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

position, followed by aspect. So, we used these four effective variables to build and evaluate each model in three SDs (Table 2). Overall, in three SDs, the best method for model building was DOMAIN (kappa = 0.83-0.87), followed by DT (0.72—0.80), MAXENT (0.63—0.68), ML (0.51—0.54) and DA (0.23—0.47) in order. SDs didn’t affect the model performance. It is clear to realize that DOMAIN, DT and MAXENT is efficient in converging the predicted distribution patter. Convergence of prediction helps ecologist to reduce the consuming of field survey. We used the independent samples aforementioned to evaluate the extrapolating ability, and the result is shown in Table 1. The result reveals that when evaluating model performance by independent samples, the kappa value of each model decreased sharply in both SD1 and SD2. In contrast, the kappa value of each model in SD3 declined slightly. The result in Table 3 indicated that the model prediction of SD1 and SD2 could not extend through watershed. 5. CONCLUSIONS The methods we adopted papered in different ratability, but the performance efficiency of these methods should be at the same grade. DOMAIN had the highest prediction accuracy, and the agreement of model performance between training dataset and test dataset in DOMAIN was also the best. Although the performance of DT and MAXENT were slightly lower than DOMAIN, but this three method had the same level of kappa coefficient in this study. Hence, we suggest that DOMAIN, DT and MAXENT a high potential in similar research and ecological application. The evaluation of SDs showed that it is hard to extend the distribution pattern through spatial (e.g. watersheds and mountain). The phenomenon is strongly established by the result of two-way extrapolation we designed. The result indicated that it is hard to extend the spatial patterns of JETs from one watershed to another and vice versa. By comparing the microclimate and micro-terrain of the two watersheds, the humidity and sunlight affected by micro-terrain of these two watersheds are remarkably different. ^ Consequently, the models merely based on topographic variables performed poorly on two-way spatial extrapolation between these two watersheds. Not surprisingly, the kappa values of predictive models developed from the merged samples of the two watersheds in SD3 just declined slightly. ^ The results suggested that the vegetation indices derived from SPOT-5 images could not improve model accuracy for a widely distributed tree species due to the limitations of spectral resolution and spatial resolution with SPOT-S imagery. Follow-up studies will attempt to extract spectral information associated with species from hyperspectral data and LIDAR DEM and use it as variable for model development so that models are applicable on a broader spatial scale. Figure 2. Predicted distribution maps of SD3 included (a) DA, (b) DT, (c) MAXENT, (d) ML, and (e) DOMAIN. r^ —H CN 6 mn 95 (AA

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