p-ISSN: 0972-6268 Nature Environment and Pollution Technology (Print copies up to 2016) Vol. 19 No. 4 pp. 1475-1482 2020 An International Quarterly Scientific Journal e-ISSN: 2395-3454
Original Research Paper Originalhttps://doi.org/10.46488/NEPT.2020.v19i04.013 Research Paper Open Access Journal Evaluation of Health Level of Land-use Ecosystem Based on GIS Grid Model
Wei He*(**)†, Zheng Li*(**) and Dingqian Jing*(**) *College of Geography and Resources Science, Sichuan Normal University, Chengdu 610101, People’s Republic of China **Key Lab of Land Resources Evaluation and Monitoring in Southwest, Ministry of Education, Sichuan Normal University, Chengdu 610101, People’s Republic of China †Corresponding author: Wei He; [email protected]
ABSTRACT Nat. Env. & Poll. Tech. Website: www.neptjournal.com The evaluation of health level of land-use ecosystem provides important support for the regional health Received: 18-03-2020 and social-economic sustainable development. To measure and study the granularity characteristics Revised: 17-4-2020 and spatial differentiation law of ecological health level of land-use in Yibin City, based on PSR model, Accepted: 15-06-2020 taking kilometre grid as an evaluation unit, spatial exploration method was used to reveal its spatial differentiation law, and the decisive force of each factor was visualized through the factor detector. Key Words: The results show that: (1) The health level of the land-use ecosystem in Yibin City is generally good, Land-use and the regional development is relatively balanced, but there are significant differences between the GIS grid model municipal districts and suburban counties. The average index of comprehensive health level of land- Spatial exploratory analysis use ecosystem fluctuates around 0.60, the land for health level accounts for 46.07% of the total area, Geographic detector the land for sub-health level accounts for 29.78%, the land for unhealthy level accounts for 24.15%. (2) The health level of land-use ecosystem had a strong spatial correlation, which was mainly positive, and there was a significant spatial agglomeration pattern, dominated by HH type or LL type clusters. (3) The difference of human activities was the main factor that affects the spatial differentiation of ecological health level of land-use in the whole city, followed by the difference of natural system resilience, and the other factors were soil properties, landform and policy regulation. Finally, it was concluded that tightening the upper limit of capacity, holding the ecological bottom line, insisting on the intensive utilization of land, optimizing the spatial layout of “production, life and ecology”, adjusting the industrial structure, and developing ecotourism will become the necessary measures for Yibin city to improve the ecological health level of land-use and build a famous ecological city of landscape culture.
and basic administrative areas such as city, county (district), INTRODUCTION township (town, sub-district) (Wei 2017), involving cities The health level of land-use ecosystem refers to a state in (Ma et al. 2014), wetlands (Zhu et al. 2012) grasslands (Zhao which the land under the jurisdiction can maintain the nor- et al. 2017) and rivers (Hao et al. 2014), using building an mal operation of self-structure and functions when used by index evaluation system of land-use ecological health based people. So that the symbiosis process among land, human, on vitality-organization-resilience (VOR) theory from the biology and external environment can be continuously de- aspects of vitality, organization, resilience and function veloped (Zhang et al. 2011). In recent years, with prominent (Liu et al. 2017). problems such as resource depletion and environmental Yibin City, a hilly area in the upper reaches of the Yangtze pollution, the ecological health of land-use, as a new goal River, was taken as the research object. Based on the PSR of land ecological environment protection and sustainable model of “human-land interaction” considering the interac- development, has attracted much attention. Therefore, it is an tion between land ecosystem and human activities, the grid urgent task for regional health and sustainable development model was used to transform the remote sensing data, land- of the social economy to evaluate the ecological health of use data and panel data in a unified way to expand the single land-use and formulate reasonable control measures. ecosystem to a composite ecosystem while realizing the Since the end of the 20th century, there are few studies on compatibility of multiple data and the refinement of spatial the evaluation of health level of land-use ecosystem at home accuracy of evaluation, to establish an appropriate ecological and abroad, mainly focusing on the connotation, mechanism, health evaluation system for land-use. The ecological health level and regulation of ecological health of land-use (Gao et level of land-use in each grid was measured taking kilometre al. 2017, Zhang & Shi 2010), based on single land ecosystem grid as the basic measure unit; ESDA space exploration 1476 Wei He et al.
technology was used to reveal the spatial differentiation law to carry out the research on the evaluation of health level of land-use ecological health level in grid scale, and factor of the land-use ecosystem in the city for the restoration and detector was used to show the decisive power of each factor protection of the land ecological environment and the estab- directly. The purpose of this paper is to enrich the research lishment of ecological protective screens. content of land-use ecological health evaluation and to pro- vide targeted countermeasures for improving the ecological Data Source and Pre-processing health level of regional land-use. Social and economic statistics in this study are mainly from the information published in Yibin Statistical Yearbook. The RESEARCH AREA, DATA SOURCES AND data of land-use status were interpreted by Landsat ETM + PREPROCESSING and OLI images downloaded from the data centre for re- sources and environmental sciences of Chinese Academy of Overview of Research Area Sciences (http://www.resdc.cn), with a spatial resolution of Yibin City, located in the southeast of Sichuan Province, 30m × 30m. Considering the needs of research, the land-use between 103°36′E and 105°20′E, 27°50′N and 29°16′N, types were determined as cultivated land, forest land, grass- has two districts and eight counties, with a total area of land, urban construction land, rural residential land, water 13,000 km2 (Fig. 1). It is the transition from Sichuan basin area, independent industrial and mining district and land for to Yunnan-Guizhou Plateau, with the terrain dominated by transportation. DEM data came from ASTER-GDEMV2 middle and low mountains and hills, alternated valleys and digital elevation data with a resolution of 30m provided flatlands, and the landscape distribution pattern, which is high by NASA (search.earthdata.nasa.gov). The tool of creating in the southwest and low in the northeast (Liu et al. 2017, fishnet in ArcGIS 10.2 was used to generate the grid of 1km Zhao et al. 2015). In 2018, Yibin city’s population totalled × 1km, and there were a total of 13,307 grids generated in 5.5429 million, with a GDP of 152.594 billion yuan, the Yibin City. urbanization rate has reached 48.5%. With the development of social economy, also increases the pressure of regional MATERIALS AND METHODS resources and environment, and brings new challenges to the Construction of PSR Evaluation Index System sustainable development of regional land ecology. Therefore, (search.earthdata.nasa.gov).under the background of the The planning tool strategyof creating of regional fishnet The in ArcGISinternal health 10.2 ofwas the used land toecosystem generate is relatedthe grid to of 1km land ecological development, it is of practical significance people’s survival and sustainable development of society. × 1km, and there were a total of 13,307 grids generated in Yibin City.
Fig. 1: GeographicalFig. 1: Geographical location location of the of study the area study area
MATERIALSVol. 19, No. 4, 2020AND • Nature METHOD Environment Sand Pollution Technology Construction of PSR Evaluation Index System The internal health of the land ecosystem is related to people's survival and sustainable development of society. The interaction between land ecosystem and human activities must be considered comprehensively when establishing the indicator system. Based on the PSR model (Zhao et al. 2015, Xu et al. 2010), build the evaluation index system for the health level of the land-use ecosystem in Yibin City. "Pressure" refers to the defect of land and resources and the impact of human activities on land and surrounding environment, which is measured by terrain fragmentation, soil erosion, population density and stress index of human activities (He et al. 2015). "State" refers to the energy and activity of land ecosystem (Jiang et al. 2009), which is measured by Landscape diversity index (Chen 2007), ecological resilience (Pan et al. 2004), ecosystem service values (Xie et al. 2008). "Response" refers to the policy measures taken by human beings for land ecological restoration, mainly including the amount of environmental protection investment and the proportion of the tertiary industry in GDP. Details are provided in Tables 1 and 2. Table 1: Ecosystem health evaluation index system. Target layer Criterion layer Index layer Formulas Safety trend Weight
P1 Soil erosion - Negative 0.089 P2 Terrain fragmentation P2 1 cos Negative 0.177 Population density P3 P3 ri mi Negative 0.102 Pressure (person/km²)
Stress index of human n
P4 P4 ti *qi A Negative 0.081 activities i1
Vital Normalized differential NIR R S1 S1 Positive 0.090 ity vegetation index NIR R Land Orga ecosystem n t t nizat S2 Landscape diversity index S2 i ln i Positive 0.089 health i1 A A ion
State Resi n t * p lienc S3 Ecological resilience S3 S * i i Positive 0.087 i1 A e
Func n
S4 Ecosystem service values S4 ti *vi Negative 0.087 tion i1
(search.earthdata.nasa.gov).(search.earthdata.nasa.gov). TheThe tooltool ofof creatingcreating fishnetfishnet inin ArcGISArcGIS 10.210.2 waswas usedused toto generategenerate thethe gridgrid ofof 1km1km
× 1km,(search.earthdata.nasa.gov).(search.earthdata.nasa.gov). and there were a total of The 13,307 The tool tool of grids creatingof creating generated fishnet fishnet inin YibinArcGISin ArcGIS City. 10.2 10.2 was was used used to generate to generate the thegrid grid of 1km of 1km × 1km,(search.earthdata.nasa.gov).(search.earthdata.nasa.gov). and there were a total of The The13,307 tool tool of grids of creating creating generated fishnet fishnet in in inYibin ArcGIS ArcGIS City. 10.2 10.2 was was used used to to generate generate the the grid grid of of 1km 1km ×(search.earthdata.nasa.gov). 1km,× 1km, and and there there were were a total a total ofThe of13,307 tool 13,307 of grids creating grids generated generated fishnet in in Yibinin ArcGIS Yibin City. City. 10.2 was used to generate the grid of 1km × ×1km, 1km, and and there there were were a atotal total of of 13,307 13,307 grids grids generated generated in in Yibin Yibin City. City. × 1km, and there were a total of 13,307 grids generated in Yibin City.
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Table 1: EcosystemTable healthTarget 1: Ecosystemevaluationlayer Criterion index health layer system. evaluation index systemIndex .layer Formulas Safety trend Weight TableTable 1Table:: Ecosystem 1: Target1 Ecosystem: Ecosystem layer health Criterionhealth health evaluation layerevaluation evaluation index index index system system system.. Index. . layer Formulas Safety trend Weight Target layer CriterionTarget layer Criterion layer IndexP1 layer SoilIndex erosion layer FormulasFormulas- NegativeSafetySafety trend trend 0.089Weight Weight TargetTarget layerTarget layer layerCriterion CriterionCriterion layer layer layer P1 IndexIndexIndexSoil layer layer erosionlayer FormulasFormulasFormulas- SafetySafetyNegative trendtrend WeightWeight0.089 Targetlayer layer Criterion layer Index layer Formulas Safety trend Weight P2P1 Terrain Soilfragmentation erosion P2 1 cos- NegativeNegative 0.1770.089 P1 P1P1 P2 SoilTerrainSoil Soilerosion erosion fragmentationerosion - -- NegativeNegative 0.0890.0890.177 Land ecosys- Pressure P1 P1Soil erosionSoil erosion - P2 - 1 cos NegativeNegative 0.0890.089 tem healthw P2 PopulationTerrain fragmentation density P2 1 cos Negative 0.177 P2 P3P2P2 TerrainTerrainTerrainPopulation fragmentation fragmentation fragmentation density P2P211coscos NegativeNegative 0.1020.1770.177 P2 P2Terrain Terrainfragmentation fragmentation P2P31ricosmi NegativeNegative 0.1770.177 Pressure P3 (person/km²) P2P31cosri mi Negative 0.102 Pressure Population density PopulationPopulation(person/km²) density density P3 Population density P3 ri mi Negative 0.102 P3 PopulationP3P3 PopulationStress density index (person/km²) densityof human P3P3nrri mmi NegativeNegative 0.102 0.102 Pressure P3 (person/km²) P3 rn ii mii Negative 0.102 Pressure P3 P4 Stress(person/km²)(person/km²) index of human P4P3 tr*i qmAi NegativeNegative 0.0810.102 Pressure P4 (person/km²) i i Negative 0.081 Pressure (person/km²)activities P4i1 n ti *qi A P4 Stress indexStress of human index ofactivities human nn Negative 0.081 StressStress index indexactivities of of human human n i1 P4 Stress index of human P4 ti *qi A Negative 0.081 P4P4 Stress index of human P4 nti *qi A Negative 0.081 Vital Normalizedactivities differential P4 NIRi1ti *qRi A VitalP4 Normalizedactivities differential P4 i1ti *qi A Negative 0.081 P4 S1 activities P4S1 i1tNIRi *qiAR PositiveNegative 0.0900.081 S1 activities i1 Positive 0.090 ity vegetationactivities index S1 NIR R State Vitality S1Vitality Normalized Normalized differentialvegetation differential vegetation index i 1NIRNIRRR Positive 0.090 Land Vital S1 Normalized differential S1 NIRNIR RR Positive 0.090 Land VitalOrga indexS1S1 Normalized differential S1S1NIR R PositivePositive 0.090 Vital ity Normalizedvegetation differential index nNIRNIRRR ecosystem ityityOrga S1 vegetationvegetation index index S1 NIRNIRt t RR Positive 0.090 ecosystemLand S1 S1 ni i Positive 0.090 Land itynizat S2 Landscapevegetation diversity index index S2 NIRlnti Rti Positive 0.089 health ity Organizat S2 vegetationLandscape indexdiversity index S2 NIR lnR Positive 0.089 Landecosystem Organization S2Orga Landscape diversity index i1 nnA A Positive 0.089 Landecosystem health n t t ecosystem Orgaion i1tiAi tiAi Orga nizat S2 Landscape diversity index S2 ti lnti Positive 0.089 ecosystem nizatnization S2S2 LandscapeLandscape diversity diversity index index S2n ln PositivePositive 0.089 ecosystemhealthhealth S2 n i1ti AlntiA health State Resi i1tiA tAi nizat ionS2 Landscape diversity index S2 i1n Aln A Positive 0.089 health State nizationResi S2 Landscape diversity index S2 tln*p Positive 0.089 health Resilience S3 Ecological resilience i1 A ni Ai Positive 0.087 ionlienc S3 Ecological resilience S3 S *i1 A ti A*pi Positive 0.087 State ion Resilienc S3 Ecological resilience S3 S * Positive 0.087 State Resi i1 nn A e ni1tti**Appi State Resi lienc S3 Ecological resilience t i * p i Positive 0.087 State Resi lience S3 Ecological resilience SS33 SS** i i Positive 0.087 lienc S3 Ecological resilience S3 S *n n Positive 0.087 Function S4Func Ecosystem service values nii11t *AAp Negative 0.087 Investment in n ti *Api lienc Funce S3 S4 EcologicalEcosystem service resilience values S3S4 S * it 1*vi i NegativePositive 0.0870.087 lienc e S3 Ecological resilience S3 S *i i Positive 0.087 tion S4 Ecosystem service values S4 i1 ti *Avi Negative 0.087 FuncR1 environmental protection i —1in n1 A Positive 0.097 e Function i1 e Func S4 Ecosystem service values n Negative 0.087 Response R1 InvestmentS4 Ecosystem in environmental4 service values pro - – SS44 ttii**vvii NegativePositive 0.087 0.097 tion S4 Ecosystem(10 yuan) service values S4 nt *v Negative 0.087 Func tion nii11 i i Response Function tection (104 yuan) i1 S4 Ecosystem service values S4 ti *vi Negative 0.087 S4 EcosystemProportion service of tertiary values S4 ti *vi Negative 0.087 tionR2 Proportion of tertiary industry in i1 Positive 0.101 tion R2 R2 Si1 GDP Positive 0.101 GDP (%)industry in GDP (%)
Note: n = Note:Number n = of Number land-use of types;land-use a =Average types; =Averageslope of cell slope grid; of r celli = Population grid; ri = Population of each district of each and district county; and m icounty; = Area mofi =the Area administrative of the administrative region; ti = region; Area of th th th th the i typeti of= Arealand-use; of the qi i= Stresstype of intensity land-use parameters; qi = Stress of intensity human activities parameters of theof humani type activities of land-use; of the R =i Reflection type of land value-use ;of R red = Reflectionband; NIR= value Reflection of red band;value of th th th th infrared band;NIR= V Reflectioni = Ecosystem value service of infrared value band;of the Vuniti = Ecosystemarea of the servicei type valueof land-use; of the unitpi = Resiliencearea of the scorei type of of the land i -typeuse; ofpi land-use;= Resilience A =Total score areaof the of i evaluation type of unit; S = Outputland-use value; A =Total of the tertiaryarea of evaluationindustry. unit; S = Output value of the tertiary industry. Table 2: Comparison of relevant parameters of land-use type.
Table Types 2: Comparison of Cultivated of relevant Forest parameters Grass of- landUrban-use type. Rural Water Independent industrial and land-use Typesland of land land construction residential area area mining district and land for Relevant index land transportationIndependent industrial parameters land- Cultiva Urban Rural Forest Grassla Water and mining district Stress intensity of human 0.55use 0.10 ted 0.23 0.95 construction0.68 residential0.12 0.68 activities land nd area and land for Relevant land land area Parameters of ecological 0.50 0.90 0.60 0.40 0.40 0.80 0.40 transportation resilience index parameters Ecosystem serviceStress value intensity of 7.93human 0.55173.00 0.1020.00 0.230.00 0.95 0.00 0.68 732.000.12 0.00 0.68 (104 yuan/ km2) activities
Parameters of ecological 0.50 0.90 0.60 0.40 0.40 0.80 0.40 Nature Environment and Pollution Technology • Vol. 19, No. 4, 2020 resilience
Ecosystem service value (104 7.93 173.00 20.00 0.00 0.00 732.00 0.00 yuan/ km2)
Spatial Exploratory Analysis Spatial exploratory analysis is a kind of data spatial description and visual analysis method which emphasizes the spatial correlation measure as the core and aims at revealing the spatial interaction mechanism of research objects, divided into global autocorrelation and local autocorrelation. Global spatial autocorrelation is mainly used to explore the distribution characteristics of attribute data values in the whole regional space. The global Moran’s I index is a desirable measure of response distribution, which is between -1 and 1. If it is greater than 0, it means positive correlation; if it is less than 0, it means a negative correlation. Local spatial autocorrelation is mainly used to analyze the distribution pattern of attribute values of each unit in the heterogeneous space, and measure the degree of local spatial correlation between each region and its surrounding regions. The larger local Moran’s I statistic represents the spatial agglomeration of regional units with similar observations, while smaller values represent spatial agglomeration of regional units with different values (Liu et al. 2018). The calculation formula of global Moran´s I index is as follows:
n n n n n 2 I nwij xi xx j x wij xi x …(1) i1 j1 i1 j1 j1
The calculation formula of local Moran’s I index is as follows:
n 1 2 Ii xi xwij x j x xi x …(2) j1 n i
Where, wij is the weight matrix based on the spatial adjacency principle; xi and xi are evaluation values of the ith and jth unit; x is the mean of the nth evaluation value. Geographical Detector Analysis The geographical detector is a kind of econometric statistical analysis method proposed by Wang et al. (2002) of Chinese Academy of Sciences to check the spatial differentiation of single-variable and to detect the causal relationship between two variables by checking the consistency of spatial distribution of two variables,
Investment in
R1 environmental protection — Positive 0.097
(104 yuan) Response Investment in Proportion of tertiary Investment in R2 R1 environmentalR2 protection S GDP —Positive 0.101Positive 0.097
industry inR1 GDP (%)environmental (10 protection4 yuan) — Positive 0.097 Response Note: n = Number of land-use types; =Average slope of cell grid; ri = Population of(10 each4 yuan) district and county; mi = Area of the administrative region; th Proportion of thtertiary ti = Area of the i type of land-use; qi = Stress Responseintensity parameters of human activities of the i type of land-use; R = Reflection value of red band; R2 Proportion of tertiaryth R2 S GDP thPositive 0.101 NIR= Reflection value of infrared band; Vi = Ecosystem service value of the unit areaindustry of the ini GDP type (%)of land -use; pi = Resilience score of the i type of land-use; A =Total area of evaluation unit; S = Output value of the tertiaryR2 industry. R2 S GDP Positive 0.101 Note: n = Number of land-use types; =Average slope industryof cell grid; in GDP ri = (%)Population of each district and county; mi = Area of the administrative region; t = Area of the ith type of land-use; q = Stress intensity parameters of human activities of the ith type of land-use; R = Reflection value of red band; Note:i n = Number of land-use types; =Averagei slope of cell grid; ri = Population of each district and county; mi = Area of the administrative region; NIR= Reflectionth value of infrared band; V = Ecosystem service value of the unit area of theth ith type of land-use; p = Resilience score of the ith type of ti = Area of the i type of land-use; qi = Stressi intensity parameters of human activities of the i type of land-use; R =i Reflection value of red band; Table 2: Comparison of relevant parameters of land-use type. th th NIRland= Reflection-use; A =Total value areaof infrared of evaluation band; V unit;i = Ecosystem S = Output service value valueof the of tertiary the unit industry. area of the i type of land-use; pi = Resilience score of the i type of Typesland -use; Aof =Total area of evaluation unit; S = Output value of the tertiary industry. Independent industrial Tableland- 2: ComparisonCultiva of relevant parametersUrban of land-useRural type . Table 2: Comparison ofForest relevant Grassla parameters of land-use type. Water and mining district use ted Types of construction residential Types landof nd area and land for Independent industrial Relevant land land- Cultiva land areaUrban Rural Independent industrial land- Cultiva Forest Grassla Urban Rural Water and mining district use ted construction residentialtransportation index parameters Forest Grassla Water and mining district use ted land nd construction residential area and land for Relevant land land nd land area area and land for Stress intensity of humanRelevant 0.55 0.10 land0.23 0.95 0.68land 0.12area 0.68 transportation index parameters transportation activities index parameters Stress intensity of human 0.55 0.10 0.23 0.95 0.68 0.12 0.68 Parameters of ecologicalStress intensity0.50 of human0.90 0.550.60 0.10 0.400.23 0.400.95 0.800.68 0.120.40 0.68 activities resilience activities 1478 Parameters of ecological 0.50 0.90 0.60 Wei He 0.40et al. 0.40 0.80 0.40 Ecosystem servicemainly value (10Parameters including4 7.93 of ecological factor173.00 detector,0.5020.00 risk0.90 detector,0.000.60 ecological0.000.40 detector732.000.40 and 0.80interaction0.00 detector.0.40 The factor resilience resilience yuan/ kmdetector2) was used to detect and identify the driving force of the spatial differentiation law of the tion and visualEcosystem analysis service method value (104 which 7.93 emphasizes 173.00 the20.00 spatial 0.00analysis method0.00 proposed732.00 by Wang 0.00et al. (2002) of Chinese correlationcomprehensiveEcosystem measure service evaluation asvalue the (10 4core index7.93 and ofaims173.00 land at ecosystemrevealing20.00 the health0.00 Academy in Yibin0.00 of City, Sciences which732.00 to check was, thein0.00 essence,spatial differentiation comparing of sin- yuan/ km2) spatialthe total interaction varianceyuan/ kmmechanism2 )of the factor of research index in objects, different divided categories gle-variable and regions and withto detect the thetotal causal variance relationship in the wholebetween two Spatial Exploratoryinto global autocorrelationAnalysis and local autocorrelation. Global variables by checking the consistency of spatial distribution of region. The calculation formula is as follows: Spatial exploratoryspatial analysis autocorrelation is a kind isof mainly data spatial used descriptionto explore the and distri visualmainly- analysistwo including variables, method mainlyfactor which includingdetector, emphasizes factorrisk detector, detector, riskecological detector, detector and interaction detector. The factor SpatialSpatial E xploratoryExploratory Analysis Analysis the spatial correlationbutionSpatial characteristics measure exploratory as the of core analysisattribute andm aimsis data a kind atvalues revealing of data in spatialthe the whole spatial description interactionecological and visual detector mechanism analysis and interaction methodof research which detector. emphasizes The factor detector Spatial exploratory analysis1 is a kind of2 data spatial descriptiondetector and was visual used analysis to methoddetect andwhich identify emphasizes the driving force of the spatial differentiation law of the regional Pspace. 1The global Moran’sn * I index is …a desirable(3) wasmainly used to including detect and factor identify detector, the driving risk force detector, of the ecologicalspatial detector and interaction detector. The factor objects, divided intothe global spatialD,F autocorrelation correlation 2 measure andD local,i as the autocorrelation.FD core,i and aims at Globalcomprehensive revealing spatial the spatial autocorrelation evaluation interaction index is mechanism mainly of land ecosystem of research health in Yibin City, which was, in essence, comparing measurethe spatial of response correlationn distribution,* measureF i1 as which the core is betweenand aims -1at revealingand differentiation thedetector spatial wasinteraction law used of the mechanismto comprehensive detect and of research identify evaluation the index driving of force of the spatial differentiation law of the used to explore the distributionobjects, divided characteristics into global autocorrelation of attribute data and values local autocorrelation.in the whole regional Global space. spatial The autocorrelation global is mainly 1. Ifobjects, it is greater divided than into 0, global it means autocorrelation positive correlation; and local autocorrelation. if theit totallandcomprehensive variance ecosystem Global of spatial healththe evaluation factor autocorrelation in Yibin index index City, in ofisdifferentwhich mainlyland was,ecosystem categories in essence, health and inregions Yibin withCity, thewhich total was variance, in essence, in the comparing whole is lessused thanW here,to 0, explore it PmeansD,F =the Detectiona distributionnegative indexcorrelation. characteristics of influence Local of spatial factorattribute D dataoncomparing the values spatial thein the differentiationtotal whole variance regional of law the space. of factor comprehensive The index global in different Moran’s I index isused a desirable to explore measure the distribution of response characteristics distribution, of attribute whichregion. data is betweenvalues theThe total calculationin the- variance1 wholeand 1. formularegional ofIf theit is factor greaterspace.is as indexfollows: The gl inobal different categories and regions with the total variance in the whole autocorrelationhealthMoran level’s I of isindex themainly landis a used desirableecosystem to analyze measure in Yibin the of distributionCityresponse. distribution, categories which and isregions between with -1 theand total 1. If varianceit is greater in the whole than 0, it means Moranpositive’s I correlation;index is a desirable if it is measure less than of response0, it means distribution, a negativeregion. which correlation. Theis between calculation Local-1 and formula spatial1. If it is is as greater follows: pattern of attribute values of each unit in the heterogeneous region. The calculation1 m formula is as follows: autocorrelation isthan mainlythan n0,D,i 0, it= used meansitNumber means to positive analyze ofpositive samples correlation; the correlation; distributionin the ifsecondary itif isit pattern lessis less areathan ofthan 0,attribute it0,P means it means values 1a negative a negativeof each correlation. ncorrelation.unit *in Localthe2 Local spatial spatial space, and measure the degree of local spatial correlation D,F 2 D,im FD,i …(3) 1 2 autocorrelationn = Number samplesis mainly in used the wholeto analyze region; the distribution patternP ofn attribute*1F i 1values ofn each* unit in the heterogeneousbetween space,autocorrelation eachand regionmeasure is andmainly the its surrounding degreeused to ofanalyze local regions. thespatial Thedistribution largercorrelation pattern between of Dattribute,F each valuesregion2 of and each Dits, i unit FinD,i the …(3) …(3) 2 n* F i1 localheterogeneous Moran’sheterogeneous F =Variance I statistic space, space, of represents andspatial and measure measuredifferentiation the spatial the thedegree agglomeration degree rule of oflocalof comprehensive local spatial spatial correlation correlation evaluation between between index each of each landregion regionecosystem and andits health;its surrounding regions. The larger local Moran’s I statistic represents the spatialWhere, aPgglomerationD,F = Detection of indexregional of influence factor D on the spatial differentiation law of comprehensive Where, PD,F = Detection index of influence factor D on of regionalsurroundingsurroundingM = units Number regions. withregions. of similar Thesamples The larger observations, larger in local the local secondaryMoran Moranwhile’s I’ ssmallerstatisticregion; I statistic represents represents Wthehere, thespatial PspatialD,F a =gglomeration Detection agglomeration index of regional of influenceregional factor D on the spatial differentiation law of comprehensive units with similar observations, while smaller values represent spatialhealth agglomerationthe level spatial of thedifferentiation of land regional ecosystem units law of within comprehensive Yibin City. health level valuesunits represent with similarspatial agglomerationobservations, while of regional smaller units values with represent health spatial level agglomeration of the land ecosystem of regional in unitsYibin with City . different values (Liuunits et with al.2 2018similar). observations, while smaller values representof spatialthe land agglomeration ecosystem in of Yibin regional City. units with different valuesF (Liu et al. 2018). nD,i = Number of samples in the secondary area differentdifferentD values,i = values Variance (Liu (Liu et in al.et the al.2018 2018secondary). ). region; n = NumbernD,i = Number of samples of samples in the secondary in the secondary area area The calculationThe formula calculation of global formula Moran´s of global I indexMoran´s is as I index follows: is as n D,i= Number samples in the whole region; TheThe calculation calculation formula formula of globalof global Moran´s Moran´s I index I index is as is follows:n as = follows:Numbern = Number samples samples in the whole in the region; whole region; PD,F is between 0 and 1, the larger the value is, the greater the influence of the index on the spatial follows: 22 2 n n n n n s F =Variance=Variance F =Variance of of spatialspatial of spatialdifferentiationdifferentiation differentiation rule rule of of rulecomprehen comprehensive of comprehensive- evaluation evaluation index index of land of land ecosystem ecosystem health; health; n n 2 n n n F n n n n n 2 I n wij xi differentiationxx j x ofw ijland-xusei x ecological …(1) health2 level. sive evaluation index of land ecosystem health; I In n w wxij xxi xxxxj x w wij x xxi x M = NumberM = Number of samples of samples in the insecondary the secondary region; region; i1 j1 iij1 ji1 jj1 ij i …(…1) (…(1)1) RESULTi1 ij11 jS1 AND ANALYSISi1 ij1 j1 j 1 j1 M = Number of samples in the secondary region; 2 2 = VarianceFD,i in the secondary region; The calculation formula of local Moran’s I index is as FD,i = Variance in the secondary region; The calculationAnalysis formulaThe o calculation fof H localealth Moran formulaLevel’s I ofof index localLand is Moran as-use follows:’ sE Icosystem index is as follows:= Variance in the secondary region; follows:The calculation formula of local Moran’s I index is as follows:P is between 0 and 1, the larger the value is, the greater To reflect the health level of the land-use ecosystem ofD,F each PdistrictD,F is between and county 0 and in Yibin1, the City,larger the the health value is, the greater the influence of the index on the spatial n n n PD,F is between 0 and 1, the larger the value is, the greater the influence of the index on the spatial indexes of1 the land-use2 ecosystem within2 2 the grid were putthe oninfluence the ArcGIS of the platform, index on and the the spatial average differentiation index of 1 1 differentiation of land-use ecological health level. Ii xi x wij Ixi jIi xxixix x wijxwixijjx jx x… (2) xi xxi x …(2) …(2) land-use ecological health level. of the healthn level of each district and county…(2) was obtaineddifferentiation by using of theland summary-use ecological statistical health data level. tool (Fig. j1 j1i j1 n ni i RESULTS AND ANALYSIS 2). Fig. 2 shows the comprehensive health status, asR wellESULT as pressure,S AND state ANALYSIS and response subsystems of the landWhere, ecosystem wij is the of weight each matrixdistrict based and countyon the spatial in Yibin adja City.- RESULTS Analysis AND of H ealthANALYSIS Level of Land-use Ecosystem Where, wij is the Wweighthere,Where, wmatrixij wisij theis basedthe weight weight on matrix the matrix spatial based based adjacencyon theon thespatialth spatial principle; adjacency adjacency xi principle;and principle; xi are x evaluationi and xi andxi are xi evaluationarevalues evaluation values values cency principle; xi and xi are evaluation values of the i andAnalysis oTf oH reflectealth the L evelhealth of level Land of the-use land E-cosystemuse ecosystem of each district and county in Yibin City, the health th In th2018,th th theth average thindex of comprehensiveth th health level of the land-use ecosystem in Yibin City of the ith and jthj unit; unit;of ofthe thex i isis i and thethe and jmeanmean unit;j unit; of x thethe isx the nisnth the meanevaluationevaluation mean of theof value. thenvalue. evaluationn evaluation value. value.TAnalysiso indexes reflect of theof Health the health land Level level-use ecosystemofof Land-usethe land within-use Ecosystem ecosystem the grid were of each put districton the ArcGIS and county platform, in Yibin and City, the average the health index fluctuatedGeographicalGeographical around D 0.60. etectorDetector Junlian Analysis A nalysisCounty, Pingshan Countyindexes andof of Xingwenthe the health land- Countyuselevel ecosystem of exceededeach district within the and city's the county grid average, were was obtainedput on the by ArcGIS using the platform, summary and statistical the average data toolindex (Fig. GeographicalGeographical Detector ADetectornalysis Analysis To2). reflect Fig. 2the shows health the level comprehensive of the land-use health ecosystem status, ofas eachwell as pressure, state and response subsystems of the with the highest value of 0.65 (Pingshan County andof Xingwenthe health County);level of eachother district districts and and cou countiesnty was wereobtained by using the summary statistical data tool (Fig. The geographical ThedetectorThe geographical geographical is a kind detector of detector econometric is a is kind a kind of statistical econometric of econometric analysis statistical statistical method analysisdistrict land analysisproposed ecosystemmethodand method county by proposed Wang of proposedin each Yibin et by al. district Wang (2002)City,by Wang ettheand al. et health county(2002) al. (2002) indexes in Yibin of City. the 2). Fig. 2 shows the comprehensive health status, as well as pressure, state and response subsystems of the Theslightlyof geographical ofChinese Chinese lower Academy detectorAcademythan the of is average ofSciencesa kind Sciences of level, econometricto checkto with check thethe statisticalthe spatiallowest spatial differentiation of differentiation0.59land-use (NanxiIn ecosystem of2018, District). singleof singlethe-variable within average -variable the and index grid andto detectwere ofto comprehensivedetect put the on the the ArcGIS health level of the land-use ecosystem in Yibin City of Chinese Academy of Sciences to check the spatial differentiationland of ecosystemsingle-variable of each and district to detect and countythe in Yibin City. causal relationship between two variables by checking the consistencyfluctuated of spatial around distribution 0.60. Junlian of two County, variables, Pingshan County and Xingwen County exceeded the city's average, causal relationship betweencausal relationship two variables between by checking two variables the consistency by checking of the spatial Inconsistency 2018, distribution the of average spatial of twodistribution index variables, of comprehensiveof two variables, health level of the land-use ecosystem in Yibin City with the highest value of 0.65 (Pingshan County and Xingwen County); other districts and counties were fluctuated around 0.60. Junlian County, Pingshan County and Xingwen County exceeded the city's average, slightly lower than the average level, with the lowest of 0.59 (Nanxi District). with the highest value of 0.65 (Pingshan County and Xingwen County); other districts and counties were slightly lower than the average level, with the lowest of 0.59 (Nanxi District).
Fig. 2: HealthFig. 2:level Health index level ofindex land of -land-useuse ecosystem ecosystem inin each district district and and county county of Yibin. of Yibin.
It is necessary to deal with the differences in ecological health degreeFig. of2: Healthland-use level in indexthe study of land area-use at ecosystem in each district and county of Yibin. Vol.different 19, No. 4, levels 2020 • to Nature clarify Environment them. Natural and Pollution breaks (Jenks)Technology method inIt isArcGIS necessary was to used deal to with divide the differencesthe values ofin ecological health degree of land-use in the study area at ecosystem health level into different discrete classes by comparingdifferent and levels analysing to clarify the them. GVF N(theatural Goodness breaks (Jenks)of method in ArcGIS was used to divide the values of
Variance Fit) value. The comprehensive index of land-use ecosystemecosystem healthhealth level intoin Yibin different City discrete was divided classes by comparing and analysing the GVF (the Goodness of Fig. 2: Health level index of land-use ecosystem in each district and county of Yibin. into three levels, which respectively represent different healthVariance conditions, Fit) value. in order The comprehensive of healthy, sub index-healthy of land -use ecosystem health level in Yibin City was divided It isinto necessary three levels, to deal which with respectively the differences represent in ecological different healthhealth conditions,degree of landin order-use ofin healthy,the study sub area-healthy at and unhealthy. The regional characteristics of each level refer to the definitions of areas with different health differentand levels unhealthy. to clarify The them.regional N aturalcharacteristics breaks (Jenks) of each method level refer in toArcGIS the definitions was used of to areas divide with the different values health of level of the land ecosystem (Xu et al. 2010) (Table 3). ecosystemlevel healt of theh level land intoecosystem different (Xu discrete et al. 201 classes0) (Table by comparing 3). and analysing the GVF (the Goodness of Variance Fit) value. The comprehensive index of land-use ecosystem health level in Yibin City was divided into three levels, which respectively represent different health conditions, in order of healthy, sub-healthy and unhealthy. The regional characteristics of each level refer to the definitions of areas with different health level of the land ecosystem (Xu et al. 2010) (Table 3). HEALTH LEVEL OF LAND-USE ECOSYSTEM BASED ON GIS GRID MODEL 1479
Table 3: Classification of the health status of land-use ecosystem. Health Classification Area km2 Area Regional characteristics status standard percentage (%) Healthy [0.63，0.73] 6131 46.07 Reasonable ecological structure, strong system vitality, small external pres- sure, no ecological abnormality, perfect ecosystem function, stable system and sustainable state Sub- [0.59，0.63) 3962 29.78 Relatively reasonable ecological structure, stable system, large external healthy pressure, more ecologically sensitive areas, a small number of ecological abnormalities, basic ecological functions, and sustainable state Unhealthy [0.38，0.59) 3214 24.15 Defects in the ecological structure, low system vitality, high external pressure, many ecological abnormalities, weak ecological function to meet the needs of maintaining the ecosystem, and degradation of the ecosystem platform, and the average index of the health level of each there were many sensitive areas in the land ecosystem of the district and county was obtained by using the summary statis- city, they had normal ecological functions and sustainable tical data tool (Fig. 2). Fig. 2 shows the comprehensive health ecological economy. status, as well as pressure, state and response subsystems of The area of regions in healthy level was 6,131 km2, the land ecosystem of each district and county in Yibin City. accounting for 46.07% of the total area, which was mainly In 2018, the average index of comprehensive health level distributed in the middle and west of Pingshan County, the of the land-use ecosystem in Yibin City fluctuated around middle and south of Junlian county and Xingwen County. 0.60. Junlian County, Pingshan County and Xingwen County The main work in these places is to maintain and prevent. exceeded the city’s average, with the highest value of 0.65 (Pingshan County and Xingwen County); other districts and Spatial Exploratory Analysis on Health Level of Land- counties were slightly lower than the average level, with the use Ecosystem lowest of 0.59 (Nanxi District). It is necessary to deal with the differences in ecological Moran’s I was selected as the evaluation standard of the health degree of land-use in the study area at different levels spatial difference law for the health level of the land-use to clarify them. Natural breaks (Jenks) method in ArcGIS ecosystem by using the spatial exploratory analysis method, was used to divide the values of ecosystem health level into revealing the characteristics of the spatial agglomeration different discrete classes by comparing and analysing the pattern. The LISA cluster map was drawn at the signifi- GVF (the Goodness of Variance Fit) value. The comprehen- cance level of 5% (Fig. 4B) according to Moran’s I index sive index of land-use ecosystem health level in Yibin City of health level of the land-use ecosystem. The ecosystem was divided into three levels, which respectively represent by using the spatial exploratory analysis method revealed different health conditions, in order of healthy, sub-healthy the characteristics of the spatial agglomeration pattern. The and unhealthy. The regional characteristics of each level refer LISA cluster map was drawn at the significance level of 5% to the definitions of areas with different health level of the (Fig. 4B) according to Moran’s I index of health level of the land ecosystem (Xu et al. 2010) (Table 3). land-use ecosystem. According to the classification standard, the health level Fig. 4 A shows that Moran’s I value of health level of of the land-use ecosystem of Yibin City was classified and the land-use ecosystem in Yibin City was 0.447179 in 2015. evaluated. According to the distribution of Moran scattered points, the relationship between each region and a geographical phe- It is observed from Fig. 4 that the areas with unhealthy nomenon or attribute value in the surrounding region can be land ecosystem were 6,131 km2, accounting for 24.15% of qualitatively determined. Points with strong spatial positive the total. These are mainly concentrated in the transition area correlation will fall into the HH (“high-high”) and LL (“low- between the plain and hilly area in the northeast of Yibin City low”) quadrants, i.e., the health level of the areas around and the plateau and basin in the south. Where there were those with high health level will be high, the same to areas unreasonable ecological structure and low vitality, large with low level, which are homogeneous; points with strong areas of abnormal ecological environment, and damaged spatial negative correlation will fall into the LH (“low- high”) ecosystem to some extent. and HL (“high-low”) quadrants, that is, the health level of The area of regions in sub-healthy level was larger than the region is opposite to that of the surrounding areas, which that in unhealthy level, accounting for 29.78% of the total are heterogeneous. According to the distribution of attribute area, which was mainly distributed in Cuiping District, values, the health level of the land-use ecosystem in Yibin Nanxi district and Jiang’an County, indicating that although City has a strong spatial correlation.
Nature Environment and Pollution Technology • Vol. 19, No. 4, 2020
Table 3: Classification of the health status of land-use ecosystem. Health Classification Area Area km2 Regional characteristics status standard percentage (%) Reasonable ecological structure, strong system vitality, small external Healthy [0.63，0.73] 6131 46.07 pressure, no ecological abnormality, perfect ecosystem function, stable system and sustainable state Relatively reasonable ecological structure, stable system, large external pressure, more ecologically sensitive areas, a small number of Sub-healthy [0.59，0.63) 3962 29.78 ecological abnormalities, basic ecological functions, and sustainable state Defects in the ecological structure, low system vitality, high external pressure, many ecological abnormalities, weak ecological function to Unhealthy [0.38，0.59) 3214 24.15 meet the needs of maintaining the ecosystem, and degradation of the ecosystem
According to the classification standard, the health level of the land-use ecosystem of Yibin City was classified and evaluated. It is observed from Fig. 4 that the areas with unhealthy land ecosystem were 6,131 km2, accounting for 24.15% of the total. These are mainly concentrated in the transition area between the plain and hilly area in the northeast of Yibin City and the plateau and basin in the south. Where there were unreasonable ecological structure and low vitality, large areas of abnormal ecological environment, and damaged ecosystem to some extent. The area of regions in sub-healthy level was larger than that in unhealthy level, accounting for 29.78% of the total area, which was mainly distributed in Cuiping District, Nanxi district and Jiang'an County, indicating that although there were many sensitive areas in the land ecosystem of the city, they had normal ecological functions and sustainable ecological economy. The area of regions in healthy level was 6,131 km2, accounting for 46.07% of the total area, which was mainly distributed in the middle and west of Pingshan County, the middle and south of Junlian county and 1480 Wei He et al. Xingwen County. The main work in these places is to maintain and prevent.
Fig. 4 A shows that Moran’s I value of health level of the land-use ecosystem in Yibin City was 0.447179 in 2015. According to the distribution of Moran scattered points, the relationship between each region and a geographical phenomenon or attribute value in the surrounding region can be qualitatively determined. Points with strong spatial positive correlation will fall into the HH ("high-high") and LL ("low-low") quadrants, i.e., the health level of the areas around those with high health level will be high, the same to areas with low level, which are homogeneous; points with strong spatial negative correlation will fall into the LH ("low- high") and HL ("high-low") quadrants, that is, the health level of the region is opposite to that of the surrounding areas, which are heterogeneous. According to the distribution of attribute values, the health level of the land-use ecosystem in Yibin City has a strong spatial correlation.
Fig. 3Fig.: Ecological 3: Ecological health health of of the land-useland-use in Yibinin Yibin City. City.
Spatial Exploratory Analysis on Health Level of Land-use Ecosystem Moran’s I was selected as the evaluation standard of the spatial difference law for the health level of the land-use ecosystem by using the spatial exploratory analysis method, revealing the characteristics of the spatial agglomeration pattern. The LISA cluster map was drawn at the significance level of 5% (Fig. 4B) according to Moran’s I index of health level of the land-use ecosystem. The ecosystem by using the spatial exploratory analysis method revealed the characteristics of the spatial agglomeration pattern. The LISA cluster map was drawn at the significance level of 5% (Fig. 4B) according to Moran’s I index of health level of the land-use ecosystem.
Fig. 4: Moran scatter diagram A and LISA cluster Diagram B of ecological health level of land-use in Yibin City.
It is observed from Fig. 4B that the health level of the Analysis of the Influencing Factors of Spatial Fig. 4: Moran scatter diagram A and LISA cluster Diagram B of ecological health level of land-use in Yibin land-use ecosystem in Yibin City was mostly non-significant Differentiation of Health Level of Land-use Ecosystem in space, and those with spatial correlation were mainly posi- City. The ecological health level of land-use is the result of the tive, mainly high-value or low-value clusters. HH (high-high cluster) is distributed in the east and west of Pingshan County, combined action of regional natural factors and human fac- It is observed from Fig. 4B that the health level of the land-use ecosystem in Yibin City was mostly the middle and south of Xingwen County, and the middle tors. Resilience and function” index system was selected as and west ofnon Junlian-significant County. in In space, the future and development,those with spatial the correlationthe geographic were exploration mainly positive, variables mainly of the high spatial-value differen or low- - ecological protectionvalue clusters. project HH should (high be-high put incluster) the first is distrplace,ibuted tiation in the of eastthe ecological and west ofhealth Pingshan level of County, land-use, the calculated middle and to ensure the ecological non-degradation of the whole county. the decisive power values of each influencing factor on the south of Xingwen County, and the middle and west of Junlian County. In the future development, the LL (low-low cluster) is scattered in the northeast and spatial differentiation of health level of land-use ecosystem south of Yibinecological City, mainly protection including project Cuiping should District, be putNanxi in the respectively.first place, to ensure the ecological non-degradation of the District, Jianganwhole County, county. Yibin County and Gongxian Coun- It is observed from Table 4 that value of the geographic ty. The LL type areaLL (lowshould-low develop cluster) new is leading scattered industries in the northeastdetector and of stresssouth fromof Yibin human City, activities mainly is including 0.350, which Cuiping is according to local conditions based on ecological economy far greater than other factors, indicating that the difference and optimizationDistrict, of theNanxi land-use District, structure. Jiangan County, Yibin ofCounty human and activities Gongxian is the County. main reason The LLfor typethe spatial area should dif- develop new leading industries according to local conditions based on ecological economy and optimization
Vol. 19, No. 4,of 2020 the land• Nature-use Environmentstructure. and Pollution Technology Analysis of the Influencing Factors of Spatial Differentiation of Health Level of Land-use Ecosystem The ecological health level of land-use is the result of the combined action of regional natural factors and human factors. Resilience and function" index system was selected as the geographic exploration variables of the spatial differentiation of the ecological health level of land-use, calculated the decisive power values of each influencing factor on the spatial differentiation of health level of land-use ecosystem respectively. It is observed from Table 4 that value of the geographic detector of stress from human activities is 0.350, which is far greater than other factors, indicating that the difference of human activities is the main reason for the spatial differentiation of ecosystem health level. By the end of 2018, the area of cultivated land and construction land in Yibin City had reached 36.69% and 7.02% respectively, but there were significant HEALTH LEVEL OF LAND-USE ECOSYSTEM BASED ON GIS GRID MODEL 1481
Table 4: Geographical exploration results of spatial differentiation determination of health level of land-use ecosystem by various influencing factors. Index level Threshold values P value Level I areas Level II areas Level III areas P1 Soil erosion [0,0.330) [0.330,0.667) [0.667,1] 0.208 P2 Terrain fragmentation [0,0.842) [0.842,0.931) [0.931,1] 0.064 P3 Population density (person / km²) [0,0.561) [0.561,0.769) [0.769,1] 0.184 P4 Stress index of human activities [0,0.085) [0.085,0.500) [0.500,1] 0.350 S1 Normalized differential vegetation index [0,0.675) [0.675,0.916) [0.916,1] 0.032 S2 Landscape diversity index [0,0.210) [0.210,0.730) [0.730,1] 0.296 S3 Ecological resilience [0,0.0017) [0.0017,0.110) [0.110,1] 0.318 S4 Ecosystem service value [0,0.028) [0.028,0.237) [0.237,1] 0.315 R1 Investment in environmental protection (104 yuan) [0,0.225) [0.225,0.553) [0.553,1] 0.054 R2 Proportion of tertiary industry in GDP (%) [0,0.364) [0.364,0.633) [0.633,1] 0.005 ferentiation of ecosystem health level. By the end of 2018, vigorously develop science and technology, actively adjust the area of cultivated land and construction land in Yibin the industrial structure, increase the proportion of the tertiary City had reached 36.69% and 7.02% respectively, but there industry, reduce the pollution and damage of the primary were significant differences among districts and counties. and secondary industries to the soil, to build the ecological The different pressure of human activities on land leads to protection barrier of the upper reaches of the Yangtze River. the destruction of regional ecological environment and the imbalance of the ecological system. Therefore, it is urgent DISCUSSION AND CONCLUSIONS to control the population, reduce the direct destruction of Evaluating the health level of the land-use ecosystem is land by human activities. the premise and foundation of planning for land space The difference in resilience of the natural system is the renovation. For spatial exploratory analysis, geographical secondary reason that affects the spatial differentiation of detector model was used to explore the spatial distribution ecosystem health level. The values of ecological resilience, characteristics of the ecosystem health status of land-use in ecosystem service value and landscape diversity index were Yibin City, and its spatial differentiation law was explained 0.318, 0.315 and 0.296, respectively, indicating that the by combining quantitative and qualitative methods. Based on ecological health status was affected by the resilience of the the existing research results, firstly, the PSR model and the system itself. Although Yibin has a large proportion of arable theory of vitality, organization, resilience and function were land and construction land, there are various types of land. In organically combined and improved to establish a compre- particular, the area of grassland and forest land accounts for hensive health assessment index system of land ecological 41.74% of the total area of the region, which strong self-reg- security in line with the actual situation of Yibin. Then, the ulation and self-restoration capacity of the ecosystem. In the health indexes were calculated by 1km × 1km grid, and the process of future development, should adjust the structure of spatial differentiation law was revealed by ESDA method. land-use, optimize the spatial layout of “production, life and Finally, the decisive power of each factor was displayed by ecology”, activate the stock, strictly observe the “red line” factor detector. All the above is the inheritance and develop- of cultivated land, increase the ecological land, adhere to the ment of previous research results. From the comparison of the road of sustainable development, and promote the internal results of this study, the temporal and spatial evolution law resilience of the ecosystem. of health level of the land-use ecosystem is more in line with Soil properties, terrain factors and policy regulation are the regional reality (Huang et al. 2019), which also confirms the other reasons for the spatial differentiation of ecosystem that human activities are the important factors affecting land health level. The hilly landform brings about different region- ecological security (Wei et al. 2020). al natural geographical characteristics, and different types of However, there are still many shortcomings: First, the soil erosion and terrain fragmentation, as a consequence, the evaluation index system is not perfect. The ecosystem is above factors lead to obvious differences in the feedback of complex but was measured only by 10 indicators, neglecting land ecosystem. Therefore, the city must implement a strict NPP, soil pollution and other factors reflecting its attributes. ecological environment protection system, severely crack At the same time, due to the availability of data, the response down on the destruction of the ecological environment, indexes were less, which needs to be further improved.
Nature Environment and Pollution Technology • Vol. 19, No. 4, 2020 1482 Wei He et al.
Second, the stress intensity of human activities, ecological and build a famous ecological city of landscape culture. resilience and ecosystem service value were the factors de- termined by reference to the existing research results. If the REFERENCES reference standards are different, the calculated values will be Chen, P. 2007. Assessment of regional ecological health based on remote different. Therefore, it needs to be corrected according to the sensing and GIS landscape scale: a case study of the new area of bay actual situation of Yibin City. Third, to facilitate the transfor- cities. Acta Scientiae Circumstantiae, 27(10): 1744-1752. mation and compatibility of multiple data, enrich the attribute Gao, J.Z., Xia, M.L., Meng, Z. and Liu, Y.Z. 2017. Diagnosis of land-use dimensions of evaluation units, and refine the spatial accuracy and ecological health under PSR framework. 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Vol. 19, No. 4, 2020 • Nature Environment and Pollution Technology