remote sensing

Article Impact of Expansion Pattern of Built-Up Land in Floodplains on Flood Vulnerability: A Case Study in the North China Plain Area

Guangpeng Wang 1,2,3 , Ziying Hu 1,2,3, Yong Liu 1,2,3, Guoming Zhang 1,2,3, Jifu Liu 1,2,3, Yanli Lyu 1,2,3, Yu Gu 1,2,3, Xichen Huang 1,2,3, Qingyan Zhang 1,2,3, Zongze Tong 1,2,3, Chang Hong 1,2,3 and Lianyou Liu 1,2,3,*

1 Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China; [email protected] (G.W.); [email protected] (Z.H.); [email protected] (Y.L.); [email protected] (G.Z.); [email protected] (J.L.); [email protected] (Y.L.); [email protected] (Y.G.); [email protected] (X.H.); [email protected] (Q.Z.); [email protected] (Z.T.); [email protected] (C.H.) 2 Engineering Research Center of Desertification and Blown-sand Control, Ministry of Education, Beijing Normal University, Beijing 100875, China 3 Faculty of Geographical Science, Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China * Correspondence: [email protected]; Tel.: +86-10-58802600



Received: 17 August 2020; Accepted: 23 September 2020; Published: 28 September 2020


Abstract: Built-up land in floodplains (BLF) is a driver and a disaster-bearing body of flood risk from a socio-hydrological perspective. The relationship between BLF growth and flood vulnerability is the key to understanding and managing flood risk. However, previous studies have focused more on the relationship between BLF growth and flood exposure, ignoring flood vulnerability. We examined the BLF expansion pattern (patch size and expansion type) in the North China Plain Area from 1975 to 2014 (1975–1990–2000–2014) using GIS (geographic information system)-based landscape analysis and revealed its relationship with flood vulnerability. The results show that the BLF area experienced rapid growth (288.26%) from dispersion to coalescence. Small patches dominated the number and area of BLF growth, and edge-expansion patches were the expansion type with the most area growth. We discovered that flood vulnerability was significantly correlated with the growth in small (R = 0.36, p < 0.01), edge-expansion (R = 0.53, p < 0.01), and outlying patches (R = 0.51, p < 0.01). Large patches were not significantly correlated with flood vulnerability (R = 0.18, p > 0.1), but there was a negative trend. Infilling patch growth was significantly associated with flood vulnerability over a long period (R = 0.27, p < 0.05). In addition, we suggest nature-based soft adaptations or village merging for small patches and outlying patches. Our findings have important scientific significance for adequately understanding the interplay between BLF growth and flood risk. It has practical implications for the formulation of integrated flood risk management strategy and the sustainable development of floodplains.

Keywords: built-up land in floodplains; impact; flood vulnerability; GIS-based landscape analysis; adaptation strategy; socio-hydrology

1. Introduction Floodplains can be defined as the periodically inundated areas by the lateral overflow of rivers [1,2]. Historically, humankind has tended to settle in floodplains because of the role of rivers as transportation

Remote Sens. 2020, 12, 3172; doi:10.3390/rs12193172 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, x FOR PEER REVIEW 2 of 30 Remote Sens. 2020, 12, 3172 2 of 29 transportation corridors and the fertility of riparian areas. It is estimated that almost one billion people currently live in floodplains [3,4]. Recently, floods are causing increasing havoc in floodplains corridorsdue to unprecedented and the fertility urbanization of riparian areas.and improper It is estimated development that almost [5]. oneIn China, billion there people were currently 157 major live inriver floodplains floods in [floodplains3,4]. Recently, during floods 1992 are– causing2015, which increasing killed havoc21,660 inpeople, floodplains affected due 1.48 to unprecedentedbillion people, urbanizationand caused economic and improper losses development of $182.18 billion [5]. In [1]. China, Thus, there understanding were 157 major the river impact floods of inbuilt floodplains-up land duringexpansion 1992–2015, on flood which risk killedand responding 21,660 people, to challenge affected 1.48s has billion become people, an urgent and caused need economicfor sustainable losses ofdevelopment $182.18 billion in floodplains. [1]. Thus,understanding the impact of built-up land expansion on flood risk and respondingFrom a tosocio challenges-hydrology has becomeperspective, an urgent built- needup land for sustainablein floodplains development (BLF) is not in only floodplains. a disaster- bearingFrom body a socio-hydrology of flooding but perspective, also a driving built-up factor land [6]in floodplains. Sivapalan (BLF) proposed is not only the concept a disaster-bearing of socio- bodyhydrology of flooding in 2012 but, which also a views driving human factor activities [6]. Sivapalan (such proposedas urbanization the concept and land of socio-hydrology-use change) as inendogenous 2012, which to water views system human dynamics activities rather (such asthan urbanization boundary conditions and land-use or external change) forcing as endogenous of river- tofloodplain water system systems dynamics [7]. The rathersocio-hydrolog than boundaryy focuses conditions on revealing or external the feedback forcing and of river-floodplainco-evolutionary systemsrole between [7]. The human socio-hydrology society and hydrologi focuses oncal revealing systems the [4,6,7]. feedback Its purpose and co-evolutionary is to guide the role sustainable between humandevelopment society challenges and hydrological facing by systems human [4 ,society6,7]. Its in purpose a changing is to guideenvironment the sustainable [4,6,7]. In development this paper, challengesBLF growth facing is considered by human to be society a driver in a of changing flood risk. environment As shown in [4 Figure,6,7]. In 1, this the paper,relationship BLF growth between is consideredBLF expansion to be and a driver flood of risk flood may risk. be Asregard showned inas Figurea two-1way, the coupling. relationship The between BLF expansion BLF expansion affects andflood flood risk riskthrough may hydrological be regardedas processes, a two-way quantity, coupling. and The expansion BLF expansion pattern as.ff ectsIn turn, flood the risk dynamics through hydrologicalof flood risk processes, due to BLF quantity, expansion and can expansion be fed patterns. back into In flood turn, risk the dynamics adaptation of strategies flood risk for due the to BLFfloodplain expansion [4]. can be fed back into flood risk adaptation strategies for the floodplain [4].

Figure 1. SchematicSchematic of of the the interplay interplay between between the the built built-up-up land in floodplain floodplainss and the flood flood r riskisk system fromfrom a socio-hydrologysocio-hydrology perspective.perspective.

Previous studies studies focused focused more more on quantifying on quantifying urban urban expansion expans andion its and impact its on impact flood exposure.on flood Forexposure. example, For Früh-Müller example, Früh et al.-Müller studied et flood al. studied exposure flood and settlement exposure expansion and settlement in northern expansion Bavaria in fromnorthern 1850 Bavaria to 2011; from they found1850 to that 2011; built-up they landfound increased that built almost-up land five-fold increased in the almost Main Riverfive-fold floodplain in the underMain River investigation floodplain [8 ].under Wu etinvesti al. quantifiedgation [8]. the Wu spatio-temporal et al. quantified changes the spatio of-temporal built-up landchanges in the of Hangzhoubuilt-up land metropolitan in the Hangzhou area from metropolitan 1978 to 2008 area and from analyzed 1978 to its 2008 socio-economic and analyzed drivers its socio [9].-economic Luo et al. revealeddrivers [9]. that Luo the et built-up al. revealed land that and the population built-up expansionland and population in the Yangtze expansion River Deltain the was Yangtz significante River forDelta the was city’s significant sustainable for developmentthe city’s sustainable from a collaborative development perspective from a collaborative [10]. Additionally, perspective Sun et[10]. al. quantifiedAdditionally and, Sun compared et al. quantified the magnitude, and compared rates, forms, the magnitude, and dynamics rates, of forms, urban and expansion dynamics for of 13 urban cities acrossexpansion the Jing-Jin-Jifor 13 cities Urban across Agglomeration, the Jing-Jin-Ji U andrban examined Agglomeration, the relationship and examined of urban the patchrelationship structure of andurban hierarchical patch structure structure and ofhierarchical urban growth structure over theof urban past four growth decades over [ 11the]. past four decades [11]. Less attention has been given to the impact of the BLF expansion pattern (patch(patch size and expansion type) type) on on flood flood vulnerability vulnerability and its and flood its adaptation flood adaptatio strategiesn [ 12strategies]. However, [12]. understanding However, theunderstanding relationship the between relationship BLF expansionbetween BLF and expansion flood vulnerability and flood playsvulnerability a crucial plays role a in crucial flood role risk managementin flood risk andmanagement the sustainable and the development sustainable of development floodplains [13 of,14 floodplains]. In recent years,[13,14]. advanced In recent progress years, inadvanced remote sensing progress techniques in remote and sensing flood inundation techniques models and flood have provided inundation suffi modelcient datas have to investigate provided thissufficient issue, data which to hasinvestigate made it this possible issue to, which identify has the made landscape it possible structure to identify of BLF the in landscape floodplains structure [15,16]. Accordingof BLF in floodplains to flood control [15,16]. practices, According the to BLF flo patchod control size ispractices directly, t associatedhe BLF patch with size flood is directly control capabilitiesassociated with due to flood the di controlfferent capabilities populations due and to resources the different they possessed populations [17 ,18 and]. We resources assume they that

Remote Sens. 2020, 12, 3172 3 of 29 large patches have more population and flood control resources, which makes them relatively less vulnerable to flooding compared to small patches. For example, the Flood Control Standard in China stipulates the flood prevention level for a city based on the size-related population and economy [17]. In addition to patch size, we believe that the expansion type of BLF may also affect flood vulnerability. First, identifying expansion types of new growth patches can reflect whether they are within the existing flood control system. For example, outlying patches have scattered distribution characteristics, and their rapid growth may lead to a dramatic increase in flood vulnerability because most of them are outside the existing flood prevention measures [19]. Second, different expansion types of BLF are closely related to patch size, which is directly associated with flood control capabilities [11]. Moreover, the impact of the BLF expansion pattern on flood vulnerability can be further fed back in flood adaptation strategies [4,20]. In the spirit of socio-hydrology, flood adaptation strategies can also change correspondingly as the impact of BLF expansion on flood vulnerability [6,7]. Therefore, the discussion of adaptation strategies under changing flood vulnerability environments reflects the feedback mechanism and co-evolution of socio-hydrology. Our analysis mentioned above is primarily derived from the flood control practice and related studies. Whether and how the BLF expansion pattern affects flood vulnerability and how we can adjust to the impact needs further empirical research. In our research, using the North China Plain Area (NCPA) as an example, we first quantified the spatio-temporal dynamics of the BLF based on remote sensing data and identified the expansion pattern of BLF growth patches in different periods (1975–1990–2000–2014) employing the GIS-based landscape analysis method. Then, the impact of the BLF expansion pattern (patch size and expansion type) on flood vulnerability was investigated. Finally, adaptation strategies were discussed to reduce flood damage. Our research focuses on the impact of the BLF expansion pattern on flood vulnerability and its adaptations from the socio-hydrology perspective. This study is of great significance for flood prevention and the sustainable development of floodplains.

2. Materials and Methods

2.1. Study Area

The North China Plain Area (NCPA) lies between 29◦410–42◦400 N and 110◦210–122◦430 E and includes the two municipalities of Beijing and Tianjin and the five provinces of Hebei, Shandong, Henan, Anhui, and Jiangsu, covering a total area of 7.8 105 km2 (Figure2). The study area is a × large Mesozoic and Cenozoic sedimentary basin composed of surrounding mountains and the North China Plain (NCP) [21]. The NCP is the second-largest plain in China with average elevations below 50 m. The annual precipitation of NCP ranges from 500 to 600 mm/yr and is concentrated in June to August [22]. The NCPA has a transition from a humid subtropical climate to a warm temperate humid and semi-humid climate from south to north [21]. In addition, there are many freshwater lakes and ecological wetlands, such as Taihu Lake, Chaohu Lake, Weishan Lake, and Baiyangdian Lake. The NCPA is the most densely populated and arable land in China. The population and regional GDP (gross domestic product) of the NCPA were 440 million and $4.84 trillion (2018), accounting for 31.56% and 36.18% of the country’s total population and GDP, respectively [23]. The NCPA has experienced rapid urbanization since the reform and opening-up. The overall annual urban expansion rate in the Beijing-Tianjin-Hebei region was 5.5% 2.0% between 1978 and 2015 [11]. The expansion ± of the built-up area and the decline of vegetated land led to an increase of 1047 million m3 in total water yield [24]. Additionally, more extreme hydrological events occurred in floodplains and caused severe damage. One hundred and fifty-seven major river floods occurred in China’s floodplains from 1992–2015. [1]. A 60-year return period extreme rainstorm hit the Beijing-Tianjin-Hebei region on 21 July 2012, with the maximum rainfall of 541 mm in Hebei Town, Fangshan District, Beijing. This extreme rainstorm resulted in wide waterlogging and torrential flood in Beijing, with 79 human casualties and a direct economic loss of $1.6 billion [25]. During 2009–2018, about 21 million people Remote Sens. 2020, 12, 3172 4 of 29 Remote Sens. 2020, 12, x FOR PEER REVIEW 4 of 30 weremillion affected people by were floods affected in the by NCPA floods eachin the year. NCPA Therefore, each year. understanding Therefore, understanding the impact the of impact the BLF expansionof the BLF on expansion flood risk on is flood paramount risk is paramount to the sustainable to the sustainable development development of floodplains. of floodplains.

FigureFigure 2. 2.The The geographicalgeographical location of the North North China China Plain Plain Area. Area.

2.2.2.2. Data Data and and Pre-Processing Pre-Processing ThreeThree datasets datasets were were employed employed toto analyzeanalyze thethe relationship between between the the B BLFLF expansion expansion pattern pattern andand flood flood vulnerability. vulnerability. The The built-upbuilt-up landland datasetdataset is a multi-temporalmulti-temporal (1975 (1975–1990–2000–2014)–1990–2000–2014) and and multi-resolutionmulti-resolution (30 (30 m, m, 250 250 m, m, 1000 1000m) m) rasterraster accessed from the the Joint Joint Research Research Centre Centre of of the the European European CommissionCommission (https: (https://ghslsys.jrc.ec.e//ghslsys.jrc.ec.europa.euuropa.eu/ghs_bu2019.php/ghs_bu2019.php).). The The built-up built-up land land was was based based on 33,202 on Global33,202 Land Global Survey Land (GLS)Survey images (GLS) andimages processed and processed using the using symbol the symbol machine machine learning learning (SML) (SML) method designedmethod fordesigned remote for sensing remote big sensing data analysis big data [16 analysis]. A per-pixel [16]. A accuracyper-pixel assessmentaccuracy assessment was performed was usingperformed a total u ofsing 342,568 a total SU of tiles342,568 derived SU tiles from derived the reference from the buildingreference footprintsbuilding footprints [15]. The [15]. balanced The accuracybalanced of accuracy the built-up of the land built data-up inland the data 2018 in versionthe 2018 is version 0.86, which is 0.86, shows which that shows the that dataset the dataset has high accuracyhas high [15 accuracy]. [15]. Moreover,Moreover, 4000 4000 random random points points werewere selectedselected for the built built-up-up and and non non-built-up-built-up land land in in different different periodsperiods of of thethe study area. area. Then Then the the random random points points were wereverified verified with high with-resolution high-resolution satellite images satellite imagesderived derived from Google from Google Earth and Earth Gaofen and Gaofensatellite satellite(GF-1). The (GF-1). overall The average overall accuracy average of accuracy the built of-up the built-updataset datasetis higher is than higher 90.63%, than and 90.63%, the highest and the accuracy highest in accuracy 2014 reached in 2014 92.48%. reached Therefore, 92.48%. the Therefore, 30 m- theresolution 30 m-resolution datasetsdatasets were mainly were used mainly in the used 2018 in theversion 2018 to version define the to define spatial the extent spatial of the extent built of-up the built-upland inland the NCPA. in the NCPA. The floodplain extent used in this paper was extracted from the inundation area of flood events The floodplain extent used in this paper was extracted from the inundation area of flood events with a 100-year return period. The flood dataset was obtained from the Global Risk Data Platform with a 100-year return period. The flood dataset was obtained from the Global Risk Data Platform (http://preview.grid.unep.ch/), which was generated by the Centro Internazionale in Monitoraggio (http://preview.grid.unep.ch/), which was generated by the Centro Internazionale in Monitoraggio Ambientale Research Foundation (CIMA). The dataset was shaped by the accumulated effects of Ambientale Research Foundation (CIMA). The dataset was shaped by the accumulated effects of

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Remote Sens. 2020, 12, x FOR PEER REVIEW 5 of 30 geomorphicgeomorphic and and hydrologic hydrologic processes processes and and has has beenbeen validatedvalidated with historical historical flood floodss [26]. [26]. Considering Considering thethe inundation inundation area area of of floods floods with with a a 100-year 100-year returnreturn period is no nott only only closely closely related related to to the the current current urban-ruralurban-rural construction construction and and socio-economic socio-economic butbut provides a more overall overall depiction depiction of of floodplain floodplain extent.extent. Therefore, Therefore, the the floodplain floodplain extent extent of of NPCA NPCA was was defined defined byby thethe inundationinundation areaarea ofof floodsfloods with a 100-yeara 100-year return return period perio (Figured (Figure3). 3).

FigureFigure 3. 3.Spatial-temporal Spatial-temporal distribution distribution ofof floodplainsfloodplains in the North North China China Plain Plain A Arearea (NCPA). (NCPA).

Moreover,Moreover, the the flood-a flood-ffaffectedected areas areas ofof 7777 prefecture-levelprefecture-level cities cities in in the the NCPA NCPA during during the the study study periodperiod were were obtained obtained from from the Dartmouth the Dartmouth Flood Observatory Flood Observatory (DFO) [27 (DFO)]. We used[27]. GIS We to use superimposed GIS to thesuperimpose vector data the layers vector to data obtain layers the to cumulative obtain the cumulative flood-affected flood areas-affected of each areas prefecture-level of each prefecture city,- demonstratinglevel city, demonstrating the spatio-temporal the spatio variations-temporal variations in flood occurrence. in flood occurrence. The dataset The was dataset utilized was utilized to reveal theto relationshipreveal the relationship between thebetween BLF expansionthe BLF expansion pattern pattern (patch size(patch and size expansion and expansion type) type) and and flood vulnerability.flood vulnerability. Besides, Besides the administrative, the administrative region region of theof the NCPA NCPA that that we we usedused originated from from the the NationalNational Geomatics Geomatics Center Center of of China China in in 2017 2017 ((http:http://www.ngcc.cn/ngcc///www.ngcc.cn/ngcc/).).

2.3.2.3. Methods Methods

2.3.1.2.3.1. Overall Overall Framework Framework TheThe overall overall framework framework is shown is shown in Figure in Figure4. The 4. research The research issue was issue put was forward put in forward the interaction in the analysisinteraction between analysis BLF between expansion BLF and expansion flood risk and systems. flood risk In thissystems. paper, In our this study paper, mainly our study used mainly the GIS (geographicused the GIS information (geographic system) informat -basedion landscape system) -based analysis landscape method. analysis First, three method landscape. First, indices three

Remote Sens. 2020, 12, 3172 6 of 29

Remote Sens. 2020, 12, x FOR PEER REVIEW 6 of 30 (occupancy ratio, change rate, and average annual change rate) were used to examine the spatial distributionlandscape and indices dynamic (occupancy change ratio, of BLF change patches rate, from and 1975 average to 2014. annual Second, change the landscaperate) were expansion used to indexexamine (LEI) the was spatial employed distribution to identify and dynamic the expansion change type of BLF of BLFpatches growth from patches 1975 to in2014. diff erentSecond, periods the landscape expansion index (LEI) was employed to identify the expansion type of BLF growth patches (1975–1990–2000–2014). Meanwhile, the sensitivity analysis was used to determine the buffer distance in different periods (1975–1990–2000–2014). Meanwhile, the sensitivity analysis was used to in LEI. Then, the Pearson correlation coefficient (PPC) was employed to analyze the relationship of BLF determine the buffer distance in LEI. Then, the Pearson correlation coefficient (PPC) was employed expansion pattern (patch size and expansion type) and flood vulnerability. Subsequently, we discussed to analyze the relationship of BLF expansion pattern (patch size and expansion type) and flood the stability of the correlation between the BLF expansion pattern and flood vulnerability using the vulnerability. Subsequently, we discussed the stability of the correlation between the BLF expansion built-up land data with different resolutions (150 m, 250 m, 500 m, and 1000 m). Eventually, policy pattern and flood vulnerability using the built-up land data with different resolutions (150 m, 250 m, suggestions500 m, and and1000 floodm). Eventually, adaptation policy strategies suggestions were and proposed flood adaptation to mitigate strategies flood losses were proposed and promote to sustainablemitigate flood development losses and in promote the floodplain. sustainable development in the floodplain.

FigureFigure 4. 4.The The overall overall frameworkframework ofof thethe impact of the b built-upuilt-up land land in in floodplains floodplains (BLF (BLF)) expansion expansion patternpattern on on flood flood vulnerability vulnerability and and itsits adaptationadaptation strategies.

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2.3.2. Landscape Metrics of Built-Up Land in Floodplains Three landscape metrics were used to examine the spatio-temporal variation of BLF in three periods (1975–1990–2000–2014). OR(i) represents the occupation ratio of the BLF area to the floodplain area in zone i. In other words, it indicates the BLF building density of the floodplain in each district and county, and its expression is shown in Equation (1).

BLF(i) OR( ) = 100 (1) i FP(i) × where zone i represents the i-th district or county of the research area; BLF(i) and FP(i) are the areas of built-up land and floodplain in the i-th district or county. To investigate the dynamics of the BLF area in the three periods, the change rate (CR) in the three periods was calculated using the Formula (2) [1].

ST + ST CR = n 1 − n 100% (2) STn × where CR is the change rate of the BLF; Tn+1 and Tn represent the BLF areas in years n+1 and n, respectively. Moreover, the average annual change rate (AACR) of BLF was calculated using the geometric average method [1]. This method calculates the average annual change rate by comparing the level of the last year of the interval with the base period. In addition, the total change rate in the interval is the continuous production of the growth rate of each period.     (Tn T ) (Tn T ) AAGR = − 1√V 1 100%AAGR = − 1√V 1 100% (3) − × − × r r (Tn T ) ϕ ϕ ϕ ϕ − 1 (Tn T ) T2 T3 Tn (Tn T ) Tn √V = − 1 = − 1 (4) ϕT1 × ϕT2 × · · · × ϕTn 1 ϕT1 − where AACR is the average annual change rate of the BLF; T1 and Tn represent the base year and the last year of the time interval, respectively. V is the chain ratio product of the BLF area each year during the study period, and its expression is shown in Equation (4). ϕTn and ϕT1 are BLF area in years of Tn and T1 (i.e., the base year) in the time interval, respectively.

2.3.3. Patch Size Classification of the Built-Up Land in Floodplains Patches are the basic components of landscape patterns, which refers to relatively homogeneous non-linear areas that differ from the surrounding background [28]. To detect the impact of different patch sizes on flood vulnerability, we initially needed to classify the patch sizes of newly growing BLF in the study area. Based on previous studies on the composition and frequency distribution of urban patches in the North China Plain, we finally divided BLF patches into four classes: 0.1, 0.1–1, 1–10, ≤ and >10 km2 [11]. Moreover, we set 1 km as the threshold value for distinguishing large (>1 km2) and small ( 1 km2) BLF patches. Then, we quantified the number and area of the new growth BLF under ≤ different patch sizes in each period using the GIS-based landscape analysis method.

2.3.4. Expansion Type Identification of New Built-Up Land in Floodplains The landscape expansion index (LEI) proposed by Liu et al. was employed to distinguish the BLF expansion types [28]. Figure5 shows the identification principle of di fferent expansion types of new BLF patches. The LEI determines the expansion types based on the buffer analysis between the pre-existing and new BLF patches (Formula (5)).

A LEI = BLF 100 (5) ABLF + ANBLF × Remote Sens. 2020, 12, 3172 8 of 29

whereRemote Sens. LEI 20 indicates20, 12, x FOR the PEER landscape REVIEW expansion index of new BLF patches; ABLF and ANBLF are the areas8 of of30 the new BLF patches and non-BLF patches in the buffer zones of the original BLF patches, respectively. Accordingrespectively. to According Figure4, the to new Figure BLF 4, patches the new can BLF be patches divided can into be three divided expansion into three types expansion [11,28]. types [11,28]. (1) If the new BLF patches belong to the infilling type, most of their buffer areas will be occupied by (1) Ifpre-existing the new BLF BLF patches patches belong (Figure to 5theA). infilling type, most of their buffer areas will be occupied by (2) preIf a-existing newly grown BLF patches patch is (Figure an edge-expansion 4A). type, the buffer zone is mixed with the pre-existing (2) IfBLF a newly patches grown and patch vacant is land an edge (i.e.,-expansion non-built-up type, land) the (Figurebuffer zone5B). is mixed with the pre-existing (3) BLFThe patches buffer zone and ofvacant outlying land patches (i.e., non is- composedbuilt-up land) exclusively (Figure of4B). vacant land (Figure5C). (3) The buffer zone of outlying patches is composed exclusively of vacant land (Figure 4C).

Figure 4. 5. TThehe identification identification of of the the different different expansion types of BLF patches patches using using landscape landscape expansion index (LEI).(LEI). (A (–AC–)C indicate) indicate infilling infill patches,ing patches edge-expansion, edge-expansion patches, andpatches, outlying and patches, outlying respectively. patches, respectively. According to the definition of LEI in Equation (5), the calculated values of LEI range from 0 to 100. TheyAccording are assigned to the definition as an infilling of LEI type in Equation if the LEI (5), values the calculated of new BLF values patches of LEI are betweenrange from 50 and0 to 100 (i.e., 50 < LEI 100). If the calculated LEI values are between 0 and 50 (excluding 0), the patches 100. They are assigned≤ as an infilling type if the LEI values of new BLF patches are between 50 and 100are classified(i.e., 50 < LEI as edge-expansion ≤ 100). If the calculated type. Moreover, LEI values if thereare between is no intersection 0 and 50 (excluding between new0), the BLF patches patch arebuff classifieder zones and as edge old patches-expansion (i.e., type. LEI = Moreover,0), new patches if there are is divided no intersection into outlying between type new [28]. BLF The abovepatch buffermethod zones was utilizedand old topatches identify (i.e., the LEI expansion = 0), new types patches of new are divi BLFded patches into outlying in the NPCA type in[28]. three The periods. above method was utilized to identify the expansion types of new BLF patches in the NPCA in three 2.3.5. Buffer Distance Setting in the Landscape Expansion Index periods. The buffer distance setting is essential for distinguishing the expansion types of new BLF patches 2.3.5.in the Buffer LEI method. Distance In Setting general, in the the bu Landscapeffer distance Expansion should beIndex roughly equal to or less than the spatial resolution of remote sensing data (i.e., the buffer distance 30 m) [29]. However, it remains a question The buffer distance setting is essential for distinguishing≤ the expansion types of new BLF patches whether using different buffer distances significantly affects the value of the LEI. To detect the effect of in the LEI method. In general, the buffer distance should be roughly equal to or less than the spatial the buffer distance, ten different buffer distances were selected to perform a sensitivity analysis on LEI. resolution of remote sensing data (i.e., the buffer distance ≤30 m) [29]. However, it remains a question The ten buffer distances were set between 1 m and 30 m, which were 1, 2, 3, 4, 5, 10, 15, 20, 25, and 30 m, whether using different buffer distances significantly affects the value of the LEI. To detect the effect respectively. Then, we calculated the standard deviation (SD) of the LEI value of each newly grown of the buffer distance, ten different buffer distances were selected to perform a sensitivity analysis on patch under ten buffer distance values, which was used to test the sensitivity of the LEI (Formula (6)). LEI. The ten buffer distances were set between 1 m and 30 m, which were 1, 2, 3, 4, 5, 10, 15, 20, 25, and 30 m, respectively. Then, we calculatedr the standard deviation (SD) of the LEI value of each Xn  j 2 newly grown patch under ten bufferSD distancei = (1 values,/n) whichM wasM¯ i used to test the sensitivity of the LE(6)I j=1 i − (Formula (6)). where SDi represents the standard deviation of the i-th new LEI patch under different buffer distances; 푛j n (n = 10) is the total number of buffer distances; M is the푗 LEI̅ value2 of the i-th newly added patch 푆퐷푖 = √(1/푛) ∑ i (푀푖 − 푀푖) (6) 푗=1

Remote Sens. 2020, 12, 3172 9 of 29

at the j-th buffer distance; M¯ i is the mean LEI value of the ith newly grown patch under different buffer distances. Subsequently, the average standard deviation (ASD) of all newly grown BLF patches were calculated to measure the sensitivity of LEI. Here, a small ASD value indicates that the LEI value is more stable when the buffer distance changes.

XN ASD = (SDi/N) (7) 1 where ASD is the average standard deviation; N is the number of new BLF patches in each period. Table1 shows the sensitivity analysis results of LEI under the change of bu ffer distance. When the buffer distance varies between 1–5 m, the calculated values of ASD in the three periods are 0.074 (1975–1990), 0.072 (1990–2000), and 0.077 (2000–2014), respectively. However, the ASD values are 0.462 (1975–1990), 0.447 (1990–2000), and 0.471 (2000–2014) when the buffer distances are set between 10 and 30 m. Sensitivity analysis results show that LEI values are relatively robust when the buffer distances are set to 1–5 m. Therefore, the buffer distance of new BLF patches is set equal to 5 m in our research.

Table 1. Sensitivity analysis of LEI under different buffer distances in three periods.

Average Standard Deviation (ASD) Buffer Distances (m) 1975–1990 1990–2000 2000–2014 1–5 0.074 0.072 0.077 10–30 0.462 0.447 0.471

2.3.6. The Relationship between the Expansion Pattern and Flood Vulnerability In terms of flood risk, we are concerned about whether different patch sizes and expansion types of new grown BLF aggravate or mitigate flood vulnerability (i.e., linear relationship). Thus, we used Pearson correlation coefficients (PPC) to investigate the relationship between flood vulnerability and the BLF expansion pattern. The PPC is a measure of the linear correlation degree between two conditioning factors, with values ranging from 1 to 1 (Formula (8)) [30,31]. Coefficient values of 1, − 1, and 0 indicate that two variables are positive correlation (linear), negative correlation (linear), − and non-linear correlation, respectively.

Pn ¯ ¯ i=1(Xi X)(Yi Y) RXY = q − q − (8) P 2 P 2 n (X X¯ ) n (Y Y¯ ) i=1 i − i=1 i − where RXY indicates the relationship between flood occurrence and the BLF expansion pattern, Xi and Yi represent the new BLF area and the cumulative flood-affected area of the i-th prefecture-level city. X¯ and Y¯ are the average values of the new BLF area and cumulative flood area of all prefecture-level cities during the corresponding periods of Xi and Yi, respectively. To detect whether the coarse resolution of built-up land affects the relationship between flood vulnerability and expansion type, we evaluated the correlation between the two at resolutions of 150, 250, 500, and 1000 m [29]. The built-up land with 250 m and 1000 m resolution was derived from the original dataset. The 150 m and 500 m datasets were resampled in the GIS using the majority method based on the 30 m resolution data.

3. Results

3.1. Spatial Distribution of Built-Up Land in Floodplains The spatial pattern of floodplains was unbalanced in the NCPA. It was primarily located in the intermediate zone of the Haihe and Yellow Rivers and the northern region of the Huaihe River basin Remote Sens. 2020, 12, 3172 10 of 29 Remote Sens. 2020, 12, x FOR PEER REVIEW 10 of 30

5 2 (Figure3). The floodplain occupied an area of 2.30 105 km2 , making up about 30% of the total study (Figure 3). The floodplain occupied an area of 2.30 ×× 10 km , making up about 30% of the total study area. However, However, affected affected by by the the spatial pattern of of the floodplains, floodplains, the the BLF is also disproportionally distributed in the NCPA. 3 2 In 2014, the BLF covered 25.56 103 km2 , which was 11.09% of the total floodplain area. Figure6 In 2014, the BLF covered 25.56 ×× 10 km , which was 11.09% of the total floodplain area. Figure 5 demonstrates the the spatial distribution of of the BLF f fromrom the provinceprovince level. BLF BLF areas areas in in Shandong, Shandong, 2 Anhui, and and Henan Henan all all exceeded exceeded 5000 5000 km km2, of, of which which the the BLF BLF area area of ofAnhui Anhui accounted accounted for for 61.14% 61.14% of its of totalits total built built-up-up land land,, whereas whereas the proportion the proportion of Shandong of Shandong and Henan and Henan was less was than less 35% than. The 35%. BLF The areas BLF 2 ofareas Jiangsu of Jiangsu and Hebei and Hebeiwere 4001.87 were 4001.87 and 4849.19 and 4849.19 km2, making km , making up 29.88% up 29.88% and 33.18% and 33.18% of their of total their built total- 2 upbuilt-up land. land.The BLF The in BLF Beijing in Beijing and Tianjin and Tianjin had hadareas areas of 74. of77 74.77 and and877.27 877.27 km2 km, of ,which of which the theBLF BLF area area of Beijingof Beijing only only accounted accounted for for 2.8% 2.8% of ofthe the total total built built-up-up land. land. However, However, the the proportion proportion of of the the BLF BLF area area inin Anhui and Tianjin was higher than in other provinces and cities, indicating relatively high flood flood exposure in two regions.

Figure 5. 6. AreaArea of of built built-up-up land land in in floodplains floodplains and and its its proportion proportion of the the total total built built-up-up land area of each province and city in 2014.

To further examine the spatial pattern of the BLF at the county scale, the occupation ratio of the BLF area to the floodplainfloodplain area was calculated by using thethe OROR(i)(i) proposed in Formula (1). Figure Figure 76 revealrevealss the spatial distribution of the OR(i)(i) valuesvalues in in ea eachch county county and and district district.. Ther Theree were were 200 200 c countiesounties and districts with an occupation rate greater than 15%, accounting for 28.57% of the whole counties and districts in the NCPA; among them,them, there were 41 counties and districts with OR(i)(i) valuesvalues greater greater than 15% 15% in in Shandong Shandong province, province, which which was was mainly mainly located located in in the the central central region region and and along along the the Yellow Yellow River. One hundred and three counties and districtsdistricts (OR(OR(i) ≥ 15%)15%) were were distributed distributed in in the vicinity of (i) ≥ the Zhengzhou Zhengzhou-Shijiazhuang-Beijing-Shijiazhuang-Beijing line, line, accounting accounting for for more more than than half half of of the entire counties and districts with OR(i)(i) valuesvalues exceeding 15%. The The remaining districts and countiescounties (OR(OR(i)(i) > 15%) were primarily located along the Grand Canal and Huaihe River in Anhui and Jiangsu Provinces.

3.2. Rapid Growth and Dynamics of Built-Up Land in Floodplains

3.2.1. Rapid Growth of Built-Up Land in Floodplains The BLF was increased by 288.26% from 6.58 103 km2 in 1975 to 25.56 103 km2 in 2014, with an × × average annual change rate of 3.45% (Table2). Two provinces with the highest increase in the BLF area were Shandong (4212.73 km2) and Hebei (3788.67 km2), which consisted of 42.18% of the total BLF growth. The increased area of Henan, Anhui, and Jiangsu was 3524.79, 3352.21, and 3406.75 km2, respectively, accounting for 54.21% of the total BLF growth area. Beijing and Tianjin had the smallest

Remote Sens. 2020, 12, 3172 11 of 29

BLF growth area, accounting for only 3.62% of the BLF growth in the NCPA. The BLF area in Jiangsu has increased by 572.45% at an average annual change rate of 5.01% in the last four decades, making it the most rapidly growing province in the BLF area. However, the BLF area in Anhui increased by 172.67% at an average annual growth rate of 2.61%, making it the province with the slowest growth in theRemote BLF Sens. area. 2020, 12, x FOR PEER REVIEW 11 of 30

FigureFigure 7. 6.The The occupation occupation ratioratio (OR(OR((i)i))) of of the the built built-up-up land land in infloodplains floodplains of each of each district district and andcounty county in in2014. 2014.

3.2. Rapid GrowthTable and 2. DynamicThe rapids of growth Built-U ofp built-upLand in F landloodplains in floodplains from 1975 to 2014. Provinces Growth Area Proportion CR AACR 3.2.1. Rapid Growth of(Cities) Built-Up Land in(km Floodplains2) (%) (%) (%) The BLF was increasedBeijing by 288.26% 49.39 from 6.58 × 10 0.263 km2 in 1975 194.57 to 25.56 × 2.81 103 km2 in 2014, with an average annual changeTianjin rate of 3.45% (Table 636.89 2). Two provinces 3.36 with 264.95 the highest 3.38 increase in the BLF area were Shandong (4212.73Hebei km2) and 3788.67 Hebei (3788.67 19.97 km2), which 357.25 consisted 3.97 of 42.18% of the total BLF growth. The increasedShandong area of Henan, 4212.73 Anhui, and 22.21 Jiangsu was 344.84 3524.79, 3352.21 3.90 , and 3406.75 km2, Henan 3524.79 18.58 235.47 3.15 respectively, accounting for 54.21% of the total BLF growth area. Beijing and Tianjin had the smallest Anhui 3352.21 17.67 172.67 2.61 BLF growth area, accountingJiangsu for only 3406.753.62% of the BLF 17.96 growth in 572.45 the NCPA. 5.01 The BLF area in Jiangsu has increased by 572.45%NCPA at an average 18971.42 annual change 100.00 rate of 5.01% 288.26 in the last 3.45 four decades, making it theNotes: most the rapidly proportion growing represents province the ratio in of the the BLF BLF growtharea. However, area to the the total BLF BLF area growth in area;Anhui CR increased and AACR by 172.67%indicate at the an change average rate annual and the averagegrowth annual rate of change 2.61%, rate making of the BLF it area,the province respectively. with the slowest growth in the BLF area. FigureFigure8 shows 7 show thes the rapid rapid growth growth of of the the BLF BLF area area in in various variou countiess counties and and districts districts of of the the NCPA NCPA in a morein a more intuitive intuitive approach. approach. There There were were 440 440 counties counties and and districts districts with with a BLF a BLF change change rate rate of moreof more than than 250%, accounting for 62.86% of all counties and districts in the research area. Counties and districts with a change rate higher than 500% accounted for 37.86% of the total, which was distributed in C-shaped belt zones around the provincial capitals (Jinan-Shijiazhuang-Zhengzhou-Hefei- Nanjing). Moreover, 61 counties and districts had BLF change rates in excess of 2000%, of which 68.85% were located in Anhui and Jiangsu to the south of the Huaihe River. Hence, the BLF area in the NCPA has experienced rapid and uneven growth over the four decades from 1975 to 2014.

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250%, accounting for 62.86% of all counties and districts in the research area. Counties and districts with a change rate higher than 500% accounted for 37.86% of the total, which was distributed in C-shaped belt zones around the provincial capitals (Jinan-Shijiazhuang-Zhengzhou-Hefei-Nanjing). Moreover, 61 counties and districts had BLF change rates in excess of 2000%, of which 68.85% were located in Anhui and Jiangsu to the south of the Huaihe River. Hence, the BLF area in the NCPA has experiencedRemote Sens. 20 rapid20, 12, andx FOR uneven PEER REVIEW growth over the four decades from 1975 to 2014. 12 of 30

FigureFigure 8. 7.The The change change rate rate (CR) (CR) of of the the built-up built-up land land in floodplainsin floodplains of eachof each county county and and district district from from 1975 to1975 2015. to 2015.

3.2.2. Dynamics ofTable the Built-Up2. The rapid Land growth in Floodplainsof built-up land in floodplains from 1975 to 2014.

We discoveredProvinces that the BLF dynamics changedProportion differently overCR time.AACR As shown in Table3, Growth Area (km2) the BLF growth rate(Cities) in the study area has experienced(%) a fast-to-slow(%) process(%) from 1975 to 2014. During 1975–1990, theBeijing BLFarea of the49.39 seven provinces and0.26cities increased194.57 2.81 by 34.01% at an average annual change rate ofTianjin 5.98%. Subsequently,636.89 the AACR of the3.36 BLF decreased264.95 to3.38 2.28% in 1990–2000 and then to 2.19% in 2000–2014.Hebei Correspondingly,3788.67 the area change19.97 rates357.25 of the BLF3.97 in the research area increased by 25% andShandong 32% in the above4212.73 two periods. The22.21 AACR of344.84 the provinces 3.90 and cities from 2000 to 2014 was less than halfHenan of the period3524.79 from 1975 to 1990.18.58 However, 235.47 the growth 3.15 area in 2000–2014 was roughly equivalent inAnhui 1975–1990 due to3352.21 its large BLF area17.67 base. 172.67 2.61 Figure9 shows theJiangsu AACR of the3406.75 BLF area in each17.96 county 572.45 and district 5.01 in different periods. During 1975–1990, 22NCPA counties and districts18971.42 had AACRs100.00 greater than288.26 20%, of3 which.45 64% were located south ofNotes: the Huaihethe proportion River represents (Figure9a). the Moreover, ratio of the theBLF numbergrowth area of countiesto the total and BLF districts growth area; with CR AACRs exceedingand 3.5%AACR and indicate 4.5% inthe 1990–2000 change rate and and 2000–2014 the average were annual 54 andchange 52, rate respectively. of the BLF Comparingarea, respectively. Figure 9b,c, the spatial distribution of counties and districts with higher AACR values in 1990–2000 (>2.5%) and 3.2.2. Dynamics of the Built-Up Land in Floodplains We discovered that the BLF dynamics changed differently over time. As shown in Table 3, the BLF growth rate in the study area has experienced a fast-to-slow process from 1975 to 2014. During 1975–1990, the BLF area of the seven provinces and cities increased by 34.01% at an average annual change rate of 5.98%. Subsequently, the AACR of the BLF decreased to 2.28% in 1990–2000 and then to 2.19% in 2000–2014. Correspondingly, the area change rates of the BLF in the research area

Remote Sens. 2020, 12, 3172 13 of 29

2000–2014Remote Sens. 20 (>203.0%), 12, x isFOR almost PEER consistent;REVIEW these counties and districts were primarily distributed south13 of of30 the Huaihe River, central Shandong, western Henan, and western and northern Hebei. increased by 25% and 32% in the above two periods. The AACR of the provinces and cities from 2000 Table 3. Dynamics of the built-up land in floodplains (BLF) in different periods. to 2014 was less than half of the period from 1975 to 1990. However, the growth area in 2000–2014 was roughly equivalent in 1975–19901975–1990 due to its large 1990–2000BLF area base. 2000–2014 Provinces Figure 8 shows the AACR of the BLF area in each county and district in different periods. During (Cities) CR AACR CR AACR CR AACR 1975–1990, 22 counties and districts(%) had AACRs(%) greater(%) than(%) 20%, of(%) which 64(%)% were located south of the Huaihe RiverBeijing (Figure 8a). 30.92 Moreover, 4.41 the 15.46 number 1.45 of counties 33.52 and 2.08 districts with AACRs exceeding 3.5% and 4.5%Tianjin in 1990– 32.032000 and 5.302000–2014 22.37 were 54 2.04 and 52, 37.49 respectively. 2.30 Comparing Figure 8b,c, the spatial distributionHebei of counties 37.03 and 6.83 districts 28.31 with higher 2.52 AACR 32.32 values 2.02 in 1990–2000 (>2.5%) and 2000–2014 (>3.0%)Shandong is almost 35.13consistent 6.48; these 26.93counties 2.41and districts 36.74 were 2.66 primarily distributed Henan 37.82 5.55 16.86 1.56 26.53 1.68 south of the Huaihe River,Anhui central 29.99 Shandong, 4.85 western 25.00 Henan, 2.52 and western 20.00 and 2.02 northern Hebei. Jiangsu 35.14 8.42 40.65 3.47 42.16 2.54 Table 3.Average Dynamics of 34.01 the built-up 5.98 land in 25.08 floodplains 2.28 (BLF) 32.68in different 2.19 periods. Notes: CR and AACR are1975 the change–1990 rate and the average1990 annual–2000 change rate of the BLF area,2000 respectively.–2014 Provinces CR AACR CR AACR CR AACR (Cities) 3.3. Expansion Types and(%) Characteristics (%) of New Growth(%) Built-Up Land(%) in Floodplains(%) (%) TheBeijing three expansion30.92 types of4.41 new BLF patches,15.46 edge-expansion,1.45 33.52 infilling, and2.08 outlying, were identifiedTianjin using32.03 the LEI in the5.30 research area.22.37 As shown2.04 in Figure37.49 10, the edge-expansion2.30 type of BLFHebei patches was37.03 dominant in the6.83 NCPA from28.31 1975 to 2014.2.52 The edge-expansion32.32 type2.02 of the BLF coveredShandong 18.74 10 5 patches,35.13with an area6.48 of 13.22 26.93103 km2, making2.41 up 50.46%36.74 and 68.25%2.66 of the total × × growthHenan patch number and37.82 area, respectively.5.55 Outlying16.86 was the1.56 second-largest 26.53 type of BLF1.68 expansion in the NPCA,Anhui with the29.99 number and area4.85 of patches25.00 accounting 2.52 for 33.23%20.00 (12.34 105)2.02 and 17.57% × (19.38 Jiangsu103 km 2) of the35.14 total number8.42 and area of expanded40.65 patches,3.47 respectively.42.16 Compared2.54 with the × above twoAverage BLF expansion34.01 types, the growth5.98 in the25.08 number and2.28 area of infilling32.68 patches was2.19 relatively small,Notes: accounting CR and AACR for only are the 16.30% change (6.05 rate and105 the) and average 14.18% annual (2.75 change103 kmrate2 of) of the the BLF total area, increased respectively. patch × × number and area, respectively.

Figure 9. Cont.

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FigureFigure 8. Average 9. Average annual annual change change rates rates (AACR) (AACR) of the built-upbuilt-up land land in in floodplains floodplains (BLF) (BLF) in di infferent different periods:periods: (a) 1975 (a) 1975–1990;–1990; (b) ( b1990) 1990–2000;–2000; and and ( (cc)) 2000 2000–2014.–2014.

3.3. ExpansionThe identification Types and of Characteristics the expansion of typeNew canGrowth manifest Built-U thep Land regularity in Floodplains of BLF growth in the study area [31]. We found that the BLF growth can be characterized by two distinct processes: dispersion and The three expansion types of new BLF patches, edge-expansion, infilling, and outlying, were coalescence. During the initial phase, dispersed BLF patches grow by outlying type and eventually identified using the LEI in the research area. As shown in Figure 9, the edge-expansion type of BLF patches was dominant in the NCPA from 1975 to 2014. The edge-expansion type of the BLF covered

Remote Sens. 2020, 12, x FOR PEER REVIEW 15 of 30 Remote Sens. 2020, 12, 3172 15 of 29 18.74 × 105 patches, with an area of 13.22 × 103 km2, making up 50.46% and 68.25% of the total growth patch number and area, respectively. Outlying was the second-largest type of BLF expansion in the formNPCA, multiple with the BLF number core areas. and Then,area of BLF patches expands accounting around the for original 33.23% core(12.34 area × 10 with5) and an edge-expansion17.57% (19.38 × type.103 km In2) theof the process, total number the patch and size area of the of expanded original BLF patches, core area respectively. gradually Compared increased. with The above the above two phasestwo BLF belong expansion to the types, BLF th dispersione growth in process. the number As the and process area of continues, infilling patches BLF growth was relatively is more small, likely toaccounting gradually for fill only non-BLF 16.30% areas (6.05 between × 105) and existing 14.18% patches(2.75 × 10 through3 km2) of an the infilling total increased expansion; patch hence number this processand area, is respectively. termed coalescence.

FigureFigure 10.9. PatchPatch number number (a (a) )and and area area ( (bb)) of of three three BLF BLF expansion expansion types types in in the the study study area in 1975 1975–2014.–2014.

The BLFidentification growth in theof the NPCA expansion has experienced type can manifest a process the of dispersion regularity and of BLF coalescence. growth in The the growth study ofarea BLF [31]. outlying We found patches that wasthe BLF the mostgrowth widely can be distributed characterized expansion by two type distinct in 1975–1990. processes: Meanwhile, dispersion edge-expansionand coalescence. patches During are the mainly initial phase, located dispersed in urban BLF areas. patches During grow 1990–2000, by outlying the growth type and of edge-expansioneventually form patches multiple became BLF core more areas. extensive Then, in BLF the NPCA,expand accompanieds around the original by an increase core area in the with patch an sizeedge of-expansion the original type. BLF In corethe process, area. With the patch the continued size of the growth original of BLF the outlyingcore area andgradually edge-expansion increased. patches,The above infilling two phase patchess belong gradually to the filled BLF thedispersion vacant landprocess. between As the BLF process patches. continues, The growth BLF of growth infilling is patchesmore likely played to gradually an increasingly fill non important-BLF areas role between during existing 2000–2014. patch Theyes through continued an infilling to fill the expansion; non-BLF regionshence this between process patches, is termed resulting coalescence. in a continuous coalescence of BLF patches. This process of dispersion-coalescenceThe BLF growth in continues the NPCA in ahas cyclic experienced pattern as thea process BLF patches of dispersion proceed and to expand. coalescence. Therefore, The BLFgrowth growth of BLF indicates outlying wave-like patches properties, was the which most could widely be distributed described as expansion a general temporal type in 1975 oscillation–1990. betweenMeanwhile, dispersion edge-expansion and coalescence patches phases.are mainly located in urban areas. During 1990–2000, the growth of edge-expansion patches became more extensive in the NPCA, accompanied by an increase in the 3.4. Patch Size Characteristics of the Growth Built-Up Land in Floodplains patch size of the original BLF core area. With the continued growth of the outlying and edge- expansionSmall patches patches, were infilling overwhelming patches gradually in the increased filled the numbers vacant of land BLF between (Figure 11 BLF). The patches number. The of BLFgrowth patches of infilling in the NCPApatche accelerateds played anand increasingly increased important by 5.43 times role in during the past 2000 four–2014. decades They (1975–2014). continued Theto fill number the non of-BLF small regions BLF patches between ( 1patches, km2) increased resulting by in 342.86a continuous104, accountingcoalescence for of 99.88%BLF patche of thes. ≤ × totalThis growth.process of Specifically, dispersion the-coalescence increasing continues number ofin BLFa cyclic patches pattern in theas the three BLF periods patches of proceed 1975–1990, to 1990–2000,expand. Therefore, and 2000–2014 BLF growth was 99.85indicates10 wave4, 108.65-like properties104, and 134.36, which could104. Amongbe described them, as small a general BLF × × × patchtemporal numbers oscillation ( 1 km between2) were dispersion 99.77 104 ,and 108.52 coalescenc104, ande phases 134.15. 104, consisting of 99.93%, 99.88%, ≤ × × × and 99.85% of the total number of increased patches, respectively. As shown in Figure 11, the number of3.4. BLF Patch patches Size Characteristics decreased from of the small Growth patches Built- toup largeLand in patches Floodplains in 1975–2014. In addition to Henan province, there were no BLF patches larger than 10 km2 in the study area in 1975. As of 2014, although Small patches were overwhelming in the increased numbers of BLF (Figure 10). The number of the total number of BLF patches soared since 1975, the number of large BLF patches (>1 km2) increased BLF patches in the NCPA accelerated and increased by 5.43 times in the past four decades (1975– by 4098, making up only 0.12% of the total. 2014). The number of small BLF patches (≤1 km2) increased by 342.86 × 104, accounting for 99.88% of The growth area of the BLF was dominated by small patches ( 1 km2) in 1975–2014. Notably, the total growth. Specifically, the increasing number of BLF patches in≤ the three periods of 1975–1990, 78.14% (4.78 104 km2) of the increased BLF area in the NCPA was small patches ( 1 km2) from 1990–2000, and× 2000–2014 was 99.85 × 104, 108.65 × 104, and 134.36 × 104. Among them≤ , small BLF 1975 to 2014 (Figure 12a). The area of BLF patches decreased from small to large in 1975 (Figure 12). patch numbers (≤1 km2) were 99.77 × 104, 108.52 × 104, and 134.15 × 104, consisting of 99.93%, 99.88%, The area distributions of different patch sizes among the provinces and cities also showed distinctions. and 99.85% of the total number of increased patches, respectively. As shown in Figure 10, the number Concretely, the areas of BLF patches in Anhui and Jiangsu provinces decreased from patches smaller of BLF patches decreased from small patches to large patches in 1975–2014. In addition to Henan than 0.1 km2 to patches larger than 10 km2 in various periods (Figure 12g,h). As of 1990, BLF patches province, there were no BLF patches larger than 10 km2 in the study area in 1975. As of 2014, although with an area between 0.1–1 km2 exceeded patches smaller than 0.1 km2 and became the largest patch the total number of BLF patches soared since 1975, the number of large BLF patches (>1 km2) category in the five provinces and cities of Beijing, Tianjin, Hebei, Shandong, and Henan (Figure 12b–f). increased by 4098, making up only 0.12% of the total.

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With the rapid area growth, BLF patches larger than 10 km2 have occurred in all provinces and cities except Beijing since 1990. The absence of patches larger than 10 km2 in Beijing was due to its floodplain area being much smaller than other provinces and cities (879.29 km2). Moreover, the floodplain in Beijing was primarily located in the outer suburbs, where small outlying patches were widely distributed (Figure3, Figure 11b, and Figure 12b). Remote Sens. 2020, 12, x FOR PEER REVIEW 16 of 30

Figure 10. 11. TheThe number number distribution distribution of of different different growth growth patch patch sizes sizes in th ine the North North China China Plain Plain Area: Area: (a) North(a) North China China Plain Plain Area; Area; (b) (Beijing;b) Beijing; (c) Tianjin; (c) Tianjin; (d) (Hebei;d) Hebei; (e) Shandong; (e) Shandong; (f) Henan; (f) Henan; (g) Anhui; (g) Anhui; and and (h) Jiangsu(h) Jiangsu..

TheSmall growth patches area dominated of the BLF the was growth dominated process ofby three small di patchesfferent expansion (≤1 km2) in types 1975 in–2014. the study Notably, area. 78.14%The average (4.78 × patch 104 km size2) of th thee increased expanded BLF BLF area was in 0.0047 the NCPA km2, of was which small the patches edge-expansion (≤1 km2) from type 1975 was tothe 2014 largest (Figure (0.0071 11a). km T2he), andarea the of BLF outlying patches type decreased was the smallestfrom small (0.0028 to large km2 in). Figure1975 (Figure 13 reveals 11). thatThe areathe number distributions of patches of different and areas patch of thesizes three among expansion the provinces types decreased and cities from also small showed to large distinctions patches.. Concretely,The number the of areas small of patches BLF patches of the in three Anhui expansion and Jiangsu types provinces was overwhelming, decreased from and patches its proportions smaller thanwere 0.1 all km higher2 to patches than 99.98%. larger In than terms 10 km of the2 in patchvarious size, periods although (Figure the proportions11g,h). As of of 1990, small BLF patches patches of withthe edge-expansion an area between and 0.1– infilling1 km2 exceeded types were patches close smaller (93.05% than and 0.1 93.94%, km2 and respectively), became the the largest area ofpatch the categoryformer was in the 4.77 five times provinces of the latter and cities (Figure of 13Beijing,a–d). ComparedTianjin, Hebei, with Shandong, edge-expansion and Henan and infilling (Figure types, 11b– f).the W ratioith the of small rapid BLF area patches growth, in theBLF outlying patches typelarger was than the 10 highest km2 have (99.96%) occurred among in the all three provinces expansion and citiestypes, except while theBeijing patch since area 1990. was theThe smallest absence (3.40of patches103 kmlarger2) (Figure than 10 13 kme,f).2 in Beijing was due to its × floodplain area being much smaller than other provinces and cities (879.29 km2). Moreover, the floodplain in Beijing was primarily located in the outer suburbs, where small outlying patches were widely distributed (Figure 3, Figure 10b, and Figure 11b).

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Figure 11. 12. AreaArea d distributionsistributions of the built built-up-up land in floodplains floodplains with different different patch sizes in the North China PlainPlainArea: Area: ( a()a North) North China China Plain Plain Area; Area; (b) Beijing;(b) Beijing; (c) Tianjin; (c) Tianjin; (d) Hebei; (d) Hebei; (e) Shandong; (e) Shandong; (f) Henan; (f) (Henan;g) Anhui; (g) andAnhui; (h) Jiangsu.and (h) Jiangsu.

3.5. TheSmall BLF patches Expansion dominated Pattern Impactthe growth on Flood process Vulnerability of three di andfferent Adaptation expansion Strategy types in the study area. The average patch size of the expanded BLF was 0.0047 km2, of which the edge-expansion type was 3.5.1. Impact of Patch Size on Flood Vulnerability and Adaptation Strategy the largest (0.0071 km2), and the outlying type was the smallest (0.0028 km2). Figure 12 reveals that the numberThe total of increase patches ofand the areas BLF areaof the was three correlated expansion to the types flood decreased occurrence from inthe small NCPA. to lar Asge shown patches. in TableThe number4, there wasof small a significant patches correlationof the three coe expansionfficient (R types= 0.36; wasp < overwhelming,0.01) between total and BLF its proportions growth area andwere flood all higher occurrence than 99.98%. from 1975–2014, In terms whichof the indicatedpatch size, that although the total the BLF proportions growth area of wassmall significantly patches of associatedthe edge-expansion with flood and vulnerability. infilling types Although were clo these correlation (93.05% and coe 93.94%,fficients respectively), were disparate the in magnitudearea of the (Rformer= 0.41, was 0.31, 4.77 and times 0.29, of the respectively) latter (Figure in the12a– threed). Compared periods of with 1975–1990, edge-expansion 1990–2000, and and infilling 2000–2014, types, the correlation ratio of small coeffi BLFcients patches were all in significant the outlying at p type< 0.01 was (Table the4 ). highest (99.96%) among the three expansiCorrelationson types, while between the patch different area patch was the sizes smallest and flood (3.40occurrences × 103 km2) (Figure were distinct12e,f). in the NCPA. The correlation between the growth of small BLF patches and flood occurrence (R = 0.36, p < 0.01) was more remarkable than that of large patches (R = 0.18, p > 0.1) from 1975 to 2014 (Table4), which indicated that small growth patches strongly influenced flood vulnerability compared to large growth patches in the NCPA. The correlations between small growth patches and flood occurrences were significant in different periods. For example, when the significance was less than 0.01, the correlation coefficients were 0.40, 0.32, and 0.31 in the periods of 1975–1990, 1990–2000, and 2000–2014, respectively. However, the growth of large patches in the three periods above has no significant correlation with flood

Remote Sens. 2020, 12, 3172 18 of 29 occurrence (p > 0.1). Moreover, the growth of large BLF patches and flood occurrence showed an inverse correlation trend between 1990–2000 (R = 0.05, p > 0.1) and 2000–2014 (R = 0.07, p > 0.1). − − These negative correlation trends suggest that an increase in the number of large patches may reduce

floodRemote vulnerabilitySens. 2020, 12, x FOR in a PEER particular REVIEW region. 18 of 30

FigureFigure 13.12. TheThe numbernumber ((aa,,cc,,ee)) andand areaarea ((bb,,dd,,ff)) distributionsdistributions ofof BLFBLF patchespatches withwith didifferentfferent patchpatch sizessizes underunder threethree expansionexpansion typestypes inin 1975–2014.1975–2014. LegendLegend abbreviations: BJ, Beijing; TJ, Tianjin; HB, Hebei; SD,SD, Shandong;Shandong; HN,HN, Henan;Henan; AH,AH, Anhui;Anhui; andand JS,JS, Jiangsu.Jiangsu.

3.5. TTablehe BLF 4. PearsonExpansion correlation Pattern Impact coefficients on Flood between Vulnerability the patch and sizes Adaptation of built-up Strategy land in floodplains and flood occurrence in the North China Plain Area.

3.5.1. Impact of Patch Size on Flood Vulnerability1975–2014 1975–1990 and Adaptation1990–2000 Strategy2000–2014 Patch Sizes The total increase of the BLF area (Nwas= correlated77) (N =to77) the flood(N =occurrence77) (N in= 77)the NCPA. As shown in Table 4, there wasTotal a significant BLF increase correlation 0.36 ** coefficient 0.41 ** (R = 0.36; 0.31 p **< 0.01) between 0.29 ** total BLF growth Large patches 0.18 0.22 0.05 0.07 area and flood occurrence from 1975–2014, which indicated −that the total− BLF growth area was Small patches 0.36 ** 0.40 ** 0.32 ** 0.31 ** significantly associated with flood vulnerability. Although the correlation coefficients were disparate Notes: Double asterisks (**) indicate a significance level at p < 0.01. in magnitude (R = 0.41, 0.31, and 0.29, respectively) in the three periods of 1975–1990, 1990–2000, and 2000–2014, the correlation coefficients were all significant at p < 0.01 (Table 4). Based on the above analysis, the growth of small BLF patches strongly correlates with flood vulnerability,Table 4. Pearson which indicatescorrelation thatcoefficients they are between more vulnerablethe patch size ands of susceptiblebuilt-up land to in flooding. floodplains However, and floodflood prevention occurrence standards in the North for China small Plain patches Area. are relatively low or even non-existent (Figure 14), which has also been supported by practical experience [32,33]. In China, the BLF patch sizes are closely 1975–2014 1975–1990 1990–2000 2000–2014 related to the populationPatch sizeSizes and economic magnitude, which directly determines the flood control standards [34]. For instance, the central(N districts = 77) of Tianjin(N = 77) and the(N core = 77) area of(N the = Binhai77) New District (a total of 2700 kmTotal2) areBLF protected increase by a0.36 249 ** km ring-like0.41 ** flood dike0.31 that** can0.29 resist ** a 200-year return period flood. In theLarge suburbs patches of Tianjin, 0.18 flood control0.22 standards are−0.05 degraded − to0.07 resist floods with a Small patches 0.36 ** 0.40 ** 0.32 ** 0.31 ** Notes: Double asterisks (**) indicate a significance level at p < 0.01.

Correlations between different patch sizes and flood occurrences were distinct in the NCPA. The correlation between the growth of small BLF patches and flood occurrence (R = 0.36, p < 0.01) was more remarkable than that of large patches (R = 0.18, p > 0.1) from 1975 to 2014 (Table 4), which indicated that small growth patches strongly influenced flood vulnerability compared to large

Remote Sens. 2020, 12, x FOR PEER REVIEW 19 of 30 growth patches in the NCPA. The correlations between small growth patches and flood occurrences were significant in different periods. For example, when the significance was less than 0.01, the correlation coefficients were 0.40, 0.32, and 0.31 in the periods of 1975–1990, 1990–2000, and 2000– 2014, respectively. However, the growth of large patches in the three periods above has no significant correlation with flood occurrence (p > 0.1). Moreover, the growth of large BLF patches and flood occurrence showed an inverse correlation trend between 1990–2000 (R = 0.05, p > 0.1) and 2000–2014 (R = 0.07, p > 0.1). These negative correlation trends suggest that an increase in the number of large patches may reduce flood vulnerability in a particular region. Based on the above analysis, the growth of small BLF patches strongly correlates with flood vulnerability, which indicates that they are more vulnerable and susceptible to flooding. However, flood prevention standards for small patches are relatively low or even non-existent (Figure 13), which has also been supported by practical experience [32,33]. In China, the BLF patch sizes are closely related to the population size and economic magnitude, which directly determines the flood Remotecontrol Sens. standards2020, 12, 3172 [34]. For instance, the central districts of Tianjin and the core area of the Binhai19 New of 29 District (a total of 2700 km2) are protected by a 249 km ring-like flood dike that can resist a 200-year return period flood. In the suburbs of Tianjin, flood control standards are degraded to resist floods 100-year return period. However, flood control standards for small settlements in the exurbs of Tianjin with a 100-year return period. However, flood control standards for small settlements in the exurbs are for 10–20-year return periods. of Tianjin are for 10–20-year return periods.

Figure 13. 14. EffectsEffects of different different patch sizes on floodflood vulnerability andand floodflood controlcontrol capacity.capacity.

Further,Further, foreignforeign floodflood controlcontrol planningplanning alsoalso provedproved thethe aboveabove argument.argument. ForFor example,example, thethe areaarea surroundingsurrounding HoHo ChiChi MinhMinh City City in in Vietnam Vietnam is is susceptible susceptible to to flooding, flooding, whereas whereas its its central central urban urban area area is protectedis protected by by ring ring dikes dikes without without being being aff ectedaffected [35 ].[35] Additionally,. Additionally a 2011, a 2011 flood flood caused caused flooding flooding in the in surroundingthe surrounding area area of Bangkok, of Bangkok, Thailand, Thailand, while while the central the central urban urban of Bangkok of Bangkok was relatively was relatively dry [36 ,dry37]. According[36,37]. According to cost–benefit to cost analyses–benefit analyses (CBAs), the (CBA initials), the investments initial investments and maintenance and maintenance costs of structural costs of protectionstructural protection of small patches of small (such patches as floodwalls(such as floodwal and seals dikes)and sea are dikes) economically are economically unviable unviable [18,38]. Therefore,[18,38]. Therefore, the low protection the low protectionlevel of the level small of patches the small is because patches of their is because scattered of distribution their scattered and limiteddistribution flood and control limited and flood disaster control mitigation and disaster resources mitigation (Figure 14 resources)[34]. All (Figure of this suggests13) [34]. All that of flood this preventionsuggests that levels flood and prevention the impact levels on flood and the vulnerability impact onvary flood across vulnerability patch sizes. vary across patch sizes. InIn termsterms ofof floodflood adaptationadaptation strategies,strategies, structuralstructural preventionprevention measuresmeasures complementedcomplemented byby softsoft adaptationadaptation strategies play an essential role in increasingincreasing thethe resilienceresilience ofof largelarge patchespatches toto floodingflooding (Figure(Figure 1514).). InIn contrast,contrast, smallsmall andand disperseddispersed patchespatches areare moremore suitablesuitable forfor softsoft adaptationadaptation strategiesstrategies basedbased onon previousprevious cost–benefitcost–benefit studiesstudies (Figure(Figure 1413))[ [34].34]. Herein, soft adaptationsadaptations refer to thethe useuse ofof availableavailable informationinformation and and low-cost low-cost strategies strategies such such as as behavioral, behavioral, institutional, institutional, and and strategic strategic changes changes to enhanceto enhance the the ability ability to to resist resist floods flood [s39 [39–41–41].]. These These measures measures include include flood flood insurance,insurance, buildingbuilding codes,codes, forecastingforecasting andand earlyearly warningwarning systemssystems (EWS),(EWS), landland useuse planning,planning, capacitycapacity buildingbuilding ofof educationeducation andand awareness,awareness, andand otherother nature-basednature-based measuresmeasures [[3939––4141].]. Remote Sens. 2020, 12, x FOR PEER REVIEW 20 of 30

Figure 14. 15. Flood a adaptationdaptation strateg strategiesies between different different patch sizes in floodplains.floodplains. ( a)) indicates the current situation of flood flood pr preventionevention for didifferentfferent patch sizes. ( b)) show showss their corresponding flood flood adaptation measures.

In addition, we suggest implementing the village merging to mitigate flood threats in floodplains where flood hazard is relatively high and BLF patches are scattered. Here, village merging refers to the displacement of rural areas distributed in high-risk areas into concentrated settlements within safe regions [42]. Recently, village merging has been widely implemented in Shandong province and achieved certain effectiveness. The integration of villages and settlements not only improves the efficiency of public resources and promotes the intensive use of land resources, but also provides more construction land space for urban development [43,44]. More crucially, village merging enables efficient use of flood control resources to reduce flood damage and contribute to the sustainable development of floodplains.

3.5.2. Impact of Expansion Type on Flood Vulnerability and Adaptation Strategy The correlation between BLF growth and flood occurrence varied depending on the expansion types. As shown in Table 5, correlations between outlying (R = 0.56, p < 0.01) or edge-expansion types (R = 0.52, p < 0.01) and flood occurrence were more significant than the infilling type (R = 0.27, p < 0.05). Therefore, the outlying and the edge-expansion types of BLF growth patches have a stronger impact on flood vulnerability in the NCPA. Although the correlation coefficients of the outlying and edge-expansion types varied in different periods, their significance level was less than 0.01, indicating that they were significantly correlated with flood vulnerability.

Table 5. Pearson correlation coefficients between the expansion types of built-up land in floodplains and flood occurrence in the North China Plain Area (NPCA).

1975~2014 1975~1990 1990~2000 2000~2014 Expansion Types (N = 77) (N = 77) (N = 77) (N = 77) Infilling 0.27 * 0.05 0.17 0.19 Edge-expansion 0.53 ** 0.39 ** 0.35 ** 0.32 ** Outlying 0.51 ** 0.52 ** 0.33 ** 0.45 ** Notes: A single asterisk (*) represents a significance level at p < 0.05; double asterisks (**) indicate a significance level at p < 0.01.

There was no significant correlation between the infilling patches and flood occurrence in the three periods of 1975–1990 (R = 0.05, p > 0.1), 1990–2000 (R = 0.17, p > 0.1), and 2000–2014 (R = 0.19, p > 0.1) (Table 5). However, the infilling patches correlated to flood occurrence when the significance level was less than 0.05 in 1975–2014. The cause is that the infilling growth area contributes to flooding vulnerability that is not apparent in a short period. When the infilling patch area increases to a certain threshold, it will affect the surface runoff’s infiltration rate, thereby changing the socio-hydrological

Remote Sens. 2020, 12, 3172 20 of 29

In addition, we suggest implementing the village merging to mitigate flood threats in floodplains where flood hazard is relatively high and BLF patches are scattered. Here, village merging refers to the displacement of rural areas distributed in high-risk areas into concentrated settlements within safe regions [42]. Recently, village merging has been widely implemented in Shandong province and achieved certain effectiveness. The integration of villages and settlements not only improves the efficiency of public resources and promotes the intensive use of land resources, but also provides more construction land space for urban development [43,44]. More crucially, village merging enables efficient use of flood control resources to reduce flood damage and contribute to the sustainable development of floodplains.

3.5.2. Impact of Expansion Type on Flood Vulnerability and Adaptation Strategy The correlation between BLF growth and flood occurrence varied depending on the expansion types. As shown in Table5, correlations between outlying (R = 0.56, p < 0.01) or edge-expansion types (R = 0.52, p < 0.01) and flood occurrence were more significant than the infilling type (R = 0.27, p < 0.05). Therefore, the outlying and the edge-expansion types of BLF growth patches have a stronger impact on flood vulnerability in the NCPA. Although the correlation coefficients of the outlying and edge-expansion types varied in different periods, their significance level was less than 0.01, indicating that they were significantly correlated with flood vulnerability.

Table 5. Pearson correlation coefficients between the expansion types of built-up land in floodplains and flood occurrence in the North China Plain Area (NPCA).

1975~2014 1975~1990 1990~2000 2000~2014 Expansion Types (N = 77) (N = 77) (N = 77) (N = 77) Infilling 0.27 * 0.05 0.17 0.19 Edge-expansion 0.53 ** 0.39 ** 0.35 ** 0.32 ** Outlying 0.51 ** 0.52 ** 0.33 ** 0.45 ** Notes: A single asterisk (*) represents a significance level at p < 0.05; double asterisks (**) indicate a significance level at p < 0.01.

There was no significant correlation between the infilling patches and flood occurrence in the three periods of 1975–1990 (R = 0.05, p > 0.1), 1990–2000 (R = 0.17, p > 0.1), and 2000–2014 (R = 0.19, p > 0.1) (Table5). However, the infilling patches correlated to flood occurrence when the significance level was less than 0.05 in 1975–2014. The cause is that the infilling growth area contributes to flooding vulnerability that is not apparent in a short period. When the infilling patch area increases to a certain threshold, it will affect the surface runoff’s infiltration rate, thereby changing the socio-hydrological cycle system [45,46]. Thus, the increase in infilling BLF patches on the long-term scale will cause flood vulnerability dynamics. As shown in Figure 16, the three BLF expansion types pose different impacts on flood vulnerability. The edge-expansion type formed new BLF areas outside the original protected BLF patches and increased the flood vulnerability (Figure 16a). For example, the planned central urban area of Nanjing has increased from 280 to 834 km2. The existing Nanjing urban scope of flood control is small, and the flood control projects in the newly expanded area are relatively weak. The booming economy and dense population have caused severe flood damage in Nanjing [47]. In addition, the edge-expansion type may also damage the existing flood prevention measures in the transition zone of new and old BLF patches. Hence, it is necessary to upgrade the flood control system for edge-expansion patches that tend to stabilize. For the BLF area that is still expanding, soft adaptation measures should be applied to enhance community resilience (Figure 16d). Outlying patches occupied vacant lands or rural areas in floodplains, and flood control standards in these areas were low or even lacked prevention measures. Therefore, the considerable expansion of small outlying patches balloons flood vulnerability in the NCPA (Figure 16b). To address this issue, Remote Sens. 2020, 12, x FOR PEER REVIEW 21 of 30

cycle system [45,46]. Thus, the increase in infilling BLF patches on the long-term scale will cause flood vulnerability dynamics. As shown in Figure 15, the three BLF expansion types pose different impacts on flood vulnerability. The edge-expansion type formed new BLF areas outside the original protected BLF patches and increased the flood vulnerability (Figure 15a). For example, the planned central urban Remotearea of Sens. Nanjing2020, 12 has, 3172 increased from 280 to 834 km2. The existing Nanjing urban scope of flood control21 of 29 is small, and the flood control projects in the newly expanded area are relatively weak. The booming economy and dense population have caused severe flood damage in Nanjing [47]. In addition, the we suggest taking necessary structural measures and village merging projects for critical outlying edge-expansion type may also damage the existing flood prevention measures in the transition zone patches, such as cultural facilities and public infrastructure. For a large number of sporadic small of new and old BLF patches. Hence, it is necessary to upgrade the flood control system for edge- outlying patches, natural-based soft adaptation measures should be taken, such as improving flood expansion patches that tend to stabilize. For the BLF area that is still expanding, soft adaptation early warning systems (Figure 16e). measures should be applied to enhance community resilience (Figure 15d).

FigureFigure 16.15. TheThe impactimpact ofof didifferentfferent expansionexpansion typestypes onon thethe floodflood vulnerabilityvulnerability ofof BLFBLF patchespatches andand thethe adaptationadaptation measures.measures. (a–c) representrepresent thethe currentcurrent floodflood preventionprevention inin thethe edge-expansion,edge-expansion, outlying,outlying, andand infillinginfilling patches,patches, respectively.respectively. ((dd––ff)) indicatesindicates floodflood adaptationadaptation measuresmeasures forfor thethe threethree expansionexpansion typestypes ofof BLFBLF patches.patches.

BasedOutlying on the patches analysis occupied above, the vacant impact lands of infilling or rural patches areas on inflood floodplains, vulnerability and is not flood evident control in astandards short period in these (Table areas5). On we are long-term low or even scale, lack infillinged preven patchestion influencemeasures. the Therefore, socio-hydrological the considerable cycle andexpansion ultimately of small lead to outlying dynamics patches in flood balloons vulnerability flood vulnerability by altering surface in the runo NCPAff [45 (Figure–47]. Therefore, 15b). To designingaddress this urban issue, open we suggest space within taking BLF necessary patches structural can slow measures down the and rapid village convergence merging projects of surface for runocriticalff to outlying mitigate patches, flood vulnerability such as cultural (Figure facilities 16f). For and example, public infrastructure. the “Sponge City” For plana large implemented number of insporadic China issmall a useful outlying tool for patches, mitigating natural floods-based and soft waterlogging adaptation [37 measures,38]. The should plan requires be taken, cities such to act as likeimproving sponges flood that early are resilient warning to systems natural disasters(Figure 15e). caused by rainwater, which can not only alleviate floodingBased but on also the e ffianalysisciently above, using freshwaterthe impact resources.of infilling patches on flood vulnerability is not evident in a short period (Table 5). On a long-term scale, infilling patches influence the socio-hydrological 4.cycle Discussion and ultimately lead to dynamics in flood vulnerability by altering surface runoff [45–47]. Therefore, designing urban open space within BLF patches can slow down the rapid convergence of 4.1. Stability Analysis of Correlation Coefficient under Different Data Resolution surface runoff to mitigate flood vulnerability (Figure 15f). For example, the “Sponge City” plan implementedAs shown in in China Table6 , is with a useful the coarsening tool for mitigating of the data floods resolution, and waterlogging the correlations [37,38]. between The total plan p BLF increase and flood occurrence from 1975 to 2014 have always been significant ( < 0.01). In terms of BLF patch sizes, the significant correlation between the growth characteristics of small BLF patches ( 1 km2) and flood occurrence is still valid with the coarser data resolution. The correlation coefficients ≤ of small BLF patches tend to increase as the data resolution decreases from 150 m to 500 m (Table6). The relationship is also remarkable even when the resolution reaches 1000 m (R = 0.46, p < 0.01). Remote Sens. 2020, 12, 3172 22 of 29

However, the relationship between the growth of large BLF patches and flood occurrence is insignificant except for the 1000 m resolution (R = 0.29, p < 0.05).

Table 6. Correlation coefficients between BLF expansion patterns and flood occurrence at different spatial resolutions.

Different Resolutions (m) Expansion Pattern 30 m 150 m 250 m 500 m 1000 m Total BLF increase 0.36 ** 0.49 ** 0.49 ** 0.48 ** 0.48 ** Large patches 0.18 0.14 0.16 0.18 0.29 * Small patches 0.36 ** 0.39 ** 0.39 ** 0.46 ** 0.46 ** Infilling 0.27 * 0.17 0.23 * 0.24 * 0.20 Edge-expansion 0.53 ** 0.35 ** 0.42 ** 0.37 ** 0.42 ** Outlying 0.51 ** 0.38 ** 0.39 ** 0.44 ** 0.36 ** Notes: A single asterisk (*) represents a significance level at p < 0.05; double asterisks (**) indicate a significance level at p < 0.01.

From the perspective of expansion types with different resolutions, the significant correlation between flood occurrence and outlying or edge-expansion patches holds when the BLF resolution changes from 30 m to 1000 m (Table6). Although the correlation coe fficient values fluctuate at different resolutions, they are remarkably correlated at the significance level p < 0.01. Moreover, the linear correlation between the infilling type and flood occurrence changes with the resolution of built-up areas. There is a linear correlation between the infilling patch and flood occurrence under 30 m, 250 m, and 500 m resolution, and the correlation coefficients are 0.27 (p < 0.05), 0.23(p < 0.05), and 0.24 (p < 0.05), respectively. However, there is no significant linear correlation between infilling patches and flood occurrence under 150 m and 1000 m resolution. Therefore, the above analysis indicates that the spatial resolution of built-up land affects the correlation between expansion pattern (patch size and expansion type) and flood vulnerability [29]. Although the correlation coefficients between small patches, expansion type, infilling type, and flood occurrence varied with the spatial resolution of built-up land, the significant correlation between the three and flood occurrence has not changed (p < 0.01). Our understanding of the correlations between small patches, expansion type, infilling type, and flood vulnerability is relatively stable to spatial resolution. In this paper, the built-up land data with a resolution of 30 m was mainly utilized to investigate the correlation between BLF expansion pattern and flood vulnerability, and we believe this is reasonable. First, it should be noted that as the data resolution decreases, the BLF becomes coarse and unreliable, which makes the correlation between the BLF expansion pattern and flood occurrence unrepresentative. For example, large BLF patches are remarkably correlated to flooding that occurs when the resolution is reduced to 1000 m (R = 0.29, p < 0.05) (Table6). Nevertheless, we still cannot conclude that there is a significant correlation between the large patches and flood vulnerability because correlations are unremarkable at accurate resolutions. Moreover, considering that the maximum width of roads and bridges in China is generally 30 m, the built-up land dataset we adopted not only ensures the accuracy of the BLF data but also segments the BLF patches connected by roads and bridges. Therefore, the shape and classification criteria of BLF patches should be considered when using high-resolution data (<30 m) for research.

4.2. Comparison with Previous Studies Previous studies focused on the impact of spatio-temporal variation in the amount of built-up land on flood exposure [8]. These studies investigated the relationship with flood exposure by quantifying the built-up land area [10,11]. Further, they discussed the possible socio-economic factors that lead to a dramatic increase in flood exposure [9]. Due to the limitation of floodplain and built-up land data, the research on the relationship between BLF expansion and flood vulnerability is less involved. Remote Sens. 2020, 12, 3172 23 of 29

In this paper, we analyzed the interaction between BLF expansion and flood risk based on a conceptual model (Figure1). We recognized that the BLF expansion pattern (patch size and expansion type) a ffects flood vulnerability based on the flood control practice. Our study provides a new perspective for a more comprehensive understanding of the relationship between BLF expansion and flood risk systems. Moreover, it provides a theoretical basis for formulating sustainable flood management strategies in floodplains. In hydrology, human activities (flood control measures and urbanization) are considered to be boundary conditions or an external forcing of river-floodplain systems rather than drivers of interactions with flood risk [5,6]. Until the proposal of socio-hydrology, human activities are regarded as the endogenous factors of water system dynamics [4]. Socio-hydrology reveals the feedback mechanisms and synergistic evolution between human societies and hydrological systems to guide sustainable development in a changing environment [7]. For example, Di Baldassarre et al. implemented socio-hydrology as a flood risk problem using the concept of social memory as a vehicle to link human perceptions to flood damage [4]. In analyzing the interaction between BLF expansion and flood risk systems, we view BLF as a driver of flood risk dynamics. Hence, there remains a need to understand how BLF expansion impacts flood risk and how it feeds back into flood adaptation strategies. There are several differences between our research and a previous study of Han et al., which examined the impact of BLF growth mode on flood vulnerability in the Yangtze River Economic Belt from 1990 to 2014 (hereafter referred to as the previous case study) [48]. First, our study differs from the resolution of the built-up land data utilized in the previous case study. Built-up land datasets for the two studies were from the Joint Research Centre of the European Commission. We employed the 2018 version while the previous case study used the 2015 version. The 2018 version includes several improvements compared with the 2015 version. (1) The 2018 version of the dataset increases the resolution to 30m, while the 2015 version is 38m. The pixel-based assessment of data precision indicates that the 2015 version has lower data precision than the 2018 version (Table7)[ 5]. (2) The generation method and workflow of 2018 version data have been improved. For example, improvements in the accuracy of the learning dataset have reduced omission and commission errors in built-up land (Table7)[ 5]. Additionally, accuracy assessment by Leyk et al. indicates that the error of built-up land in rural and scattered areas is larger than in urban areas [49]. Thus, the 2018 version dataset has a major advantage for depicting rural and scattered built-up land patches, which provided more accurate data for illustrating the relationship between BLF expansion and flood vulnerability.

Table 7. Performance metrics of built-up land data based on pixel accuracy evaluation [49].

Performance Metrics 2015 Version 2018 Version Balanced Accuracy 0.83 0.86 Omission Error 0.22 0.18 Commission Error 0.46 0.42

Second, this paper and the previous case study have consistently agreed that small patches dominate the BLF growth and have a significant linear relationship with flood vulnerability [48]. There was no significant correlation between large growth patches and flood vulnerability. However, we found a negative correlation trend between large patches and flood vulnerability as they increased. This trend suggests that an increase in the number and size of large patches in a particular study area has a positive effect on reducing flood vulnerability. Moreover, the differences primarily lie in the impact of expansion type on flood vulnerability. The previous case study in the Yangtze River Economic Belt showed that the correlation between leapfrog type (i.e., outlying type) BLF growths (R = 0.34–0.68, p < 0.01) and flood occurrence was much more reliable than the edge-expanding BLF (R = 0.16–0.46) [48]. However, we discovered that the edge-expansion type BLF (R = 0.53, p < 0.01) rather than the outlying type (R = 0.51, p < 0.01) had a stronger correlation with flood vulnerability in 1975–2014 (Table5). As shown in Table5, correlations between outlying type BLF and flood occurrence Remote Sens. 2020, 12, 3172 24 of 29 are not always stronger than edge-expansion type in each period. Therefore, we deduce that there may be more complex relationships between flood vulnerability and either outlying or edge-expanding BLFs. At least, which of the two is more strongly correlated with flood vulnerability needs further study. In addition, we conducted a more comprehensive analysis of BLF dynamics and growth rates from the reform and opening-up to 2014, which is more reliable in examining linear correlations over a relatively long period. The BLF expansion in the NPCA has experienced a process of dispersion and coalescence. The previous case study has shown no significant correlation between infilling patches and flood vulnerability. However, we found a significant correlation (R = 0.27, p < 0.05) between the growth of infilling patches and flood vulnerability in 1975–2014, although this correlation was weaker than the growth of outlying and infilling patches (Table5). Many studies have also shown that the growth of infilling patches increases impervious surfaces and thus affects surface runoff [45,46,50]. This effect may not be apparent for short periods. When the impervious surface reaches a certain threshold, it affects the hydrological cycle and exacerbates flood hazards by altering the infiltration of surface runoff. Therefore, the correlation between infilling patch growth and flood vulnerability is significant over relatively long time scales (Table5).

4.3. Policy Implications and Suggestions China has experienced unprecedented rapid urbanization since its reform and opening-up policies in 1978 [51]. The climate deterioration and the gathering of people and property have wreaked havoc on the floodplain [52]. Previous studies have revealed that China will suffer the highest direct losses due to fluvial floods in the next two decades [53,54]. To address this challenge, governments have successively issued many policies and decrees to reduce flood damage (Table8)[ 1]. According to these historical policies and decrees, the flood control policies in the 1980s and 1990s focused on building structural projects in core cities and areas prone to catastrophic floods. After the 21st century, policies and decrees have placed more emphasis on integrated risks and gradually shifted towards soft adaptation strategies to manage floods. Flood control planning was required to be incorporated into the city’s master plans. These policies have important guiding significance for regional flood control and disaster reduction.

Table 8. Policies and decrees related to BLF in China from the 1980s to 2010s.

Year Policy/Decrees Related Contents Departments Flood prevention measures should be Urban Planning Law of the People’s 1989 implemented in areas prone to MOHURD Republic of China catastrophic flooding Upgrading of flood prevention standards in Disaster Reduction Planning of the 1998 core cities and implementing integrated MOHURD, MWR, MCA People’s Republic of China (1998–2010) disaster mitigation plans China’s National Plan of Integrated Compilation of the national integrated 2007 MOHURD, MWR, MCA Disaster Reduction (2006–2010) disaster risk map Disaster prevention and reduction should Urban-Rural Planning Law of the 2007 be included in the comprehensive plans of MOHURD People’s Republic of China cities and towns China’s National Plan of Integrated Compilation of integrated risk maps at 2011 Disaster Prevention and Reduction different scales; improving flood prevention MOHURD, MWR, MCA (2011–2015) in small and medium-sized rivers Notes: MOHURD, MWR, and MCA denote the Ministry of Housing and Urban-Rural Development, Ministry of Water Resources, and Ministry of Civil Affairs, respectively.

However, rapid BLF growth in China also reflects policy deficiencies in flood risk management. Historical flood risk management policies have ignored the impact of the BLF expansion on flood vulnerability. The relationship between the BLF expansion pattern and flood vulnerability in our study can provide a scientific basis for flood risk management. First, small BLF patches should be focused on reducing their flood vulnerability. Contrary to large BLF patches, small patches have a higher vulnerability and lower resistance to flooding due to their limited resources (Figure 14). High-grade structural measures may temporarily prevent small Remote Sens. 2020, 12, 3172 25 of 29 patches from flooding. However, it is not cost–effective to use structural measures to strengthen the flood control level of small patches based on cost–benefit analysis [18]. Managing flood risk involves much more than building dikes or levees [38]. Soft adaptation strategies can play a crucial role in reducing the flood vulnerability of small patches (Figure 15)[38]. For example, China has started implementing the national “Sponge Cities” program in 16 pilot cities. A vast amount of green space has been integrated into urban design to increase infiltration and prevent surface flooding [55]. Soft adaptations also include early warning systems (EWS), risk-informed land planning, risk financing instruments, and other nature-based measures [39]. Second, the government should issue decrees to limit the sprawl of BLF patches. Flood prevention measures are generally cost-effective in densely populated and property-intensive areas [18,38]. By contrast, implementing structural measures in small and scattered outlying BLF patches is not sustainable [18]. If the cost-effectiveness of structural measures is not considered, the potential hazard of dam breaks is devastating for outlying and small patches [38]. Given rapid urbanization, we suggest that new urban areas be planned in non-floodplains to minimize the area of outlying BLF patches. At minimum, new outlying BLFs should be built in areas with existing flood control systems. Besides, political and economic instruments are the primary determinants of the BLF expansion pattern. Governments should further amend the Urban-Rural Planning Act and issue explicit decrees to regulate the disorderly expansion of BLF patches. For example, governments should guide or designate areas for BLF expansion or adopt mandatory tax policies for illegal floodplain occupation. In addition, soft adaptation strategies should be implemented to reduce flood vulnerability in outlying patches that have already expansion (Figure 16b,e). Third, flood control planning is an indispensable part of risk management and a prerequisite for urban to resist floods. However, flood control standards of most cities in China are low and lag behind socio-economic development. According to an urban flood control survey in 2013, about 90% of China’s 642 cities are threatened by flooding of varying degrees. Half of these cities (321) failed to meet the national flood control standards. In addition, 44% of the cities (282) have not completed or updated their flood control plans [56]. Therefore, we suggest that flood control planning should be pervasively incorporated into urban and rural construction planning rather than just in large and medium-sized cities. The impacts of the BLF expansion pattern on flood vulnerability should be fully considered in updated urban and rural planning. Moreover, a cost-benefit analysis should be a fundamental theory for implementing structural measures or soft adaptation strategies. Many cost–benefit analyses on flood control measures have been carried out in European and American countries [57,58]. However, the development of flood control policies lacks the support of relevant research in China. In a recent study, Du et al. conducted a cost–benefit analysis of flood control measures under different climate change scenarios in Shanghai [59]. An optimal hybrid strategy combining structural measures and soft adaptation strategies was proposed, which provides a reference for the cost–benefit analysis of flood control measures and potential adaptation strategies in other cities.

4.4. Remaining Deficiencies and Future Research Direction Our research on BLF expansion mode and flood vulnerability has several limitations. Limited by the accuracy of remote sensing data, we only studied the relationship between BLF growth and flood occurrence for resolutions greater than 30 m [29]. Whether there is still a significant correlation between BLF expansion type and flood occurrence when the resolution is less than 30 m remains to be further researched. Therefore, more accurate BLF data from remote sensing images are needed to further analyze the correlation between BLF expansion and flood occurrence. Moreover, the relationship between BLF expansion pattern and flood occurrence in this paper was examined by quantifying the BLF area and cumulative flood-affected areas. Thus, the relationship between the two needs to be further validated. For example, a study of the relationship between BLF growth and flood magnitude, peak flows, flood frequency, or flood losses could further verify the Remote Sens. 2020, 12, 3172 26 of 29 correlation between BLF expansion and flood vulnerability. In addition to significant linear correlations, there may be a more complicated relationship between flood occurrence and outlying or edge-expansion patches. We have not investigated this possible relationship in this paper. At least, it is not clear which of the outlying and edge-expansion patches have a stronger correlation with flood occurrence, i.e., more significantly associated with flood vulnerability. Despite a handful of drawbacks, our study has important implications for understanding the relationship between BLF growth and flood risk, as well as for flood prevention and disaster mitigation. In future studies, in addition to addressing the above limitations in this paper, we need to further analyze the relationship between infilling patches and flood occurrence, which is because the previous case study by Han et al. does not support this correlation [48]. In a particular study area, we are trying to find out which threshold the infilling BLF reaches will significantly affect the flood vulnerability. From the conceptual framework of BLF expansion and flood risk system (Figure1), the interplay between BLF growth and flood risk is not limited to the impact of expansion pattern on flood vulnerability. Flood risk is an integrated result of flood hazard, exposure, and vulnerability. Therefore, this study is a snapshot of the relationship between BLF growth and flood risk. Our future study will select a typical region to investigate the impact of BLF expansion on flood risk systems (flood hazard, exposure, and vulnerability). Future studies will lead to a more comprehensive understanding of the interaction between BLF expansion and flood risk and propose corresponding disaster prevention and mitigation strategies for the sustainable development of floodplains.

5. Conclusions Floods are causing increasing havoc in China’s floodplains due to unprecedented urbanization and improper development. Thus, understanding the relationship between BLF expansion and flood risk is of great significance for flood prevention and disaster reduction. Previous studies have focused on the relationship between BLF area and flood exposure while ignoring the relationship between BLF expansion pattern and flood vulnerability as well as its adaption strategies, which plays a crucial role in the sustainable development of floodplains and the formulation of flood control policies. Using the NCPA as an example, we examined the spatial distribution and dynamics of BLF patches using the GIS-based landscape analysis in 1975–2014. The spatial distribution of BLF was uneven. Districts and counties with high occupancy rates (ORi > 15%) were mainly distributed along the Yellow River, Zhengzhou-Shijiazhuang-Beijing line, the Grand Canal-Huaihe River, and the center of Shandong province. The area of BLF increased by 288.26% from 1975 to 2014, and the most rapid growth of BLF occurred in 1975–1990 (CR = 34.01%, AACR = 5.98%). Besides, the BLF expansion in the NPCA has experienced a dispersion-coalescence process. Small patches dominated the number and area of BLF growth. Edge-expansion patches were the expansion type with the most area growth in the NCPA. We viewed BLF growth as a driver of flood risk from a socio-hydrological perspective. Our study discovered that flood vulnerability was significantly impacted by small (R = 0.36, p < 0.01), edge-expansion (R = 0.53, p < 0.01), and outlying patches (R = 0.51, p < 0.01). Unlike previous studies, we discovered no significant linear correlation between large patches and flood vulnerability (R = 0.18, p > 0.1) but a negative trend. It was not the outlying patches that had a stronger correlation with flood vulnerability. Furthermore, infilling patch growth was significantly associated with flood vulnerability over long periods, but this correlation was weaker than edge-expansion and outlying patches. Governments should issue decrees to limit the sprawl of BLF patches. Soft adaptation measures and village merging should be taken to reduce flood damage in small and outlying patches. Future research will improve the resolution of remote sensing data (less than 30 m) and further validate the correlation with flood vulnerability by analyzing the BLF expansion pattern in relation to flood frequency, flood magnitude, peak discharges, and flood losses. Flood risk is an integrated result of flood hazard, exposure, and vulnerability. Therefore, this study is a snapshot of the relationship between BLF growth and flood risk. Future studies will select a representative region to investigate the Remote Sens. 2020, 12, 3172 27 of 29 impacts of BLF expansion on flood hazard, exposure, and vulnerability. These studies provide a more comprehensive understanding of the interplay between BLF growth and the flood risk system and finding a sustainable and integrated development strategy for floodplains.

Author Contributions: Conceptualization, G.W., and L.L.; data curation and investigation, G.W., Z.H., Y.L. (Yong Liu), G.Z., J.L., Y.L. (Jifu Liu), Y.G., X.H., Q.Z., Z.T., C.H., and L.L.; methodology, G.W., and L.L.; writing, G.W., and L.L. All authors have read and agreed to the published version of the manuscript. Funding: The study was supported by the National Key Research and Development Project (Grant No. 2017YFC1503000). Acknowledgments: The authors would like to thank the three anonymous reviewers for their valuable comments and the two editors (Cris Wang and Helen Wang) for their help with this article. Conflicts of Interest: The authors declare no conflict of interest.

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