XXII ISPRS Congress 2012: Technical Commission VIII

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

Bibliographic data

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

'onverted into a agine software, n position layer d as follows. o-date SPOT-5 y using Spatial ained by a GPS ntial correction later use. certain pixel P pixel P and the it is referred to nto eight equal osition in plant cted by using could result in rent bands in oncept of the ined in Hoffer to derive the r infrared band nce of middle output value is rrain position, e overlaid by ion “AOI (area the concurrent ese clipped-out ding predictive ample and the of background | Sperduto and be more than 3. lowing Pereira correlation. he comparison an-Dau (SD2)” 3)”. SDI had SD3 had all of Ds, the dataset all, used for split-sample evaluation. We used 2/3 of all as the training dataset for modeling, and used the remaining 1/3 as test dataset for model evaluation. To build the predictive models for each SD, we used five methods, DA, DT, MAXENT, ML, DOMAIN. 3.5.1 Discriminant Analysis (DA): DA is an algorithm that tries to find the most robust boundary within variables for group participation. A grouping variable and few discriminant variables are implemented in DA to establish the discriminant function to participate the original samples into few categories (Lowell, 1991). The following equation is the typical structure of discriminant function. Y=bp tb Xt bXy ti. ob Xt... HAN Where Y = the grouping variable Xi = discriminant virables 3.5.2 Decision Tree (DT): DT (also called Classification and Regression Trees, CART) is a non-parametric classification algorithm for data mining with both classifying and predicting capability. DT could build classified rules from observations or some experiences (Guisan and Zimmermann, 2000). Decision tree algorithm sequentially partitions the dataset with some important predictors in order to maximize differences on a dependent variable. The decision pathways originate from a starting node (root) that contains all observations, then classify step by step into binary subsets based on the important predictors, and so on. Finally, it will end at multiple nodes containing unique subsets of observations. Terminal nodes are assigned a final outcome based on group membership of the majority of observations (De’ath and Fabricius, 2000; Bourg ef al., 2005; O’Brien et al., 2005). 3.5.3 Maximum Entropy (MAXENT): One of novel methods used in ecology field is MAXENT. It can build robust and stable prediction models by applying incomplete information and small sample size (Kumar and Stohlgren, 2009; Phillips ef al, 2006). Entropy means the uniform condition in thermodynamic. The axiom of MAXENT is to searching the maximum entropy of species distribution under limited conditions. When reaching the maximum entropy, the species distribution is similar to the natural condition. ~~ Max, = Min, > ES P(x)= | À, duisi mm, - linear PredietorNormalizer‘ / Z Where f, (x)— min, : hinge feature; max, —min, ^, : weight coefficient Linear Predictor Normalizer: a constant for numerical stability Z: a scaling constant that ensures that P sums to 1 over all grid cells The MAXENT software is free and online available (http;/www.cs.princeton.edu/-schapire /[MAXENT). 3.54 Maximum Likelihood (ML): ML is a widely used method in classification algorithm (Wu and Shao, 2002; Mclver and Fridel, 2002). ML algorithm is based on the probability to assign the pixel to one of the predefined Kk class with maximum likelihood (Atkinson and Lewis, 2000; Lo and Yeung, 2002). 3.5.5 DOMAIN: This method assigns a classification value to the candidate area according to a point-to-point similarity metric, and also base on this criterion to find the area where environment is similar with the sample data. Sum of the standardized distance between two points of each environment variable is used to quantify the similarity. And equalization of variable contribution is achieved by standardizing the environment variables. The classification value of each pixel in the study area is decided by the maximum similarity between each pixel a set of data points. It is necessary to set a similarity threshold to converge the predicted distribution pattern (Carpebter et al, 1993; Hernandez et al, 2006). In this study, the similarity threshold was set in 0.97, in which the kappa coefficient was reasonable. 3.6 Model Evaluation and Assessment The test and training data sets are used to evaluate the model performance and reliability. In each data set, the evaluation indices contain producer's accuracy (PA), user's accuracy (UA) and overall accuracy (OA). Kappa agreement coefficient is extremely important to assess the agreement between predicted map and reference test dataset. The kappa coefficient compares the marginal and diagonal value in matrix fairly due to the calculation containing not only PA and UA but also OA (Referred and Fitzpatrick-Lins, 1986; Congalton, 1991; Paine and Kiser 2003). Furthermore, in the model evaluation of “Tong-Feng (SD1)” model, the test dataset of “Kuan-Dau” JET samples were used as independent samples to evaluate the ability of extrapolating predicting model through space. Again, we treat the same process evaluating “Kuan-Dau (SD2)” model with the test dataset of “Tong-Feng” samples. In the evaluation in “merged samples of two watersheds (SD3)” model, we split the test set into two subsets according to the watersheds’ boundary. The two subsets of SD3's test sample were used as two independent sample sets to demonstrate that the model performance was still reasonable when using these two subsets solely. 4. RESULTS AND DISCUSSION We calculated the statistics of five environmental factors corresponding to the entire study area and all of the JET samples in two watersheds and compared the difference in statistics between them, as shown in Table 1. The elevation range of the “Tong-Feng” and “Kuan-Dau” JET samples (1,122-2,027 m and 1,076—1,559, respectively) were within the nature distribution range, from low elevation to 2,200 m above sea level. The means of slope statistics in "Tong-Feng" and “Kuan-Dou” samples were 22° and 27°, respectively. The mean slope of all JET samples is obviously lower than that of the entire study area; consequently, this result is due to the nature behavior of JET. JETs prefer to grow on the flat areas beside ridges with unclosing canopy structure, where they are illuminated by abundant solar radiation. This behavior could be demonstrated by the mean of terrain position statistics. The predicted distribution maps of SD3 used to represent overall prediction showed in Figure 2. At the earlier stage of this result, each method eliminated vegetation index from the effective variables because the contribution of vegetation index in model performance is less than 1 percent. The most important effective variables were slope, aspect and terrain

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