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TWO-WAY SPATIAL EXTRAPOLATION AND VALIDATION ON ECOLOGICAL
PATTERNS OF ELAEOCARPUS JAPONICUS BETWEEN MAIN WATERSHEDS
IN HUISUN OF CENTRAL TAIWAN
S. Y. Su*, N. J. Lo", W. I Chang', K. Y. Huang”
^Graduate student, Dept. of Forestry, Chung-Hsing University, Taiwan, E-mail: j82831079(g hotmail.com
"Specialist, EPMO, Chung-Hsing University, Taiwan, E-mail: nil@dragon.nchu.edu.tw
* Director, Hsinchu Forest District Office, Taiwan, E-mail: weii@forest.gov.tw
‘Professor, Dept. of Forestry, Chung-Hsing University, Taiwan, E-mail: kyhuang@dragon.nchu.edu.tw
250 Kuo-Kuang Road, Taichung 402, Taiwan, R. O. C.
Commissions: VIil/6
KEY WORDS: Forestry, GIS, Modeling, Pattern, SPOT, Prediction, Accuracy, Performance.
ABSTRACT:
Spatial extrapolation has become a sine qua non and an ad hoc major research focus in applied ecology in the latter half 20" century.
Progressive innovations in data acquisition and processing technologies over the last few decades, especially in the fields of 3S (RS,
GIS and GPS) and statistical modeling method, have greatly enhanced ecologists’ capacity to face the challenge by enabling them to
to describe patterns in nature over larger spatial scales and a greater level of details than ever before. Elaeocarpus japonicas
(Japanese Elaeocarpus tree, JET) was selected for applying in the concurrent developed technology, such as ecological distribution
modeling and ecological extrapolation. The GPS-located JET samples were introduced in a GIS for overlaying with five
environmental layers (elevation, slope, aspect, terrain position and vegetation index derived from two-date SPOT-5 images) for
ecological information extraction and model building. We created three sampling designs (SD), Tong-Feng samples for SDI,
Kuan-Dau samples for SD2, and the merge of the two former datasets for SD3, according to watersheds, and the three SDs were used
individually to test the extrapolation ability of predictive models.
The results of the two-way extrapolation indicated it is hard to
extend the predicted distribution patterns through different watersheds. The main reasons resulting in this outcome were the
difference in microclimate and micro-terrain between these two watersheds. Consequently, the models built with SD3 were the
more robust. The information of vegetation index in this study poorly improved the models, so we will adopt the hyperspectral data
to overcome the shortage of the SPOT-5 images.
1. INTRODUCTION
To plant right tree at right place is the most critical concept in
plantation project and forest management. Different tree
species need different habitat conditions, which are as the same
as the concept of Odum (1997) proposed, ecological niche.
Different environmental conditions result in different tree
species composition. The niche breadth of each species is not
the same, but equally means the species with wider niche
breadth could adapt border environmental conditions.
Presence or absence of a tree species will mainly decide by the
interaction of numerous environmental factors, those usually
contain direct factors and indirect factors. Generally, direct
factors are referred climate, soil and biotic factors, as well as
indirect factors are composted with topographic factors
(including elevation, slope, aspect and terrain position). To
obtain broad-extent and high accuracy data of direct factors is
really difficult because of that the field data collecting stations
are fragmentary result in introducing serious error when
performing spatial interpolation (Prudhomme and Reed, 1999;
Marquinez et al., 2003). In the contrast, by introducing the
digital elevation model (DEM) to a geographic information
system (GIS), we can derive the high accuracy and
broad-extent data of indirect factors, such as elevation, slope,
aspect, and terrain position.
Nowadays, ecologists especially value the ecological modelling
techniques. The specialists can apply the 3S technology to
* Corresponding author.
extract the point data and related data for ecological model
building, and the potential distribution map can be produced.
According to predicted accurate distribution maps, ecologists
can reduce the field survey tasks to save labor and fund
spending. The predicted map also can be used to evaluate the
ability of model extrapolation, and help the ecologist to
evaluate the area inaccessible but we are interested in.
It is extraordinarily necessary to acquire the spatial information
for parametric or non-parametric algorithm to build species
distribution models. We can compare the distribution maps of
different algorithms to realize the performance of different
models. Felicísimo (2004) applied discriminant analysis (DA)
and decision tree (DT) along with GIS to predict the suitable
habitat of tree species. Maximum entropy (MAXENT), with
increasing application in ecology field, is a promising tool in
many domains. MAXENT doesn’t suffer the statistical
assumption and limitation, and it can use only fewer point data
and incomplete information to build robust predictions (Phillips
et al., 2006; Kumer and Stohlgren, 2009). The advantages of
MAXENT modelling are very indispensable in ecological
related field because it is unusual to collect abundant and
representative point data in field survey. Maximum likelihood
(ML) algorithm is commonly used in multispectral image
classification (Mu and Shao, 2002; Mclver and Friedl, 2002).
Carpenter (1993) and Hernandez (2006) used DOMAIN to
modeling species potential distribution. Carpenter (1993) also
proposed DOMAIN is variable sensitivity, and perform well
