XXII ISPRS Congress 2012: Technical Commission VIII

Shortis, M.; Shimoda, H.; Cho, K.

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663822514

Title:: Technical Commission VIII

Scope:: 590 Seiten

Year of publication:: 2014

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663822514

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Shortis, M.; Shimoda, H.; Cho, K.

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VIII/6: Agriculture, Ecosystems and Bio-Diversity]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: 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

Document type:: Multivolume work

Structure type:: Chapter

38, 2012 ctivity for the razil. Part A: and Forest Sheffield, K., cations of DPI iverland region > Murray River ent of Primary Sheffield, K., Measurement, d management stralia. Tatura, [cAllister, A., 2011) SEBAL- in Horticultural 922, pp. 141- 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

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