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

with limited site data. The target tree species of this study is Elaeocarpus japonicas (Japanese Elaeocarpus tree, JET), a kind of evergreen tree species. It widely spread in whole Taiwan from low upland to mountainous areas with elevation 2200 m above sea level. JET is also founded in Japan and China. JET is a kind of dominant tree species in the Huisun forest station. It is usually founded on the ridge with thinner soil layer, direct sunlight and water stress. JET is a kind of pioneer tree species in second succession, and therefore it plays a necessary role in ecosystem. We aimed at applying 3S (GIS, GPS and RS) technology to derive elevation, slope, aspect and terrain position from DEM and vegetation index (derived from the two-date SPOT-5 images), and using these five environmental layers to build predictive models. In this study, we adopted five methods (DA, DT, MAXENT, ML and DOMAIN) and three sampling designs (SD) to build “Tong-Feng (SD1)” model, “Kuan-Dau (SD2)” model and “two watersheds (SD3)”, eventually we totally built 15 models. The models’ reliability and performance were evaluated, and used as the criteria of model comparison. 2. STUDY AREA We chose the study area with rectangular shape, which covers the Huisun Forest Station and has the total area of 17,136 ha. The Huisun Forest Station is in central Taiwan, situated within 242°-24’5" N latitude and 12131217” E longitude. The station is the property of National Chung-Hsing University, and study area ranges in elevation from 454 m to 3,419 m, and its climate is temperate and humid. Hence, the study area has nourished many different plant species and is a representative forest in central Taiwan. It comprises five watersheds, including two larger watersheds, Kuan-Dau at west and Tong-Feng at east. All of the JET samples were collected from the two watersheds by using a GPS (Figure 1.). 3. METHODS AND METERIAL 3.1 Data Collection The collected data contained DEM with 5 m x 5 m resolution, orthophoto maps with 1: 10,000 scale and two-date SPOT-5 images taken in 2004/07/10 and 2005/11/11. The JET samples were acquired by field survey with Trimble PRO XR series GPS system. Furthermore, an expandable antenna rod with 5m in length and a laser ranging were adopted with GPS for enhancing the capacity of the system. All of the JET point data were field-collected from Tong-Feng and Kuan-Dau watersheds (© SPOT Image Copyright 2004 and 2005, CSRSR NCU). 5 3.2 Data Processing Slope and aspect data layers were generated from 5 x 5 m DEM by using ERDAS Imagine software module. The ridges and valleys in the study area were used together with DEM to derive terrain position layer. The main ridges and valleys over the study area were directly interpreted from the contour lines shown on the orthophoto base maps; these lines were then digitized to establish the data layer of main ridges and valleys by using ARC/INFO software for later use. The data layer of main ridges and valleys in a vector format was converted into a new data layer in a raster format by ERDAS Imagine software, and then combined with DEM to generate terrain position layer (Skidmore, 1990). The equation is expressed as follows. Vegetation indices were derived from the two-date SPOT-5 images, one in autumn, the other in summer, by using Spatial Modeler of ERDAS Imagine. JET samples obtained by a GPS were corrected by using post-processed differential correction and converted into ArcView shapefile format for later use. Pj - PV / (PV 4 PR) Where PV = the Euclidean distance between a certain pixel P and the nearest valley pixel; PR - the Euclidean distance between a certain pixel P and the nearest ridge pixel; When Pj = 0.0, it is referred to valley; P; = 1.0, it is referred to ridge. The P; from 0.0 to 1.0 is partitioned into eight equal intervals. The change in water content and pigment composition in plant owing to the season or stress can be detected by using multi-date imagery. These two phenomena could result in changing plant's spectral reflectance of different bands in multi-band image (Jensen, 2005). The concept of the vegetation index adopted in this study is explained in Hoffer (1978). The following equation is used to derive the vegetation index data layer. NIB umm = MER ver NIR — MIR summer summer Vegetation Index — Where NIR summer/autumn iS the reflectance of near infrared band during summer and autumn, and the reflectance of middle infrared is denoted as MIR summer/autuma- The output value is scaled in 8-bits data type. 3.3 Overlaying the Environmental Layers The layers of elevation, slope, aspect, terrain position, vegetation index, and JET sample data were overlaid by ERDAS Imagine software. We used the function “AOI (area of interest)” in ERDAS imagine software to clip the concurrent environment factor value of JET locations. These clipped-out data were used as independent variable for building predictive model. 3.4 Target and Background Samples Target sample is the GPS-located JET point sample and the concurrent environment factor value. The ratio of background to target we adopted was followed the criteria Sperduto and Congalton (1996) proposed that the ratio should be more than 3. The sampling strategy is randomly selected following Pereira and Itami (1991) suggested avoiding spatial autocorrelation. 3.5 Sampling Designs and Model Building We designed three sampling designs (SD) for the comparison of model reliability, “Tong-Feng (SD1)”, “Kuan-Dau (SD2)" and “merged samples of two watersheds (SD3)”. SDI had 104 individual JET samples, and SD2 had 80. SD3 had all of the 184 JET samples. For each of these three SDs, the dataset was split into two subsets, 2/3 and 1/3 of all, used for SZ -— €) ON m >> ay ss (t3

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