sustainability

Article Analyzing Trends of Dike-Ponds between 1978 and 2016 Using Multi-Source Remote Sensing Images in Shunde District of South China

Fengshou Li 1, Kai Liu 1,* , Huanli Tang 2, Lin Liu 3,4,* and Hongxing Liu 4,5

1 Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangdong Provincial Engineering Research Center for Public Security and Disaster, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China; [email protected] 2 Guangzhou Zengcheng District Urban and Rural Planning and Surveying and Mapping Geographic Information Institute, Guangzhou 511300, China; [email protected] 3 Center of Geo-Informatics for Public Security, School of Geographic Sciences, Guangzhou University, Guangzhou 510006, China 4 Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH 45221, USA; [email protected] 5 Department of Geography, the University of Alabama, Tuscaloosa, AL 35487, USA * Correspondence: [email protected] (K.L.); [email protected] (L.L.); Tel.: +86-020-8411-3044 (K.L.); +1-513-556-3429 (L.L.); Fax: +86-020-8411-3057 (K.L. & L.L.)


Received: 27 August 2018; Accepted: 26 September 2018; Published: 30 September 2018


Abstract: Dike-ponds have experienced significant changes in the Pearl River Delta region over the past several decades, especially since China’s economic reform, which has seriously affected the construction of ecological environments. In order to monitor the evolution of dike-ponds, in this study we use multi-source remote sensing images from 1978 to 2016 to extract dike-ponds in several periods using the nearest neighbor classification method. A corresponding area weighted dike-pond invasion index (AWDII) is proposed to describe the spatial evolution of dike-ponds, both qualitatively and quantitatively. Furthermore, the evolution mechanisms of dike-ponds are determined, which can be attributed to both natural conditions and human factors. Our results show that the total area of dike-ponds in 2016 was significantly reduced and fragmentation had increased compared with the situation in 1978. The AWDII reveals that Shunde District has experienced three main phases, including steady development, rapid invasion and a reduction of invasion by other land use types. Most dike-ponds have now converted into built-up areas, followed by cultivated lands, mainly due to government policies, rural area depopulation, and river networks within Shunde. Our study indicates that the AWDII is applicable towards the evaluation of the dynamic changes of dike-ponds. The rational development, and careful protection, of dike-ponds should be implemented for better land and water resource management.

Keywords: dike-ponds; land use changes; DISP; Landsat; Shunde District

1. Introduction Dike-ponds are a predominant and typical traditional agricultural production mode that was developed in the Pearl River Delta region of south China by local farmers [1]. Pond-breeding fish form in the low-lying, artificially dug fields, while clay in the dikes is used for planting mulberry-dominated trees, which thus form the basic dike-pond pattern. Dike-ponds represent the integration of terrestrial ecosystems (dike) and freshwater ecosystems (pond), and they have become one of the most important types of ecological landscapes in dense river network areas [2,3]. The land-water interaction between

Sustainability 2018, 10, 3504; doi:10.3390/su10103504 www.mdpi.com/journal/sustainability Sustainability 2018, 10, 3504 2 of 27 dikes and ponds is very significant because of the unique natural conditions of the area where dike-ponds are located, as well as their individual system characteristics. Dike-ponds not only effectively solve some natural environment problems that often occur in low-lying areas, and in turn preserve ecological balance, but they also create huge benefits in terms of driving economic growth [4]. The energy exchange, conversion, and material recycling processes within the system are intense and frequent. Dike-ponds are considered a model of traditional Chinese agriculture practices, and they are an important agricultural cultural heritage in the world [5,6]. Researchers have long sought a better understanding of the various aspects of dike-ponds. The study of dike-ponds began in 1980s whereby the basic structure and functions of dike-ponds was explained [4,7,8]. In addition, scholars also discussed the land–water interactions that occur between dikes and ponds. Such researchers measured the productivity of dike-ponds based on their natural environment, energy flow, material flow and the input and output of the major types of dike-pond (mulberry-dike-fish-pond, fruit-dike-fish-pond, sugar-dike-fish-pond and flower-dike- fish-pond) [9,10]. According to the available research on dike-ponds, reformation of the original dike-pond was conducted in low-lying lands such as Southeast Asia, North America, Australia, and other tropical and subtropical regions to introduce three-dimensional planting [11,12]. In the 1990’s, the Pearl River Delta region became a pioneer in reform and opening up, but was deemed to excessively pursue economic benefits. The situation led to the phenomenon of heavy fishpond breeding and light base planting. In-depth studies of dike-ponds fell into stagnation then, where the focus switched to merely analyzing the economic benefits that existing dike-ponds bring about [13–16]. At the beginning of 21th century, the traditional dike-pond had been severely damaged due to the high level of economic development in the Pearl River Delta. A series of subsequent ecological and environmental problems have aroused the attention of the public. For example [17–19], by being mainly focused on the repair and renovation of dike-ponds by exploring the mechanisms related to their construction and regulation. Zhao [20] and Wu [21] demonstrated the impact of the socio-economic development of dike-ponds, while other scholars [22,23] evaluated the ecological environment quality of dike-ponds. As a field of research, the agricultural landscape of dike-ponds provides a reference for the continuation of traditional ecological service functions, the restoration of agricultural ecosystems and the sustainable development of regional ecological economies [24–27]. At present, the study of dike-ponds includes a combination of field survey data of local areas, using geographic information systems (GIS) and remote sensing (RS) spatial analysis methods. The assistance of RS technology has the advantage of the timeliness of data acquisition in field investigations and their wide and large geographic coverage [28–30]. Some existing studies refer to the monitoring of land use changes both in terms of their distribution and area with multi-temporal data [30,31]. Ye used data from the Landsat TM from 1990, 2000, and 2006 to analyze dike-pond changes in the Pearl River Delta [32]. He divided the region into four types of dike-ponds based on a comprehensive expansion coefficient. Wang used data from three periods in 1990, 2000, and 2008 to extract the dike-pond distribution, while determining the dynamic change processes experienced by dike-ponds in the study area [33]. However, most of these analyses involved short time spans and were performed at only a single scale, making it difficult to obtain spatial information of multi-temporal dike-pond areas at larger scales, as well as the spatial distribution of dike-ponds before the 1980’s. Some studies stated that the Key Hole (KH) of Declassified Intelligence Satellite Photographs (DISP) series can scan both generally and in detail [34,35]. For the period from the 1960s to 1970s, the Pearl River Delta was a key area of interest for the United States, who archived considerable DISP data covering this region. Considering the available data, DISP images were selected as a source of RS data to determine the river network distribution and classify land use. Many literature works concluded that multi-source data can improve the image resolution and accuracy of land use classification [36–39]. Taking the Landsat Thematic Mapper(TM) images provided by the National Aeronautics and Space Agency (NASA) as a main data source, the evolution of dike-ponds from the 1980’s to the present can be realized with the help of KH-9 images [40–42]. Sustainability 2018, 10, 3504 3 of 27

Landscape patterns and their dynamic change processes are crucial parts of landscape ecology [43]. Human activities and their environmental effect are frequently discussed in the global change research [44]. The dike-ponds, which are typical ecological landscapes, are especially studied and explored with the help of pollen and palaeoecological records, within an interdisciplinary study [45–47]. Scholars have attempted to measure the conversion of land use quantitatively with feasible indexes, such as the freedom, dispersion, and goodness of construction land expansion [48–50]. Previous studies refer to other indices like the landscape expansion index (LEI) to better reveal spatio-temporal land use dynamics [51]. Unfortunately, the land use types that these indices determine remain relatively limited. For other types of land use which are not always considered, such as wetlands, and it is hard to use such indices to measure their dynamic change. Indeed, there exist very few publications regarding indices that effectively reflect the development trends of wetlands, especially dike-ponds. Therefore, evaluation methods and/or applicable models need to be further formulated. In the present study, we propose a new index called the area weighted dike-pond invasion index (AWDII), which is a variant of the landscape invasion index (LII). The modified parameter, which is based on the LEI, improves our ability to effectively evaluate dike-ponds. The estimation and monitoring of dike-pond changes are necessary to establish an integrated land and water resource management system [52,53]. As a typical agricultural mode in the Pearl River Delta, it is of great practical significance to implement ecological agriculture construction and sustainable development of regional social economy in developed regions. Therefore, in this study we assemble a series of RS images that span 38 years and which have sufficient spatial resolution to classify land use and extract the distribution of dike-ponds for each period of research. A corresponding evaluation indicator is constructed to analyze the spatial evolution of dike-pond distributions, both qualitatively and quantitatively. By combining the natural environment characteristics in Shunde District with social economics, the factors which led to the dike-pond changes were performed.

2. Materials and Methods

2.1. Study Area The study area, Shunde, is located at 22◦4001500–23◦004000 N and 113◦103500–113◦2303000 E in the Southern part of Foshan City, Guangdong Province. It covers an area of about 806 km2 (Figure1). The area has a warm, humid, south Asian subtropical monsoon climate with sufficient sunshine, abundant precipitation, and other unique natural conditions. The territory has a flat land dominated by alluvial plains and rivers, while sporadic hillocks can be found in Southern and Western regions. The Northwest land is slightly higher than the Southwest, and the average elevation of most areas is 0.2 to 2 m above sea level [54]. Several rivers flow across Shunde District, including Xijiang River and the Shunde District Waterway. There are currently about 16 tributaries, and the total length of them reaches 210 km. Shunde District has jurisdiction over 10 towns (Chencun, Junan, Xingtan, Longjiang, Lecong, Beijiao, Daliang, Ronggui, Lunjiao, and Leliu). These 10 towns are distributed on both sides of the main river channel in Shunde, which has become one of the main ways to communicate with the outside and has provided a link and means of interaction among the towns in the district. Sustainability 2018, 10, 3504 4 of 27 Sustainability 2018, 10, x FOR PEER REVIEW 4 of 27

FigureFigure 1. Location1. Location of theof the study study area. area. 2.2. Data and Pre-Processing 2.2. Data and Pre-Processing In this study, Declassified Intelligence Satellite Photographs (DISP) and Landsat MSS/TM/OLI In this study, Declassified Intelligence Satellite Photographs (DISP) and Landsat MSS/TM/OLI images were used. To obtain a large time range of recorded RS data as far as possible, the Landsat images were used. To obtain a large time range of recorded RS data as far as possible, the Landsat MSS data were acquired on 2 November 1978, and the archived DISP data KH-9 is close to MSS data MSS data were acquired on 2 November 1978, and the archived DISP data KH-9 is close to MSS data on time. KH-9 are single-band images with high spatial resolution, between 6 and 9 m, while the on time. KH-9 are single-band images with high spatial resolution, between 6 and 9 m, while the Landsat MSS images have low spatial resolution of about 78 m over four bands. To obtain enhanced Landsat MSS images have low spatial resolution of about 78 m over four bands. To obtain enhanced images with both high spatial resolution and multispectral features, image fusion was performed. images with both high spatial resolution and multispectral features, image fusion was performed. Among the available KH-9 images, we used two cloud-free images of good quality obtained on Among the available KH-9 images, we used two cloud-free images of good quality obtained on 17 17 December 1975 and 18 January 1976 (i.e., 1 month apart), respectively, which were combined to December 1975 and 18 January 1976 (i.e., 1 month apart), respectively, which were combined to obtain obtain an image that covered the entire study area (i.e., Shunde District). Dominated by agricultural an image that covered the entire study area (i.e., Shunde District). Dominated by agricultural developments between 1975 and 1978, Shunde District can be regarded as being constant due to developments between 1975 and 1978, Shunde District can be regarded as being constant due to only only small changes occurring in land use over the three-year period. Therefore, image fusion was small changes occurring in land use over the three-year period. Therefore, image fusion was undertaken with the KH-9 image and the MSS image that was acquired 3 years later in 2 November undertaken with the KH-9 image and the MSS image that was acquired 3 years later in 2 November 1978. Through several comparison experiments, it was found that the Brovey method resulted in 1978. Through several comparison experiments, it was found that the Brovey method resulted in a a fused image of higher quality, which hence served as the data source for the subsequent analysis in fused image of higher quality, which hence served as the data source for the subsequent analysis in 1978. To avoid inconsistency in resolutions, the resolution of Landsat images in 1978 was normalized 1978. To avoid inconsistency in resolutions, the resolution of Landsat images in 1978 was normalized to that of KH-9 image in 1975. The required image pre-processing varied according to the range of to that of KH-9 image in 1975. The required image pre-processing varied according to the range of single scene images from the different data sources, the projection information of the images, and their single scene images from the different data sources, the projection information of the images, and application purposes (Table1). their application purposes (Table 1).

Sustainability 2018, 10, 3504 5 of 27 SustainabilitySustainabilitySustainability 20182018 2018,, 1010, 10,, xx, FORFORx FOR PEERPEER PEER REVIEWREVIEW REVIEW 55 5ofof of 2727 27

TableTableTableTable 1. Summary 11. . 1 SummarySummary. Summary of the ofof of thethe data the datadata data used usedused used and andand and pre-processing prepre pre--processingprocessing-processing steps stepssteps steps taken takentaken taken in inin thisin thisthis this study. study.study. study.

GeometricGeometricGeometricGeometric ImageImageImageImage ImageImageImage ImageImageImageImage SatelliteSatelliteSatelliteSatellite SensorSensorSensor Sensor AcquiAcquiAcqui Acquisitionsitionsitionsition TimeTime Time Time CorrectionCorrectionCorrectionCorrection MosaicMosaicMosaicMosaic ClippingClippingClipping FusionFusionFusionFusion √ √ √ √ KHKH-4- 4 19671967-12-12-18-18 √√√ * * √√√ √√√ √√√ DISP KHKHKHKH-4--44 - 4 196719671967--1212 1967-12-18-12--1818-18 √√ ** * * √√ √√ √√ DISPDISPDISPDISP √ √ √√√ √√ √ √√ KHKHKHKH-9--99 - 9 197519751975--1212 1975-12-17/1976-01-18-12--17/197617/1976-17/1976--0101-01--1818-18 √√√ ** * * √√√ √√√ √√√ √√ √ √√ √ √√ MSSMSSMSSMSS 197819781978--1111 1978-11-02-11--0202-02 √√√ ×× × √√√ √√√ Landsat 1988-12-10, 1993-11-22, 2000- LandsatLandsatLandsatLandsat 1988198819881988-12-10,--1212-12--1010-10,, 19931993, 1993 1993-11-22,--1111-11--2222-22,, 20002000, 2000 2000-09-14,-- - √√ √ √√ TMTMTMTM √√√ ×× × √√√ × ×× × 090909--1414-14,, 20052005, 20052005-07-18,--0707-07--1818-18,, 20112011, 2011 2011-06-01--0606-06--0101-01 √√ √ √√ LandsatLandsatLandsatLandsat OLIOLIOLI OLI 201620162016--1212 2016-12-07-12--0707-07 √√√ ×× × √√√ × ×× × √ √√ Note:Note:Note:Note:means √√ √ themeansmeans means pre-processing thethe the prepre pre--processingprocessing-processing step was implemented;stepstep step waswas was implemented;implemented; implemented;× means the ×× means× pre-processingmeans means thethe the prepre pre- step-processingprocessing-processing was not stepstep implemented; step waswas was notnot not

* meansimplemented;implemented;implemented; image rotation * * means means* wasmeans implemented imageimage image rotationrotation rotation before waswas was geometric implementedimplemented implemented correction. beforebefore before geometricgeometric geometric correction.correction. correction.

2.3. Classification2.3.2.3.2.3. ClassificationClassification Classification and andand Accuracy and AccuracyAccuracy Accuracy Assessment AssessmentAssessment Assessment ThereThereThereThere are differences are are are differences differences differences in the in incharacteristics in the the the characteristics characteristics characteristics of the target of of of the the the categories target target target categories categories categories between betweenthe between between fused the theand the fused fusedindividual fused and and and Landsatindividualindividualindividual TM images. LandsatLandsat Landsat Interpretation TMTM TM images.images. images. InterpretationInterpretation ofInterpretation the two types ofof of thethe the of twotwo RS two imagestypestypes types ofof of in RSRS RS the imagesimages images fused inin MSSin thethe the fused andfused fused KH-9 MSSMSS MSS and dataand and KHKH-9- 9data data (R:4 (R:4 G:3 G:3 B:2) B:2) and and Landsat Landsat TM TM image image (R:7 (R:7 G:5 G:5 B:3) B:3) band band comb combinationsinations are are respectively respectively (R:4KH G:3KHKH--9 B:2)9- 9datadata data and (R:4(R:4 (R:4 Landsat G:3G:3 G:3 B:2)B:2) B:2) TM andand and image LandsatLandsat Landsat (R:7 TMTM G:5TM imageimage B:3)image band (R:7(R:7 (R:7 G:5 combinationsG:5 G:5 B:3)B:3) B:3) bandband band combcomb are comb respectivelyinationsinationsinations areare are displayedrespectivelyrespectively respectively in displayeddisplayed in in Table Table 2 2and and described described in in Table Table 3 .3 . Tabledisplayeddisplayed2displayed and described inin in TableTable Table in 22 and Table2and and describeddescribed 3described. inin in TableTable Table 33. . 3 .

TableTableTable 2.22. . 2 ComparisonComparison. Comparison between betweenbetween between the thethe the fused fusedfused fused images imagesimages images (MSS (MSS(MSS (MSS and andand KH-9)and KHKH KH- and-9)9)-9) andand Landsat and LandsatLandsat Landsat TM. TM.TM. TM. FusionFusion Image Image Features Features LandsatLandsat TM TM Image Image Features Features FusionFusionFusionFusion ImageImage Image Image FeaturesFeatures Features LandsatLandsatLandsatLandsat TMTM TM TM ImageImage Image FeaturesFeatures FeaturesFeatures CategoriesCategoriesCategoriesCategories (R:4(R:4(R:4(R:4 G:3G:3 G:3 B:2) B:2) B:2) (R:7(R:7(R:7(R:7 G:5G:5 G:5 B:3)B:3) B:3)B:3)

Dike-pondDikeDikeDike--pondpond-pond

BuiltBuiltBuilt--upup-up Built-up area areaareaarea

CultivatedCultivatedCultivated CultivatedCultivatedCultivated land llandandland

Sustainability 2018, 10, 3504 6 of 27

Table 2. Cont.

SustainabilitySustainabilitySustainability 20182018 2018,, 1010, 10,, xx, FORFORx FOR PEERPEER PEERFusion REVIEWREVIEW REVIEW Image Features Landsat TM Image Features 66 6ofof of 2727 27 Categories (R:4 G:3 B:2) (R:7 G:5 B:3)

ForestForestForestForest

RiverRiverRiverRiver

TableTableTableTable 3. Description 3.3. 3. DescriptionDescription Description of ofof the of thethe targetthe targettarget target categories. categoriescategories categories.. .

CategoriesCategoriesCategoriesCategories DescriptionsDescriptionsDescriptions Descriptions A combination of light blue or dark blue regular patches and surrounding AA A combinationcombination combinationA combination ofof of lightlight light of light blueblue blue blue oror or darkdark ordark dark blueblue blue blue regularregular regular regular patchespatches patches patches andand and and surroundingsurrounding surrounding 11 1 1 yellowyellowyellowyellow linearlinear linear linear featuresfeatures features features whichwhich which which areare are aremeshedmeshed meshed meshed Dike-pondDikeDikeDike--pondpond-pond AA A combinationcombination combinationA combination ofof of darkdark dark of dark blueblue blue blue regularregular regular regular patchespatches patches patches andand and and surroundingsurrounding surrounding surrounding greengreen green linearlinear linearlinear 22 2 2 featuresfeaturesfeaturesfeatures whichwhich which which areare are meshedmeshed are meshed meshed 11 1 1GrayGrayGray- Gray-black,-black,black,-black, unevenuneven uneven uneven internalinternal internal internal shade,shade, shade, shade, irregularirregular irregular irregular shapeshape shape shape Built-upBuiltBuiltBuilt--upup-up area areaarea area 22 2 2GrayGrayGray- Gray-purple,-purple,purple,-purple, unevenuneven uneven uneven internalinternal internal internal shade,shade, shade, shade, irregularirregular irregular irregular shapeshape shape shape CultivatedCultivatedCultivated 11 1 1LightLightLight Light yellowyellow yellow yellow oror or brownbrown brown or brown bar,bar, bar, bar, uniformuniform uniform uniform colorcolor color color shade,shade, shade, shade, regularregular regular regular patchespatches patches Cultivated land landlandland 22 2 2Green,Green,Green, Green, uniformuniform uniform uniform colorcolor color color shade,shade, shade, shade, relativelyrelatively relatively relatively regularregular regular regular patchespatches patches patches 11 1 1Brown,Brown,Brown, Brown, relativelyrelatively relatively relatively uniformuniform uniform uniform internalinternal internal internal shade,shade, shade, shade, darkerdarker darker darker inin in the inthe the the middlemiddle middle middle partpart part ForestForestForestForest 22 2 2Green,Green,Green, Green, relativelyrelatively relatively relatively uniformuniform uniform uniform internalinternal internal internal shade,shade, shade, shade, darkerdarker darker darker inin in the inthe the the middlemiddle middle middle partpart part 11 1 1LightLightLight Light blue,blue, blue, blue, unevenuneven uneven uneven width,width, width, width, uniformuniform uniform uniform internalinternal internal internal shade,shade, shade, shade, longlong long long curvedcurved curved curved lineslines lines RiverRiverRiverRiver 22 2 2DarkDarkDark Darkblue,blue, blue, blue, unevenuneven uneven uneven width,width, width, width, uniformuniform uniform uniform internalinternal internal internal shade,shade, shade, shade, longlong long long curvedcurved curved curved lineslines lines Note:Note:Note: 11 means1means means fusion fusionfusion fusion image imageimage image (R:4 (R:4(R:4 (R:4 G:3 G:3G:3 G:3 B:2); B:2);B:2); B:2); 2means 22 means2means means Landsat LandsatLandsat Landsat TM TM imageTM TM imageimage image (R:7 G:5(R:7(R:7 (R:7 B:3). G:5G:5 G:5 B:3).B:3). B:3).

ECognitionECognition Developer Developer 9.0 9.0 was was used used for for multi multi-scale-scale image image segmentation, segmentation, the the scale scale of of which which ECognitionECognitionECognition Developer Developer Developer 9.0 was 9.0 9.0 wasused was used for used multi-scale for for multi multi-scale image-scale image segmentation, image segmentation, segmentation, the scale the the of scale scale which of of which varied which variedvaried according according to to the the spatial spatial resolution resolution of of the the image image and and the the object object characteristics. characteristics. Through Through accordingvariedvaried to according according the spatial to to the resolution the spatial spatial resolutionof resolution the image of of the and the image the image object and and the characteristics. the object object characteristics. characteristics. Through Through repeated Through repeatedrepeatedrepeated experimentsexperiments experiments ofof of settingsetting setting thethe the segmentationsegmentation segmentation parameters,parameters, parameters, thethe the segmentationsegmentation segmentation scasca scaleslesles ofof of thethe the sevenseven seven experiments of setting the segmentation parameters, the segmentation scales of the seven period periodperiodperiod imagesimages images were were were finally finally finally determined determined determined to to to be be be 70, 70, 70, 30, 30, 30, 15, 15, 15, 15, 15, 15, 15, 15, 15, 20, 20, 20, andand and 100,100, 100, while while while other other other relevant relevant relevant images were finally determined to be 70, 30, 15, 15, 15, 20, and 100, while other relevant parameters parametersparametersparameters includeinclude include thethe the huehue hue indexindex index (0.9),(0.9), (0.9), shapeshape shape indexindex index (0.1),(0.1), (0.1), tightnesstightness tightness indexindex index (0.5)(0.5) (0.5),, andand, and smoothnesssmoothness smoothness indexindex index include the hue index (0.9), shape index (0.1), tightness index (0.5), and smoothness index (0.5). (0.5).(0.5).(0.5). AA A certaincertain certain numbernumber number ofof of classificationclassification classification featuresfeatures features werewere were selectedselected selected fromfrom from thethe the mean,mean, mean, standardstandard standard deviation,deviation, deviation, A certain number of classification features were selected from the mean, standard deviation, object objectobjectobject lengthlength length shapeshape shape area,area, area, etc.etc. etc. ofof of eacheach each object.object. object. CombinationCombination Combination ofof of thesethese these featuresfeatures features waswas was obtainedobtained obtained toto to maximizemaximize maximize lengththethethe shape classification classification classification area, etc. distance, distance, of distance, each object. and and and wewe we Combination used used used the the the nearest nearest nearest of these neighbor neighbor neighbor features classification classification wasclassification obtained method method method to maximize to to to realize realize realize the classificationautomaticautomaticautomatic distance, classificationclassification classification and ofof we of thethe the used RSRS RS images.images. theimages. nearest LandLand Land neighbor useuse use classificationclassification classification classification resultsresults results weremethodwere were acquiredacquired acquired to realize afterafter after automatic manuallymanually manually classificationcorrectingcorrectingcorrecting of thethe thethe automaticautomatic RSautomatic images. classificationclassification classification Land use classification resultresult result ofof of eacheach each results image.image. image. were ClassificationClassification Classification acquired after validationvalidation validation manually iis s i ansan correctingan integralintegral integral the automaticandandand essentialessential essential classification partpart part ofof of anyany any image resultimage image-- ofclassificationclassification-classification each image. processprocess process Classification [55][55] [55].. Therefore,Therefore,. Therefore, validation thethe the accuracyaccuracy is accuracy an integral ofof of thethe the classificationandclassification classification essential partresultsresults ofresults any waswas image-classification was assessedassessed assessed byby by randomlyrandomly randomly process collectingcollecting collecting [55]. Therefore, aa certainacertain certain number thenumber number accuracy ofof of samplesample sample of the objectsobjects objects classification ffor orfor variousvarious various results typestypes types was ofof of assessedgroundgroundground by randomlyobjectsobjects objects toto to avoid collectingavoid avoid contingencycontingency contingency a certain andand number and improveimprove improve of sample thethe the qualityquality quality objects ofof of for accuracyaccuracy accuracy various evaluationevaluation typesevaluation of ground.. ForFor. For thethe the objects landland land to avoiduseuseuse mapsmaps contingency maps ofof of earlierearlier earlier and years,years, years, improve thethe the relevantrelevant relevant the quality topographictopographic topographic of accuracy mapsmaps maps, evaluation., andand, and photographsphotographs photographs For the werewere were land consideredconsidered considered use maps toto of to choosechoose earlier choose validationvalidationvalidation samples.samples. samples. TheThe The overalloverall overall classifclassif classificationicationication accuracyaccuracy accuracy andand and globalglobal global KappaKappa Kappa coefficientcoefficient coefficient forfor for eacheach each periodperiod period fromfromfrom 19781978 1978 toto to 20162016 2016 werewere were carriedcarried carried out.out. out. Sustainability 2018, 10, 3504 7 of 27 years, the relevant topographic maps, and photographs were considered to choose validation samples. The overall classification accuracy and global Kappa coefficient for each period from 1978 to 2016 were Sustainability 2018, 10, x FOR PEER REVIEW 7 of 27 carried out. Figure2 2 shows shows thethe workflowworkflow of of the the image image pre-processing, pre-processing, classification, classification, and and analysis. analysis.

DISP KH-9 DISP KH-9 1975-12-17 1976-01-18 Raw Landsat images

Mosaicking Landsat MSS 1978-11-02 Geometric correction

Brovey fusion Clipping &Clipping

Remote sensing image 1978 TM-1988 TM-1993 TM-2000 TM-2005 TM-2011 OLI-2016

Multi-scale Optimal combination segmentation of features

K-nearest neighbor classifier

Classified map Manual correction

Accuracy assessment

AWDII Dike-pond extraction

Analysis of dike-pond change

Figure 2 2.. WorkflowWorkflow of the research.research.

2.4.2.4. AWDII The landscape expansion index (LEI) was proposed to identify three types of urban expansion, i.e., infilling infilling type, type, edge edge-expansion-expansion type type,, and and outlying outlying type type to to analyze analyze the the dynamic dynamic changes changes of oftwo two or ormore more temporal temporal phases phases of oflandscape landscape patterns patterns [56] [56. ].At At present, present, the the LEI LEI is is usually usually applied applied to spatial patternspatterns and process descriptions descriptions of of construction construction land land expansion expansion [51,57,58 [51,57,58]; ];however, however, it is it difficult is difficult to touse use them them to determine to determine dynamical dynamical changes changes of landscape of landscape patterns patterns that thatcontain contain wetlands wetlands because because they theymostly mostly fluctuate fluctuate rather rather than than present present a single a single trend trend.. In In addition addition to to the the proposed proposed definitiondefinition and and quantificationquantification index LEI LEI [59] [59],, we also propose a landscape invasion index index (LII) (LII) that that can be used for cases where a certain land type invades another land type, and which can describe the spatial pattern and extent of the the invasion invasion within within a a specific specific stage. stage. The The LI LIII can can be be calculated calculated using using Equation Equation (1): (1):

((AAi i−−A0A)0 ) LIILII == (1(1)) (Ai + A0) (Ai + A0 ) where A0 is the original patch area, and Ai is the area of patch occupied by a certain landscape adjacent where 퐴0 is the original patch area, and Ai is the area of patch occupied by a certain landscape to the original patch. When A = 0, there is no original patch adjacent to the invading patch. At that adjacent to the original patch. 0When A0 = 0, there is no original patch adjacent to the invading patch. time, LII = 1, that is, it belongs to the outlying invasion type. When A = 0, LII = −1, and the original At that time, LII = 1, that is, it belongs to the outlying invasion type. iWhen Ai = 0, LII = −1, and the − patchoriginal does patch not does invade not anyinvade other any landscape. other landscape. When LII Whenis between LII is between1 and − 1,1 and the invasion1, the invasion mode mode is the edge-invasionis the edge-invasion type. type. This study considers dike-ponds as the sole wetland research object. Therefore, the area weighted dike-pond invasion index (AWDII) is proposed, which can be calculated using Equation (2): n a AWDII (LII i ) =  i  (2) i=1 A Sustainability 2018, 10, 3504 8 of 27

This study considers dike-ponds as the sole wetland research object. Therefore, the area weighted dike-pond invasion index (AWDII) is proposed, which can be calculated using Equation (2):

n ai AWDII = ∑ (LIIi × ) (2) i=1 A where LIIi is the LII of the invading patch i, n is the total number of patches in the invaded landscape, ai is the area of the invading patch i, and A is the total invaded area of the landscape. According to Equation (1), the value range of LII is [−1, 1], which implies that the range of AWDII is also [−1, 1]. The larger AWDII is, the larger the area of the invading dike-pond is relative to the original dike-pond area is, or in other terms, the more severely the dike-pond is being invaded.

3. Results

3.1. Land Use Classification Results and Accuracy Analysis To analyze the land use distribution in Shunde, five land use types were identified including built-up areas, cultivated lands, dike-ponds, forests, and rivers. Figure3 shows the superposition of an RS image with the results of the dike-pond extraction, which illustrates the distribution of the main research objects of this paper in each chosen period. The results of the seven periods (1978, 1988, 1993, 2000, 2005, 2011, and 2016) indicate that the total area of dike-ponds in 2016 was significantly reduced and more fragmented compared with 1978, i.e., after approximately 40 years of development. Comparing the classification results from 1978 to 2016, the trends of the dike-pond distribution changes were qualitatively obtained. The dike-ponds extended from West to East between 1978 and 1988. In 1978, the dike-ponds were mainly in the Western, Central, and Southern regions of Shunde District, which were continuously distributed and densely packed. In 1988, the dike-ponds in these areas stayed constant and some began to appear in the Eastern parts of Shunde District, though overall they have a more scattered distribution. The distribution changed little from 1988 to 1993. Compared to1988, the dike-ponds in the Wstern, Central, and Southern regions remained lumped and densely distributed in 1993, while those in Eastern regions were scattered and distributed relatively compactly. Locally, the conversion to other land use types from dike-ponds and the contrary circumstances simultaneously existed. The fragmentation of dike-ponds in the Western and Central regions increased significantly between 1993 and 2000. The contiguousness of the dike-ponds in Shunde District was broken in 2000, where the largest changes occurred in the Western and Northern parts. It can be observed from the image from 2000 that these changes are mainly due to urban land expansion that occupied regions previously populated by dike-ponds. The land changes in the Central and Southern areas were relatively small, and they still contained the most concentrated distributions within the entire study area. The changing trends of dike-pond distribution were basically the same both from 2000 to 2005 and from 2005 to 2011, that is, the distribution range of dike-ponds continued to shrink and the degree of fragmentation increased. Since 2000, the contiguous area of the dike-pond landscape in Shunde District was broken by other types of land use, and its area declined year by year. Dike-ponds in the Western, North-Central, and Eastern regions experienced the largest changes. By 2011, they were only sporadically distributed in these areas. In the South and South-Central parts, dike-ponds also shrank and the degree of fragmentation increased. However, compared with other regions, they were still more densely distributed, and became the main areas of dike-pond occurrence. The distribution pattern of dike-ponds has not changed substantially from 2011 to 2016, although its total area has further decreased due to invading, built-up areas. Throughout the whole study area, the fragmentation of dike-ponds became more obvious, especially in the Northwest and Eastern regions. Dike-ponds remained tightly spaced along the river networks, but were sparsely distributed in the Northern and Southern parts of Shunde. As of 2016, the total area of dike-ponds was less than one third of the entire study area. Sustainability 2018, 10, 3504 9 of 27 Sustainability 2018, 10, x FOR PEER REVIEW 9 of 27

FigureFigure 3. 3.Distribution Distribution ofof dike-ponddike-pond in Shunde Shunde District District from from 1978 1978 to to 2016 2016.. Sustainability 2018, 10, x FOR PEER REVIEW 10 of 27 Sustainability 2018, 10, 3504 10 of 27 The classification results were assessed by randomly collecting a certain number of sample objects, i.e., various types of ground objects, to determine the overall classification accuracy and The classification results were assessed by randomly collecting a certain number of sample objects, global Kappa coefficient for each period from 1978 to 2016 (Table 4). Error matrix calculation i.e., various types of ground objects, to determine the overall classification accuracy and global Kappa information is shown in Appendix A. The results show that the overall accuracy of the RS image coefficient for each period from 1978 to 2016 (Table4). Error matrix calculation information is shown classifications during the seven periods from 1978 to 2016 is higher than 89%, and the Kappa in AppendixA. The results show that the overall accuracy of the RS image classifications during coefficient is more than 0.86. The high classification accuracy demonstrates that the use of object- the seven periods from 1978 to 2016 is higher than 89%, and the Kappa coefficient is more than 0.86. oriented methods and artificially assisted corrections to classify images can improve the classification The high classification accuracy demonstrates that the use of object-oriented methods and artificially efficiency compared with pure manual interpretation, and ensure the reliability of the classification assisted corrections to classify images can improve the classification efficiency compared with pure results. manual interpretation, and ensure the reliability of the classification results.

Table 4. Overall accuracy assessment of the classification results. Table 4. Overall accuracy assessment of the classification results. Accuracy Measures Overall Accuracy (%) Kappa Coefficient Accuracy Measures Overall Accuracy (%) Kappa Coefficient 1978 91.55 0.894 1978 91.55 0.894 1988 92.92 0.911 1988 92.92 0.911 1993 93.30 93.30 0.916 0.916 2000 90.94 90.94 0.886 0.886 2005 89.49 89.49 0.868 0.868 2011 92.90 92.90 0.910 0.910 2016 91.29 0.890 2016 91.29 0.890

RS images from from the the seven seven periods periods from from 1978 1978 to to2016 2016 were were taken taken to calculate to calculate the area the areaof each of eachland landuse type use and type their and relative their relative proportion proportion to the total to the area. total The area. changes The changes in land use in land over use the overpast the38 years past 38are years given are in givenFigure in 4.Figure Between4. Between 1978 and 1978 2000, and the 2000, area theof dike area-pond of dike-pond wetlands wetlands dominated dominated all land use all landtypes use in the types study in the area, study with area, an area with ratio an areaof over ratio 45% of and over a 45% maximum and a maximum of 50%. The of area 50%. of The dike area-pond of dike-pondwetlands increased wetlands increasedfirst, reaching first, reachinga peak in a 1993, peak inand 1993, then and decreased, then decreased, to become to become the second the second most mostprevalent prevalent land use land type use after type 2000. after 2000.Between Between 1978 and 1978 1993, and the 1993, proportion the proportion of cultivated of cultivated land in land the instudy the studyarea was area in was the insecond the second most dominant most dominant type, while type, whilethe combined the combined cultivated cultivated land plus land dike plus- dike-pondsponds in the in area the area was was more more than than 70%, 70%, which which reached reached a peak a peak of of 86.62% 86.62% in in 1978 1978.. This This reflects reflects the dominant agriculturalagricultural economyeconomy beforebefore thethe 1990’s1990’s inin Shunde.Shunde. Over the past 38 years, the number of built built-up-up areas in Shunde District District has continued continued to increase, increase, and it it became became the the most most prevalent prevalent land land use use type type in the in whole the whole study study area in area 200 in5, accounting 2005, accounting for 54.43% for 54.43%of the totalof the area total in area 2016. in 2016. The Thesteady steady declining declining trend trend of of forests forests reflect reflect their their increasing increasing level level of exploitation, while the the area area of of rivers rivers stayed stayed stable stable at ataround around 7.5% 7.5% over over the thelast last 38 years. 38 years. Since Since the area the areaof forests of forests and andrivers rivers was was much much smaller smaller than than the the other other land land types, types, the the absolute absolute value value of of their area changechange was not very considerable. It can be observed from Figure4 4 that that thethe totaltotal areaarea ofof forests forests and and riversrivers remainedremained relativelyrelatively steadysteady duringduring thethe four-decadefour-decade period.period.

Figure 4.4. ChangingChanging area of land use types in Shunde District between 1978 and 2016.2016. SustainabilitySustainability 20182018,, 1010,, 3504x FOR PEER REVIEW 1111 of of 2727

3.2. Dike-Pond Trends 3.2. Dike-Pond Trends Next, we took the towns of Shunde District as land use units. With the land use classification resultsNext, acquired we took for the seven towns periods of Shunde from District 1978 to as 2016, land information use units. Withpertaining the land to the use administrative classification resultsscope of acquired each town for thein Shunde seven periods District from was 1978 obtained to 2016, so informationthat the distribution pertaining of to dike the- administrativeponds in each scopetown could of each be town extracted. in Shunde We Districtcalculated was the obtained area of sodike that-ponds the distribution in each town of dike-ponds and their proportion in each town of couldthe overall be extracted. dike-pond We area calculated in the district, the area which of dike-ponds were also incompared each town and and analyzed their proportion for the different of the overallperiods. dike-pond area in the district, which were also compared and analyzed for the different periods. Figure5 5 showsshows thethe relative dike dike-pond-pond areas areas of of all all towns towns in in ea eachch period. period. From From 1978 1978 to 2016, to 2016, the thedike dike-pond-pond areas areas of Xingtan of Xingtan and and Leliu Leliu are are ranked ranked first first and and second, second, respectively, respectively, among among the the 10 10 towns. towns. TheThe area of dike dike-ponds-ponds in in Xingtan Xingtan accounts accounts for for more more than than 20% 20% of ofthe the total total area area of dike of dike-ponds-ponds in the in thestudy study area, area, while while the the proportion proportion in in Leliu Leliu were were above above 14%, 14%, where where both both decreased decreased first first and then increased,increased, andand whichwhich were were accompanied accompanied by by fluctuations fluctuations in in their their relative relative proportions. proportions. Xingtan Xingtan and and the Leliu,the Leliu, which which are locatedare located in the in Southwestthe Southwest and and Central Central part part of Shunde, of Shunde, respectively, respectively, were were within within the mainthe main distribution distribution area area of dike-ponds of dike-ponds in the in the overall overall scale scale analysis. analysis. Over Over the the past past 38 years,38 years, the the area area of 2 dike-pondsof dike-ponds in Chencunin Chencun ranged ranged from from 0.86 0.86 to 8.51 to km8.51 ,km which2, which is relatively is relatively small small compared compared with otherwith towns.other towns. It accounts It accounts for less for than less than 3% of 3% the of total the total area area of dike-ponds of dike-ponds in Shunde in Shunde and and as lowas low as 0.4%as 0.4% in 2011.in 2011. Chencun Chencun is located is located in the in the Northern Northern part part of Shunde, of Shunde, and itand has it fewer has fewer areas areas compared compared with otherwith towns.other towns. After 2000,After the2000, area the of area dike-ponds of dike- inponds each townin each showed town showed a downward a downward trend except trend for except Ronggui, for whereRonggui, its dike-pondwhere its dike area-pond in 2016 area was in higher2016 was than higher that inthan 2011. that in 2011.

Figure 5.5. Stacked bar graph of dike-ponddike-pond area of the 10 towns in Shunde District fromfrom 19781978 toto 2016.2016.

InIn can can be be concluded concluded that that the the dike-pond dike-pond distribution distribution in the the different different towns towns was was asymmetric asymmetric according toto thethe spatialspatial analysis.analysis. Xingtan and Leliu in Shunde District contain the largest dike-ponddike-pond areas, whilewhile ChencunChencun hashas thethe smallestsmallest dike-ponddike-pond areaarea betweenbetween 19781978 andand 2016.2016. We havehave shownshown herehere thethe differencedifference betweenbetween thethe dike-ponddike-pond proportionsproportions of eacheach town. In 1978, thethe dike-ponddike-pond proportions of Xingtan and Longjiang were larger than the other towns, and were similarsimilar atat 78.94%78.94% andand 78.99%,78.99%, respectively.respectively. In the process ofof extractingextracting thethe dike-ponds,dike-ponds, thethe spatialspatial resolutionresolution and and spectral spectral characteristics characteristics of the of images the images inevitably inevitably affected the affected accuracy the of accuracy the classification of the results.classification Therefore, results. a difference Therefore, of a 0.05% difference in the dike-pondof 0.05% in area the proportiondike-pond ofarea Xingtan proportion and Longjiang of Xingtan in 1978and Longjiang can be neglected, in 1978 andcan be are neglected, hence considered and are tohence be approximately considered to equal.be approximately equal. During the period from 1978 to 1988, the dike-ponddike-pond areaarea proportionsproportions ofof XingtanXingtan andand LongjiangLongjiang werewere bothboth moremore thanthan 70%,70%, andand rankedranked inin thethe toptop two.two. InIn 1993,1993, thethe dike-ponddike-pond proportionproportion ofof LecongLecong began to surpasssurpass LongjiangLongjiang and Xingtan,Xingtan, and was rankedranked firstfirst inin Shunde.Shunde. However, it fell down slightlyslightly toto 60.49%60.49% followedfollowed after after Xingtan Xingtan with with a a decline decline of of 2%. 2%. After After 2000, 2000, the the dike-pond dike-pond proportion proportion in Xingtanin Xingtan stayed stayed the the highest, highest, though though its its proportion proportion gradually gradually decreased, decreased, and and it it was was seenseen thatthat LecongLecong and Junan werewere rankedranked secondsecond inin 20002000 andand 2005,2005, respectively.respectively. Leliu remained the secondsecond highesthighest proportionproportion after 2005. From our results, we predict that Xingtan might be the town with the largest proportionproportion ofof dike-pondsdike-ponds inin thethe nearnear future.future. Sustainability 2018, 10, x FOR PEER REVIEW 12 of 27 Sustainability 2018, 10, x FOR PEER REVIEW 12 of 27 SustainabilitySustainability 20182018,, 1010,, 3504x FOR PEER REVIEW 1212 of 2727 Sustainability 2018, 10, x FOR PEER REVIEW 12 of 27 SustainabilityAccording 2018, 10to, xthe FOR above PEER REVIEW analysis, it can be summarized that the dike-pond area proportion12 of 27 of According to the above analysis, it can be summarized that the dike-pond area proportion of each Accordingtown has changed to the above in two analysis, ways over it canthe pastbe summarized 38 years. The that 10 townsthe dike were-pond divided area intoproportion two types of each Accordingtown has changed to the above in two analysis, ways over it can the be past summarized 38 years. thatThe 10 the towns dike-pond were areadivided proportion into two of types each eachwith townClassAccording hasA, thechanged to proportion the above in two ofanalysis, ways which over itcontinuously canthe pastbe summarized 38 years.decreased The that ,10 and thetowns Class dike were -B,pond the divided areaproportion proportion into two of which types of townwith Class has changed A, the proportion in two ways of w overhich the continuously past 38 years. decreased The 10, and towns Class were B, the divided proportion into two of which types withincreasedeach Class town first A,has the andchanged proportion then indecreased. two of ways which The over continuously different the past 38trends years. decreased are The shown 10, and towns in Class Figure were B, thedivided6. proportion into two of types which withincreased Class first A, the and proportion then decreased. of which The continuously different trends decreased, are shown and in Class Figure B, the6. proportion of which increasedwith Class first A, theand proportion then decreased. of which The continuously different trends decreased are shown, and Classin Figure B, the 6. proportion of which increasedincreased first first and and thenthen decreased.decreased. TheThe different trends trends are are shown shown in in Figure Figure 6..

FigureFigure 66.. TwoTwo differentdifferent evolutionevolution trendstrends ofof dikedike--pondpond proportionproportion inin eacheach town.town. Figure 6. Two different evolution trends of dike-pond proportion in each town. FigureFigure 6.66.. Two different evolution trends trends of ofof dike dike-ponddike-pond-pond proportion proportionproportion in inin each eacheach town. town.town. BasedBased onon thethe statisticalstatistical datadata ofof dikedike--pondpond areaarea inin eacheach town,town, thethe yearyear (i.e.(i.e.,, time)time) waswas usedused asas thethe Based on the statistical data of dike-pond area in each town, the year (i.e., time) was used2 as the independentBased on variable the statistical (x) and data the of dike dike-pond-pond area area was in each the town,dependent the year variable (i.e., time)(y, unit: was km used2 ). Linearas the independentBasedBased onon variable thethe statisticalstatistical (x) and datadata the of dike dike-ponddike-pond-pond area area area was in in each each the town,dependent town, the the year year variable (i.e. (i.e.,, time) time)(y, unit: was was usedkm used2). as Linear as the the independent variable (x) and the dike-pond area was the dependent variable (y, unit: km2). Linear independentindependentandindependent quadratic variablevariablevariable polynomial (((xx)) andand functions thethe dike-ponddikedike were--pondpond constructed area area was was the tothe fit dependentdependent the evoluti variable variablevariableon trends ( y((y,y ,ofunit:, unit:unit: the km twokmkm2).22 ).types).Linear LinearLinear for and quadratic polynomial functions were constructed to fit the evolution trends of the two types for andandtheand 10 quadraticquadratic quadratic towns, as polynomialpolynomial classified in functionsfunctions Table 5 .were were constructed constructed to to fit fitfit the the evoluti evolutionevolutionon trends trends of of the the two two types types for for the 10 towns, as classified in Table 5. thethethe 1010 10 towns,towns, towns, as asas classifiedclassifiedclassified inin TableTable5 5. .. Table 5. Two types of dike-pond evolution fitting function on town. Table 5. Two types of dike-pond evolution fitting function on town. TableTable 5.5.. Two typestypes ofof dike-ponddikedike--pondpond evolution evolution fitting fittingfitting function functionfunction on onon town town.town. . Types Towns Fitted Curves Functions R² Types Towns Fitted Curves Functions R² TypesTypesTypes TownsTownsTowns Fitted Fitted Curves Curves FunctionsFunctions Functions R²R² R2 JunanJunan yy == −−0.0056x0.0056x ++ 11.68911.689 0.83440.8344 JunanJunan yy = = − −0.0056x0.0056x + +11.689 11.689 0.83440.8344 JunanJunan y =y − =0.0056x−0.0056x + 11.689 + 11.689 0.8344 0.8344

Lecong y = −0.0153x + 31.022 0.8763 Lecong y = −0.0153x + 31.022 0.8763 LecongLecongLecong yy = = −y −0.0153x =0.0153x−0.0153x + +31.022 31.022 + 31.022 0.87630.8763 0.8763

Leliu y = −0.0101x + 20.703 0.9229 Class A Leliu y = −0.0101x− + 20.703 0.9229 ClassClassClass A A A LeliuLeliuLeliu yy = = −y −0.0101x =0.0101x0.0101x + +20.703 20.703 + 20.703 0.92290.9229 0.9229 Class A Leliu y = −0.0101x + 20.703 0.9229

Longjiang y = −0.0147x + 29.843 0.9666 LongjiangLongjiangLongjiang yy = = −y −0.0147x =0.0147x−0.0147x + +29.843 29.843 + 29.843 0.96660.9666 0.9666 Longjiang y = −0.0147x + 29.843 0.9666

− XingtanXingtanXingtan yyy = = =−y −0.0083x− =0.0083x0.0083x0.0083x + ++17.115 17.11517.115 + 17.115 0.97670.97670.9767 0.9767 Xingtan y = −0.0083x + 17.115 0.9767 Xingtan y = −0.0083x + 17.115 0.9767

Sustainability 2018, 10, 3504 13 of 27

Sustainability 2018, 10, x FOR PEER REVIEW 13 of 27 Sustainability 2018,, 10,, xx FORFOR PEERPEER REVIEWREVIEW Table 5. Cont. 13 of 27 Sustainability 20182018,, 1010,, xx FORFOR PEERPEER REVIEWREVIEW 1313 of 2727 Types Towns Fitted Curves Functions R2

2 Beijiao y = −0.0009x 2 + 3.5686x−3561.3 0.7888 Beijiao y = −0.0009x22 + 3.5686x−3561.3 0.7888 BeijiaoBeijiao y =y − =0.000−0.0009x9x2 + 23.5686x+ 3.5686x−3561.3−3561.3 0.78880.7888

2 Chencun y = −0.0003x 2 + 1.0258x−1020.6 0.8155 Chencun y = −0.000− 3x22 + 21.0258x−1020.6− 0.8155 ChencunChencun y =y − =0.0000.0003x3x2 + 1.0258x+ 1.0258x−1020.61020.6 0.81550.8155

2 Class B Daliang y = −0.0004x 2 + 21.7216x−1719.2 0.8328 ClassClass B B DaliangDaliang y =y − =0.000−0.0004x4x22 + 1.7216x+ 1.7216x−1719.2−1719.2 0.83280.8328 Class B Daliang y = −0.0004x2 + 1.7216x−1719.2 0.8328 Daliang y = −0.0004x + 1.7216x−1719.2 0.8328

2 Lunjiao y = −0.0005x 2 + 22.1476x−2144.5 0.9023 LunjiaoLunjiao y =y − =0.000−0.0005x5x22 + 2.1476x+ 2.1476x−2144.5−2144.5 0.90230.9023 Lunjiao y = −0.0005x2 + 2.1476x−2144.5 0.9023

2 2 RongguiRonggui y = y− =0.000−0.0005x5x 2 + 2.0255x+ 2.0255x−2016.9−2016.9 0.86640.8664 Ronggui y = −0.0005x22 + 2.0255x−2016.9 0.8664 Ronggui y = −0.0005x2 + 2.0255x−2016.9 0.8664

2 2 Each function fitting result had a high correlation coefficient, R2 2, (0.79 ≤ R22 ≤ 0.98), which was EachEach functionfunction fittingfittingfitting resultresultresult had hadhada aa high highhigh correlation correlationcorrelation coefficient, coefficient,coefficient, R RR,22, (0.79, ((0.79≤ ≤ R R22 ≤≤ 0.98),0.98),), whichwhich waswas Each function fittingfitting resultresult hadhad aa highhigh correlationcorrelation coefficient,coefficient, RR2,, ((0.79 ≤ R2 ≤ 0.98),), whichwhich waswas used to to evaluate evaluate how how well well each each function function could could effectively effectively reflect reflect the thedike dike-pond-pond area areachanges changes in each in used to evaluate how well each function could effectively reflect the dike--pond area changes in each town.used to The evaluate dike-pond how wellarea eachproportion function of couldthe Class effectively A towns reflect decreased the dike wit-pondh different area changes curvatures, in each in eachtown.town. town. TheThe dikedike The-- dike-pondpond area areaproportion proportion of the of Class the Class A towns A towns decreased decreased wit withh different different curvatures, curvatures, in whichtown. TheJunan dike had-pond the smallestarea proportion curvature of andthe ClassLecong A hadtowns the decreased largest. We wit canh different see from curvatures, Figure 6 that in inwhich which Junan Junan had had the the smallest smallest curvature curvature and and Lecong Lecong had had the the largest. largest. We We can can see see from from Figure 66 that thatthat thewhich deceleration Junan had rate the smallestof dike-pond curvature areas and in each Lecong period had wasthe largest.not the Wesame. can Among see from the Figure five fitting6 that thethe decelerationdecelerationdeceleration raterate ofofof dike-ponddikedike--pond areasareas inin eacheach periodperiod waswas notnot thethe same.same. Among the fivefive fittingfitting curves,the deceleration Xingtan has rate the of highestdike-pond deg reeareas of fitting,in each indicating period was that not it decreasedthe same. atAmong a more the uniform five fitting rate. curves,curves, XingtanXingtan hashas the the highest highest deg degreedegreeree of of fitting, fitting,fitting, indicating indicating that that itit decreased decreased atat aa moremore uniformuniform rate.rate. Ascurves, for the Xingtan Class hasB towns, the highest the rate deg ofree increase of fitting, and indicating decrease thatof the it decreaseddike-pond at areas a more in auniform given town rate. AsAs forfor thethe Class Class B B towns, towns, the the rate rate of of increase increase and and decrease decrease of theof the dike-pond dike--pond areas areas in a in given a given town town was wasAs for inconsistent the Class Bfrom towns, 1978 the to rate2016, of and increase there andwas decreasealso a difference of the dike in -thepond rates areas of declin aine given between town inconsistentwas inconsistent from from 1978 to1978 2016, to and2016, there and was there also was a difference also a difference in the rates in the of declinerates of between declineine differentbetweenbetween differentwas inconsistent towns. The from curve 1978 of to Beijiao 2016, wasand morethere symmetrical,was also a difference that is, the in rates the ratesof increase of decl andine decreasebetween towns.different The towns. curve The of Beijiao curve of was Beijiao more was symmetrical, more symmetrical, that is, the that rates is, of the increase rates of and increase decrease and were decrease both weredifferent both towns. relatively The curve consistent. of Beijiao Daliang was more and symmetrical, Lunjiao had that similar is, the increaserates of increase and decrease and decrease rates. relativelywere both consistent. relatively Daliang consistent. and Daliang Lunjiao hadand similar Lunjiao increase had similar and decrease increase rates. and Comparatively,decrease rates. Comparatively,were both relatively the dike consistent.-pond areas Daliang in Ronggui and fell Lunjiao faster had than similar those in increase Chencun. and decrease rates. theComparatively, dike-pond areas the dike in Ronggui--pond areas fell faster in Ronggui than those fellfell fasterfaster in Chencun. thanthan thosethose inin Chencun.Chencun. Comparatively,We marked the two dike types-pond of townsareas in on Ronggui the map fell of fasterShunde than District those (inFigure Chencun. 7) based on the data in WeWe markedmarked twotwo typestypes ofof townstowns onon thethe mapmap ofof ShundeShunde DistrictDistrict (Figure(Figure7 7))) based basedbased on onon the thethe data datadata in inin FigureWe 5. markedThis figure two shows types thatof towns the two on types the map have of obvious Shunde spatial District differences, (Figure 7) wherebased Classon the A data towns in FigureFigure5 5... This ThisThis figure figurefigure shows showsshows that thatthat the thethe two twotwo types typestypes have havehave obvious obviousobvious spatial spatialspatial differences, differences,differences, where wherewhere Class ClassClass A AA towns townstowns areFigure located 5. This in figureWestern, shows Central that theand two Southern types have parts, obvious and Class spatial B towns differences, are located where in Class the EastA towns and areare locatedlocated in in Western, Western, Central Central and and Southern Southern parts, parts, and and Class Class B towns B towns are located are located in the in East the and East North and Northare located of Shunde. in Western, A combination Central and of theSouthern spatial parts,distribution and Class map B and towns the arestatistical located data in the of theEast dike and- ofNorth Shunde. of Shunde. A combination A combination of the of spatial the spatial distribution distribution map and map the and statistical the statistical data ofdata the of dike-pond the dike-- pondNorth areaof Shunde. proportion A combination in each town of the quantitatively spatial distribution reflect map the evolutionand the statistical processes data of ofthe the spatial dike- areapond proportion area proportion in each intown each quantitatively town quantitatively reflect the reflect evolution the processes evolution of processes the spatial ofdistribution thethe spatial spatial distributionpond area proportion patterns of in dike each-ponds town in quantitatively Shunde. From reflect 1978 the to 2016, evolution the dike processes-ponds of were the mainly spatial patternsdistribution of dike-ponds patterns of in dike Shunde.--ponds From in Shunde.1978 to 2016, From the 1978 dike-ponds to 2016, were the dike mainly--ponds distributed were mainly in the distributeddistribution in patterns the Western, of dike C-entralponds and in Shunde.Southern From regions, 1978 and to 2016,they spread the dike to- pondsthe East were and mainlyNorth Western,distributed Central in the and Western, Southern Central regions, and and Southern they spread regions,regions, to the andand East theythey and spreadspread North betweentoto thethe East 1978 and and North 2000. betweendistributed 1978 in andthe 2000.Western, After C entral2000, the and dike Southern-pond arearegions, in the and Eastern they spreadand Northern to the partsEast and decreased North Afterbetween 2000, 1978 the and dike-pond 2000. After area 2000, in the the Eastern dike--pond and Northernarea inin thethe parts Eastern decreased and Northern at a faster parts rate, decreased but the atbetween a faster 1978 rate, andbut the2000. dike After-pond 2000, area the proportion dike-pond in area the Northin theeast Eastern gradually and Northern returned partsto the decreased historical dike-pondat a faster rate, area but proportion the dike-- inpond the area Northeast proportion gradually in the returned Northeast to gradually the historical returned level ofto 1978.the historical levelat a faster of 1978. rate, but the dike-pond area proportion in the Northeast gradually returned to the historical levellevel ofof 1978.1978. levellevel ofof 1978.1978. Sustainability 2018, 10, 3504 14 of 27 Sustainability 2018, 10, x FOR PEER REVIEW 14 of 27 Sustainability 2018, 10, x FOR PEER REVIEW 14 of 27

Figure 7. Spatial distribution of the Class A and Class B towns in Shunde. FigureFigure 7. 7Spatial. Spatial distribution distribution ofof thethe Class A and Class Class B B towns towns in in Shunde. Shunde. 3.3. Invasion Evaluation of Dike-Pond 33..3.3. InvasionInvasion EvaluationEvaluation ofof DikeDike--PondPond In this study, we used the AWDII to measure the spatial pattern and extent of dike-ponds InIn thisthis study,study, wewe usedused thethe AWDIIAWDII toto measuremeasure thethe spatialspatial patternpattern andand extentextent ofof dikedike--pondsponds beingbeing being invaded by other land use types in each period. By overlaying and analyzing the conversion invadedinvaded byby otherother landland useuse typestypes inin eacheach period.period. ByBy overlayingoverlaying andand analyzinganalyzing thethe conversionconversion ofof dikedike-- ofpondsponds dike-ponds andand nonnon and--dikedike non-dike-ponds--pondsponds betweenbetween between diffedifferentrent different years,years, thethe years, transformationtransformation the transformation ofof dikedike--pondpond of dike-pond areasareas inin eacheach areas inperiodperiod each period waswas obtainedobtained was obtained ((FigureFigure 88 (Figure).). WeWe firstfirst8). calculated Wecalculated first calculated thethe LIILII valuesvalues the LIIofof eacheach values patch,patch, of and eachand thenthen patch, determineddetermined and then determinedthethe AWDIIAWDII the ofof AWDIIeacheach area,area, of eachwhichwhich area, waswas which usedused toto was evaluateevaluate used to thethe evaluate amountamount the ofof amount invasioninvasion of experiencedexperienced invasion experienced byby dikedike-- bypopo dike-pondsndsnds inin eacheach in regionregion each ( region(TableTable 66 (Table).). 6).

Figure 8. Dike-pond expansion/invasion during each period. FigureFigure 8. 8Dike-pond. Dike-pond expansion/invasionexpansion/invasion during during each each period. period. Sustainability 2018, 10, 3504 15 of 27

Table 6. Calculated AWDII (area weighted dike-pond invasion index) values.

Period Region 1978–1988 1988–1993 1993–2000 2000–2005 2005–2011 2011–2016 Beijiao −1.000 −1.000 −0.986 −0.474 −0.232 −0.652 Chencun −1.000 −0.891 −1.000 −0.200 −0.191 −0.956 Daliang −1.000 −1.000 −1.000 −0.492 −0.409 −0.944 Junan −0.847 −0.962 −1.000 −0.879 −0.665 −0.807 Lecong −0.891 −0.980 −0.789 −0.582 −0.479 −0.481 Leliu −0.908 −0.983 −0.759 −0.699 −0.726 −0.875 Longjiang −0.890 −0.874 −0.610 −0.742 −0.673 −0.642 Lunjiao −1.000 −1.000 −1.000 −0.698 −0.612 −0.827 Ronggui −1.000 −1.000 −0.967 −0.395 −0.258 −1.000 Xingtan −0.785 −0.953 −0.861 −0.889 −0.813 −0.750 Shunde −1.000 −1.000 −0.871 −0.439 −0.456 −0.570

Between 1978 and 1993, the AWDII of Shunde District was −1, illustrating Shunde District was not invaded during this period. Among the towns, the AWDII values of Beijiao, Daliang, Lunjiao, and Ronggui remained at −1, and it could also be observed from Figure8 that the area of these four towns kept increasing. Xingtan and Longjiang had the largest AWDIIs in 1978–1988 and 1988–1993, respectively, showing that they had experienced serious dike-pond invasion compared to the other towns. During 1993–2005, the AWDII values of Shunde District rose from −0.871 to −0.439, mainly due to extensive reclamation of land and urban development. Dike-ponds began to be invaded by other land types, and the protection of dike-ponds was not as good as before. Between 1993 and 2000, Lecong and Leliu were occupied more densely than the other towns. After 2000, Chencun was the most seriously invaded, the AWDII of which reached to −0.2, followed by Ronggui (−0.395) and Daliang (−0.492). The situation of expropriation of dike-ponds in 2005–2011 was slightly better than that in 2000–2005, but the invasion degree was larger. Among the ten towns, Chencun was occupied most heavily, the AWDII value of which was as high as −0.191, followed by Beijiao (−0.232) and Ronggui (−0.258). Up to 2016, the ecological environment caused by the dike-pond invasion by other land types had attracted the public’s eye, following the development of modernization. In recent years, the situation of dike-pond invasion has improved compared with previous periods. However, it can be clearly concluded that dike-ponds in Lecong, Longjiang and Beijiao were still invaded in spite of the control. There was obvious protection for Ronggui and Chencun on account of the low AWDII values. On the whole, the dike-ponds in Shunde District experienced steady development, rapid invasion and a gradual reduction of invasion in the period from 1978 to 2016.

3.4. Comparison of Typical Dike-Pond Trends Two specific examples with different dike-pond evolutions were selected to describe their concrete changes. Figure9 shows the changes in the dike-pond proportion of Xingtan and Longjiang (relative to the total area of the town) during the period from 1978 to 2016. As mentioned previously, in 1978, the proportions in Xingtan and Longjiang were 78.99% and 78.94%, respectively, which could be considered as them having the same starting point of their evolutionary trends. After approximately 40 years of development, the distribution scope in both towns has reduced. In 2016, the proportions in Xingtan and Longjiang decreased to 45.39% and slightly lower than 30%, respectively. The dike-pond change trend curves of the two towns are also displayed in Figure9, which show that the decreasing trends of the two towns have been quite different since 1978, and especially after 1993, the dike-pond proportion of Longjiang dropped sharply. In contrast, the rate of decline of dike-ponds in Xingtan was relatively slow. Next, let us take 1993 as a boundary point, so that we further analyze two periods (i.e., before and after 1993). From 1978 to 1993, the decreasing trend of dike-pond proportion Sustainability 2018, 10, 3504 16 of 27

inSustainability both towns 2018, were10, x FOR similar, PEER REVIEW although the proportion in Xingtan was slightly lower than that16 of in27 Longjiang in 1988. After 1993, the rate of decline in two towns began to diverge greatly. The decreasing ratethat ofof dike-pondthe previous proportion period, while in Xingtan Longjiang was showed scarcely a differentdrastic decline from thatwith of a therelatively previous larger period, rate whilefrom the Longjiang previous showed period. a drastic decline with a relatively larger rate from the previous period.

Figure 9.9. Comparison of dike-ponddike-pond area proportionsproportions inin XingtanXingtan andand LongjiangLongjiang fromfrom 19781978 toto 2016.2016.

AsAs aa resultresult ofof aa SouthernSouthern tourtour ofof thethe chiefchief architectarchitect ofof reformreform andand openingopening upup toto GuangdongGuangdong ProvinceProvince inin 1992,1992, thethe pacepace ofof reformreform inin thethe PearlPearl RiverRiver DeltaDelta waswas acceleratedaccelerated byby thethe majormajor historichistoric event.event. It could be inferred that the development of dike-pondsdike-ponds inin Shunde District was mainlymainly affected byby thethe expansionexpansion ofof urbanurban landland use.use. Therefore, wewe speculate that Longjiang pushed forward aa period ofof construction construction and and urbanization, urbanization, which which meant meant that that more more dike-ponds dike-ponds were were occupied occupied by built-up by built areas-up afterareas 1993. after 1993. BasedBased onon thethe changeschanges ofof dike-pondsdike-ponds and the other types of landland use in XingtanXingtan and Longjiang, thethe reasons reasons for for the the differences differences in their in their dike-pond dike-pond evolutions evolutions is discussed. is discussed. Xingtan, Xingtan, a well-known a well-known water townwater in town the Pearl in the River Pearl Delta River region, Delta is region, densely is intertwined densely intertwined with river with networks river in networks the Southwest in the ofSout Shunde.hwest of Dike-ponds Shunde. Dike used-ponds to be theused main to be agricultural the main agricultural form in Xingtan form in because Xingtan of because their contained of their fertilecontained land. fertile Since land. the Since reform the and reform opening and opening up, Xingtan up, Xingtan has paid has attention paid attention to the to adjustment the adjustment and optimizationand optimization of agricultural of agricultural structures structures while developing while developing industry, industry, where the where science the and science technology and industry,technology ecological industry, agriculture, ecological agriculture and water, countryand water culture country form culture the three form majorthe three industrial major industrial strategy focistrategy of the foci town of the [60 town]. Table [60]7. Tableshows 7 shows the land the use land changes use changes quantitatively quantitatively in Xingtan in Xingtan from from 1978 1978 to 2016.to 2016. In In 1978, 1978, the the area area of of dike-ponds dike-ponds accounted accounted for for a a large large proportion, proportion, 79.06%, 79.06%, ofof thethe totaltotal areaarea ofof thethe town,town, whilewhile aboutabout 35.60%35.60% ofof thethe town’stown’s dike-pondsdike-ponds were convertedconverted to built-upbuilt-up areas, wherewhere onlyonly slightlyslightly moremore thanthan halfhalf ofof thethe dike-pondsdike-ponds werewere preserved.preserved. FromFrom 19781978 toto 2016,2016, dike-ponds,dike-ponds, cultivatedcultivated lands,lands, forests,forests, andand riversrivers werewere allall convertedconverted toto built-upbuilt-up areasareas toto varyingvarying degrees,degrees, whichwhich increasedincreased 2 fromfrom 2.012.01 toto 38.6938.69 kmkm2, and accounted for 31.82% of thethe town’stown’s totaltotal areaarea inin 2016.2016. DespiteDespite thethe sharpsharp changechange inin dike-pondsdike-ponds andand built-upbuilt-up areas,areas, forestsforests andand riversrivers comprisedcomprised onlyonly aa littlelittle bothboth inin termsterms ofof areaarea andand proportion.proportion. Longjiang is is located located in in western western Shunde. Shunde. It has It has superior superior agricultural agricultural production production conditions, conditions, and andits river its river network network is dense. is dense. When When the the dike dike-ponds-ponds in inthe the Pearl Pearl River River Delta Delta emerged emerged in in the the late Ming DynastyDynasty [61[61]], it, it also also became became a typical, a typical, and representative, and representative, typeof type argo-ecological of argo-ecological model, which model, resulted which inresulted it being in a major it being economic a major source. economic After source. the reform After and the opening reform up, and Longjiang opening took up, Longjiang manufacturing took industrymanufacturing as the industry main body as the of economicmain body development, of economic development, the pillar industries the pillar of i whichndustries include of which the manufactureinclude the manufacture of furniture, of small furniture, household small appliances, household textiles,appliances, food, textiles, beverages, food, plastics, beverages, etc. plastics, etc. Table8 shows the land use changes in Longjiang from 1978 to 2016. During this period, the proportionTable 8 shows of dike-ponds the land inuse Longjiang changes decreased in Longjiang dramatically from 1978 from to 2016. 78.91% During to 27.41% this period,of the total the townproportion area. As of ofdike 2016,-ponds about in 56.52% Longjiang of the decreased dike-pond dramatically area in Longjiang from has78.91% been to converted 27.41% of to the built-up total areas,town area. and onlyAs of about 2016, 33.29%about 56.52% of the of dike-ponds the dike-pond have area been in preserved. Longjiang Whilehas been developing converted industry, to built- up areas, and only about 33.29% of the dike-ponds have been preserved. While developing industry, Longjiang focused on developing its agriculture in terms of producing high yields, high quality, and high economic efficiency. Therefore, the dike-ponds and livestock breeding have been maintained. Sustainability 2018, 10, 3504 17 of 27

Longjiang focused on developing its agriculture in terms of producing high yields, high quality, and high economic efficiency. Therefore, the dike-ponds and livestock breeding have been maintained. Apart from dike-ponds, a large proportion of cultivated lands and forests were converted to built-up areas, which increased from 0.63 to 38.54 km2, which account for a town proportion increase from 0.9% to 54.69%.

Table 7. Land use transfer matrix of Xingtan between 1978 and 2016 (unit: km2).

2016 Built-up Cultivated Dike-Pond Forest River Total (1978) 1978 Area Land 1.74 0.02 0.25 2.01 Built-up area 86.52% 1.06% 12.42% 1.65% Cultivated 1.79 2.18 2.87 0.10 0.37 7.32 land 24.44% 29.82% 39.28% 1.38% 5.08% 6.02% 34.22 10.03 51.13 0.66 0.08 96.13 Dike-pond 35.60% 10.44% 53.19% 0.69% 0.08% 79.06% 0.15 0.06 0.20 1.84 2.25 Forest 6.66% 2.66% 8.79% 81.89% 1.85% 0.79 0.40 0.82 0.23 11.64 13.88 River 5.69% 2.89% 5.93% 1.68% 83.80% 11.42% 38.69 12.70 55.28 2.84 12.09 121.59 Total (2016) 31.82% 10.45% 45.46% 2.33% 9.94% 100.00%

Table 8. Land use transfer matrix of Longjiang between 1978 and 2016(unit: km2).

2016 Built-up Cultivated Dike-Pond Forest River Total (1978) 1978 Area Land 0.63 0.63 Built-up area 99.54% 0.90% Cultivated 2.07 0.46 0.41 0.01 2.95 land 70.08% 15.68% 14.03% 0.21% 4.19% 31.43 5.18 18.51 0.44 0.05 55.61 Dike-pond 56.52% 9.31% 33.29% 0.79% 0.08% 78.91% 4.07 1.42 0.15 2.77 8.42 Forest 48.33% 16.92% 1.82% 32.93% 11.95% 0.34 0.25 0.24 2.03 2.86 River 11.89% 8.76% 8.23% 71.12% 4.06% 38.54 7.32 19.32 3.21 2.09 70.47 Total (2016) 54.69% 10.38% 27.41% 4.56% 2.96% 100.00%

To sum up, the dike-pond area proportions of Xingtan and Longjiang both showed decreasing trends from 1978 to 2016, but they declined at very different rates after 1993. The rate of dike-pond curtailment in Xingtan remained steady after 1993, while that in Longjiang performed saliently.

3.5. Relationship between River Network and Dike-Ponds In the territory of Shunde, several major rivers run through the area, which are a primary means of communication among the ten towns. The influence of the main river on each town can be considered as consistent. Outside the main river, the rivers in Shunde District are densely distributed. They compose the river network which is one of the objects analyzed in this paper. The limitation of spatial resolution made it difficult to obtain information on rivers from the KH-9 image. Visually comparing the KH-4A image obtained in 1967 and the KH-9 remote sensing image in 1978, the river network distribution in Shunde District was not significantly different between these two periods. Sustainability 2018, 10, 3504 18 of 27

Sustainability 2018, 10, x FOR PEER REVIEW 18 of 27 Therefore, we used the high-resolution KH-4A image as a RS data source to obtain high-precision river networkThe information.total length of the river network, the river network density (the ratio of the length of the riverThe network total lengthto the oftotal the area river of network, the town) the and river the network dike-pond density proportion (the ratio in of each the length town ofin theShunde river networkDistrict are to theshown total in area Table of 9 the. Xingtan town) andhad the dike-pondlargest river proportion length of 240.72 in each km, town and in its Shunde river network District arewas shown also the in largest, Table9. reaching Xingtan had1.98 the km largest per square river kilometer. length of 240.72 The length km, and of the its river network was alsoonly the76.67 largest, km in reaching Ronggui, 1.98 which km perwas square the shortest, kilometer. and Theits river length network of the riverdensity network was the was small onlyest 76.67 among km inthe Ronggui, ten towns, which which was was the only shortest, 0.96 km and per its square river networkkilometer. density was the smallest among the ten towns, which was only 0.96 km per square kilometer. Table 9. Length and density of the river network in each town in Shunde in 1967. Table 9. Length and density of the river network in each town in Shunde in 1967. Towns Length (km) Density (km/km2) BeijiaoTowns Length165.06 (km) Density1.80 (km/km 2) ChencunBeijiao 80.47 165.06 1.801.59 DaliangChencun 92.71 80.47 1.591.16 JunanDaliang 105.72 92.71 1.161.36 Junan 105.72 1.36 Lecong 127.12 1.63 Lecong 127.12 1.63 LeliuLeliu 141.79 141.79 1.561.56 LongjiangLongjiang 134.31 134.31 1.821.82 LunjiaoLunjiao 93.34 93.34 1.581.58 RongguiRonggui 76.67 76.67 0.960.96 Xingtan 240.72 1.98 Xingtan 240.72 1.98

Comparing the dike-ponddike-pond proportion in 1978 (Figure(Figure5 5)) withwith thethe river river network network informationinformation inin 19671967 ( (TableTable9 9),), inin general, the the trend trend of of the the river river network network density density was was in line in line with with the trend the trend of the of dike the- dike-pondpond proportion proportion (Figure (Figure 10). This10). Thismeans means that the that towns the towns with withthe high the highdensity density of river of river networks networks had hada correspondingly a correspondingly larger larger proportion proportion of ofdike dike-ponds.-ponds. In Inparticular, particular, the the density density o off river river networks in Beijiao and Chencun were higher, but the proportion of dike dike-ponds-ponds did not correspondingly rise up. ItIt isis inferredinferred that that the the relationship relationship between between the the river river network network density density and the and proportion the proportion of dike-ponds of dike- inponds each in town each can town be affected can be affected by other by types other of landtypes use. of land To this use. end, To a this statistical end, a analysisstatistical was analysis performed was onperformed the land on use the classification land use classification information ofinformation 1978 in Shunde, of 1978 which in Shunde, also considers which also the proportionconsiders the of eachproportion type of of land each in type each of town land (Table in each 10 town). (Table 10).

Figure 10.10. TheThe relationshiprelationship between river network density in 1967 and thethe dike-ponddike-pond proportion in each town inin 1978.1978.

Sustainability 2018, 10, 3504 19 of 27

Sustainability 2018, 10, x FOR PEER REVIEW 19 of 27 Table 10. The proportion of various types of land use in each town in 1978. Table 10. The proportion of various types of land use in each town in 1978. Towns Built-Up Area Cultivated Land Dike-Pond Forest River Towns Built-Up Area Cultivated Land Dike-Pond Forest River Beijiao 1.06% 80.93% * 11.60% 0.89% 5.53% Beijiao 1.06% 80.93% * 11.60% 0.89% 5.53% Chencun 1.66% 81.94% * 10.85% 2.30% 3.24% Chencun 1.66% 81.94% * 10.85% 2.30% 3.24% Daliang 1.87% 79.88% * 5.12% 7.05% 6.07% JunanDaliang 0.74%1.87% 16.41%79.88% * 57.95%5.12% * 7.20%7.05% 6.07% 17.69% LecongJunan 2.64%0.74% 19.51%16.41% 73.54%57.95% * * 0.00%7.20% 17.69% 4.31% LeliuLecong 2.04%2.64% 17.58%19.51% 71.66%73.54% * * 0.56%0.00% 4.31% 8.16% LongjiangLeliu 0.89%2.04% 4.18%17.58% 78.94%71.66% * * 11.92%0.56% 8.16% 4.06% LunjiaoLongjiang 1.03%0.89% 67.85%4.18% * 22.25%78.94% * 11.92% 0.00% 4.06% 8.86% RongguiLunjiao 2.99%1.03% 62.29%67.85% * * 21.30%22.25% 2.08%0.00% 8.86% 11.34% XingtanRonggui 1.65%2.99% 6.01%62.29% * 78.99%21.30% * 1.87%2.08% 11.34% 11.48% Xingtan 1.65%Note: * means the largest6.01% proportion of area78.99% in the town. * 1.87% 11.48% Note: * means the largest proportion of area in the town. It can be concluded from Table 10 that the dike-ponds in Junan, Lecong, Leliu, Longjiang, and XingtanIt can be accounted concluded for from the Table largest 10 proportion that the dike of- theponds total in areaJunan, of theLecong, town. Leliu, For theLongjiang other towns,, and cultivatedXingtan accounted land was the for main the largest land use proportion type. This of was the taken total areaas a basis of the to town. divide For the the towns other in Shundetowns, cultivated land was the main land use type. This was taken as a basis to divide the towns in Shunde District into two categories. Junan, Lecong, Leliu, Longjiang, and Xingtan were classified as Class C, District into two categories. Junan, Lecong, Leliu, Longjiang, and Xingtan were classified as Class C, and the others were Class D. This has led to a deeper understanding of the C and D-class towns, of and the others were Class D. This has led to a deeper understanding of the C and D-class towns, of which the dominant land use types were dike-ponds and cultivated land, respectively. In practice, which the dominant land use types were dike-ponds and cultivated land, respectively. In practice, the rivers in the Class D towns are a rich source of water for agricultural lands other than dike-ponds. the rivers in the Class D towns are a rich source of water for agricultural lands other than dike-ponds. As a result, the trends of the two curves were not identical, as shown in Figure 10, when considering As a result, the trends of the two curves were not identical, as shown in Figure 10, when considering the proportion of dike-ponds and river network density of the two classes of towns. the proportion of dike-ponds and river network density of the two classes of towns. The curves of the river network density and the dike-pond proportion (to the total area of the The curves of the river network density and the dike-pond proportion (to the total area of the corresponding town) are shown in Figure 11. For the towns of Class C, their river network density corresponding town) are shown in Figure 11. For the towns of Class C, their river network density curves are consistent with the trends of the dike-pond proportion curve, illustrating that the higher curves are consistent with the trends of the dike-pond proportion curve, illustrating that the higher network density is, the higher dike-pond proportion is. For towns of Class D, the towns with high network density is, the higher dike-pond proportion is. For towns of Class D, the towns with high river network densities generally had large dike-pond proportions. In particular, Ronggui had a large river network densities generally had large dike-pond proportions. In particular, Ronggui had a large dike-ponddike-pond areaarea butbut aa lowlow riverriver networknetwork density.density. This is because Ronggui Ronggui is is surrounded surrounded by by a a main main riverriver channel, channel, andand itsits dike-pondsdike-ponds areare mainlymainly distributeddistributed a alonglong the main river river channel. channel. In In the the river river networknetwork analysis, analysis, only only the the length length of the of river the stream river stream was considered was considered and the main and channel the main information channel wasinformation ignored, wasthus ignored, inferring thus that inferring the main that river the channel main riverhad a channel greater hadimpact a greater on the impact distribution on the of dike-pondsdistribution than of dike the inner-ponds stream than ofthe the inner river. stream It can of be the concluded river. It that can the be river concluded network that density the river had thenetwork same influencedensity had on the dike-ponds same influence of both on Class dike C-ponds and D. of With both anClass increase C and of D. river With network an increase density, of theriver proportion network of density, dike-ponds the proportion increase accordingly. of dike-ponds In general, increase the accordingly. river network In general, density of the Class river C towns,network dominated density of by Class dike-ponds, C towns, was dominated higher thanby dike that-ponds of Class, was D. higher than that of Class D.

FigureFigure 11. 11.TheThe relationship relationship between between river river network densitydensity and and dike-pond dike-pond proportion. proportion.

Sustainability 2018, 10, 3504 20 of 27

4. Discussion

4.1. Impact of Government Policies on Dike-Pond Area Reduction This study illustrated the government policies and river network both had significant influence on the evolution of dike-ponds in Shunde District. In order to discuss the decision-making activities of the government, especially the influence of local governments’ decision in terms of the evolution of dike-ponds in Shunde, we selected the two sites as the research objects, which had the same starting point but discrepant end points. That is, the difference of the dike-pond proportion of both towns in 1978 was relatively small, but larger in 2016 (Figure9). In 1978, China began to implement reforms and an opening-up policy, where the south took a leading role compared with other regions in the country. As the Pearl River Delta region is at the forefront of reform and opening-up, industrial restructuring, urban development and other measures became more compact. Over the past 38 years, the proportion of built-up areas in Shunde District has increased from 1.68% to 54.43%. Under the influence of built-up areas and other types of land use, the number and spatial distribution pattern of dike-ponds in Shunde District have changed, from which it could be inferred that the country’s macroeconomic policies were inextricably linked to the evolution of dike-ponds in Shunde. The dike-ponds in each town were analyzed, and it was shown that the total dike-pond area and its proportion (relative to the total area of the town) in different types of towns had different trends. Indeed, even in the same type of town, the evolution curve of dike-ponds was not exactly the same. The difference in the evolution of dike-ponds was closely related to the enthusiasm of local farmers for cultivation. The market demand, economic benefits, and social response brought about by farming products have a direct impact on the enthusiasm of farmers. Therefore, the policies of local government play an important, perhaps leading role, in the industrial structure and economic development of the whole region [62]. It was an important turning point in the process of China’s reform and opening up in 1992 because the pace of development began to accelerate across the country, especially in the Pearl River Delta. After reform and opening up, Xingtan and Longjiang adopted different development strategies driven by government policies in two directions. After about four decades, the two towns, which had the same evolutionary starting point, have large differences in their conservation of dike-ponds. The great policy has promoted the expansion of the built-up areas, which in turn led to a reduction of dike-pond area. It can be concluded that the country’s macroeconomic policies have had an indelible impact on dike-ponds.

4.2. Impact of River Network on Dike-Pond Distribution Another important factor that affects dike-pond evolution is known as natural environment, more specifically, the river network. Some predecessors have conducted extensive research on the distribution area characteristics, ecological patterns, and system operation mechanisms of dike-ponds [63–65]. These investigations indicated that dike-ponds may be the most successful agro-ecological model for the transformation of low-lying waterlogged lands. In other words, the need for humans to transform low waterlogging land has promoted the formation and promotion of the dike-pond. From this, a main natural condition necessary for dike-pond construction must be a rich source of water. After 1978, the dike-ponds in each town in Shunde District evolved with different trends under the influence of the national macro-policy and local government decision-making. Moreover, urbanization became a major factor in the evolution of dike-ponds after reform and opening up. Statistics showed that prior to reform and opening up, dike-ponds in Shunde District were dominant over other the types of land use in 1978. Therefore, in order to avoid the influence of other factors after the reform and opening up, the article took the dike-ponds in Shunde District that existed before the reform and explored the relationship between the distribution of the river network in Shunde District and the distribution of dike-ponds. More precisely, we explored the distribution of dike-ponds of the various towns in Shunde District in 1978 and the relationship with the river network distribution during this period. From what the study showed in 3.5, it can be inferred that the proportion of dike-ponds increase Sustainability 2018, 10, 3504 21 of 27 synchronously with a rise of river network density. Therefore, river network is another important factor besides government policies which is regarded as water resource of dike-ponds helping ecological cycle and large-scale development.

4.3. Impact of Rural Area Depopulation on Dike-Pond area Reduction The migration of the rural population to the cities will affect the area of the dike-ponds from two aspects. First of all, the reduction of rural population is bound to decrease the effective development and management. The hysteretic sludge cleaning of dike-ponds will directly lead to insufficient water cycle. Additionally, the yield will not be able to keep up with the requirement due to the unsatisfied feeding, which will give rise to economic benefit reduction. In this case, a large amount of area of dike-ponds can be influenced in Shunde District. Secondly, the rural area depopulation can be a stimulus to the rise of urban population, thus increasing the urban burden to some extent and speeding up the process of urbanization. In need of more living space, more dike-ponds with relatively low economic output value are invaded by expanding urban construction area. As a result, the area of dike-ponds continue to decrease transferring to built-up area.

4.4. Changes of Dike-Pond Role in Water Management Due to the long-term waterlogging in the low-lying areas of the Pearl River Delta in history, flood disasters were frequent in the past, thus bringing about original dike-ponds that helps drainage and disaster reduction. Since 1949, flood control in China has entered a new period of development [66]. The public concentrated on the construction and reinforcement of river embankments, opened new flood discharge channels, and completed water conservancy projects. Therefore, the flood control system of major rivers has been gradually enhanced and improved. The result of this is that the role of dike-ponds was gradually weakened in flood control and disaster mitigation. In the 1970’s and 1980’s, the dike-ponds developed into an agricultural model with higher economic benefits [14]. Combining the terrestrial and aquatic ecosystems, the utilization efficiency of freshwater resources reached optimal. Moreover, considering its large amount of storage, the dike-ponds without industrial pollution are widely regarded as one of the main freshwater sources of domestic water in rural areas. As a result, the resource-based ecological service function has become the most essential part of water management. With the acceleration of urbanization, the area of dike-ponds has been reduced, and the fragmentation has increased. While pipelines are generally installed in rural areas to supply water by reservoirs, the function of dike-ponds cannot be underestimated conserving water storage. Because the water level in dike-ponds is artificially controlled, the water is allocated to cultivated land for agricultural irrigation besides supplying the demand for its own aquatic products. Generating convenience and benefits, the major functions of dike-ponds has been changing with time and the current situation, but have always played a significant and irreplaceable role in water management.

4.5. Limitation The development of dike-pond system can be influenced by many factors including natural environment and socio-economic aspects. Among them, the former contains regional climate, geological conditions, river basin locations, etc., while the latter includes population, regional agricultural structure, agricultural production efficiency, urbanization process, economic structure, macroeconomic policies, and so on. In the Results part of this paper, only government policies and river network were considered and discussed. In fact, some other factors such as irreversible urbanization process, also affect the evolution of dike-ponds. Thus, more research needs to be done on the contribution of different factors to dike-pond evolution. In addition, the paper intends to analyze the relationship between dike-pond evolution and affecting factors from a quantitative point of view in the design of the experiment. However, the number of selected objects is relatively small that is not convincing enough. For example, only two representative Sustainability 2018, 10, 3504 22 of 27 towns were selected in the analysis of impact of government decisions on the dike-ponds. If more than a few can be chose for comparison, it will be more completely accepted. In terms of the above discussion, a deeper exploration can be carried out from the following research. Firstly, the multi-temporal and high spatial resolution RS images of Shunde District is supposed to be acquired to explore the evolution characteristics of the internal structure in dike-ponds, comparing the differences between towns. What is more, combining with other non-RS data like statistical yearbooks and official reports, the factors affecting the dike-pond evolution can be analyzed more thoroughly and accurately, so as to provide a more comprehensive reference for the restoration and construction of dike-ponds. Lastly, we have already discussed the evolution and its factors regarding dike-ponds as target objects in the former part. However, in turn, considering that dike-pond is a typical wetland ecosystem, the area change and fragmentation of dike-ponds will also have a feedback effect on the environment, which is a significant field we can explore.

5. Conclusions In this paper, DISP and Landsat images were used to derive land use information over the period from 1978 to 2016. Ultimately, seven land use maps were produced to extract the dike-pond distribution, which had a classification accuracy greater than 89% and a kappa coefficient of more than 0.86. The results of comparison among the seven chosen periods indicate that the area of dike-ponds in 2016 was significantly reduced, and fragmentation had increased compared with that in 1978, after 38 years of development. It was seen that most of the dike-ponds that had disappeared by 2016 were due to their conversion into built-up areas, and to a lesser degree, cultivated land. Among the ten administrative regions in Shunde District, two main classes were constructed to describe the trend of dike-pond area proportion change of each town: Class A, where the proportion of dike-ponds continuously decreased, and Class B, where the proportion of dike-ponds increased first and then decreased. Combined with the calculated AWDII values, the dike-ponds in Shunde District experienced three main phases over the past 38 years: steady development, rapid invasion and a gradual reduction of invasion by other land use types. It indicated that AWDII are of great significance to explain other types of wetlands with fluctuated and complex trends. There are some main factors that may affect the evolution of dike-pond, namely government policies, the river network, and rural area depopulation, which all belong to socio-economic and the natural environmental factors. To overcome the situation where dike-ponds showed a decreasing trend caused by continuous urbanization and intemperate cultivation, the harmony should be made a priority between economic development and ecological environment. Based on free satellite data and open source software, the processing workflow and eventual results can be used generally for spatio-temporal monitoring wetland evolution, more rational water resource management as well as supporting directives for the conversation of inland freshwater ecosystem.

Author Contributions: F.L., K.L. and H.T. designed the study, performed the experiments, analyzed the results and wrote the manuscript. L.L. contributed in designing the study, editing the manuscripts and providing funding to the project, and H.L. provided some useful suggestions for the paper’s results and discussions. Funding: This research is founded by the National Science Foundation of China (Grant No. 41001291, 41531178), the Natural Science Foundation of Guangdong (Grant No. 2016A030313261), the Science and Technology Planning Project of Guangdong Province (Grant No. 2017A020217003). Acknowledgments: The authors would like to thank the anonymous referees and the editors for their valuable comments, and also appreciate International Science Editing (http://www.internationalscienceediting.com) for editing this manuscript. Conflicts of Interest: The authors declare no conflict of interest. Sustainability 2018, 10, 3504 23 of 27

Appendix A. Error Matrix of Classification Accuracies in Shunde District from 1978 to 2016

Table A1. Error matrix of classification in 1978.

Evaluated Image Sample Number Built-up Cultivated River Forest Dike-Pond Sum Area Land River 57 1 3 1 2 64 Forest 0 56 1 2 0 59 Reference Built-up area 6 1 76 2 0 85 image Cultivated land 0 3 2 64 2 71 Dike-pond 2 0 0 3 83 88 Sum 65 61 82 72 87 367 Producer’s accuracy 94.00% 100.00% 96.60% 100.00% 100.00% - User’s accuracy 100.00% 100.00% 96.60% 95.00% 100.00% - Overall accuracy: 91.55%; Overall kappa coefficient: 0.894

Table A2. Error matrix of classification in 1988.

Evaluated Image Sample Number Built-up Cultivated River Forest Dike-Pond Sum Area Land River 43 0 2 1 1 47 Forest 0 45 1 2 0 48 Reference Built-up area 2 0 71 5 0 78 image Cultivated land 1 2 0 72 1 76 Dike-pond 1 3 0 1 71 76 Sum 47 50 74 81 73 325 Producer’s accuracy 91.49% 93.75% 91.03% 94.74% 93.42% - User’s accuracy 91.49% 90.00% 95.95% 88.89% 97.26% - Overall accuracy: 92.92%; Overall kappa coefficient: 0.911

Table A3. Error matrix of classification in 1993.

Evaluated Image Sample Number Built-up Cultivated River Forest Dike-Pond Sum Area Land River 61 1 2 1 1 66 Forest 0 67 1 2 1 71 Reference Built-up area 4 0 74 0 0 78 image Cultivated land 1 2 2 83 1 89 Dike-pond 2 3 2 1 91 99 Sum 68 73 81 87 94 403 Producer’s accuracy 92.42% 94.37% 94.87% 93.26% 91.92% - User’s accuracy 89.71% 91.78% 91.36% 95.40% 96.81% - Overall accuracy: 93.30%; Overall kappa coefficient: 0.916 Sustainability 2018, 10, 3504 24 of 27

Table A4. Error matrix of classification in 2000.

Evaluated Image Sample Number Built-up Cultivated River Forest Dike-Pond Sum Area Land River 55 1 3 2 1 62 Forest 0 55 3 0 4 62 Reference Built-up area 0 1 61 1 3 66 image Cultivated land 0 1 0 71 3 75 Dike-pond 2 3 2 1 69 77 Sum 57 61 69 75 80 342 Producer’s accuracy 88.71% 88.71% 92.42% 94.67% 89.61% - User’s accuracy 96.49% 90.16% 88.41% 94.67% 86.25% - Overall accuracy: 90.94%; Overall kappa coefficient: 0.886

Table A5. Error matrix of classification in 2005.

Evaluated Image Sample Number Built-up Cultivated River Forest Dike-Pond Sum Area Land River 38 2 0 5 1 46 Forest 1 46 2 0 0 49 Reference Built-up area 2 1 65 1 3 72 image Cultivated land 1 1 1 51 3 57 Dike-pond 0 3 1 3 64 71 Sum 42 53 69 60 71 295 Producer’s accuracy 82.61% 93.88% 90.28% 89.47% 90.14% - User’s accuracy 90.48% 86.79% 94.20% 85.00% 90.14% - Overall accuracy: 89.49%; Overall kappa coefficient: 0.868

Table A6. Error matrix of classification in 2011.

Evaluated Image Sample Number Built-up Cultivated River Forest Dike-Pond Sum Area Land River 53 0 0 1 3 57 Forest 0 45 1 1 0 47 Reference Built-up area 1 2 76 2 1 82 image Cultivated land 1 3 2 83 2 91 Dike-pond 2 1 1 1 70 75 Sum 57 51 80 88 76 352 Producer’s accuracy 92.98% 95.74% 92.68% 91.21% 93.33% - User’s accuracy 92.98% 88.24% 95.00% 94.32% 92.11% - Overall accuracy: 92.90%; Overall kappa coefficient: 0.910

Table A7. Error matrix of classification in 2016.

Evaluated Image Sample Number Built-up Cultivated River Forest Dike-Pond Sum Area Land River 51 0 1 1 2 55 Forest 0 47 2 0 1 50 Reference Built-up area 1 2 64 3 2 72 image Cultivated land 1 3 2 73 1 80 Dike-pond 3 1 1 2 69 76 Sum 56 53 70 79 75 333 Producer’s accuracy 92.73% 94.00% 88.89% 91.25% 90.79% - User’s accuracy 91.07% 88.68% 91.43% 92.41% 92.00% - Overall accuracy: 91.29%; Overall kappa coefficient: 0.890 Sustainability 2018, 10, 3504 25 of 27

References

1. Zhong, G. A Deeper Realization of Mulberry Dike-Fish Pond System. Trop. Geogr. 1984, 4, 129–135. 2. Zhong, G.; Cai, G. Eco-Economic Model of Basic (Cultivated Land) Pond System in China—A Case Study of the Pearl River Delta and the Yangtze River Delta. Ecol. Econ. 1987, 53, 15–20. 3. Liao, X. Theory and Technology Mode of Mulberry Based Fish Pond Ecosystem in Low-Lying Water Ponds. Guangxi Seric 1999, 36, 30–31. 4. Zhong, G. The Characteristics of Dike-Pond System and Practical Significance. Sci. Geogr. Sin. 1988, 45, 161–187. 5. Ding, J.; Wen, Y.; Shu, Q. Studies on Sustainable Development of Aquaculture in the Dike-Pond Ecosystem. Chongqing Environ. Sci. 2001, 23, 12–14. 6. Nie, C.; Luo, S.; Zhang, J.; Li, H.; Zhao, Y. The Dike-Pond System in the Pearl River Delta: Degradation Following Recent Land Use Alterations and Measures for Their Ecological Restoration. Acta Ecol. Sin. 2003, 23, 1851–1860. 7. Zhong, G. The Structural Characteristics and Effects of the Dyke-Pond System in China. Outlook Agric. 1989, 18, 119–123. 8. Zhong, G. The Types, Structure and Results of the Dike-Pond System in South China. GeoJournal 1990, 21, 83–89. 9. Zhong, G. Some Problems About the Mulberry-Dike-Fish-Pond Ecosystem on the Zhujiang Delta. Chin. J. Ecol. 1982, 10–11. [CrossRef] 10. Zhong, G. Mulberry-Dike-Fish-Pond on the Zhujiang Delta—A Complete Artificial Ecosystem of Land-Water Interaction. Acta Geogr. Sin. 1980, 35, 200–209. 11. Korn, M. The Dike-Pond Concept: Sustainable Agriculture and Nutrient Recycling in China. Ambio 1993, 25, 6–13. 12. Lo, C. Environmental Impact on the Development of Agricultural Technology in China: The Case of the Dike-Pond (‘Jitang’) System of Integrated Agriculture-Aquaculture in the Zhujiang Delta of China. Agric. Ecosyst. Environ. 1996, 60, 183–195. [CrossRef] 13. Huang, Y.; Liu, T. Dike-Pond Ecosystes and Their Soils. J. South China Agric. Univ. 1995, 102–107. 14. Li, H.; Luo, S. Eco-Economic Analysis on the Chinampa Systems in Maoming City. Rural Eco-Environ. 1992, 8, 41–45. 15. Yang, Y. Problems and Countermeasures of Sustainable Development of Agricultural Ecology in the Pearl River Delta. Guangdong Agric. Sci. 1995, 14–16. 16. Zhang, J.; Zhong, G. Analysis of Harmonizing Human and Geographic Environment Relationship in the Dike-Pond Econ-Ecological System. Ecol. Sci. 1993, 55–59. 17. Li, H.; Ling, W.; Yang, J.; Zhang, M. Biremediation of the Hchs Residue in Sediments of Dike-Pond System by Growing Ipomoea Aquatic and Using Different Fertilizer. J. Agro-Environ. Sci. 2007, 26, 2251–2256. 18. Li, H.; Luo, S.; Nie, C. Reconstruction and Control of Modern in Tensive Dike-Pond System in Shunde. Chin. J. Ecol. 2005, 24, 108–112. 19. Zhu, G. Ponderation Over the Flood Control Functions of the Artifical Landforms in Prd. Trop. Geogr. 2012, 32, 378–384. 20. Zhao, Y. The Effect of Scoeconomical Development on the Dike-Pond System in Zhujiang Delta. J. Zhongkai Agrotech. Coll. 2001, 14, 28–33. 21. Wu, J. Land-Pool Farming and Economic Restructuring in Shunde County during the Times of Ming, Qing Dynasties and the Republic of China. Ancient Mod. Agric. 2011, 1, 96–104. 22. Li, M.; Nie, C.; Long, X. Establishment of Indicator System for Assessing Eco-Environment Quality on Dike-Pond System. J. Agro-Environ. Sci. 2007, 26, 386–390. 23. Lu, H.; Peng, S.; Lan, S.; Chen, F. Energy Value Evaluation of Dike-Pond Agro-Ecological Engineering Modes. Chin. J. Appl. Ecol. 2003, 14, 1622–1626. 24. Han, X.; Yu, K.; Li, D.; Wang, S. Building the Landscape Security Pattern of Dike-Pond System with Urban Functions. Area. Res. Dev. 2008, 27, 107–110. 25. Lin, M.; Feng, R.; Ji, S. Analysis on Mode Change and Landscape Pattern of the Dike-Pond Agriculture in Zhongshan. Guangdong Agric. Sci. 2014, 41, 184–189. Sustainability 2018, 10, 3504 26 of 27

26. Liu, K.; Wang, S.; Xie, L.; Zhuaang, J. Spatial Evolution Analysis of Dike-Pond Systems in Foshan City. Trop. Geogr. 2008, 28, 513–517. 27. Yang, D.; Ye, C. Analyses and Simulation on Landscape Fragmentation of Dike—Pond Based on Ca Model in Pearl River Delta. Hubei Agric. Sci. 2016, 55, 3932–3937. 28. Kennedy, R.E.; Townsend, P.A.; Gross, J.E.; Cohen, W.B.; Bolstad, P.; Wang, Y.Q.; Adams, P. Remote Sensing Change Detection Tools for Natural Resource Managers: Understanding Concepts and Tradeoffs in the Design of Landscape Monitoring Projects. Remote Sens. Environ. 2009, 113, 1382–1396. [CrossRef] 29. Schneider, A. Monitoring land Cover Change in Urban and Peri-Urban Areas Using Dense Time Stacks of Landsat Satellite Data and a Data Mining Approach. Remote Sens. Environ. 2012, 124, 689–704. [CrossRef] 30. Zurqani, H.A.; Post, C.J.; Mikhailova, E.A.; Schlautman, M.A.; Sharp, J.L. Geospatial Analysis of Land Use Change in the Savannah River Basin Using Google Earth Engine. Int J. Appl Earth Obs. 2018, 69, 175–185. [CrossRef] 31. Hauser, L.T.; Nguyen Vu, G.; Nguyen, B.A.; Dade, E.; Nguyen, H.M.; Nguyen, T.T.Q.; Le, T.Q.; Vu, L.H.; Tong, A.T.H.; Pham, H.V. Uncovering the Spatio-Temporal Dynamics of Land Cover Change and Fragmentation of Mangroves in the Ca Mau Peninsula, Vietnam Using Multi-Temporal Spot Satellite Imagery (2004–2013). Appl. Geogr. 2017, 86, 197–207. [CrossRef] 32. Ye, C. Change Characteristics and Spatial Types of Dike-Pond in the Pearl River Delta. J. East China Inst. Technol. 2013, 36, 315–322. 33. Wang, X.; Xia, L.; Deng, S.; Pan, Z. Spatial Temporal Changes in Dike-Pond Land in Nanhai District Based on RS and GIS. Resour. Ind. 2011, 13, 55–60. 34. Pang, Z. The Revelation of Contemporary American Photographic Reconnaissance Satellite. China Ordnance Ind. 2001, 57–60. 35. Yang, X. An Overview of Foreign Reconnaissance Satellites. For. Space Technol. 1979, 43–48. 36. Chen, B.; Chen, L.; Huang, B.; Michishita, R.; Xu, B. Dynamic Monitoring of the Poyang Lake Wetland by Integrating Landsat and Modis Observations. ISPRS J. Photogramm. 2018, 139, 75–87. [CrossRef] 37. Liu, Y.; Li, R.; Yang, L. Image Classification Method in Landuse Dynamic Detection Based on Multi-Source Remote Sensing Data—A Case Study in the Loess Plateau of Northern Shaanxi Province. Bull. Soil Water Conserv. 2006, 26, 63–66. 38. Ottinger, M.; Clauss, K.; Kuenzer, C. Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data. Remote Sens-Basel. 2017, 9, 1–23. [CrossRef] 39. Zhai, T.; Jin, G.; Deng, X.; Li, Z.; Wang, R. Research of Wuhan City Land Use Classification Method Based on Multi-Source Remote Sensing Image Fusion. Resour. Environ. Yangtze Basin 2016, 25, 1594–1602. 40. He, Z.; Guan, L.; Kang, Q.; Wang, X.; Wang, C. Sustainable Development of Mulberry-Dike and Fish-Pond Farming in Pearl River Delta by Remote-Sensing Technology. Acta Sci. Nat. Univ. Sunyatseni 1998, 37, 68–73. 41. Wang, X.; Kang, Q. Application of tm Image in Investigating Changes of Dike-Pond Land on the Pearl River Delta—Samples as Shunde Nanhai Dike-Pond Region. Remote Sens. Land Resour. 1997, 3, 8–14. 42. Yee, A. New Developments in Integrated Dike-Pond Agriculture-Aquaculture in the Zhujiang Delta, China: Ecological Implications. Ambio 1999, 28, 529–533. 43. Turner, M.G. Landscape Ecology: The Effect of Pattern on Process. Annu. Rev. Ecol. Syst. 1989, 20, 171–197. [CrossRef] 44. Li, Y.; Zhou, L.; Cui, H. Pollen Indicators of Human Activity. Chin. Sci. Bull. 2008, 53, 1281–1293. [CrossRef] 45. Mercuri, A.M. Genesis and Evolution of the Cultural Landscape in Central Mediterranean: The ‘Where, When and How’ through the Palynological Approach. Landsc. Ecol. 2014, 29, 1799–1810. [CrossRef] 46. Xinwen, Z.; Chuanxiu, L.; Shuangxi, C.; Changsheng, H.; Zhuo, Z.; Haogang, D.; Min, Z.; Wen, C.; Fengmei, L.; Yiyong, L.; et al. Quantitative Reconstruction of Holocene Climate based on Pollen Data from Drill Hole qzk6 in Pearl River Delta. Geol. Bull. China 2014, 33, 1621–1628. 47. Yang, S.; Zheng, Z.; Huang, K.; Zong, Y.; Wang, J.; Xu, Q.; Rolett, B.V.; Li, J. Modern Pollen Assemblages from Cultivated Rice Fields and Rice Pollen Morphology: Application to a Study of Ancient Land Use and Agriculture in the Pearl River Delta, China. Holocene 2012, 22, 1393–1404. [CrossRef] 48. Almeida, C.M.D.; Monteiro, A.M.V.; Camara, G.; Soares-Filho, B.S.; Cerqueira, G.C.; Pennachin, C.L.; Batty, M. GIS and Remote Sensing as Tools for the Simulation of Urban Land-Use Change. Int. J. Remote Sens. 2005, 26, 759–774. [CrossRef] Sustainability 2018, 10, 3504 27 of 27

49. Bhatta, B.; Saraswati, S.; Bandyopadhyay, D. Quantifying the Degree-of-Freedom, Degree-of-Sprawl, and Degree-of-Goodness of Urban Growth from Remote Sensing Data. Appl. Geogr. 2010, 30, 96–111. [CrossRef] 50. Kumar, J.A.V.; Pathan, S.K.; Bhanderi, R.J. Spatio-Temporal Analysis for Monitoring Urban Growth—A Case Study of Indore City. J. Indian Soc. Remote Sens. 2007, 35, 11–20. [CrossRef] 51. Liu, X.; Li, X.; Chen, Y.; Tan, Z.; Li, S.; Ai, B. A New Landscape Index for Quantifying Urban Expansion Using Multi-Temporal Remotely Sensed Data. Landsc. Ecol. 2010, 25, 671–682. [CrossRef] 52. Badjana, H.; Helmschrot, J.; Selsam, P.; Wala, K.; Flugel, W.; Afouda, A.; Akpagana, K. Land cover Changes Assessment Using Object-Based Image Analysis in the Binah River Watershed (Togo and Benin). Earth Space Sci. 2016, 2, 403–416. [CrossRef] 53. Zhang, L.; Nan, Z.; Xu, Y.; Li, S. Hydrological Impacts of Land Use Change and Climate Variability in the Headwater Region of the Heihe River Basin, Northwest China. PLoS ONE 2016, 11, 1–25. [CrossRef] [PubMed] 54. Liang, H. Study on Lucc in Typical County of the Pearl River Delta; Guangzhou University: Guangzhou, China, 2007. 55. Sinha, P.; Kumar, L.; Drielsma, M.; Barrett, T. Time-Series Effective Habitat Area (eha) Modeling Using Cost-Benefit Raster Based Technique. Ecol. Inform. 2014, 19, 16–25. [CrossRef] 56. Liu, X.; Li, X.; Chen, Y.; Qin, Y.; Li, S.; Chen, H. Landscape Expansion Index and Its Applications to Quantitative Analysis of Urban Expansion. Acta Geopgr. Sin. 2009, 64, 1430–1438. 57. Zeng, Y.; He, L.; Jin, W.; Wu, K.; Xu, Y.; Yu, F. Quantitative Analysis of the Urban Expansion Models in Changsha-Zhuzhou-Xiangtan Metroplan Areas. Sci. Geogr. Sin. 2012, 32, 544–549. 58. Zhou, X.; Chen, L.; Xiang, W. Quantitative Analysis of the Built-Up Area Expansion in Su-Xi-Chang Region, China. Chin. J. Appl. Ecol. 2014, 25, 1422–1430. 59. Wu, P.; Zhou, D.; Gong, H. A New Landscape Expansion Index Definition and Quantification. Acta Ecol. Sin. 2012, 32, 4270–4277. 60. Xingtan Government Online. Available online: http://www.shunde.gov.cn/xingtan/zjxt/?Sid=1 (accessed on 11 June 2018). 61. Manqiang, W. An Analysis on the Status of Land-Use of Foshan Region in History; Ji’nan University: Guangzhou, China, 2012. 62. Dai, W.; Si, S. Government Policies and Firms’ Entrepreneurial Orientation: Strategic Choice and Institutional Perspectives. J. Bus. Res. 2018, 93, 23–36. [CrossRef] 63. Guo, C.X.; Xu, S.J. The New Visual Angle and Advance on Researches of the Dike-Pond System in China. Wetland Sci. 2011, 9, 75–81. 64. Li, Y.; Liu, K.; Liu, Y.; Zhu, Y. The Dynamic of Dike-Pond System in the Pearl River Delta during 1964–2012; Springer: Cham, Switzerland, 2017; pp. 47–59. 65. Ye, C.; Feng, Y. Ecological Risk Assessment for Pearl River Delta based on Land Use Change. Trans. Chin. Soc. Agric. Eng. 2013, 29, 224–232. 66. Wuying, S. The Hubei Province and Flood Relief Study (1949–1956); Huazhong Normal University: Wuhan, China, 2013.

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).