Eco-Engineering, 23（3）,75-80, 2011

Original Paper

Analysing Habitat Characteristics for Korean Water Deer
(Hydropotes inermis argyropus) in Korea Using Remote Sensing
and Landscape Metrics

Jihyang Jung, Yo Shimizu and Kenji Omasa*

Department of Biological and Environmental Engineering, Graduate school of Agriculture and Life Science,
The University of Tokyo
1-1-1, Yayoi, Bunkyo-ku, Tokyo, Japan（Received April 14, 2011; Accepted June 8, 2011）

ABSTRACT
Korean water deer (Hydropotes inermis argyropus) is an endemic subspecies and one of the common species in Korea.
However, deer have been threatened by human activities, which have resulted in the population decrease. To conserve andmanage their populations, understanding Korean water deer’s habitat characteristics, which depend on the structure of
the landscape, and identifying the relationship between those would be a key component. Our main aim was first to makelandcover maps from remote sensing imagery, and to determine some habitat metrics for Korean water deer at two different
home range scales (25 ha and 100 ha), and finally to compare them between habitat and non-habitat of the Korean water
deer.
This study analyzed spatial patterns at two scales in the Chungnam province environment by using the PCA-ECHO
classification technique based on Landsat ETM+ remote sensing data (2001) and 19 habitat metrics. Study results show that
habitat metrics for forest and open areas are more obviously distinguished within a 25 ha home range scales. Especially,
more continuous and less fragmented forest patches and open area patches with a high edge density and connectivity werethe main characteristics of Korean water deer’s habitat. As a part of habitat management, these results could be used as agood proxy for assessing the habitat quality of Korean water deer.

Key words : GIS, Habitat, Korean water deer, Landscape metrics, Remote sensing

become more serious in farmlands and their surroundings.

1. Introduction
Korean water deer (Hydropotes inermis argyropus) is an en-

Therefore, effective habitat management plans for deer should
be adopted.

demic subspecies in Korea. The species can be found throughout the Korean peninsula and in southeast China. However,

Population dynamics are independently and interactively

as many forest habitats have been lost to agriculture, forestry

influenced by the spatial arrangement, size, type, and diver-plantations and urban development, deer populations havesity of patches. That is, the distribution, abundance and diver-declined dramatically. This species is now listed on the In-

sity of animal species in an area are affected by the structural

ternational Union for Conservation Nature (IUCN) red list

characteristics of a landscape such as habitat type, resource-

as Vulnerable (VU) in 2008 (Harris and Duckworth, 2008).

patch size, edge length, configuration (Forman et al., 1976)

Moreover, recently crop loss and damage caused by deer has

and disturbance or manmade landscape structure (Fritz et al.,*Corresponding author : Email: aomasa@mail.ecc.u-tokyo.ac.jp© The Society of Eco-Engineering（5）75

2003). Therefore, assessments based on simple measures ofmountains, and the Gyeryong mountains which pass through-habitat quality, which do not take the effects of landscape pat-out the province. Forest area covers about 449,533 ha (52.1%).

tern into account, are unlikely to provide accurate estimates

Forests are dominated by coniferous forest (197,839 ha),of important aspects of landscape ecology such as population

deciduous broad-leaved forest (115,813 ha), and mixed

persistence, survival, and reproduction (Hansson et al., 1995).

forest (107,291 ha). Other land uses are as follows: ar-This study was primarily addressed to detect the habitat

able land (268,417 ha (31.1%)), water area (67,734 ha (7.8%)),spatial structure of Korean water deer using geographic in-commercial/industrial/residential area (38,616 ha (4.5%)),formation systems (GIS) and remote sensing. Various envi-

transportation area (25,958 ha (3%)), and the others (12,663

ronment map layers derived from remote sensing are a cost-ha (1.5%)) (Chungnam Provincial Government, 2009).

effective representation of the natural landscape and provide

Korean water deer are an evidently edge species, preferring

an appropriate source of spatial information. Landscape met-

habitat characterized by shrubs and small trees and wildly dis-

rics are yielded through accurate landcover classes based on

tributed in mountains and fields of the Korean Peninsula (Rhimremote sensing data and might be used for management andand Lee, 2007). Although Korean water deer are reported toconservation planning of wildlife habitats.

be moderately widespread in Korea, currently they are now

Previous studies on Korean water deer have mainly focuseddecreasing due to poaching and habitat destruction (Harris andon their ecological or individual characteristic such as ana-

Duckworth, 2008). Commonly, 2~4 Korean water deer live

tomical features, taxonomic classification, epidemiological in-

together and appear unlikely to colonize areas > 20 km from avestigation and genetic testing (Zhang, 2000; Kim et al., 2005;

source population. They eat various kinds of herbaceous plants

Park et al., 2009). Although Jung (2007) developed the Habi-

such as reeds, rough grasses and crops (Guo and Zhang, 2002).

tat Suitability Model for identifying habitat use information,
the habitat model did not thoroughly consider spatial patterns

2.2 Landcover map production

and physical structure. Therefore, it is important to find out

Chungnam province landcover map with 30 x 30 m resolu-

relationship between spatial patterns and the distribution oftion was created using Landsat ETM+ remote sensing data

Korean water deer to preserve them effectively. Habitat char-(by providing Maryland Uni. 2001/09/23) to update the land-

acteristics that consider Chungnam province’s environments

cover map produced by the Ministry of Environment of Korea

as key indicators of habitat quality, will help the authorities(1997‑1999). We applied the PCA-ECHO method proposed

to implement the program related to habitat monitoring andby Lu et al. (2007) to develop the landcover map using Mul-

management.

tispec software program. Areas covered by cloud/shadow are

By examining the Chungnam province landscape, this study

excluded from the analysis. We should stress that the area ofaims to 1) make landcover maps derived from remote sensing

cloud cover affecting the image was fairly small (about 8.4%)

imagery, 2) detect important habitat metrics for Korean water

and was largely limited to the southeastern areas of the Chun-deer at two different home range scales (25 ha and 100 ha),gnam province.

and 3) compare them between habitat and non-habitat of the

We used a feature extraction method, Principal Components
Analysis (PCA), to reduce the data dimensionality and com-

Korean water deer.

putational cost. PCA is often used to compress the informa-

2. Methods
2.1 Study site and species

tion content of original image bands into a fewer number oftransformed components that recover as much variability in

The study area, Chungnam province (about 862,900 ha)

the data as possible (Gonzalez and Woods, 1993). Then, Ex-with an average altitude of 100 m, is located in the middle oftraction and Classification of Homogeneous Objects (ECHO)South Korea (35°38'~ 37°71'N, 125°31'~127°38'E). This area

was applied to the extracted images. ECHO procedure ishas temperate monsoon climate with four distinctive seasons.

based on the groups rather than individual pixels, which use

The yearly average temperature is 12.8℃ and the annual aver-the spectral information from neighbor pixels to allocate it toage precipitation totals 1,577.5 mm. The mountains in Chun-a probable class, and subsequently subdivide the landscapegnam include the Charyeongsanmaek Mountain, the Gaya

into an arbitrary mosaic inadequately related to the actual

76（6）Eco-Engineering

the 200 random points as a non-habitat area outside of the
95% Kernel area (Fig. 1 right).
2.4 Landscape pattern metrics
In order to compute landscape pattern metrics, 25 ha and100 ha scaled samples were applied. Because Korean water
deer’s home range is known for approximately 25 ha (Cookand Ferrell, 1983) and an extended area of 100 ha is considFig. 1 Landcover map and result of Kernel estimation of study
area, Chungnam province.

ered as a potential home range deer can commonly traverse in
a 24 h period.
We measured landscape configuration and composition

landcover pattern (Campbell, 1996). After mapping throughwithin 25 ha and 100 ha samples using the Patch Analyst

the classification method, the kappa statistic was used to con-

Extension FRAGSTATS software in ArcView (Elkie et al.,firm classification accuracy. Across different ecoregions, the1999). Calculated landscape pattern indices are regarded ascore components of deer habitat are consistent; water, food,

habitat metrics. Each metric is integrated over all the patches

and cover. We considered that forest is suitable for sleepingof a given landcover type (i.e., forest, open area, water area,and hiding cover and open area is an integrative place whichand development area) and represents the amount and spatial

can provide many functions such as resting, reproductive, anddistribution of a single landcover type. 19 habitat metrics

feeding covers. Thus, we reclassified cover types into fourwhich are divided into 5 groups were computed for eachhabitat categories, i.e. forest, open area (grass land, wetland

landcover type (Table 1) and equations and definitions for theand farm land), water area, and development area (Fig. 1).

metrics can be found in the Fragstats Spatial Pattern Software
user guide (McGarigal and Marks, 1995).

2.3 Kernel home range estimation
Home ranges were estimated based on probability Kernel’,

2.5 Data analysis

which are regions around each point location containing some

Stepwise discriminant analysis (DA) was used to quan-

likelihood of animal presence. It is considered the most robust

tify the statistical significance of the identified habitat metricsof the probabilistic techniques (Hooge et al., 1999). The two

across two sub-regions, habitat and non-habitat area, because

probabilities (95% and 50%) are most commonly used in the

DA function maximizes the distance between the means of theliterature. The 95% area is considered the area of active use

extracted metrics. The function score is given by

and 50% area, on the other hand, is considered the core area

Z = α1X1 +α2 X 2 + … + αn X n + Cof activity. Therefore, 95% kernel is more suitable for separat-

where the α's are discriminant coefficients, the X's areing habitat area within Chungnam province. It was estimatedhabitat metrics and C is a constant. In executing the stepwise

by using the Animal Movement Extension of ArcView (Rodg-

procedure, we determined Wilk’s Lamda as the criterion anders and Carr, 1998).

used the probability of F-value with Pin = 0.05 and Pout = 0.10.

For this study, we used deer observation data for seven

And then, we conducted T-test to compare significant differ-

years (2000-2006), which were derived from the 2nd and 3rdence of habitat metrics selected by each DA. The results show

National survey on the Natural Environment Report (Ministry

habitat information about the present habitat conditions andof Environment of Korea). A total of 183 stations were locatedhabitat metrics which can be used to assess habitat quality as

within the site and regarded as habitats because there wereindicators.

no big environmental changes for seven years. To avoid overcounting due to deer’s reappearance, we excluded overlap-

3. Results and Discussion

ping samples in the buffer zone (< 100 m). To compare the dif-

PCA-ECHO classification method yielded an overall accu-

ferences between habitat and non-habitat area, the Chungnamracy of 94.30% (Kappa, 0.923). The result was a pair of quite

province was subdivided into two sub-regions. We determined

believable classification maps whose patterns seem to closely

Eco-Engineering（7）77

Table 1 Habitat metrics used in this study and their brief definitions(McGarigal and Marks, 1995)
Metric (unit)

Landcover type

Description

Area/Density/Edge
CA (m2): Total class area

Sum of the areas of all patches

PLAND (%): Percentage of Landscape

Percentage the landscape comprised of the patchtype

PD (n/100ha): Patch density

Number of patches of the patch type divided bytotal area

TE (m): Total Edge

An absolute measure of total edge length of apatch type

ED (m/ha): Edge Density

Sum of the lengths of all edges divided by the
total area

GYRATE# (m): Radius of gyration

Mean distance between each cell in the patch
and the centroid

LSI (none): Landscape Shape Index

Measure of class aggregation. LSI ≥1, LSI = 1
when the landscape consists of a single compact
patch

Shape
SHAPE# (none): Shape index

Patch perimeter divided by the minimum
perimeter possible for a maximally compact patch

FRAC# (none): Fractal Dimension Index

Mean patch fractal dimension with the additionof individual patch area weighting applied to eachpatch

PARA# (none): Perimeter-Area Ratio

Ratio of the patch perimeter to area

ForestOpen area
Development area
Water area

Habitat scale
25 ha

100 ha

98.0%**
94.3%**
86.7%**
90.3%**

78.7%**
79.5%**
70.5%**
63.5%**

in non-habitat in forested areas within a 25 ha home range.
SHAPE, PROX, and GYRATE had a meaningful difference
within the 100 ha home range (P < .01). In this study, the appearance of Korean water deer relied on a larger and more
complicated shaped forest patch area and a high proximity in
distribution. Especially, high connectivity or less fragmented
patches for the 25 ha home range were important. This result
can be interpreted that the probability of occurrence of deerin suitable patches increases with proximity and connectivity.
That is, more connected forest patches likely provide easierConnectivity
COHESION(none): Patch Cohesion Index

Table 2 Prediction accuracy of discriminant analysis for land cover

movement for deer because forests provide cover and resting
Measure of physical connectedness. COHESION
approaches 0 as the proportion of the landscapecomprised of the focal class decreases.

Contagion/Interspersion
DIVISION (%)
Probability that two randomly chosen pixels in: Landscape Division Index the landscape are not situated in the same patch

places from inclement weather and disturbance by humans or
predators. The DA results showed that if the 25 ha samples
were suitable for habitat, the discriminant function scores (Z =0.687 SHAPE−0.015 COHESON + 3.199 DIVISION + 0.061

Isolation/Proximity
PROX# (none): Proximity Index#

SIMI (none): Similarity Index

Sum of patch area divided by the nearest
edge-to-edge distance squared between the patch
and the focal patch.
Sum of all neighboring patches with similarity
coefficient between the focal patch andneighboring patch, divided by the nearest
edge-to-edge distance squared# : Area-weighted mean of each habitat metric.

PROX−0.027 SIMI−2.211) would be over 1.07 for forest
patches.
In the case of open area in the 25 ha habitat, the six habitat
metrics (i.e., CA, PLAND, SIMI, PARA, PROX, and PD)
showed a significant difference between habitat and nonhabitat. In the 100 ha habitat, the two habitat metrics, PLAND
and SIMI, were significantly different (P < .01). The selec-

depict reality. This method would be applied to provide infor-

tion of different vegetation communities by herbivores should

mation about landcover change.

reflect foraging and refuge-seeking strategies (Andersen etClassification results were a simple summary of the numberal., 1998). If an increase in the amount of edge is related toand percentage of subjects classified correctly or incorrectly,

an increase in the availability of resources, also, if greater re-and used to assess how well the discriminant function works.

source availability is offered by habitat edges, then more time

Total percentage correctly classified into habitat’ or non-would also be spent near these edges, which is consistent with

habitat’ groups ranged from 86.7% to 98.0%. On the otherthe fact that edges are good browsing habitats for water deer.

hand, in the case of 100 ha home range scale, prediction accu-

As a feeding place, spatial patterns for open area would beracy fell dramatically by 63.5% in water areas (Table 2).

proper to meet the discriminant function score (Z = -0.621 CA

The results of the T-test for the habitat metrics showed that

+ 0.249 PLAND + 0.012 PD + 0.002 PARA−0.036 PROX +there were distinct differences between the habitat and non-

0.029 SIMI−6.03) below 1.98. And degree of similarity had

habitat in Chungnam province (Table 3).

a tendency to increase in the extension area (25 ha: ΔSIMI =Except SHAPE metric, COHESION, DIVISION, PROX,
and SIMI in the habitat were significantly different from those78（8）61.022, 100 ha: ΔSIMI = 320.371).
In the development area in both the 25 ha and 100 ha home

Eco-Engineering

Table 3 Canonical discriminant function derived from habitat metrics and mean of habitat metrics between habitat andnon-habitat for 25 ha and 100 ha home range
Landcover type

ForestHabitat scale

25 ha

100 haOpen area

25 ha

100 haDevelopment area

25 ha

100 haWater area

25 ha

100 haMetricCanonical discriminantfunction coefficients (α)/ Standardized ones

Habitat typeNon-habitat

Habitat

PROX#**.061 / 1.343

1.451

49.192

SIMI#**DIVISION**
SHAPE#COHESION**(Constant)
GYRATE#**SHAPE#**PROX#**(Constant)
PLAND**CA**
SIMI#**
PARA#**PROX#*
PD**(Constant)
SIMI#**
PLAND**(Constant)
PROX#**
SIMI#**
ED**(Constant)
ED**
PROX#**(Constant)
SIMI#**FRAC#**(Constant)
LSI**(Constant)-.027 /-1.085
3.199 / .991
.687 / .361-.015 / -.310-2.211 / .012 / 1.132-.463 / -.390.027 /.340-1.818 / .249 / 5.799-.621 / -3.777.029 / 1.443.002 /.627-.036 / -.234.012 /.143-6.030 / .003 /.522-.026 / -.514.033 / .862 / 1.612-.650 / -1.515
0.11 /.556-.762 / .028 / 1.241-.067 / -.385-2.138 / .017 /.739
17.359 /.450-19.640 / 1.844 / 1.000-2.897 / -103.800.824
1.655
74.790

2.554
.507
1.750
95.441

157.685
2.214
6.529

311.480
2.550
10.38954.43114.08764.696419.662
3.111
16.62024.4426.357
3.674
678.093
4.789
20.629240.71153.405561.14222.6432.422
2.422
104.715

0.062
1.602
71.244107.8216.936

66.731
4.112

111.670
1.069

.006
1.018

1.766

1.284

This table includes only metrics selected by the stepwise procedures.
** At the 0.01 significant level (2-tailed)* At the 0.05 significant level (2-tailed)#: Area-weighted mean of each habitat metricrange, edge density (ED) and the degree of proximity (PROX)

developmental pressure for many years, resulting in interfer-

were significantly different (P < .01). Edge density in a non-ence with the deer’s approach to the water area. Therefore,

habitat was larger than that in a habitat. Development area

habitat metrics for the water area could not be utilized as habi-

patches were closer and more contiguous in the non-habitat

tat indicators in the environment which have been strongly

area than habitat ones regardless of home range size. Overall,affected by anthropogenic factors.

non-habitat areas were more strongly influenced by anthropogenic factors.

Overall, high connectivity and proximity of forest patches,high edge density of open area, and low edge density of devel-

For water areas, FRAC and SIMI metrics showed a sig-

opment area were important factors for the 25 ha habitat area.

nificant difference (P < .01) in the 25 ha home range and there

However, results for the 100 ha range as a potential home

was difference in LSI metric of shape index in 100 ha. Cookrange couldn’t show distinct habitat characteristics (Table

and Ferrell (1983) reported that water deer prefer water areas2). It suggests that there were significant limitations associ-and areas nearby. However, the water area has experienced

ated with home range scales to analyze habitat characteristics.

Eco-Engineering（9）79

In this study, habitat metrics were basically computed on thebasis of species distribution data (presence/absence data). An

Gonzales, R. and Woods, R., 1993: Digital Image Processing.
Addison-Wesley, Reading, MA, 716 pp.

absence data (lack of observation) may have three causes: 1)

Guo, G. P. and Zhang, E. D., 2002: The distribution of the Chinese

The species was present but was not detected. 2) The habitatwater deer (Hydropotes inermis) on the Zhoushan archipelago,

is suitable, but the species is not yet in that habitat or no lon-

Zhejiang Province, China. Acta Theriologica Sinica, 22, 98‑107.

ger present. 3) The habitat is actually not suitable. Continuous

Hansson, L., Fahrig, L. and Merriam, G., 1995: Mosaic Landscapes andmonitoring of its appearance or not will help assess habitat

Ecological Processes. Chapman and Hall, London, pp. 136‑148.

quality more explicitly. The results will provide suggestions

Harris, R.B. and Duckworth, J.W., 2008: 2008 IUCN Red List of

for allowable developments and habitat management.

Threatened Species. At http://www.iucnredlist.org/apps/redlist/
details/10329/0. International Union for Conservation Nature

Acknowledgements(IUCN), Gland. Accessed 5 December 2010.

This study was conducted in cooperation with the Ecologi-

Hooge, P. N., Eichenlaub W. M. and Solomon E.K., 1999: The animal

cal Restoration Engineering Lab at Ewha Womans University

movement program. At http://www.absc.usgs.gov/glba/gistools.

(EWU) and with the Korea Environment Institute (KEI) and

U.S. Geological Survey (USGS), Alaska Biological Science Cen-with the Ministry of Environment of Korea. We would like toter, Anchorage, pp.1‑28. Accessed 15 October 2010.

thanks to Rho, Paik-Ho and Lee, Sang-Don for ideas, and suggestions on previous drafts of this study.

Jung, J.H., 2007: Habitat Suitability Model for Korean water deer
(Hydropotes inermis argyropus) using GIS. Master Thesis, Ewha
Womans University, Seoul, 56 pp.

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