Ecological Indicators 72 (2017) 399–410

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Ecological Indicators

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Original Articles

Regional ecosystem health response to rural land use change: A case

study in City,

a,∗ a a a,b

Peng Jian , Liu Yanxu , Li Tianyi , Wu Jiansheng

a

Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China

b

Key Laboratory for Environmental and Urban Sciences, School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen

518055, China

a r t i c l e i n f o a b s t r a c t

Article history: Quantitative analysis of the response of ecosystem health to rural land use change is required to compre-

Received 22 December 2015

hend the human-nature coupling mechanism and to explore the process of global environmental change,

Received in revised form 13 August 2016

which can interpret the ecological effects of regional land use and land cover change comprehensively.

Accepted 16 August 2016

However, the existing regional ecosystem health assessment largely ignored either the internal connec-

Available online 3 September 2016

tion of ecosystem health to land use patterns or the internal representation of ecosystem services to

ecosystem health. Using Lijiang City of China as a study area, the average normalized difference vege-

Keywords:

tation index (NDVI), landscape metrics, and ecosystem elasticity coefficient based on different land use

Regional ecosystem health assessment

types were used as quantitative indicators. Then the coefficient of spatial neighboring effect was intro-

Ecosystem services

duced to characterize the adjacency effect on ecosystem services, and to generate the index of integrated

Land use change

Lijiang City, China ecosystem health. The results showed the change of land use was close to 30% at county level from 1986

to 2006, and forest land was the primary land use type. With respect to the declining physical health of

ecosystems in all the four counties, the integrated health experienced a slight increase in Lijiang County.

The vast majority of towns’ ecosystem physical health and integrated health declined, while more than

70% of towns did not change distinctly. Ecosystem physical health had distinct influence on the inte-

grated ecosystem health, and ecosystem vitality was the main factor affecting the condition of physical

health. Emphasized in the interconnection of pattern and process, this study provided an ecosystem

health approach to assessing the integrated ecological effects of regional land use change.

© 2016 Elsevier Ltd. All rights reserved.

1. Introduction Rapport, 1998). Maintaining healthy ecosystems is fundamental to

guarantee the achievement of sustainable socio-economic develop-

The upsurge of industrialization and urbanization with ment, as natural ecosystems provide material basis and ecological

expanded breadth and intensity of human activity has changed services for human survival (Rapport and Maffi, 2011). As the con-

the global ecosystem at unprecedented speed and scale, leading to cept of health portrays essential features of a sustainable future

ecosystem degradation and significant threats to the survival and (Patten and Costanza, 1997), when the health of natural ecosys-

development of human society. Facing the impacts of ecosystem tem deteriorates severely from rapid socio-economic development,

degradation on human sustainable development, increasing atten- ecosystem health is considered as the purpose and basis of environ-

tions have been paid to how to monitor, evaluate, and regulate the mental management (Costanza, 1992, 2012). At the core position

state and sources of risk, and the safety or sustainability degree of integrated ecosystem assessment, ecosystem health is regarded

of ecosystem health (Calow, 1992; Cairns et al., 1993; Dernbach as one of the most important issues for ecosystem management in

and Mintz, 2011; Peng et al., 2007; Rapport, 1989; Suter, 1993; macro ecological studies (Belaoussoff and Kevan, 2003; Burger and

Wicklum and Davies, 1995). Ecosystem health refers to the abil- Gochfeld, 2001; Pinto et al., 2013; Tiwari et al., 1998; Watson et al.,

ity to meet the reasonable requirements of human society and the 2005). Therefore, because ecosystem health represents integrated

ability to self-maintain and update the ecosystem (Lackey, 2001; regional ecological quality from the two aspects of ecosystem struc-

ture and function, this concept is an important foundation for

integrated ecosystem assessment and management at macro scale.

Land use change has not only brought tremendous changes for ∗

Corresponding author.

the structure of surface landscapes, but also affected materials

E-mail address: [email protected] (J. Peng).

http://dx.doi.org/10.1016/j.ecolind.2016.08.024

1470-160X/© 2016 Elsevier Ltd. All rights reserved.

400 J. Peng et al. / Ecological Indicators 72 (2017) 399–410

circulation and energy flow of landscapes, resulting in signifi- is insufficient, tourism resources, biodiversity and water resources

cant change in regional climate, soil, biodiversity, hydrology and highlight the huge potential for regional development. In the view

water resources, and profoundly impacting on regional ecologi- of land use structure, forest land, grassland, and dryland account

cal processes (Lavigne and Gunnell, 2006; Nainggolan et al., 2013; for a considerable proportion of the land use, while the lowest

Niedertscheider et al., 2012; Torres et al., 2014). Land use and land proportion is construction land (Peng et al., 2008).

cover change (LUCC) affects the ability of ecosystems to provide There are four counties and one district in Lijiang City now,

the services and biodiversity on which humans ultimately depend i.e. Gucheng District, Yulong County, , Huap-

(Wan et al., 2015). A comparative LUCC study that compares differ- ing County, and Ninglang County. However, before 2002, Gucheng

ent change processes could help to highlight the essential features District and Yulong County were combined as one Lijiang County.

of regional land use change and to provide an effective assess- As a result, the four counties of Lijiang, Yongsheng, Huaping, and

ment of the ecological effects of regional land use change (Napton Ninglang were selected for comparative study. Generally speaking,

et al., 2010; Turner, 2010). Currently, quantitative assessment has the socioeconomic development was obviously not in equilibrium

become mainstream in single factor analysis of LUCC’s ecologi- in the four counties of Lijiang City. The most densely populated

cal effects (Imhoff et al., 1997; Weng, 2001; Weng and Lo, 2001; was Yongsheng County, with the least for Ninglang County. The

Yokohari et al., 2001). However, to reveal the integrated ecological gross domestic product of Lijiang County accounted for the highest

effects of LUCC besides the single ecological processes, compre- proportion of the whole city (35–50%), while the economic aggre-

hensive indicators are still in need (Shi et al., 2009), in spite of the gate of Ninglang County was minimal (10–16%). Lijiang County was

difficulty to accurately measure the contribution of land use pat- also the heart of the city’s tourism development, while tourism

terns to the variety of regional eco-environmental quality (Zhang accounts for a very small proportion of the city’s total tourism rev-

et al., 2015). enue in Yongsheng County and . Overall, the levels

Ecosystem services can directly link regional land use with eco- of socioeconomic development of the four counties were, Lijiang

logical quality; and the proportional relationship between different County, Huaping County, Yongsheng County, and Ninglang County

land use types and economic value of ecosystem services calcu- in descending order. The similar natural environmental basis and

lated by Costanza et al. (1997), has been widely applied to quantify different stages of socioeconomic development which triggers land

global, national, or regional ecological quality corresponding to a use change had make the study area one of the ideal targets for a

certain land use structure. Thus, the quantification of ecosystem comparative LUCC study.

services can help to make an integrated assessment of ecological

effects with the change of quantitative structure of regional land 2.2. Data source and processing

use types (Li et al., 2007). The introduction of regional ecosystem

health indicators based on ecosystem services and land use pat- The study used the Landsat TM remote sensing image data.

terns to characterize eco-environmental quality will establish a The detailed stripe number/date were as follows: 131041/1986-11-

direct bridge between LUCC and regional ecological state. However, 02, 131042/1986-11-02, 132041/1986-11-25, 131041/2006-01-25,

although applying ecosystem health indicators to comprehensively 131042/2006-01-25, and 132041/2006-02-01. Related fieldwork

measure the influence of LUCC on regional ecological quality is a data in 2000, 2001, 2002, and 2005 were adopted as auxiliary for

meaningful exploration, the detailed process is still obscure. interpretation. After image geometry correction, this study used

Unlike the rapid urbanization with substantial transformation supervised classification and visual correction methods to obtain

to construction land in the east of China, the southwest of the land use maps in different periods with the support of the remote

nation is mountainous with abundant biological resources, expe- sensing image processing software ERDAS 8.4 and geographic infor-

riencing slow but noticeable land use change (Peng et al., 2008). mation system ARC/INFO 8.3. There were 8 kinds of land use

Lijiang City in the southwest of China has been identified as a classified, i.e. paddy field, dryland, forest land, grassland, bare land,

globally significant region for its rich biodiversity (Xu and Wilkes, water body, glacier and snow, and construction land. The over-

2004), which is an ideal study area to perform a regional ecosystem all classification accuracy in 1986 and 2006 was 83.2% and 85.9%,

health assessment. Thus, besides the theoretical object of complet- respectively; and the Kappa coefficient was 0.77 and 0.83, respec-

ing a quantitative assessment of the LUCC’s ecological effects in tively. Then FRAGSTATS 3.3 was used to calculate the landscape

the view of ecosystem health, two detailed research objects were: metrics and the transformation matrix of landscape components

firstly, to clarify the process of regional land use change in Lijiang during the study period. Thereafter, the study analyzed the char-

City; and secondly, to integrate landscape composition and config- acteristics of land use change. Meanwhile, according to the band

uration metrics, as well as the neighborhood effects of ecosystem ratio derived from band 3 and band 4, comparable normalized dif-

services in the paradigm of ecosystem health assessment. ference vegetation index (NDVI) values of each pixel during the

study period were also obtained.

2. Study area and data source 3. Methods

2.1. Study area 3.1. Land use change measurement

Lijiang City is located in the middle-upper stream of the Jin- In this study, the transformation matrix method was used to

sha River in the northwest of Province, covering an area quantify transition probabilities of different land use types, and

2 ◦ 

of 21,219 km . The longitude of Lijiang City ranges from 99 23 to then, a comparative analysis was undertaken. That is, for any two of

◦  ◦  ◦ 

101 31 E, with the latitude from 25 59 to 27 51 N. Located in the the land use maps Ai*j and Bi*j, using the formula (1) of map algebra

joint position of Qinghai–Tibet Plateau and the Yunnan–Guizhou method, we can obtain land use change map Ci*j from periods A to

Plateau, and crossing the two geomorphic units of Hengduan B, which reflects the change in the land use types and their spatial

Mountains Canyon and Western Yunnan Plateau, Lijiang City has distribution.

substantially more mountains than plains, with over 2000 m above

= × + ≤

Ci∗j Ai∗j 10 Bi∗j(applicable for land use types 10) (1)

sea level accounts for 92.3% of the total area (Fig. 1). Lijiang City

has the multi-year average precipitation of about 1000 mm, and Landscape ecology, which emphasizes on the interaction

the annual mean temperature of 12.6–19.9 C. Although the heat between spatial patterns and ecological process, provides effi-

J. Peng et al. / Ecological Indicators 72 (2017) 399–410 401

Fig. 1. The location of Lijiang City, China.

cient concepts and methods for ecosystem health assessment (Suo abilities, but an expression of carrying capacity (Rapport, 1992).

et al., 2008; Turner et al., 1989, 2001). Measures of landscape pat- Lack of ecosystem services and management would result in the

terns generally include three aspects: morphological features of decline of ecological carrying capacity, thereby reducing health

individual units of the landscape, spatial configuration features of level. Therefore, an integrated healthy ecosystem should high-

components of the landscape, and diversity features of the overall light the contribution of ecosystem services, and could benefit the

landscape. In this study, to measure and analyze land use change of ecosystem through homeostasis, the absence of disease, diversity

the study area, we selected landscape diversity index to measure or complexity, stability or resilience, vigor or scope, and a balance

overall diversity features; aggregation index and fragmentation between ecosystem components (Gao et al., 2013).

index to quantify spatial configuration features; and area-weighted

mean patch fractal dimension (AWMPFD) to indicate morphologi-

3.2.1. Ecosystem vigor

cal features (Peng et al., 2010).

Natural ecosystem vigor is generally characterized through

In detail, landscape diversity reflects differences of the num-

vegetation productivity indicators. As vegetation production is

ber and area percentage of each landscape component. A higher

significantly positive correlated to the normalized difference vege-

value means more kinds of landscape components or more bal-

tation index (NDVI) (Phillips et al., 2008), NDVI was usually chosen

anced area proportion of each component. Landscape aggregation

as an indicator to measure the vigor of natural ecosystems. Based

reflects the agglomeration degree of different landscape compo-

on the band ratio method, NDVI is defined as the ratio of the dif-

nents. A higher value means the landscape is constituted by a few

ference between the near-infrared waveband (NIR) and the visible

agglomerated large patches, with a smaller value for scattered small

red band (R), which can be expressed as NDVI = (NIR-R)/(NIR + R).

patches. Landscape fragmentation reflects the interference degree

For Landsat TM images, R and NIR are the third and fourth bands,

of anthropogenic activities on the landscape to a certain extent. The respectively.

landscape fractal dimension reflects the complexity of patches; the

lower the value is, the more regular shaped patches there are, and

3.2.2. Ecosystem organization

the greater are the patches affected by natural or human distur-

Regional ecosystem organization can be quantitatively eval-

bances. When the AWMPFD takes the average fractal dimension of

uated from three aspects: landscape heterogeneity, landscape

each patch, it is based on weighted area, and mainly reflects the

connectivity, and the connectivity of patches with important

changing shape trends of large patches; thus, fractal dimension is

ecological functions, which includes such landscape metrics as

more capable of reflecting change in landscape patterns. The val-

landscape diversity, landscape fractal dimension, landscape frag-

ues of landscape diversity index and fragmentation index are both

mentation, landscape aggregation, forest land fragmentation, and

greater than 0; the value of aggregation index is in the range of

patch cohesion of forest land. The diversity of the landscape,

[0,1], with [1,2] for AWMPFD.

which expresses the richness and area evenness of landscape patch

types, can be used as a core index to determine landscape hetero-

3.2. Indicators for ecosystem health assessment

geneity. Because of the dominant area proportion of forest land,

the fragmentation of forest land is the core index to determine

Assessing integrated ecosystem health that couples ecological,

patch connectivity of important ecological functions. Thus this two

economic, and human health together, needs to integrate human

indexes were set as the highest weight. Because fragmentation is

values and ecological processes. Promoting health to the regional

regarded as a core index to determine connectivity, the weight of

level means the integration of natural, social, and health sciences.

landscape fragmentation should be higher. Therefore, the index

Ecosystem health was usually measured through three aspects

weights in the formula were set as follows:

(Costanza, 1992; Rapport, 1989; Rapport et al., 1998): vigor, which

meant activity, metabolism, or primary productivity; organization, O

= 0.35 × LH + 0.3 × LC + 0.35 × CC

representing diversity and connectivity between ecosystem com-

= 0.25 × DI + 0.1 × FD + 0.2 × FN1 + 0.1 × RC + 0.25 (2)

ponents; and resilience, which referred to the capability to rebound

×FN2 + 0.1 × COHESION

from perturbations. Additionally, health is not the antonym of dis-

402 J. Peng et al. / Ecological Indicators 72 (2017) 399–410

where O is the index of regional ecosystem organization, LH is land- lower than the paddy field (Peng et al., 2015). As a result, the coef-

scape heterogeneity index, LC is landscape connectivity index, CC ficients of ecosystem services were set as follows: forest land 1.0,

is patch connectivity index of important ecological functions, DI is paddy field 0.325, dryland 0.295, grassland 0.426, bare land 0.035,

landscape diversity, FD is landscape fractal dimension, FN1 is land- water body 0.932, glacier and snow 0.015, and construction land

scape fragmentation, RC is landscape aggregation, FN2 is forest land 0.015.

fragmentation, and COHESION is patch cohesion of forest land. However, the ecosystem services coefficients only considered

functional differences and the area ratio of different ecosystems,

and did not involve the spatial neighboring interaction effect of

3.2.3. Ecosystem elasticity

various kinds of ecosystems. In a view of ecosystem services flow,

A healthy ecosystem that is resistant and resilient to exter-

regional ecosystem services can be delivered to the spatial neigh-

nal disturbances can keep species composition and productivity

boring area (Bagstad et al., 2013). For example, the forest ecosystem

relatively stable throughout a long time (López et al., 2013). Elas-

is beneficial for the services provision of adjacent ecosystems, while

ticity, one of the important standards to assess ecosystem health,

the construction land has an adverse effect. To interpret the eco-

refers to the ecosystem’s ability of structure and behavioral pat-

logical connectivity between different land use types, an affinity

tern to rebound to the initial stage in the presence of human or

matrix has been proposed with assigned values in 10 divisions from

natural disturbances. That is to say, elasticity can be characterized

0 to 1 in the Barcelona Metropolitan Area (Marulli and Mallarach,

through the resistance and resilience to external disturbances. The

2005). However, because some ecological interactions among var-

weights of resistance and resilience were determined based on the

ious land use types would be negative, the 10 divisions from −5 to

probability of external disturbances exceeding or not exceeding

5 is regarded to be more reasonable. The human-dominated land

the self-adjustment ability of regional natural ecosystems. If the

use types usually deliver a negative effect on the spatial neighbor-

external disturbances have a high damage to natural ecosystems,

ing area, while the adjacency of natural ecosystems may benefit

the resilience should be emphasized; otherwise if the external

each other. In particular, the ecosystem in forest land and grass-

disturbances have not exceeded the self-adjustment ability of

land usually deliver more ecosystem services to the surroundings

natural ecosystems, the resistance is more important. Because

than paddy field and dryland because of their multifunctionality

regional development in Lijiang City is not fast with relatively low

(Palacios-Agundez et al., 2015; Santika et al., 2015). Therefore, the

human disturbances, the weight of resistance should be higher than

coefficients of spatial neighboring effect on ecosystem services of

resilience. Thus, the weight of resilience and resistance were set

different land use types were assigned with a range of [−5%, 5%]

as 0.3 and 0.7, respectively. Then the regional ecosystem elasticity

(Table 2). The ecosystem services of image pixels in the study area

could be calculated as follows:

were determined by their land use types and land use type of their

E = 0.3 × Resil + 0.7 × Resist (3) four adjoining points, with regional ecosystem services quantified

by the average value of all the pixels, which could be expressed as

where E is the index of regional ecosystem elasticity, Resil follows:

represents ecosystem resilience, and Resist refers to ecosystem

ESp = ESc × + SNc /

resistance. That is to say, for each land use type in Lijiang City, the (100 ) 100 (4)

elasticity coefficient is equal to 30% of resilience coefficient plus

where ESp is ecosystem services of the pixel, ESc is the coefficient

70% of resistance coefficient (Table 1).

of ecosystem services for specific land use type, and SNc is the

In the number determination of resilience and resistance coef-

coefficient of spatial neighboring effect.

ficients, a decimal score was used to assign the coefficients as [0.1,

1] with the interval of 0.1. The self-adjustment ability of the 8

3.3. Ecosystem health assessment

land use types had been ranked. The glacier and snow which is

sensitive to climate change should have the lowest ability. In con-

As mentioned above, the three aspects of vigor, organization,

sideration of ecosystem integrity, natural ecosystems may recover

and elasticity can provide comprehensive characterization of an

or resist more easily when experiencing external disturbances,

ecosystem’s ability to maintain its structure and function steadily,

while human-dominated ecosystems may have a lower ability and

which can be defined as ecosystem physical health. In accordance

will thus suffer greater destruction (Peng et al., 2015). Therefore,

with the definition of the ecosystem health index proposed by

construction land and dryland were determined with lower num-

Costanza (1992), and considering the comparability of the physical

bers, while higher numbers for grassland and water body. Besides,

health index and ecosystem services index, this study modified the

according to the different implication of resilience and resistance,

product of vigor, organization, and elasticity, to the cube root of the

the numbers were not in consistence. The ecosystem in bare land

product (Lu and Li, 2003; Suo et al., 2008):

is difficult to resist external disturbances, whereas the simple or



sometimes inferior ecosystem is easy to recover; and the ecosys-

PH = 3

V × O × E (5)

tem in forest land with high ecosystem integrity is robust to resist

external disturbances, whereas it needs more time to recover to the

where PH is the index of regional ecosystem physical health, V is

complicate ecosystem structure and function.

regional ecosystem vigor, O is regional ecosystem organization, and

E is the elasticity of regional ecosystems.

3.2.4. Ecosystem services The integrated health level of regional ecosystems depends on

Natural restriction and social preference are necessary condi- the ecosystem’s own health status. Comparing with the physical

tions to maintain ecosystem health (Rapport, 1995). Therefore, health focusing on the status of ecosystem itself, the judgement

ecosystem services determine the regional ecosystem health, and criteria of regional integrated ecosystem health are whether the

their quantity and sustainability are key standards to assess ecosys- supply of ecosystem services can meet human demands. Ecosys-

tem health. Ecosystem services coefficients for each land use type tems with sufficient biodiversity to maintain multifunctionality can

were determined referring to the ratio among the average ecosys- provide humans with a broad range of health through sustainable

tem services’ values of different ecosystem types calculated by provision of ecosystem services (Speldewinde et al., 2015). Even if

Costanza et al. (1997). According to the practical situation of ecosys- ecosystem’s own physical health is good enough, if it cannot pro-

tem services in the study area, the ecosystem services coefficients vide humans with sufficient ecosystem services, it is still unhealthy

were assigned as [0,1] taking forest land as a standard, with dryland for humans. Therefore, regional integrated ecosystem health equals

J. Peng et al. / Ecological Indicators 72 (2017) 399–410 403

Table 1

Ecological elasticity coefficient of land use types in Lijiang City.

Land use type Paddy field Dryland Forest land Grassland Bare land Water body Glacier and snow Construction land

Resilience coefficient 0.3 0.4 0.5 0.8 1.0 0.7 0.1 0.2

Resistance coefficient 0.6 0.5 1.0 0.7 0.2 0.8 0.1 0.3

Elasticity coefficient 0.51 0.47 0.85 0.73 0.44 0.77 0.10 0.27

Table 2

Spatial neighboring effect coefficient on ecosystem services of land use types in Lijiang City.

Adjacent pixel type Target pixel type

Paddy field Dryland Forest land Grassland Bare land Water body Glacier and snow Construction land

Paddy field +4 +2 −1 −1 −1 −5 −1 +1

Dryland +1 +2 −1 −1 −1 −4 −1 +1

Forest land +5 +4 +5 +5 +4 +5 +3 +4

Grassland +2 +2 +3 +2 +3 +3 +2 +2

Bare land −3 −3 −1 −3 +1 −4 −1 −1

Water body +4 +2 +5 +4 +4 +5 +1 +4

− − − − −

Glacier and snow 5 5 4 1 1 −2 +1 −1

Construction land +2 +2 −2 −3 −2 −5 −3 +1

*Numerical value shows the variation percentage of target pixel type resulted from adjacent pixel type.

to the square root of the product of physical health and ecosystem slightly higher proportion with Ninglang County for a little lower

services: proportion, and water body in Yongsheng County accounted for a



higher proportion than the other counties.

H =

PH × ESV (6)

There was no significant change in the land use structure in the

four counties during 1986–2006 (Fig. 2), but the area of each land

where H is the index of regional integrated ecosystem health, and

use type presented different changes in each county. In details,

ESV is regional ecosystem services.

the more prominent change was that glacier and snow had dis-

In the standardization of original indexes mentioned above,

appeared in Yongsheng County and Huaping County, and there

maximum difference normalization method with the range of [0,1]

had been a general increase of construction land in each county.

was used. For the temporal change assessment, a distinct judge-

In Lijiang County, forest land increased and grassland decreased

ment of data sequence was carried out using a standard score

significantly; Yongsheng’s forest land and grassland decreased sig-

method. It was assumed that data sequence of change value of

nificantly while dryland and bare land increased; and forest land

physical health index or integrated health index for all the assess-

decreased while dryland and grassland increased slightly in Huap-

ing towns was Xi (i = 1,2,3, . . . n) with n for the number of assessing

ing County and Ninglang County. In general, for the transformation

towns, the arithmetic mean of the data sequence was X¯ , and then,

of each county’s land use quantitative structure, the biggest change

the distinct change of a township can be judged as follows:

occurred in Lijiang County, followed by Yongsheng County, and

Xi − X¯

> the minimum change occurred in Huaping County and Ninglang

Distinctly positive change :   1 (7)

2 County, which were associated closely with total economy of each X − X¯

i ⁄n county.

There were large differences in the extent of landscape diver-

Xi − X¯

<

Distinctly negative change :   −1 (8) sity and its variation in the study area from 1986 to 2006 (Table 3).

2

Yongsheng’s landscape diversity index was significantly higher

Xi − X¯ ⁄n

than those of the other three counties, indicating it had a more

balanced area proportion of land use types. The landscape diversity

4. Results

index of Lijiang County had a larger decline in the study period, indi-

cating a significant increase in the differences of area proportion of

4.1. Land use change

land use types. This was mainly caused by the increase of forest

land, which accounted for the absolute area ratio of the county.

Affected by the similar natural environment, the land use char-

On the contrary, Yongsheng’s landscape diversity index increased

acteristics of the four counties were relatively similar. Forest land

significantly while the indexes for both Ninglang County and Huap-

accounted for the absolute proportion of more than 60%, with grass-

ing County increased slightly. In particular, as glacier and snow

land and dryland each for a considerable proportion of about 10%,

disappeared completely at the end of the study period, the num-

and paddy fields for about 4%. Both the proportions of bare land

ber of land use types in Yongsheng County and Huaping County

and water body were low of about 1%, with the lowest propor-

decreased, indicating the land use structure gradually balanced.

tion of less than 1% for construction land. The similarity of land use

The proportional decline of forest land was the direct reason for

structure indicated that the differences of socioeconomic develop-

the increase of landscape diversity index in the three counties.

ment in the four counties of Lijiang City were not large enough,

The indexes of landscape aggregation of the four counties were

which had not resulted in the discriminating land use transforma-

at medium levels (Table 3), showing the existence of a few large

tion. This might because each county’s economy was too low to

patches of forest land, paddy fields, and glacier and snow. The land-

induce significant changes of land use. With regard to the structural

scape aggregation index increased in Lijiang County and decreased

differences of land use for the four counties within the study period,

in the other three counties, showing that the influencing degree

forest land accounted for a little higher proportion in Lijiang County

of a few large patches increased over time in Liang County, but

and Huaping County, dryland in Yongsheng County and Ninglang

tended to weaken in the other three counties. The overall land-

County accounted for a slightly higher proportion than the other

scape fragmentation of the four counties was relatively low in the

two counties, paddy field in Yongsheng County accounted for a

404 J. Peng et al. / Ecological Indicators 72 (2017) 399–410

Fig. 2. Land use patterns in Lijiang City during 1986–2006.

Table 3

Landscape metrics change in each county of Lijiang City from 1986 to 2006.

Landscape diversity Landscape aggregation Landscape fragmentation

1986 2006 1986 2006 1986 2006

Lijiang County 0.9778 0.8720 0.6697 0.7100 0.0664 0.0566

Yongsheng County 1.0805 1.1706 0.6378 0.5712 0.0685 0.0904

Ninglang County 0.9391 0.9648 0.6643 0.6476 0.0858 0.0931

Huaping County 0.8574 0.9139 0.7011 0.6482 0.0703 0.0895

study period. However, the changing trend was dissimilar in the est in Dayan Town in Lijiang County (0.23). In 2006, the highest

four counties; the landscape fragmentation index of Lijiang County was in Shitou Town in Lijiang County (0.71), and the lowest was

declined slightly, reflecting that the orderliness of human activi- remained in Dayan Town in Lijiang County (0.00). The vigor of

ties in Lijiang County was enhanced. The landscape fragmentation Dayan Town remained at the minimum with substantial decline,

index tended to increase in the other three counties, among which which was mainly due to the town being the seat of local govern-

the maximal occurred in Yongsheng County, showing that the ment at both county and municipal levels, with the largest urban

impact of anthropogenic disturbance intensity to landscape evolu- built-up area among all the towns. Urban built-up areas in Dayan

tion increased significantly in the counties of Yongsheng, Ninglang, town had expanded quickly during the study period, leading to

and Huaping. a relatively low ratio of natural vegetation area which was occu-

As shown in Table 4, there was similar AWMPFD between each pied by construction land continually. In contrast, the high vigor

land use type in the four counties on the whole, with the AWMPFD towns were far from the economic centers of the four counties,

of forest land highest in each county, showing that its patch shape with traffic isolation and sparse population of townships. During

was relatively complex with low regularity, and forest land was 1986–2006, due to the enhancement of human activities, some

the least one of land use types affected by the anthropogenic dis- forest land and grassland were transformed to arable land and con-

turbances. The AWMPFD of dryland and grassland ranked second, struction land. Thus the vast majority of towns showed significantly

while that of bare land, paddy field, water body, and glacier and lower vigor, indicating decreased regional ecosystem vitality. The

snow were relatively low. Construction land was the greatest one largest decline occurred in Xinyingpan Town of Ninglang County,

affected by anthropogenic activity, which had the simplest and and the smallest decline occurred in Chenghai Town of Yongsheng

most regular geometrical patch shape and the minimum AWMPFD. County. During 1986–2006, the relative differences of vigor at town

It should also be noted that spatial distribution of glacier and scale showed a slight decreasing trend, with the difference between

snow was strictly restricted by climatic factors, being distributed the maximum and minimum vigor values decreased from 0.77 to

generally in high mountains and extending from the tops of hills 0.71.

down to nearby snow lines. Thus, glacier and snow patches had The ecosystem organization level changed mainly in the middle

relatively simple and single geometrical shape, being mostly arc- and south part of the study area (Fig. 3). In 1986, Judian Town of

shaped, and its landscape fractal dimension was correspondingly Lijiang County had the highest ecosystem organization (0.63), and

lower, although glacier and snow was influenced by the minimal Paomaping Town of Ninglang County had the lowest (0.19). In 2006,

anthropogenic disturbance in all land use types. Shitou Town of Lijiang County had the highest (1.00), while the low-

est was remained Paomaping Town (0.00). During 1986–2006, the

4.2. Change of ecosystem health indicators ecosystem organization showed an increase in Lijiang County, with

downward trends in the other counties. Among them, the largest

Classified with natural breaks method, the change of ecosys- increase occurred in Shitou Town of Lijiang County, reaching 0.64,

tem vigor level was most obvious (Fig. 3). In 1986, the highest with Songping Town of Yongsheng County for the largest decline

vigor was in Jin’an Town in Lijiang County (1.00), with the low- of −0.48. During 1986–2006, the relative differences of ecosystem

J. Peng et al. / Ecological Indicators 72 (2017) 399–410 405

Table 4

AWMPFD Change of land use types in Lijiang City from 1986 to 2006.

Paddy field Dryland Forest land Grassland Bare land Water body Glacier and snow Construction land

Lijiang County 1986 1.1438 1.2109 1.4224 1.2688 1.1786 1.2223 1.2841 1.0785

2006 1.1667 1.2442 1.3903 1.2391 1.1928 1.2013 1.1848 1.1380

Yongsheng County 1986 1.1452 1.2762 1.4151 1.2182 1.1542 1.1106 1.1494 1.0817

2006 1.1591 1.2689 1.3879 1.2236 1.1849 1.2456 / 1.1086

Ninglang County 1986 1.1522 1.2224 1.4346 1.2273 1.1835 1.1143 1.1500 1.0976

2006 1.1703 1.2635 1.4289 1.2595 1.1615 1.1329 1.1397 1.0587

Huaping County 1986 1.1867 1.2106 1.3938 1.2083 1.1779 1.1805 1.1396 1.0576

2006 1.1747 1.2303 1.4002 1.2173 1.1779 1.1664 / 1.0981

Fig. 3. Change of ecosystem health indicators for each town in Lijiang City from 1986 to 2006.

organization at town scale increased slightly in the four counties; highest ecosystem services was in Songping Town of Yongsheng

the difference between the maximum and minimum of the index County (0.96), and the lowest was in Dayan Town of Lijiang County

increased from 0.44 to 1.00. (0.24). In 2006, the highest was in Liming Town of Lijiang County

The ecosystem elasticity level changed inconsistent in the dif- (1.00), and the lowest was still in Dayan Town (0.00). The ecosystem

ferent parts of the study area (Fig. 3). In 1986, the highest ecosystem services of Dayan Town remained at the minimum with substan-

elasticity was in Songping Town of Yongsheng County (0.97) and tial decline, which was also mainly due to the area proportion of

the lowest was in Dayan Town of Lijiang County (0.43). In 2006, the construction land being the highest with continue increase, consid-

highest was in Liming Town of Lijiang County (1.00) with the lowest ering that ecosystem services coefficient of construction land was

for Dayan Town too (0.00). The ecosystem elasticity of Dayan Town the lowest in all land use types. In contrast, there was the highest

remained at the minimum with substantial decline, which was area ratio of forest land in the towns with the highest ecosystem

mainly because the town had the highest area proportion of con- services. During 1986–2006, the ecosystem services of the towns

struction land with sustained urban growth. As shown in Table 1, in Lijiang County showed a rising trend. On the contrary, there

the elasticity coefficient of construction land was just higher than were decline trends of ecosystem services for most of the towns

that of glacier and snow in all land use types, and was far lower of the other three counties. The largest increase occurred in Long-

than the others. Furthermore, in the towns with the higher ecosys- pan Town of Lijiang County, reaching 0.20, and the largest decline

tem elasticity, the intensity of human activities was low, together occurred in Dayan Town of Lijiang County, reaching −0.24. During

with the high area proportion of forest land. During 1986–2006, the 1986–2006, ecosystem services showed moderately relative differ-

ecosystem elasticity of the towns in Lijiang County showed a rising ences at town scale; and the difference between the maximum and

trend, in contrast with the downward trend of ecosystem elas- minimum of the index increased from 0.72 to 1.00. In contrast with

ticity for most towns in Ninglang County and Yongsheng County. the ecosystem services coefficients of all land use types, it could

The largest increase occurred in Longpan Town of Lijiang County, be inferred that the area proportion of forest land and construction

and the largest decline occurred in Dayan Town of Lijiang County. land was also an important factor affecting the ecosystem services

During 1986–2006, ecosystem elasticity showed not far relative dif- of towns.

ferences. The difference between the maximum and minimum of

the index increased from 0.54 to 1.00. In contrast with the elasticity 4.3. Ecosystem health change

coefficients of all land use types, it could be inferred that the area

proportion of forest land and construction land was an important The change of ecosystem physical health was more obvious in

factor affecting the ecosystem elasticity of towns. the west part than in the east part of the study area (Fig. 4). In

The ecosystem services level increased in the west part and 1986, the highest town of the ecosystem physical health index

decreased in the east part of the study area (Fig. 3). In 1986, the was Songping (0.99) of Yongsheng County, and the lowest was

406 J. Peng et al. / Ecological Indicators 72 (2017) 399–410

Fig. 4. Change of ecosystem physical health for each town in Lijiang City from 1986 to 2006.

Dayan (0.31) of Lijiang County. In 2006, the highest town of the increase, i.e. Shigu Town, Daju Town, Jiuhe Town, Liming Town,

ecosystem physical health index was Shitou (1.00) of Lijiang County Longpan Town, Ludian Town, Shitou Town, Tacheng Town, and

and Dayan remained the lowest town (0.00). During 1986–2006, Tai’an Town. On the contrary, only eight towns experienced dis-

the ecosystem physical health of towns in Lijiang County showed tinct decrease, i.e. Dayan Town, Jin’an Town and Jinjiang Town of

mainly a descending trend, but there were also nine towns increas- Lijiang County, Yongxing Town of Huaping County, Guanghua Town

ing slightly. The ecosystem physical health index of all the towns in and Songping Town of Yongsheng County, and Chanzhanhe Town

the other three counties showed a downward trend. Among them, and Jinmian Town of Ningliang County. Comparing the distinct

the growth of Shitou Town in Lijiang County reached a maximum changes of ecosystem physical health and integrated ecosystem

of 0.25, and the largest decline occurred in Songping Town of Yong- health, it could be found that the towns of distinct change of inte-

sheng County, reaching −0.46. In the study period, each town of the grated ecosystem health were highly similar but not completely in

four counties scored a little differently in the ecosystem physical consistence with that of ecosystem physical health.

health but their differences tended to increase slightly. The rela- In 1986, the highest town of integrated ecosystem health among

tive difference at town scale between the maximum and minimum the four counties was Songping (1.00) of Yongsheng County, and

values increased from 0.68 to 1.00. the lowest was Dayan (0.27) of Lijiang County. In 2006, the highest

On account of township differences, the average change rate town was Shitou (1.00) of Lijiang County with Dayan (0.00) still

of the ecosystem vigor was significantly higher than the ecosys- for the lowest. From 1986–2006, the integrated ecosystem health

tem organization and the elasticity, and therefore, ecosystem vigor of towns in Lijiang County mainly showed a rising tendency, and

could be regarded as the major influencing factor for ecosystem only eight towns decreased slightly. The vast majority of towns in

physical health in the four counties. The change of ecosystem the other three counties showed decreasing trends, and only four

physical health index showed that for the vast majority of towns, towns increased slightly, i.e. Yongbei Town, Chenghai Town, and

ecosystem physical health did not change distinctly. Only eight Qina Town of Yongsheng County, and Yongning Town of Ningliang

towns decreased distinctly, i.e. Dayan Town and Jinjiang Town of County. Among all the towns of the study area, Jiuhe Town of Lijiang

Lijiang County, Xingquan Town and Yongxing Town of Huaping County had the largest increasing rate of 23.07%, with Dayan Town

County, Guanghua Town and Songping Town of Yongsheng County, of Lijiang County for the largest decreasing rate of −100%. During

and Chanzhanhe Town and Jinmian Town of Ningliang County. the study period, the relative differences of integrated ecosystem

Meanwhile, the ecosystem physical health in such nine towns of health at town scale increased obviously. The relative difference

Lijiang County increased distinctly as Shigu, Daju, Jiuhe, Liming, between the maximum and minimum values increased from 0.73

Longpan, Ludian, Shitou, Tacheng, and Tai’an. to 1.00. Comparing the spatiotemporal differences of ecosystem

Among the four counties, Huaping County had the highest index physical health and integrated ecosystem health at town scale, the

of integrated ecosystem health in 1986. The integrated ecosystem synchronicity of the two indicators could be found, which referred

health of the other three counties were similar, with Yongsheng to a distinct influence of ecosystem physical health on integrated

County for the lowest. In 2006, the highest was Lijiang County, with ecosystem health, although the township differences of ecosystem

Huaping County and Ninglang County taking jointly the second services were larger than that of ecosystem physical health.

place, and Yongsheng County was the lowest. During 1986–2006,

integrated ecosystem health of Lijiang County increased slightly,

5. Discussion

while those of the other three counties decreased. Comparing the

integrated ecosystem health, ecosystem services, and ecosystem

5.1. Indicators and models for ecosystem health assessment

physical health, it could be found that integrated ecosystem health

was influenced by its two components in roughly the same amount

Ecosystem health is a system diagnostic for ecosystem state

in each county, and the influence of the ecosystem services was

characteristics. Health is a relative concept based on subjective

slightly higher than that of ecosystem physical health.

human expectations, while there is no such thing as absolute health

The distinctness judgement of integrated ecosystem health

standard. Thus, regional ecosystem health assessment should focus

change at town scale showed that the vast majority of town-

on exploring temporal dynamics and spatial differences of regional

ships’ integrated ecosystem health did not change distinctly (Fig. 5).

ecosystem health, rather than artificial judgement about whether

There were also nine towns of Lijiang County showing distinct

in a specific region the natural ecosystems or ecosystems’ mosaic

J. Peng et al. / Ecological Indicators 72 (2017) 399–410 407

Fig. 5. Change of integrated ecosystem health for each town in Lijiang City from 1986 to 2006.

are healthy at a particular time point. Therefore, it should be more to interpret the inner relationship among the dimensions of ecosys-

reasonable and practical to highlight whether regional ecosystems tem health at regional scale. In this study, representative indicators

are healthier or weaker than to judge whether regional ecosystems of vigor–organization–elasticity incorporated with the index of

are healthy or not. ecosystem services revised through a spatial adjacency matrix,

A new idea from this study is to assess regional ecosystem health helped to form a new framework of vigor-organization-elasticity-

from the perspective of land use change. In a view of regional envi- services for regional ecosystem health assessment, with more

ronmental change, land use change can effectively integrate natural effective spatial explicit quantification.

ecological processes with socioeconomic development, and has The quantification of each indicator is not arbitrary. To measure

become a current research frontier of global environmental change the vigor indicator, primary productivity is the most commonly

(Peng et al., 2008). LUCC has provided an important integrated used index (Costanza and Michael, 1999; Boesch, 2000), which

approach for geography to analyzing the human-nature coupling can be estimated by NDVI (Li et al., 2014). At ecosystem or land-

mechanism, exploring the process of global environmental change, scape scale, the organization is defined as the aggregation of the

and promoting regional sustainable development. In particular, the structural and functional aspects of landscape (Müller, 2005), and

ecological effects have been regarded as one of the key issues of this attribute of landscape organization with structural and func-

LUCC studies. Focusing on global scale together with practicing tional interactions can be simplified by landscape metrics in the

at regional scale, is an analytical paradigm of geography to study discipline of landscape ecology (Turner, 2005; Turner et al., 1989,

traditional human–land relationship. As an important basis and 2001). Land use is an important aspect in the concept of ecosystem

fundamental prerequisite of ecological security, ecosystem health resilience (Colding, 2007; Foster et al., 2003), and the separation

can represent the integrated functional status of natural ecosys- of resilience and resistance can make the assessment of ecosys-

tems, and thus can be used to quantify regional eco-environmental tem health more reasonable (Whitford et al., 1999). In terms of the

quality and associated ecological effects of LUCC. Therefore, the ecological connectivity index built on land use types (Marulli and

ecosystem health assessment focusing on the “change” instead of Mallarach, 2005), the ecosystem services flow can be determined

“threshold” will contribute to highlight the essential characteristics in a landscape connectivity view, which considers that the ecosys-

of regional LUCC. tems or landscape components can be benefited from the spatial

Based on the classical framework of vigor–organization– adjacent ecosystems or landscape components (Peng et al., 2015).

resilience, integrated indicators and a fuzzy comprehensive eval- Although the coefficients and weights were set to be fit for the land-

uation model are often used to conduct regional health state scape characteristics in Lijiang City, the establishment of indicators

assessment (Su et al., 2010; Yu et al., 2013). Although some new is reasonably conveying the initial object to integrate the land use

quantifications have been introduced such as the models of set indices for ecosystem health assessment. Therefore, the model of

pair analysis and emergy indicators (Su et al., 2009; Su and Fath, integrated ecosystem health assessment with the practical quan-

2012), further understanding of classical framework is still needed tifications of the four aspects of indicators is the main contribution

408 J. Peng et al. / Ecological Indicators 72 (2017) 399–410

of this study to land use change research, which can be applied to to assess ecosystem quality comprehensively (Shen et al., 2016;

other cities or countries after an adjustment of parameter setting Tecchio et al., 2015). Therefore, promoting ecological connotations

of the coefficients and weights. of ecosystem health in theory and methodology dimensions should

Moreover, the weight determination for the parameters of be a rewarding task in the disciplinary affiliation of ecology.

ecosystem organization, ecosystem elasticity and ecosystem ser- Without doubt, no model can depict all goals, but the practi-

vices should be highlighted. The number of weight can affect cal evaluation of ecosystem health is necessary to pay attentions

the results, which is consequential and the exact difference to the key indicator dimensions (Rapport, 1995). As a complex

of even 0.1 precision cannot be ignored. However, ecosystem concept, research on ecosystem health is not limited to understand-

health has a definitiveness framework with its calculation in non- ing from naturalistic perspectives, but also reducing the negative

definitiveness, neither with weight determination. The importance impact of population growth and consumption on ecosystems

of each index could make sense, whereas the importance degree under the background of inevitable ecosystem change. Ecosystem

has indeterminacies. The indeterminacies cannot be removed in the health should be realized based on biological protection, together

weighting solution as this kind of weight scoring for an integrated with restricting human activities, and emphasizing environmental

indicator is hard to be validated. Therefore, the weights in this arti- sustainability (Belaoussoff and Kevan, 1998; Kevan et al., 1997a,

cle is perhaps of individual uniqueness, and should be revised when 1997b). Precautionary measures that can ensure ecological sus-

introducing the framework to other regions in the world. Facing tainability would be helpful to guarantee ecosystem services from

these limits, a label of “wicked problem” should be introduced. long-term perspectives, which are beneficial to health (Hales et al.,

This paradigm highlights that numerous problems in planning, 2004). Therefore, beyond the traditional assessment framework of

management, and policy-making are by “nature wicked” with vigor, organization, and resilience which indicated a naturalistic

five properties: irreversible consequentiality, non-definitiveness ecosystem quality, the core of health assessment should be high-

in problem solution, indeterminacy in problem formulation, non- lighted both the biophysical processes and the ecological services

solubility, and individual uniqueness (Xiang, 2013). For the weights for humanity.

of parameters in assessing ecosystem organization, ecosystem elas- Currently, global ecological problems, such as forest degrada-

ticity and ecosystem services, these five properties all appeared, tion, watershed pollution, and fish reduction are mainly resolved

which meant that the weight determination with accurate preci- by the help of specific disciplines. From a multi-disciplinary

sion might be inexistence. Nevertheless, besides being awareness integration perspective of integrated ecosystem health, the eco-

and take acceptance to “wicked problem”, researchers can also logical problems of environmental degradation, species protection,

make adaptation to it (Xiang, 2013). In the article, the logistic and human existence can be answered in a comprehensive way

ranking orders of parameter importance have been described, and (Bebianno et al., 2015). The land use changes of particular land-

importance of the weights has reasonable interpretation. There- scape is such a media that endow the ecosystem health study an

fore, although the weight determination is not perfect, the data integrated view (Walker et al., 2001). However, insufficient atten-

processing is far from quantified in arbitrary. The researchers’ full tions are paid to conditions, processes, and causal relationships of

experience and perception of this local study area could make ecosystem change resulting from land use change. The influenc-

sense. ing mechanisms of land use change on ecosystem health is still

difficult to identify. Although this study aimed to investigate the

5.2. Measurement goals and future research directions health change of natural ecosystems, it highlighted that the inter-

action of human socioeconomic systems and natural ecosystems

As an overall guideline, ecosystem health goes beyond the char- remained to be analyzed more deeply. Important directions for

acterization of ecological pressure levels, as the latter is concerned future research included the integration of socioeconomic indi-

only with its own specific ecological issues, whereas ecosystem cators, the identification of driving factors that affected health

health considers a wide range of socioeconomic, public health, and corresponding mechanisms, and possible regulatory measures.

legal, and policy fields (Rapport and Singh, 2006). Considering Moreover, the links between socioeconomic drivers, urbanization

human center doctrine related aspects of ecosystem properties patterns, and their influences on the ecosystem functions and thus

such as beliefs, morals, values, and ethics, the methodology of ecosystem health had to be deeply investigated.

ecosystem assessment for integrated environmental management

should never be unique in the multiple connotation of health

(Kapustka and Landis, 1998). Therefore, the determination of mea- 6. Conclusion

surement goals should be the prerequisite work in ecosystem

health assessment. Assessing regional ecosystem health is fundamental for explor-

The measurement goals of ecosystem health can be roughly ing the ecological effects generated by global environmental

divided into two parts: one is to describe the integrity of ecosystem change, and is important for analyzing comprehensively the

structure and function under the pressure of human activity; and human-nature coupling mechanism. However, existing studies

the other is to understand the influence on human health from the seldom considered the impact of land use pattern change and

evolution of ecosystem structure and function. After a bright start, ecosystem services on regional ecosystem health. Quantitative

many ecosystem health studies tended to support the human cen- research on the ecosystem health response to regional land use

ter doctrine, which set human health state as the measurement goal change could contribute to comprehensively judging the macro-

in assessing specific ecosystem structure and function (Boetzkes, ecological effects of land use change, which is an important

1998; Light, 1998). However, the research issue derived from this integrated approach of LUCC study.

human center doctrine is rooted in public health rather than ecol- This study analyzed ecosystem health response to land use

ogy. In the main focus of ecological influences, this public health change in the four counties of Lijiang City in China during

perspective cannot clarify the ecological importance and natural 1986–2006. The extent of land use change was the highest in

constraints, especially when countering with the question of what Ninglang County of 30.22% changed, with the lowest in Lijiang

causes environmental problems, namely ecological response. In County for 24.37% changed. Landscape diversity in Yongsheng

comparison, the ecosystem health indicator rooted in ecology is County was the highest, and landscape diversity and landscape

capable of characterizing the overall ecological function state of fragmentation only decreased in Lijiang County. The integrated

natural ecosystems, and is the core content and fundamental way ecosystem health was slightly better than ecosystem physical

J. Peng et al. / Ecological Indicators 72 (2017) 399–410 409

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