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
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, Yongsheng 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 Huaping County. 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 Yunnan 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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Linking ecosystem resistance, resilience, and stability in steppes of North
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