Delineation of the Urban-Rural Boundary Through Data Fusion: Applications to Improve Urban and Rural Environments and Promote Intensive and Healthy Urban Development

International Journal of Environmental Research and Public Health

Article Delineation of the Urban-Rural Boundary through Data Fusion: Applications to Improve Urban and Rural Environments and Promote Intensive and Healthy Urban Development

Jun Zhang * , Xiaodie Yuan, Xueping Tan and Xue Zhang

School of Architecture and Planning, Yunnan University, Kunming 650500, China; [email protected] (X.Y.); [email protected] (X.T.); [email protected] (X.Z.) * Correspondence: [email protected]

Abstract: As one of the most important methods for limiting urban sprawl, the accurate delineation of the urban–rural boundary not only promotes the intensive use of urban resources, but also helps to alleviate the urban issues caused by urban sprawl, realizing the intensive and healthy development of urban cities. Previous studies on delineating urban–rural boundaries were only based on the level of urban and rural development reflected by night-time light (NTL) data, ignoring the differences in the spatial development between urban and rural areas; so, the comprehensive consideration of NTL and point of interest (POI) data can help improve the accuracy of urban–rural boundary delineation. In this study, the NTL and POI data were fused using wavelet transform, and then the urban–rural boundary before and after data fusion was delineated by multiresolution segmentation. Finally, the delineation results were verified. The verification result shows that the accuracy of Citation: Zhang, J.; Yuan, X.; Tan, X.; delineating the urban–rural boundary using only NTL data is 84.20%, and the Kappa value is Zhang, X. Delineation of the 0.6549; the accuracy using the fusion of NTL and POI data on the basis of wavelet transform is Urban-Rural Boundary through Data 93.2%, and the Kappa value is 0.8132. Therefore, we concluded that the proposed method of using Fusion: Applications to Improve Urban and Rural Environments and wavelet transform to fuse NTL and POI data considers the differences between urban and rural Promote Intensive and Healthy development, which significantly improves the accuracy of the delineation of urban–rural boundaries. Urban Development. Int. J. Environ. Accurate delineation of urban–rural boundaries is helpful for optimizing internal spatial structure in Res. Public Health 2021, 18, 7180. both urban and rural areas, alleviating environmental problems resulting from urban development, https://doi.org/10.3390/ assisting the formulation of development policies for urban and rural fringes, and promoting the ijerph18137180 intensive and healthy development of urban areas.

Academic Editor: Paul B. Tchounwou Keywords: urban health; POI; urban environment; urban and rural development; urban and rural fringe Received: 10 June 2021 Accepted: 2 July 2021 Published: 5 July 2021 1. Introduction Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in As a boundary that separates urban from rural areas, the urban–rural boundary published maps and institutional afﬁl- refers to a city or region that is constantly changing in the process of urban expansion [1]. iations. Therefore, delineating the urban–rural boundary has become one of the most important connotations of new urbanization, and is important for alleviating various urban issues [2]. However, rapid urbanization construction is mainly driven by capital, which inevitably leads to the disorderly expansion of cities. The direct manifestation of a disorderly expansion is those cities constantly consumes rural land resources, and the use rate of these Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. resources is very low [3,4]. Therefore, the accurate delineation of the urban–rural boundary This article is an open access article can be used to effectively curb the rural resource plundering by urban areas, and improve distributed under the terms and the relationship between urban and rural areas, so as to promote the intensive use of urban conditions of the Creative Commons resources and achieve the healthy development of cities [5]. Attribution (CC BY) license (https:// Although rapid urbanization contributes to the rapid development of a regional econ- creativecommons.org/licenses/by/ omy and the rapid expansion of urban areas to rural areas, it has also created urban–rural 4.0/). environmental problems, including low efﬁciency of land use, environmental pollution,

Int. J. Environ. Res. Public Health 2021, 18, 7180. https://doi.org/10.3390/ijerph18137180 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2021, 18, 7180 2 of 19

ecosystem damage, housing tension, traffic congestion, and other urban problems which are increasingly prominent and may seriously affect the use of urban resources and the high-quality development of urban cities. These problems all result from the disorderly expansion of a city caused by a chaotic urban–rural boundary [6,7]. As a result of the imbalance between rapid urban development and urban governance, these urban environmental problems have gradually evolved into serious urban issues that plague urban planners [8]. One method of solving these urban issues is to intensively use urban resources and restrict the disorderly expansion of cities to alleviate the pressure caused by rapid urban development, and then realize the healthy development of urban cities [9]. However, at present, urban development resources are wasted, mainly manifested in the disorderly expansion of urban to rural areas [10]. For example, under the background of urban integration, many new cities have been built, but the use rate of these new cities is extremely low, resulting in the emergence of many “ghost towns” [11]. In summary, unreasonable urban expansion directly leads to the excessive waste of urban resources and affects the healthy development of urban and rural areas. Therefore, the accurate delineation of the urban–rural boundary is conducive to curbing disorderly urban expansion and promoting the intensive and efficient use of resources to achieve the healthy development of urban cities [12]. With the continuous progress of the national territory development plan, the goal of optimizing urban space, breaking through the dual urban–rural structure, and realizing the rapid and healthy development of cities was introduced [13,14]. Therefore, the accurate delineation of urban–rural boundaries helps with distinguishing urban from rural areas for the formulation of reasonable urban and rural development planning methods, to then further alleviate the environmental problems in the process of urban and rural development, and achieve the healthy development of urban and rural areas [15]. However, due to the significant differences between urban and rural areas in terms of the economic development level and infrastructure, urban–rural boundaries were previously delineated based on economic, population, and other statistical data [16]. In previous studies, the statistical data of the whole urban and rural areas were generally divided into different levels, with the higher level being the urban area and the lower level being the rural area, then the urban and rural areas were delineated. However, such delineation of urban–rural boundaries is highly subjective and less accuracy, which is why the delineation of the urban–rural boundary based on statistical data has been gradually replaced [17]. As data that can reflect the differences in urban spatial surface information, urban remote sensing has gradually replaced the use of traditional statistical data, such as administrative indicators, economic, and population statistical data, due to their advantages, including a larger spatial scale, wider collection range, and more connected time scale [16,18,19]. As one of the most widely used remote sensing data, NTL data mainly reflect the difference between urban and rural development levels by representing the light brightness of urban buildings and infrastructure at night [20,21]. Due to the characteristics of NTL data, NTL data can directly reflect the economy, population distribution, and development levels between urban and rural areas [22,23]. Since differences exist in the level of economic development between urban and rural areas, these areas can be judged according to these differences, which is reflected by the brightness of night lights, and then the boundary between urban and rural areas can be delineated, which makes NTL data more capable of expressing the heterogeneity of urban–rural space than traditional statistical data [24]. Due to the difference in time scale and spatial resolution, NTL data can be mainly divided into the following three types: the Defense Meteorological Program Operational Linescan System, Suomi National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite, and Luojia-01 data. The time scale of the Defense Meteorological Program Operational Linescan System data is 1992 to 2013, and the spatial resolution is 1000 m. Due to the limitation of the time scale, DMSP/OLS data are less used in current urban space research [25]. Although the spatial resolution of NPP/VIIRS data is only Int. J. Environ. Res. Public Health 2021, 18, 7180 3 of 19

500 m, which is higher than that of DMSP/OLS data, a spatial resolution of both 1000 m and 500 m is still relatively large for a single city. Therefore, these two kinds of data are mainly applied in macroscopic large-scale studies such as those of urban agglomeration. As a result, the application of these two data in small-scale research of a single city is relatively inadequate [26,27]. In October 2018, Luojia-01, an experimental satellite launched by Wuhan University, began providing free NTL data with a spatial resolution of 130 m to researchers around the world. Compared to DMSP/OLS and NPP/VIIRS data, the improved spatial resolution of Luojia-01 data enables the more detailed study of urban. Conversely, the higher spatial resolution of Luojia-01 also provides a more complete urban spatial structure. Therefore, the current applications of Luojia-01 data have mainly focused on the identification of the urban spatial structure, extraction of urban built- up areas, and delineation of urban–rural boundaries [28–30]. However, although the higher spatial resolution of Luojia-01 data has considerably improved the accuracy of urban space research, especially in the delineation of urban–rural boundaries, it is still necessary to further improve the delineation accuracy of urban–rural boundaries due to the disadvantages produced by light spillover in the NTL data [31]. POI data represent urban spatial geographic entities fed back to virtual space. As one of the urban spatial data, POI data can reflect the morphological structure of urban functions by presenting its density agglomeration situation in a geographical space, which has contributed to its increasingly extensive application in urban space [32]. In other words, due to the different development levels of urban and rural areas, significant differences exist in the infrastructure conditions of urban and rural areas. Urban and rural areas can be identified according to the infrastructure conditions, which are reflected by the POI agglomeration density, and then the boundary between urban and rural areas can be delineated. However, POI data have two shortcomings: Although POI data reflect the functional form of a certain area by expressing the size and attributes of density, the development differences of different regions in urban space lead to different density sizes. This is mainly reflected in an obvious trend: a decreasing number of POI agglomerations from the urban area to the rural fringe, which is also why POI is mainly applied in the identification of urban centers [33], delineation of urban functional areas [34], extraction of urban built-up areas [35], and the delineation of urban–rural boundaries. Another shortcoming of POI data is that they are obtained from outside of the actual geographic space of the urban city when calculating the density, which results in the urban form and urban–rural boundaries identified using the POI data being insufficiently realistic. However, with the in-depth study of POI data, it was found that a strong spatial correlation exists between NTL and POI data in urban spaces [36]: the denser the urban geographical entities, the stronger the city light [37]. It was shown in related studies that the data produced by the fusion of NTL and POI data can also be used in related studies of urban space, and the accuracy of the fused data in identifying urban morphology and delineating urban–rural boundaries is considerably higher, which indicates that data fusion will become one of the important directions in future urban studies [38,39]. The main principle of data fusion is to fuse the observation data obtained by different methods for the same target, so that the fused data simultaneously reflect the information characteristics of different data, which produces a more comprehensive and accurate judgment of the study object. As data fusion can improve the use efficiency of data, it has been widely used in urban studies as a method to improve the accuracy of the results [40]. Although the main methods of fusion of NTL and POI data are arithmetic average and weighted fusion, the effects produced by these fusion methods do not reflect the characteristics of the two kinds of data to the maximum extent [41–43]. One of the most basic and important data fusion methods is image fusion because image pixel fusion can be analyzed from the perspective of the most basic pixel scale to achieve the best fusion effect. The advantages of wavelet transform, which is used to fuse image pixels on multiple scales and frequencies, make it one of the most important methods for image pixel fusion, being widely used and applied [44,45]. Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 4 of 19

Int. J. Environ. Res. Public Health 2021, 18, 7180 4 of 19 scales and frequencies, make it one of the most important methods for image pixel fusion, being widely used and applied [44,45]. As one of the most important provincial capitals in Western China, as well as one of the citiesAs one with of thethe mostfastest important urbanization provincial rate in capitals China in in the Western past decade, China, as Kunming well as one had of a thehigh cities urban with and the rural fastest developmen urbanizationt rate rate of close in China to 70% in theat the past end decade, of 2019 Kunming [29]. However, had a highwith urbanthe rapid and development rural development of the ratecity, of a closeseries to of 70% urban at theenvironmental end of 2019 [problems29]. However, have witharisen, the for rapid example, development the ghost of town the city,of Chen a seriesggong of urbandemonstrates environmental the serious problems waste of have ur- arisen,ban resources for example, and the the bottleneck ghost town in of urban Chenggong construction. demonstrates Therefore, the serious the accurate waste ofdelinea- urban resourcestion of the and urban–rural the bottleneck bounda in urbanry of Kunming construction. will Therefore,help to better the understand accurate delineation the current of thesituation urban–rural of its urban boundary development, of Kunming so as will to helpformulate to better more understand reasonable the planning current situationand con- ofstruction its urban schemes development, to promote so as the to intensive formulate use more of urban reasonable and rural planning resources and constructionand, thus, to schemesachieve the to promotehealthy development the intensive of use Kunming. of urban In and this rural study, resources NTL and and, POI thus, data to were achieve sep- thearately healthy used development to delineate the of Kunming.urban–rural In boundary this study, of NTL Kunming, and POI and data then were a new separately method usedwas constructed. to delineate With the urban–rural this method, boundary wavelet oftransform Kunming, fusion and is then used a newto fuse method these wastwo constructed.kinds of data With for the this delineation method, wavelet of the transform urban–rural fusion boundary. is used toFinally, fuse these the results two kinds of the of data for the delineation of the urban–rural boundary. Finally, the results of the urban–rural urban–rural boundary delineation were further compared and verified in this study. boundary delineation were further compared and veriﬁed in this study. Kunming is the city with the highest level of urbanization and the most concentrated Kunming is the city with the highest level of urbanization and the most concentrated population in Southwest China. With the proposal and development of China’s One Belt population in Southwest China. With the proposal and development of China’s One Belt and One Road initiative, the urban construction in Kunming, as the western and central and One Road initiative, the urban construction in Kunming, as the western and central city of China and in Southeast Asia, has accelerated in recent years, and its urban expan- city of China and in Southeast Asia, has accelerated in recent years, and its urban expansion sion has been obvious. The consequent serious urban and rural environmental problems has been obvious. The consequent serious urban and rural environmental problems have have seriously challenged the healthy development of the city [40]. Therefore, the accurate seriously challenged the healthy development of the city [40]. Therefore, the accurate delineation of the urban–rural boundary in Kunming in this study will contribute to a delineation of the urban–rural boundary in Kunming in this study will contribute to a better understanding of the current urban and rural areas of the city, which can be used better understanding of the current urban and rural areas of the city, which can be used toto provideprovide solutionssolutions toto alleviatealleviate thethe environmentalenvironmental problemsproblems generatedgenerated inin thethe processprocess ofof urbanurban andand rural rural development development and and achieve achieve a healthier a healthier development development of both of both urban urban and rural and areasrural inareas Kunming. in Kunming.

2.2. Materials andand MethodsMethods 2.1.2.1. Study Area ◦ 0 ◦ 0 ◦ 0 ◦ 0 LocatedLocated atat 102102°1010 ′–103–103°4040 ′ EE andand 2424°2323 ′–26–26°2222 ′ N (Figure(Figure 11),), KunmingKunming hashas ﬁvefive major major urbanurban areas:areas: WuhuaWuhua District,District, XishanXishan District,District, GuanduGuandu District,District, ChenggongChenggong District,District, andand PanlongPanlong District.District.

FigureFigure 1.1. MapMap ofof thethe studystudy area.area.

The data used in this study mainly included Luojia-01 and POI data, of which Luojia- 01 data are available as a free download from the high-resolution earth observation system

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The data used in this study mainly included Luojia-01 and POI data, of which Luojia- 01 data are available as a free download from the high-resolution earth observation system data of Hubei Province and the application network (http://59.175.109.173:8888/In- dex.html, accessed on 30 May 2021). The Luojia-01 data of Kunming from October 2018 to October 2020 were obtained by visiting the website on 1 December 2020. Then, after monthly average processing of these data, the NTL data of Kunming were obtained, as shown in Figure 2. The POI data can be applied to electronic map navigation, which were obtained from online maps. The electronic map abstracts every building, infrastructure, and road in an urban geographical area into a point-type dataset, including four basic attributes—name, address, coordinate and category—which can be calculated, analyzed, and managed in a geographic information system. Since the aggregation of POI data in the geographic information system represents the amount of infrastructure in the region, it can express the Int. J. Environ. Res. Public Health 2021,level18, 7180 of infrastructure development in the region, whereas the aggregation of POI5 with of 19 the same attribute can also represent the spatial structure characteristics of different functions of the city. POI data were also obtained by visiting the application programming interface of Amap (https://www.amap.com/, accessed on 2 December 2020). In this study, datawe obtained of Hubei 22 Province and 447,201 and thecategories application and networkquantities, (http://59.175.109.173:8888/Index. respectively, of POI data in Kun- htmlming, accessedas of October on 30 2020 May through 2021). Thethe application Luojia-01 data programming of Kunming interface from October of Amap. 2018 Since to OctoberPOI data 2020 directly were obtained obtained through by visiting the interface the website contained on 1 Decembersome repeated 2020. and Then, meaning- after monthlyless data,average such as place processing names of and these landmarks, data, the it NTL was datanecessary of Kunming to screen, were check, obtained, filter, and as shownclean the in FigurePOI data2. (Figure 3).

FigureFigure 2.2. NTLNTL datadata ofof Kunming.Kunming.

The POI data can be applied to electronic map navigation, which were obtained from online maps. The electronic map abstracts every building, infrastructure, and road in an urban geographical area into a point-type dataset, including four basic attributes—name, address, coordinate and category—which can be calculated, analyzed, and managed in a geographic information system. Since the aggregation of POI data in the geographic information system represents the amount of infrastructure in the region, it can express the level of infrastructure development in the region, whereas the aggregation of POI with the same attribute can also represent the spatial structure characteristics of different functions of the city. POI data were also obtained by visiting the application programming interface of Amap (https://www.amap.com/, accessed on 2 December 2020). In this study, we obtained 22 and 447,201 categories and quantities, respectively, of POI data in Kunming as of October 2020 through the application programming interface of Amap. Since POI data directly obtained through the interface contained some repeated and meaningless data, such as place names and landmarks, it was necessary to screen, check, ﬁlter, and clean the POI data (Figure3).

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FigureFigure 3.3.POI POI datadata distributiondistribution inin Kunming.Kunming.

2.2.2.2. StudyStudy MethodsMethods 2.2.1.2.2.1. KernelKernel DensityDensity EstimateEstimate (KDE)(KDE) TheThe KDEKDE methodmethod mainlymainly reflectsreflects thethe regionalregional differencesdifferences inin physicalphysical geographicgeographic spacespace byby calculatingcalculating thethe degreedegree ofof agglomerationagglomeration ofof pointpoint andand lineline elementselements inin aa virtual virtual geographic space. On the premise of identifying different classification intervals, KDE can geographic space. On the premise of identifying different classification intervals, KDE can combine the similarity value with the maximum inter-category difference [46,47], which combine the similarity value with the maximum inter-category difference [46,47], which enables the urban POI density distribution calculated by KDE to reflect the difference in enables the urban POI density distribution calculated by KDE to reflect the difference in urban activities between the urban center and the suburbs [48]. urban activities between the urban center and the suburbs [48]. n 1 S − C f (s) = k i (1) ∑ j2 1 h− ()i ==1 ( ) (1) ℎ where f (s) is the function value calculated by the KDE of the spatial position, j is the thresholdwhere ( of) distanceis the function attenuation, valuen calculatedis the position by the in spaceKDE whereof the thespatial distance position, is less j is than the orthreshold equal to of the distance attenuation attenuation, threshold, n is the and positionk is the in spatial space weightingwhere the distance function, is in less which than differentor equal searchto the radiiattenuationh have an threshold, important and impact k is onthe the spatial results. weighting Therefore, function, it was necessary in which todifferent select an search appropriate radii h methodhave an toimportant determine impact the search on the radius results. value Therefore, in this study. it was necessary to select an appropriate method to determiner the search! radius value in this study. 1 −0.2 h = 0.9 × min SD, Dm × × n , (2) ℎ=0.9×， ×ln2 ×., (2)

wherewhereh ℎis is the the search search radius, radius,n is the is the number number of elementsof elements in thein the space, space,SD is the is the standard stand- distance, and Dm is the median distance. ard distance, and is the median distance. 2.2.2. Wavelet Transform 2.2.2. Wavelet Transform Wavelet transform is an algorithm that fuses the time domain and the frequency of the Wavelet transform is an algorithm that fuses the time domain and the frequency of image function in image fusion. Compared to other fusion algorithms, wavelet transform the image function in image fusion. Compared to other fusion algorithms, wavelet trans- can fully consider the interactive relationship between the time domain and frequency of an form can fully consider the interactive relationship between the time domain and fre- image by constantly changing the scale under the premise of ensuring the operation in the quency of an image by constantly changing the scale under the premise of ensuring the time domain, which increases the accuracy of the result of image fusion [49]. Additionally, operation in the time domain, which increases the accuracy of the result of image fusion wavelet transform can decompose the image information with the assistance of high-pass [49]. Additionally, wavelet transform can decompose the image information with the as- and low-pass ﬁlters with its unique microscope-type focusing function to unify the steps of thesistance image of in high-pass the time andand frequencylow-pass filters domains with [50 its]. unique microscope-type focusing function Theto unify dynamic the steps time-frequency of the image change in the window time and of frequency wavelet transform domains can[50]. amplify some part ofThe the dynamic image featurestime-frequency by localizing change the window spatial of frequency, wavelet transform which enables can amplify the image some part of the image features by localizing the spatial frequency, which enables the image

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signal to be decomposed into two independent spatial and temporal signals without losing the information contained in the original image signal [51]. The formula of wavelet transform is as follows:

+∞ 1 Z t − b WT(α, τ) = f (t)ϕ(t) = √ f (t) ϕ dt (3) α α −∞

where f (t) is the signal vector, ϕ(t) is the basic wavelet function, α is scale, τ is translation, and b is a parameter. Regarding the principle of wavelet transform, the original image can be regarded as a two-dimensional matrix in the wavelet transform, assuming that the area of the matrix is N × N, and there is N = 2n (where N is a non-negative integer). The two- dimensional matrix is broken down into four subregions after each wavelet transform, although the area of the four sub-regions is only 1/4 of the original matrix, as shown in Figure4. The subregions each contain the wavelet coefficients of the corresponding frequency bands. Therefore, wavelet transform can also be understood as interval sampling in the horizontal and vertical directions [52]. In the second round of wavelet transform, the wavelet coefficients generated by the convolution of the LL band in the horizontal direction with a low-pass filter are an approximate representation of the image:

0 Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW f (m, n) = f j−1 (x, y), ϕ(x − 2m, y − 2n) 8 of(4) 19 2j 2

FigureFigure 4. 4.The The principle principle of of wavelet wavelet transform. transform.

TheAfterHL waveletfrequency transform band isdecomposition, the wavelet coefficient the high- and generated low-frequency by convolution components in the of horizontalthe image directionin different with directions a low-pass could filter be andobtained; then convolution the high-frequency in the vertical component direction con- withtained a high-passthe detailed filter, part which of the represents original image, the singular which is characteristics locally amplified of the [53]. horizontal Detailed directioninformation of the of image:the different images was compared in the appropriate wavelet transform domain, fusion was realized in the wavelet domain scale, and finally inverse transfor- 1 D 1 E ( ) = − ( ) ( − − ) mation was conductedf2 j(Figurem, n 4). f2j 1 x, y , ψ x 2m, y 2n . (5)

2.2.3. Multiresolution Segmentation As an object-oriented bottom-up image segmentation algorithm, multiresolution segmentation is implemented on the basis of the use of an image analysis software such as eCognition. Multiresolution segmentation is mainly used to segment a target image by merging adjacent elements, which can preserve the heterogeneity of the image in the maximum average segment and the homogeneity in the maximum segment, so multiresolution segmentation has been widely used in image segmentation [54]. The scale, shape, and compactness of the target image are determined by multiresolution segmentation with appropriate scale parameters. After the image is multiresolution-segmented using the appropriate scale parameters, the image segmentation effect is the most accurate only when the number of images obtained, the average value of image pixels, and the weighted mean variance of the image area are all the largest. However, the current best method of determining the appropriate scale parameters is visual interpretation, which is inefficient and cannot meet the needs of large-scale image computing. Therefore, researchers have developed a tool named estimation of scale parameters to quickly determine the appropriate scale parameters for image segmentation. The scale parameters determined through this tool improve the efficiency and accuracy of image interpretation [55]. The theoretical framework of research is shown in Figure 5.

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The LH frequency band is the wavelet coefficient generated by convolution in the horizontal direction with a high-pass filter and then convolution in the vertical direction with a low-pass filter, which represents the singular characteristics of the vertical direction of the image: 2 D 2 E ( ) = − ( ) ( − − ) f2j m, n f2j 1 x, y , ψ x 2m, y 2n . (6) The HH band is the wavelet coefficient generated by the convolution of the high- pass filter in the horizontal and vertical directions, which represents the diagonal edge characteristics of the image:

2 D 3 E ( ) = − ( ) ( − − ) f2j m, n f2j 1 x, y , ψ x 2m, y 2n . (7)

After wavelet transform decomposition, the high- and low-frequency components of the image in different directions could be obtained; the high-frequency component contained the detailed part of the original image, which is locally ampliﬁed [53]. Detailed information of the different images was compared in the appropriate wavelet transform domain, fusion was realized in the wavelet domain scale, and ﬁnally inverse transformation was conducted (Figure4).

2.2.3. Multiresolution Segmentation As an object-oriented bottom-up image segmentation algorithm, multiresolution segmentation is implemented on the basis of the use of an image analysis software such as eCognition. Multiresolution segmentation is mainly used to segment a target image by merging adjacent elements, which can preserve the heterogeneity of the image in the maximum average segment and the homogeneity in the maximum segment, so multiresolution segmentation has been widely used in image segmentation [54]. The scale, shape, and compactness of the target image are determined by multiresolution segmentation with appropriate scale parameters. After the image is multiresolution- segmented using the appropriate scale parameters, the image segmentation effect is the most accurate only when the number of images obtained, the average value of image pixels, and the weighted mean variance of the image area are all the largest. However, the current best method of determining the appropriate scale parameters is visual interpretation, which is inefﬁcient and cannot meet the needs of large-scale image computing. Therefore, researchers have developed a tool named estimation of scale parameters to quickly determine the appropriate scale parameters for image segmentation. The scale parameters determined through this tool improve the efﬁciency and accuracy of image interpretation [55]. The theoretical framework of research is shown in Figure5. Int. J. Environ. Res. Public Health 2021, 18, 7180 9 of 19 Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 9 of 19

Figure 5. Method for delineation of the urban boundary. Figure 5. Method for delineation of the urban boundary. 3. Results 3. Results 3.1.3.1. Urban–Rural Urban–Rural BoundaryBoundary Delineated by by Different Different Data Data 3.1.1.3.1.1. Urban–Rural Urban–Rural BoundaryBoundary Delineated by by Luojia-01 Luojia-01 Data Data TheThe resultsresults ofof Luojia-01Luojia-01 NTL data processing processing are are shown shown in in Figure Figure 2.2 .NTL NTL data data of of Kunming.Kunming. From From the the NTL NTL figure figure of of Kunming, Kunming, the the range range of of high high NTL NTL values values is is mainly mainly located lo- incated Wuhua in Wuhua District, District, Dongfeng Dongfeng Square Square of Panlong of Panlong District, District, Luosiwan Luosiwan International International Trade Trade City of Guandu District, Chenggong University Town, and Changshui Airport of City of Guandu District, Chenggong University Town, and Changshui Airport of Guandu Guandu District. The NTL values of other regions, especially the western part of Xishan District. The NTL values of other regions, especially the western part of Xishan District, District, are relatively low. The distribution of NTL’s range of high and low values also are relatively low. The distribution of NTL’s range of high and low values also reflects the reflects the obvious differences in the level of urban and rural development in Kunming. obvious differences in the level of urban and rural development in Kunming. The scale, The scale, shape, and compactness parameters of the multiresolution segmentation deter- shape, and compactness parameters of the multiresolution segmentation determined by mined by estimation of scale parameters are 17, 0.5, and 0.6, respectively. estimation of scale parameters are 17, 0.5, and 0.6, respectively. The NTL image was segmented by the determined segmentation parameters, and the urban–ruralThe NTL boundary image was was segmented delineated, by as the shown determined in Figure segmentation 6. We found parameters, that the urban and the urban–ruralareas identified boundary by the was NTL delineated, data are mainly as shown concentrated in Figure6 .in We Wuhua found District, that the Dongfeng urban areas identifiedSquare of by Panlong the NTL District, data are Chenggong mainly concentrated University inTown, Wuhua and District, Changshui Dongfeng Airport Square of ofGuandu Panlong District, District, for Chenggonga total area of University 454.27 km2, Town, accounting and Changshuifor 17.37% of Airport the total of admin- Guandu 2 District,istrative for area. atotal The urban–rural area of 454.27 boundaries km , accounting delineated for by 17.37% the NTL of data the totalare severely administrative frag- area.mented The and urban–rural complicated boundaries due to the delineated obvious diff byerences the NTL in data the arenight-time severely light fragmented in the dif- and complicatedferent areas dueof the to urban the obvious space. Therefore, differences on in the the whole, night-time the effect light of in urban–rural the different bound- areas of thearies urban delineated space. by Therefore, NTL data on is thepoor. whole, the effect of urban–rural boundaries delineated by NTL data is poor.

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Figure 6. Urban–rural boundary delineated by NTL data. FigureFigure 6.6.Urban–rural Urban–rural boundaryboundary delineateddelineated byby NTLNTL data. data.

3.1.2.3.1.2.3.1.2. Urban–Rural Urban–RuralUrban–Rural Boundary BoundaryBoundary Delineated DelineatedDelineated after afterafter the thethe Fusion FusionFusion of ofof POI POIPOI and andand Luojia-01 Luojia-01Luojia-01 Data DataData POIPOIPOI datadatadata representrepresent the the actualactual unitunit unit ofof of eacheach each citycity city inin in aa virtual avirtual virtual geographicgeographic geographic space,space, space, soso POIPOI so POIdatadata data cancan reflect canreflect reflect thethe developmentdevelopment the development statusstatus status ofof urbanurban of urban infrastructureinfrastructure infrastructure byby describingdescribing by describing itsits densitydensity its densityagglomeration.agglomeration. agglomeration. SignificantSignificant Significant differencesdifferences differences existexist inin exist thethe indensitydensity the density distributiondistribution distribution ofof POIPOI of data,data, POI data,espe-espe- especiallyciallycially fromfrom from thethe urban theurban urban centercenter center toto thethe to fringefringe the fringe andand finally andfinally finally toto thethe to countrysidecountryside the countryside [56].[56]. BasedBased [56]. Based onon thethe onkernelkernel the kernel densitydensity density estimateestimate estimate ofof POIPOI of inin POI KunmingKunming in Kunming ususinging usingFormulasFormulas Formulas (1)(1) andand (1) (2),(2), and thethe (2), spatialspatial the spatial distri-distri- distributionbutionbution ofof POIPOI of POIinin KunmingKunming in Kunming waswas wasobtained,obtained, obtained, asas shsh asownown shown inin FigureFigure in Figure 7.7. FromFrom7. From FigureFigure Figure 6,6, the6the, the POIPOI POIdensitydensity density distributiondistribution distribution showsshows shows anan obviousanobvious obvious circularcircular circular decliningdeclining declining patternpattern pattern fromfrom from thethe the urbanurban urban centercenter center toto tothethe the urbanurban urban fringe.fringe. fringe. TheThe The POIPOI POI densitydensity density isis the isthe the highesthighest highest inin inthethe the urbanurban urban center,center, center, decreasingdecreasing decreasing withwith with in-in- increasingcreasingcreasing distancedistance distance fromfrom from thethe the urbanurban center, center, until until itit reachesreachesreaches zero.zero.zero. Therefore, Therefore,Therefore, we wewe concluded concludedconcluded thatthatthat an anan obviousobviousobvious correlation correlation exists exists betweenbetween between thethe the distributiondistribution distribution ofof of POIPOI POI inin in urbanurban urban spacespace space andand and thethe thelevellevel level ofof urbanurban of urban andand and ruralrural rural development,development, development, thatthat is thatis,, fromfrom is, from thethe urbanurban the urban centercenter center toto thethe to urbanurban the urban fringe.fringe. fringe.WithWith aa With decreasingdecreasing a decreasing levellevel levelofof urbanurban of urban andand andruralrural rural development,development, development, thethe the POIPOI POI densitydensity density alsoalso also tendstends tends toto todecrease.decrease. decrease.

FigureFigureFigure 7. 7.7.The TheThe KDE KDEKDE of ofof POI. POI.POI.

POIPOIPOI datadata data reflectreflect reflect thethe the developmentdevelopment development differencesdifferences differences in in in infrastructure infrastructure infrastructure in in in different different different areas areas areas of of of the the the citycitycity byby by describingdescribing describing thethe the aggregationaggregation aggregation of of of data data data in in ina a a virtualvirtual virtual geographicgeographic geographic space, space, space, whereas whereas whereas NTL NTL NTL data data data reflectreflectreflect the thethe level levellevel of ofof urban urbanurban and andand rural ruralrural development developmentdevelopment by byby describing describingdescribing the thethe brightness brightnessbrightness of ofof lights lightslights in inin different areas of the city, so a significant spatial correlation exists between the POI density

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different areas of the city, so a significant spatial correlation exists between the POI density and the NTL brightness in an urban space [57]. This is why we attempted to use wavelet transformand the NTL to fuse brightness POI and in anNTL urban data. space The [ma57].in This principle is why of we image attempted fusion tois usethat wavelet the fusedtransform image can to fusehighlight POI and the NTLcharacteristic data. The parts main of principle the original of image image. fusion The characteristic is that the fused partimage of the canoriginal highlight image the corresponds characteristic to partsthe absolute of the original value of image. the wavelet The characteristic coefficient in part the ofprocess the original of wavelet image transform. corresponds In other to the wo absoluterds, as valuelong as of the waveletabsolute coefficientvalue of the in the waveletprocess coefficient of wavelet is transform.the maximum, In other the words,characteristic as long part as the of absolute the original value image of the after wavelet waveletcoefficient transform is the is the maximum, most obvious, the characteristic that is, the image part fusion of the can original achieve image the best after effect. wavelet Whentransform POI and is NTL the most data obvious,were fused, that the is, theoptima imagel scale fusion of image can achieve fusion the in this best study effect. de- When terminedPOI and by NTLthe difference data were in fused, wavelet the optimaltransfor scalem coefficients of image fusiongenerated in this by studydifferent determined data wasby seven the difference(Figure 8). in wavelet transform coefficients generated by different data was seven (Figure8).

Figure 8. Variation in wavelet coefficient of variance. Figure 8. Variation in wavelet coefficient of variance. The image fused by wavelet transform is shown in Figure9. After using multires- olutionThe image segmentation fused by wavelet to segment transform the fused is shown NTL_POI in Figure image, 9. After we foundusing thatmultireso- the scale lutionparameters segmentation scale, to shape, segment and compactnessthe fused NT determinedL_POI image, by we the found estimation that the of scale scale parame- pa- rametersters were scale, 16, shape, 0.5, and and 0.4, compactness respectively. determ Accordingined by to the the estimation determined of scale scale parameters, parameters the wereurban–rural 16, 0.5, and boundary 0.4, respec couldtively. be According delineated, to the as shown determ inined Figure scale9. parameters, Figure 10 shows the ur- that ban–ruralthe urban boundary area identified could be bydelineated, NTL_POI as data shown mainly in Figure concentrated 9. Figure in 10 Dongfeng shows that Square the of urbanPanlong area identified District, Luosiwan by NTL_POI International data main Tradely concentrated City of Guandu in District,Dongfeng Chenggong Square of Uni- Panlongversity District, Town, Luosiwan and Changshui International Airport Trade of Guandu City of District, Guandu with District, a total Chenggong area of 427.93 Uni- km2, versityaccounting Town, and for 16.36%Changshui of the Airport total administrative of Guandu District, area. Fromwith thea total results area ofof the 427.93 urban–rural km2, accountingboundaries for 16.36% delineated of the by total NTL_POI administrative data, the area. NTL_POI From fusion the results data of comprehensively the urban–rural con- boundariessidered delineated the differences by NTL_POI in urban data, and ruralthe NTL_POI development fusion levels data comprehensively and urban development con- sideredwithin the the differences region, which in urban resulted and inrural the identifieddevelopment urban levels scope and being urban more development complete and withinboundary the region, information which resulted being more in the abundant identified due urban to the scope correction being ofmore the complete fragmentation and of boundarythe urban–rural information boundaries. being more abundant due to the correction of the fragmentation of the urban–rural boundaries.

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FigureFigure 9. 9.Fusion Fusion ofof NTLNTL andand POIPOI data. data. Figure 9. Fusion of NTL and POI data.

Figure 10. Urban–rural boundaries delineated by NTL_POI data. FigureFigure 10. 10.Urban–rural Urban–rural boundaries boundaries delineated delineated by by NTL_POI NTL_POI data. data. 3.2. Comparative Analysis of Data 3.2.3.2. Comparative Comparative Analysis Analysis of of Data Data Figure 11 compares the results from before and after data fusion. By analyzing Figure Figure 11 compares the results from before and after data fusion. By analyzing Figure 10, 10, it Figurecan be 11found compares that many the results similarities from before exist between and after the data urban fusion. boundaries By analyzing delineated Figure it can be found that many similarities exist between the urban boundaries delineated by by10, NTLit can and be foundNTL_POI that data. many Firstly, similarities the spatial exist betweenstructures the of urban Kunming boundaries reflected delineated by these NTL and NTL_POI data. Firstly, the spatial structures of Kunming reﬂected by these two twoby NTL kinds and of NTL_POIdata are similar; data. Firstly, secondly, the thespatial distribution structures ranges of Kunming of high andreflected low values by these of kinds of data are similar; secondly, the distribution ranges of high and low values of NTL NTLtwo kinds and NTL_POI of data are data similar; from secondly, the urban the center distribution to the fringe ranges to of the high countryside and low values are also of and NTL_POI data from the urban center to the fringe to the countryside are also similar, similar,NTL and that NTL_POI is, the range data offrom high the values urban is center mainly to distributed the fringe into thethe vicinitycountryside of Dongfeng are also that is, the range of high values is mainly distributed in the vicinity of Dongfeng Square, Square,similar, Luosiwan,that is, the rangeUniversity of high Town values and is Chan mainlygshui distributed Airport, inwhile the vicinityin other of regions Dongfeng the Luosiwan, University Town and Changshui Airport, while in other regions the distribution distributionSquare, Luosiwan, is mainly University medium Town and low,and whichChangshui indicates Airport, that while the NTL_POI in other regionsfused data the is mainly medium and low, which indicates that the NTL_POI fused data fully retained the fullydistribution retained is the mainly characteristics medium ofand NTL low, data which and indicatescan reflect that the urbanthe NTL_POI and rural fused develop- data characteristicsfully retained ofthe NTL characteristics data and can of NTL reﬂect data the and urban can and reflect rural the development urban and rural level develop- within thement city level by describing within the thecity brightness by describing value the of brightness light. value of light. mentBy level comparing within the NTL_POI city by describing data to NTL the data,brightness we found value that of light. in the actual process of delineatingBy comparing urban–rural NTL_POI boundaries, data to NTL NTL data data, only we determinedfound that inwhether the actual the regionprocess be- of longsdelineating to urban urban–rural or rural areas boundaries, by describing NTL thdatae threshold only determined of light brightness, whether the which region causes be- largelongs errorsto urban to aor certain rural areas extent. by Fordescribing example, th elight threshold brightness of light is highbrightness, for main which roads, causes airports,large errorsports, toand a certainother areasextent. of Forthe example,city, which light produce brightness an obvious is high forbrightness main roads, contrast air- withports, the ports, surrounding and other urban areas areas of the in city,the delineation which produce of urban–rural an obvious boundaries, brightness resulting contrast inwith errors the surroundingin the delineation urban of areas urban–rural in the delineation boundaries. of urban–ruralIn the vicinity boundaries, of Changshui resulting Air- port,in errors although in the the delineation light intensity of urban–rural is high and boundaries. the light range In the is vicinitywide, only of Changshui a small part Air- of theport, area although was classified the light as intensity an urban is area, high and and most the light of the range area isis wide,an uninhabited only a small area part such of asthe the area airport was classifiedrunway and as an ancillary urban area,facilities, and asmost shown of the in areaFigure is an11a. uninhabited In addition, area the lightsuch as the airport runway and ancillary facilities, as shown in Figure 11a. In addition, the light

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values near the main traffic network are significantly contrasted with the surrounding areas, which resulted in the traffic road network being delineated into an urban area, especially the main road leading to Xishan District, as shown in Figure 11c. However, although most residential areas, shopping malls, and other areas are located in the urban center, only a few lights are produced at night, which created obvious light holes in the NTL image. These light holes fragment the internal space of the urban city, thereby affecting the actual scope of the city and the delineation of the urban–rural boundary. This is also a concentrated manifestation of the difference in the level of urban and rural development in the process of urbanization. NTL_POI data, which fuses POI data, maintained the characteristics of NTL data while adding the development differences in urban infrastructure reflected by POI data. Therefore, NTL_POI data not only fully considered the level of the light value in an area, but also compared the concentration of urban POI density when delineating the urban– rural boundary, which increased the clarity of the urban scope and urban–rural area in the main urban traffic network, airport, and internal space. Therefore, we concluded that although NTL data can represent the spatial structure within a city by reflecting the level of urban and rural development, due to the limitations of NTL data the data can also produce high values in non-urban areas, which has a stronger impact on the scope of the city. However, NTL_POI data fused with POI data considered urban functions based on urban Int. J. Environ. Res. Publiclight Healthbrightness,2021, 18 ,so 7180 that NTL_POI data could comprehensively consider the aggregation 13 of 19 degree of POI in areas with significant differences in light values, which plays a key role in complementing the detailed information of urban interior space and fringes.

Figure 11. ComparisonFigure 11. Comparison before and before after data and fusionafter data ((a,d fusion) are the ((a, airportd) are the areas airport identified areas byidentified NTL and by NTL_POINTL data respectively;and (b,e NTL_POI) are Dongfeng data Squarerespectively; and Guandu (b,e) are Ancient Dongfeng Town Square identified and byGuan NTLdu and Ancient NTL_POI Town data identified respectively; and by NTL and NTL_POI data respectively; and (c,f) are Xishan Villa Area identified by NTL and (c,f) are Xishan Villa Area identified by NTL and NTL_POI data respectively). NTL_POI data respectively). By comparing NTL_POI data to NTL data, we found that in the actual process of 3.3. Comparativedelineating Verification urban–rural of Urban–Rural boundaries, Boundary NTL Delineation data only Results determined whether the region belongs By comparingto urban the orurban–rural rural areas boundaries by describing delineated the threshold by NTL of lightand NTL_POI brightness, data which in causes large Figure 12, it canerrors be found to a certain that, firstly, extent. the For urban example, areas light delineated brightness by NTL is high and for NTL_POI main roads, airports, data are 454.27ports, and 427.93 and other km2, areasrespectively, of the city, accounting which produce for 17.37% anobvious and 16.36% brightness of the total contrast with the area of the administrativesurrounding region, urban areas respectively. in the delineation Secondly, of from urban–rural the perspective boundaries, of urban– resulting in errors in rural boundaries,the delineationalthough the of urban–rural boundaries.boundary delineated In the vicinity by NTL of Changshui and NTL_POI Airport, although data are all mainlythe light concentrated intensity is in high the andvicinity the light of Changshui range is wide, Airport, only Luosiwan, a small part and of the area was Guandu ancientclassified town, asthe an urba urbann boundary area, and delineated most of the by area NTL is an data uninhabited is larger areathan suchthat as the airport runway and ancillary facilities, as shown in Figure 11a. In addition, the light values near the main traffic network are significantly contrasted with the surrounding areas, which resulted in the traffic road network being delineated into an urban area, especially the main road leading to Xishan District, as shown in Figure 11c. However, although most residential areas, shopping malls, and other areas are located in the urban center, only a few lights are produced at night, which created obvious light holes in the NTL image. These light holes fragment the internal space of the urban city, thereby affecting the actual scope of the city and the delineation of the urban–rural boundary. This is also a concentrated manifestation of the difference in the level of urban and rural development in the process of urbanization. NTL_POI data, which fuses POI data, maintained the characteristics of NTL data while adding the development differences in urban infrastructure reflected by POI data. Therefore, NTL_POI data not only fully considered the level of the light value in an area, but also compared the concentration of urban POI density when delineating the urban– rural boundary, which increased the clarity of the urban scope and urban–rural area in the main urban traffic network, airport, and internal space. Therefore, we concluded that although NTL data can represent the spatial structure within a city by reflecting the level of urban and rural development, due to the limitations of NTL data the data can also produce high values in non-urban areas, which has a stronger impact on the scope of the city. However, NTL_POI data fused with POI data considered urban functions based on urban light brightness, so that NTL_POI data could comprehensively consider the aggregation Int. J. Environ. Res. Public Health 2021, 18, 7180 14 of 19 Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 14 of 19

degree of POI in areas with significant differences in light values, which plays a key role in delineated by NTL_POIcomplementing data. Thirdly, the detailed in the information vicinity of Changshui of urban interior Airport space (Figure and 12a,d), fringes. the urban boundary delineated by NTL is far larger than that delineated by NTL_POI data because although3.3. functional Comparative urban Verification facilities of ne Urban–Ruralar Changshui Boundary Airport Delineation are lacking, Results there are strong lights, so mostBy airport comparing runways the urban–rural are classified boundaries as urban area delineated by NTL bydata. NTL Addition- and NTL_POI data in ally, in GuanduFigure ancient 12, ittown can be(Figure found 12b,c), that, firstly, an urban the urban void areas phenomenon delineated occurs by NTL in and the NTL_POI data urban–rural boundaryare 454.27 delineated and 427.93 by km NTL,2, respectively, so NTL data accounting did not delineate for 17.37% Guandu and 16.36% ancient of the total area town as an urbanof the area administrative due to its low region,light value respectively. at night. However, Secondly, fromGuandu the ancient perspective town of urban–rural is the main urbanboundaries, area in Kunming. although Notably, the urban–rural significant boundary differences delineated also exist by between NTL and the NTL_POI data urban and ruralare boundaries all mainly delineated concentrated by NTL in the and vicinity NTL_POI of Changshui data, resulting Airport, from Luosiwan, the fact and Guandu that Luosiwan,ancient as an area town, connecting the urban the boundary main urban delineated area of by Kunming NTL data and is larger Chenggong than that delineated New District, isby still NTL_POI under construction. data. Thirdly, Theref in theore, vicinity although of Changshui the light Airportvalue here (Figure is rela- 12a,d), the urban tively strong atboundary night, the delineated development by NTLof urban is far infrastructure larger than that is slow. delineated by NTL_POI data because Therefore,although we concluded functional that since urban the facilities NTL data near only Changshui consider Airport the light are intensity lacking, of there are strong a region whenlights, delineating so most the airport urban–rural runways boun aredary, classified in airports, as urban ports, area major by NTL traffic data. net- Additionally, in works, and constructionGuandu ancient areas townwith strong (Figure lights, 12b,c), the an urbandelineated void phenomenonurban–rural boundaries occurs in the urban–rural will be larger boundarythan the actual delineated urban–rural by NTL, boundaries, so NTL data whereas did not in delineateresidential Guandu areas and ancient town as shopping mallsan with urban weak area lights, due to the its boundaries low light value will be at night.smaller However, than the Guanduactual urban– ancient town is the rural boundaries.main The urban data area fused in Kunming.with POI Notably,data not significantonly comprehensively differences also consider exist between the the urban development differenceand rural boundariesbetween light delineated intensity by and NTL urban and NTL_POIinfrastructure, data, but resulting also con- from the fact that sider the influenceLuosiwan, of the asdevelopment an area connecting state of theurban main infrastructure urban area ofon Kunming the urban and scope Chenggong New and urban–ruralDistrict, boundary is still in underthe region construction. with an obvious Therefore, light although intensity the contrast, light value which here is relatively increases the accuracystrong at and night, reliability the development of the urban–rural of urban boundary infrastructure division. is slow.

Figure 12. ComparisonFigure 12. Comparison of urban–rural of urban–rural boundary delineation boundary resultsdelineation ((a,c )results are the (( airporta,c) are part the airport of the urban-rural part of the boundary delineated byurban-rural NTL and boundary NTL_POI respectively;delineated by ( bNTL,d) areand the NTL_POI Guandu respectively; ancient town (b and,d) are Luosiwan the Guandu part ofancient the urban-rural town and Luosiwan part of the urban-rural boundary delineated by NTL and NTL_POI respec- boundary delineated by NTL and NTL_POI respectively). tively.). Therefore, we concluded that since the NTL data only consider the light intensity In this study,of a region1000 random when delineatingpixel points thelocated urban–rural in Kunming boundary, were selected in airports, to verify ports, major trafﬁc the urban–ruralnetworks, boundaries and delineated construction by areas NTL with and strong NTL_POI lights, data. the delineatedWe found urban–ruralafter on- boundaries site confirmationwill with be larger Google than Earth the that actual among urban–rural the 1000boundaries, selected pixel whereas points, in181 residential pixel areas and points were locatedshopping in urban malls areas with and weak 819 lights, pixel the points boundaries were located will be in smallerrural areas. than The the actual urban– verification resultsrural of boundaries. the urban–rural The databoundary fused confusion with POI datamatrix not delineated only comprehensively by NTL and consider the NTL_POI datadevelopment after the verification difference of between random light pixel intensity points are and shown urban infrastructure,in Table 1. From but also consider Table 1, the accuraciesthe inﬂuence of the of urban–rural the development boundary state delineated of urban infrastructure by NTL and onNTL_POI the urban data scope and urban–

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rural boundary in the region with an obvious light intensity contrast, which increases the accuracy and reliability of the urban–rural boundary division. In this study, 1000 random pixel points located in Kunming were selected to verify the urban–rural boundaries delineated by NTL and NTL_POI data. We found after on-site confirmation with Google Earth that among the 1000 selected pixel points, 181 pixel points were located in urban areas and 819 pixel points were located in rural areas. The verification results of the urban–rural boundary confusion matrix delineated by NTL and NTL_POI data after the verification of random pixel points are shown in Table1. From Table1, the accuracies of the urban–rural boundary delineated by NTL and NTL_POI data are 84.20% and 93.20%, respectively, and the Kappa values are 0.6549 and 0.8132, respectively. From the perspective of the overall accuracy of urban–rural boundary delineation, after the fusion with POI data the accuracy of urban–rural boundary delineation improved by 9% and the Kappa value improved by 0.1583. Therefore, we concluded that the accuracy of urban–rural boundary delineation was improved significantly compared to the data without fusion, which proves that NTL_POI data have a higher accuracy in urban–rural boundary delineation. Therefore, overall, NTL_POI fused with POI data are more authentic and reliable than urban–rural boundaries delineated by NTL data alone, and the delineation accuracy is also significantly improved.

Table 1. Veriﬁcation of confusion matrix for urban–rural boundary delineation.

Data Urban Rural Accuracy Kappa Urban 168 21 84.20% 0.6549 NTL Rural 145 674 Urban 171 10 93.20% 0.8132 NTL_POI Rural 58 761

4. Discussion The rapid and disordered expansion of cities has led to a series of urban problems, which have seriously challenged urban planners when making urban development de- cisions. As a result, the intensive use of urban resources and the rapid and healthy development of cities have become priorities that cannot be ignored in urban planning. Delineating the boundary between urban and rural areas is an important step in urban planning, which is helpful in limiting the urban sprawl from the aspect of policy making so as to achieve the healthy development of an urban city. On the basis of describing the characteristics of NTL and POI data in an urban space, as well as combining the advantages and disadvantages of the two types of data in urban and rural environment applications, we constructed a new method of using wavelet transform to fuse NTL and POI data to delineate the urban–rural boundary. In addition, through the comparison and verification of the research results, we proved that the fusion of NTL_POI with POI data by wavelet transform produces a significant improvement in the accuracy of delineating urban–rural boundaries. As data that can directly reflect the level of urban and rural development, NTL data have been important basic data for the study of urban space, including the delineation of urban–rural boundaries [58]. With the deepening of research on NTL data, new methods have been applied to the study of NTL data. However, due to the problems due to light overflow and the oversaturation of NTL data, new methods provided limited improvement in the accuracy of urban–rural boundary delineation [59]. In addition, studies have shown that the urban functions expressed by POI data can be well-combined with those of NTL data, which play an important role in the extraction of urban built-up areas. Therefore, we used wavelet transform to fuse POI and NTL data at the pixel scale to delineate urban– rural boundaries. Compared to the results before fusion, after data fusion, the accuracy of delineating urban–rural boundaries was 93.20%, which surpasses the accuracy of 90% obtained by studies such as the Global Artificial Impervious Area [60,61]. Accurate fusion Int. J. Environ. Res. Public Health 2021, 18, 7180 16 of 19

of urban and rural development level and urban infrastructure differences can effectively improve the results of urban–rural boundary delineation. The fusion of POI and NTL data by wavelet transform compensates for the shortcomings of NTL data alone in urban–rural boundary delineation, increasing the accuracy of urban–rural boundary delineation. Traditional urban–rural boundaries often refer to a regional-scale indicator system established using socio-economic data, or urban–rural boundaries that are only delineated by spatial analysis of geographic data. These simple urban–rural boundaries have little guiding significance for the discrimination of urban and rural development levels and the formulation of policies aimed toward healthy urban and rural development. Although the NTL data, reflecting the differences in the level of urban and rural development, are some of the better data for distinguishing urban from rural areas, the results of using a single type of data in the urban space are highly inaccurate. However, POI data can reflect the functional differences between urban and rural areas by describing the amount of POI agglomeration, so the urban–rural boundary can be delineated more accurately after the fusion of the two kinds of data. In addition, we used wavelet transform to fuse POI data and NTL data at the pixel scale. Compared to other data fusion methods, wavelet transform highlights the characteristics of NTL and POI data in urban and rural areas after fusing them, which results in the delineated urban–rural boundary better reflecting the land use contradiction between urban and rural areas. Therefore, the urban–rural boundary delineated by this study has an important guiding value for the accurate identification of urban and rural development levels and the formulation of urban and rural planning policies. Although we proposed a new method to fuse POI and NTL data, which can significantly improve the accuracy of urban–rural boundary delineation, the research still has certain limitations and needs to be further developed. Notably, the urban–rural boundary will change with ongoing urban and rural development. Therefore, the delineation of the city boundary should be a dynamic process. At present, the accurate delineation of urban– rural boundaries is conducive to the identification of and environmental improvement in urban and rural areas to achieve the healthy development of these areas. However, continuous simulation and prediction of urban–rural boundaries according to the future development of cities must be conducted because only dynamic urban–rural boundaries can generate a greater value for the formulation of urban planning and development policies.

5. Conclusions Accurate delineation of urban–rural boundaries provides an important basis for judging urban and rural areas to limit urban sprawl, improve urban and rural environments, and promote the healthy development of these areas. Based on the use of NTL data to delineate urban–rural boundaries, we proposed an image fusion method based on wavelet transform to fuse NTL and POI data at the pixel scale. By comparing and verifying the confusion matrix, we found that the accuracy of NTL data in delineating urban–rural boundaries is 84.20% and the Kappa value is 0.6549, whereas the accuracy of NTL_POI data in delineating urban–rural boundaries is 93.2% and the Kappa value is 0.8132. Therefore, we concluded that the fusion of NTL and POI data by wavelet transform can not only effectively compensate for the error in NTL data in areas with signiﬁcant light differences, but can also more accurately delineate urban–rural boundaries with the use of the differences in urban and rural development levels and infrastructure development. In this study, urban and rural boundaries were delineated by fusing urban and rural development levels with urban and rural infrastructure; knowing these boundaries helps to restrict urban sprawl, alleviate urban issues, and intensively use urban resources to achieve the healthy development of the city. Moreover, it can assist in the formulation of planning policies to improve the urban and rural regional environment, which can play an important role in guiding urban and rural planning. Int. J. Environ. Res. Public Health 2021, 18, 7180 17 of 19

Author Contributions: Conceptualization, J.Z. and X.Y.; methodology, J.Z. and X.Y.; software, X.Y.; validation, J.Z.; formal analysis, X.Y.; investigation, J.Z., X.T. and X.Y.; data curation, X.Y.; writing— original draft, J.Z. and X.Z.; writing—review and editing, X.Y. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by the 12th postgraduate research and innovation project of Yunnan University (No. 2020Z53). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data Availability DOI:10.5281/zenodo.4923685. Acknowledgments: Thanks to all editors and commenters. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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