dal 
ote 
0). 
nd 
Ce, 
ids 
the 
ral 
13, 
ch, 
ive 
lds 
ts, 
ice 
25, 
ry. 
do 
itu 
ce. 
2). 
ile 
  
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
MODELING SPATIAL DISTRIBUTION OF A RARE AND ENDANGERED PLANT 
SPECIES (Brainea insignis) IN CENTRAL TAIWAN 
Wen-Chiao Wang?, Nan-Jang Lo", Wei-I Chang, Kai-Yi Huang? 
“Graduate student, Dept. of Forestry, Chung-Hsing University, Taiwan E-mail: chiao87219 € yahoo.com.tw 
"Specialist, EPMO, Chung-Hsing University, Taiwan E-mail: njl Gdragon.nchu.edu.tw 
* Director, HsinChu FDO, Forest Bureau, Council of Agriculture, Taiwan E-mail: weii @ forest.gov.tw 
Professor, same as with author-a E-mail: kyhuang 9 dragon.nchu.edu.tw (corresponding author) 
250 Kuo-Kuang Road, Taichung, Taiwan 402, Tel: +886-4-22854663; Fax: +886-4-22854663 
Commissions: VII/4 methods for land cover classification 
KEY WORDS: Forestry, Ecology, Modeling, Prediction, Algorithms, Pattern, Performance, Accuracy. 
ABSTRACT: 
With an increase in the rate of species extinction, we should choose right methods that are sustainable on the basis of appropriate 
science and human needs to conserve ecosystems and rare species. 
Species distribution modeling (SDM) uses 3S technology and 
statistics and becomes increasingly important in ecology. Brainea insignis (cycad-fern, CF) has been categorized a rare, endangered 
plant species, and thus was chosen as a target for the study. Five sampling schemes were created with different combinations of CF 
samples collected from three sites in Huisun forest station and one site, 10 km farther north from Huisun. Four models, MAXENT, 
GARP, generalized linear models (GLM), and discriminant analysis (DA), were developed based on topographic variables, and were 
evaluated by five sampling schemes. The accuracy of MAXENT was the highest, followed by GLM and GARP, and DA was the 
lowest. More importantly, they can identify the potential habitat less than 10% of the study area in the first round of SDM, thereby 
prioritizing either the field-survey area where microclimatic, edaphic or biotic data can be collected for refining predictions of 
potential habitat in the later rounds of SDM or search areas for new population discovery. However, it was shown unlikely to 
extend spatial patterns of CFs from one area to another with a big separation or to a larger area by predictive models merely based on 
topographic variables. Follow-up studies will attempt to incorporate proxy indicators that can be extracted from hyperspectral 
images or LIDAR DEM and substitute for direct parameters to make predictive models applicable on a broader scale. 
1. INTRODUCTION 
Biodiversity is very important for humans and all other species 
on the Earth. Without the diversity of species, ecosystems are 
more fragile to natural disasters and climatic change. With an 
increase in the rate of species extinction, we must conserve 
ecosystems and rare species by choosing right methods that are 
sustainable on the basis of appropriate science and human 
needs. Forest resources in Taiwan are very abundant, but 
environmental carrying capacity of the island is vulnerable, 
thus when using them we must think of conservation at the 
same time. 
Species distribution modeling (SDM) could apply in 
conservation and protection rare species, ecology, 
epidemiology, disaster and management in forestry (Pearson ef 
al., 2007; Asner et al., 2008; Cayuela er al., 2000). SDM 
needs to utilize the combination of 3S technology and statistics, 
and has become increasingly important in ecology (Cóté and 
Reynolds, 2002; Guisan and Thuiller, 2005). Nowadays a 
variety of statistical methods have been used to model 
ecological niches and predict the geographical distributions of 
species, such as BIOCLIM, maximum entropy (MAXENT), 
DOMAIN, genetic algorithm for rule-set prediction (GARP), 
generalized linear models (GLM), generalized additive model 
(GAM) and discriminant analysis (DA) (Elith er. al, 2006; 
Hernandez et al, 2006; Guisan et al, 2007; Peterson et al, 
2007; Wisz et al., 2008; Ke et al., 2010). 
SDM is based on the environmental conditions of known sites 
to predict unknown area, and also to identify the relationship 
between the species and environment. The distribution 
pattern of natural vegetation is associated with four types of 
environmental factors, including climatic, physiographic, 
edaphic, and biotic factors (Su, 1987). For SDM, it is 
desirable to predict a species distribution on the basis of 
ecological (direct) parameters (i.e. climate, soil, and biotic 
factor) that are to be the causal, driving forces for its 
distribution. Data for such direct parameters, however, are 
generally difficult or expensive to measure, soil data are even 
more difficult to derive, and they tend to be less accurate than 
pure topographic characteristics (Guisan and Zimmermann, 
2000). Moreover, biotic factor is extremely difficult to 
estimate due to the fine spatiotemporal resolution and 
potentially complex nature of biotic dimensions (Barve er. al., 
2011). On the other hand, indirect parameters (e.g. 
topographic variables: elevation, slope, aspect) are most easily 
measured by remote sensing and are often used because of their 
good correlation with observed species patterns (Guisan and 
Zimmermann, 2000) Hence, SDM should be run on an 
iterative basis with topographic data in initial rounds and 
climatic data, soil data, or biotic data, when available, in later 
rounds since not all the data needed by SDM for the four types 
of factors aforementioned are readily available at one time. 
In this study, we used four methods: MAXENT, GARP, GLM, 
and DA to build models and to predict the potential habitat of a 
rare plant together with five different sampling schemes. Our 
study area falls within a homogeneous climatic zone with one 
degree of latitude; therefore, we took account of the area's 
microclimate, which in turn affects species’ distribution. 
Indeed, the topography of an area influneces the microclimate 
of that area (Su, 1987). Furthermore, fine spatial-resolution 
soil data and biotic data were not available up to the present. 
Hence, we did run the four aforementioned SDM models on an 
iterative basis by incorporating elevation, slope, aspect, terrain 
position, and vegetation index derived from SPOT images in 
the first round. We designed five sampling schemes from two 
areas: 1) a small range with the distance of 0.7 km between 
sampling sites and 2) a large range with the distance of about 
10 km between sampling sites. We evaluated these models in