape patterns and the underlying ecological effects needs to be further explored with the ecolandscape characterization in our study.

To examine the region difference of the inner-city landscape on different spatial regions, we applied all the selected landscape metrics to the inner-city areas at two regions of 2.5-km radius and 5-km radius from the urban center. According to the NDLM value shown in Table 4, Tokyo has a very high normalized LPI, indicating that the largest patch of this inner-city region is enormous different across different regions. Nevertheless, the landscape patterns of inner cities vary little between two regions, that is, with most of the normalized metrics below 0.2. By averaging the NDLM value for the corresponding cities, New York achieves the highest consistency degree, followed by Seoul and Shanghai. Tokyo presents the poorest region consistency among all the comparison cites. 5. Discussion The overall objective of this study is to quantify and compare the eco-landscape patterns among current metropolitan inner cities. To attain this goal, a novel method to map ecological land cover by coupling multi-source images was introduced. This new method exploits an effective extraction and cooperation of the spatial-spectral features of the multi-source imageries. However, despite a significant improvement in the urban surface mapping, the proposed method still needs to be further enhanced with the aim of comprehensively understanding urban landscapes. In the era of constantly enriching remotely sensed data, more multi-source satellite images can be exploited to refine the land-cover classification. Besides, our study characterized the ecolandscape under a satellite view, additionally, the landscape from the human view also provides significant knowledge for the urban landscape understanding. In current study, Seiferling et al. (2017) has proved the potential of human-view data in urban landscape 113

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Fig. 7. Comparison of urban surface mappings.

6. Conclusion

1) We proposed a cost-effective framework for urban surface mapping. The proposed framework depends only on the open access remotely sensed imagery and that would benefit a wider urban application of the remote sensing technique; 2) We introduced a new land cover classification method with an effective cooperation of the multi-source images. Unlike fusing the spectral and spatial information of multi-source images firstly, we make use of the spectral and spatial information of multi-source imagery in

Landscape structures have a wide range of impacts on the urban ecology and human living environment. In this study, we developed a new framework for UELC mapping, and accordingly, some in-depth analysis on landscape pattern among six world-class cities are carried out. The main novelties and contributions of our study can be concluded as follows: 114

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Fig. 8. Statistics of the landscape metrics at class level.

Fig. 9. Statistics of the landscape metrics at landscape level. 115

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Table 4 NDLM values for the region difference assessment.

Shanghai Tokyo London New York Paris Seoul

PLAND

LPI

FRAC_AM

ED

SHEI

CONTAG

Average

0.044 0.181 0.172 0.061 0.121 0.152

0.16 0.605 0.109 0.163 0.255 0.063

0.012 0.004 0.035 0.008 0.01 0.002

0.089 0.071 0.206 0.028 0.137 0.031

0.138 0.101 0.071 0.008 0.232 0.065

0.017 0 0.041 0.003 0.044 0.026

0.08 0.16 0.11 0.05 0.13 0.06

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different steps by their respective properties. Through the quantitative evaluation in the Shanghai and Seoul test cases, the proposed method achieved the highest overall accuracy of above 91% among the comparison methods; 3) The landscapes among six world-class cities are analyzed and compared in our study, due to the lack of high-precision land cover data, the similar research is rarely to be carried out previously. We carried out a horizontal comparisons of landscape attributes among the six metropolitan inner cities by using six widely used landscape metrics of PLAND, LPI, AMFRAC, ED, SHEI, and CONTAG. Depending on the results, New York features the highest UELC percentage and Tokyo features the lowest. London and Paris show a significantly superior ED among the six inner-city regions, which is followed by Shanghai, New York, Seoul, and Tokyo. From the analysis at the whole landscape level, the UELC and non-UELC land cover show the highest evenness and lowest spatial aggregation degrees in London and New York, respectively, while Tokyo is the opposite. According to the studies of the ecological effects of land cover (Du et al., 2016; Su et al., 2017; Tian et al., 2014), the inner-city region of London shows the best ecological quality of the six study cities, whereas the worst is found in Tokyo. Some common characteristics also can be found in the urban cores of the representative world cities. The UELC coverage in all inner-city regions are less than 50%, and the UELC is mainly composed of green space except for New York. The shape complexity of the UELC patches is at a low level in all the study cities. When focusing on the aspect of configurational characteristics, the ED of the UELC varies among the study cities, and the green space makes the major contribution to the edge effect; 4) We proposed a new normalized difference landscape metric (NDLM) in this study. Although a variety of landscape metrics have been proposed in previous study, however, almost none of them is aiming at measuring the landscape difference between two regions. Therefore, the new proposed NDLM is very supplement to the existing landscape metrics. Through analyzing the landscape difference at different spatial regions by using NDLM in this study, the landscape structure shows a low region difference in all the inner cities. Acknowledgement This research was supported by the National Natural Science Foundation of China (No. 41631178, 51608366). We also want to thank the anonymous reviwers and the editoral team for their valuable comments for improving the paper, and thank Ziwen Tan and Fukai Peng for the language improvement of our manuscript. References Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. Isprs J. Photogramm. Remote. Sens. 58 (3), 239–258. Blaschke, T., Hay, G.J., Kelly, M., Lang, S., Hofmann, P., Addink, E., Feitosa, R.Q., Meer, F.V.D., Werff, H.V.D., Coillie, F.V., 2014. Geographic object-based image analysis– towards a new paradigm. Isprs J. Photogramm. Remote. Sens. 87, 180–191. Belgiu, M., Csillik, O., 2017. Sentinel-2 cropland mapping using pixel-based and objectbased time-weighted dynamic time warping analysis. Remote Sens. Environ. 204, 509–523. Buyantuyev, A., Wu, J., 2010. Urban heat islands and landscape heterogeneity: linking

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