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Technical Commission VII


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
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.
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.
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