APPLICATION OF SPATIAL MODELLING APPROACHES, SAMPLING STRATEGIES
AND 3S TECHNOLOGY WITHIN AN ECOLGOCIAL FRAMWORK
Hou-Chang Chen“, Nan-Jang Lo", Wei-I Chang’, and Kai-Yi Huang“ *
"Graduate student, Dept. of Forestry, Chung-Hsing University, Taiwan, R. O. C., E-mail: zkchris@hotmail.com
"Specialist, EPMO, Chung-Hsing University, Taiwan, R. O. C., E-mail: njl@dragon.nchu.edu.tw
° Director, HsinChu FDO, Forest Bureau, Council of Agriculture, Taiwan R. O. C., E-mail: weii@forest.gov.tw
4 "Professor, Dept. of Forestry, Chung-Hsing University, Taiwan, R. O. C., E-mail: kyhuang@dragon.nchu.edu.tw
250 Kuo-Kuang Road, Taichung, Taiwan 402, R. O. C., Tel: +886-4-22854663; Fax: +886-4-22854663
Commission VIII/7
KEY WORDS: Modelling, Statistics, Experimental, Technology, Ecology, Forestry.
ABSTRACT:
How to effectively describe ecological patterns in nature over broader spatial scales and build a modeling ecological framework has
become an important issue in ecological research. We test four modeling methods (MAXENT, DOMAIN, GLM and ANN) to
predict the potential habitat of Schima superba (Chinese guger tree, CGT) with different spatial scale in the Huisun study area in
Taiwan. Then we created three sampling design (from small to large scales) for model development and validation by different
combinations of CGT samples from aforementioned three sites (Tong-Feng watershed, Yo-Shan Mountain, and Kuan-Dau watershed).
These models combine points of known occurrence and topographic variables to infer CGT potential spatial distribution. Our
assessment revealed that the method performance from highest to lowest was: MAXENT, DOMAIN, GLM and ANN on small spatial
scale. The MAXENT and DOMAIN two models were the most capable for predicting the tree’s potential habitat. However, the
outcome clearly indicated that the models merely based on topographic variables performed poorly on large spatial extrapolation
from Tong-Feng to Kuan-Dau because the humidity and sun illumination of the two watersheds are affected by their microterrains
and are quite different from each other. Thus, the models developed from topographic variables can only be applied within a
limited geographical extent without a significant error. Future studies will attempt to use variables involving spectral information
associated with species extracted from high spatial, spectral resolution remotely sensed data, especially hyperspectral image data, for
building a model so that it can be applied on a large spatial scale.
1. NTRODUCTION
Building ecological modeling framework has been the core of
ecological research since the latter half of the 20% century
(Guisan and Zimmermann, 2000). It can provide a measure of
a species’ occupancy potential in areas not covered by
biological surveys and consequently is becoming an
indispensable tool to conservation planning and forest
management. Technological innovation over the last few
decades, especially in the fields of remote sensing (RS) and
geographic information systems (GIS), greatly enhanced
scientists’ capacity to meet this challenge by giving them the
ability to describe patterns in nature over broader spatial scales
and at a greater level of detail than ever before (Guisan and
Zimmermann, 2000). Besides, advances in statistical
fechniques enhance the ability of researchers to tease apart
complex relationships, while effectively incorporated of RS and
GIS tools permit more accurate descriptions of spatial patterns
and suggest directions for species distribution. ^ Several
alternative methods have been used to predict the geographical
distributions of species (Elith et al, 2006). We used
maximum entropy (MAXENT), DOMAIN modeling
(DOMAIN), generalized linear model (GLM) and artificial
neural networks (ANN) to build model because they are easy to
use and produce useful prediction in other research (Carpenter
etal, 1993; Lek and Guegan, 1999; Guisan et al., 2002; Elith
et al, 2006; Phillps et al., 2006: Phillps et al., 2008).
Despite the extensive use of species distribution models, some
Important conceptual, biotic and algorithmic uncertainties need
to be clarified in order to improve predictive performance of
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Corresponding author.
these models (Araûjo and Guisan, 2006). For instance,
species ecological characteristics, sample size, model selection
and predictor contribution (Araûjo and Guisan, 2006). Hence,
it must be interpreted carefully of species’ occupancy potential
in areas not covered by biological surveys. Generally, models
for species with broad geographic ranges and environmental
tolerance tend to be less accurate than those for species with
smaller geographic ranges and limited environmental tolerance
(Thuiller ef al., 2004; Elith er al., 2006).
According to species characteristic, the target species chosen
for this study was Schima superba (Chinese guger trees, CGT,),
which are widespread with elevation ranging from 300 to 2,300
m in central Taiwan, is one of the fine broad-leaf tree species
and good for fitment. CGTs have high water content and
dense crown closure, and high dispersal ability; therefore, they
have excellent fire resistance characteristics and can grow to
form a fire line (Liu ef al., 1994). In this study, we consider
different types of predictive models, as well as the complex
environment of study area and the ways in which ecological
relationships are affected by changes in scale. Hence, it was
intended to develop models for predicting the potential habitat
of the tree species, and has the following five steps. (1)
In-situ data (CGTs) were collected from the Tong-Feng
watershed, Yo-Shan Mountain area, and Kuan-Dau watershed
in the Huisun study area in central Taiwan by using GPS. (2)
GIS technique was used to overlay the layer of CGTs with
environmental variables. (3) Three sampling schemes were
created for model development and validation via different
combinations of CGT samples taken from aforementioned three
sites. (4) MAXENT, DOMAIN, GLM, and ANN were used to
