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/3: Atmosphere, Climate and Weather]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: AN INVESTIGATION OF LOCAL EFFECTS ON SURFACE WARMING WITH GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) Y. Xue, T. Fung, J. Tsou

Document type:: Multivolume work

Structure type:: Chapter

Figure 8. Coefficients contribution of Distance from coast in daytime model 04/17/06 The local comparisons of the relative magnitudes of CC among environmental variables are carried out to investigate the relative contributions of the independent variables to local surface temperature variation. The local dominant factors which contribute most to location specific surface temperature variation represented with the factor possessing the site specific biggest CC can be mapped and compared to differentiate the relative contribution of environmental factors. It was found that the local dominant factors to surface temperature variation are varying in space and time.This uncovered the local disparities of surface heating mechanism, which also indicates the dynamics of local effect of environmental factors on local surface heating in space and time, at one specific location, the order in relative magnitudes of environmental indicators contributing to local surface temperature variation is varied with geographical location and time. Especially in urban area it is difficult to generate a local pattern regarding the relative importance of environmental factor on site specific surface temperature due to the highly variation of the local dominant factors, which may in part due to the relative low resolution in sampling and measurement of some factors like population density and road density comparing with the intensive variation of urban landscape. The varying relationships between urban surface temperature and local environmental setting in space and time represented with the referred environmental factors made the emerged pattern of local dominant factors in terms of component contribution (CC) challenging to be interpreted and explained which may be due to limitations in the definition and measure of environmental indicators (Holt and Lo, 2008). It also made the clear delineation in the relative importance of environmental indicators on surface temperature variation, in particular surface warming difficult due to the complexity induced by the local varying effect in space and time. 5. CONCLUSIONS In this research, GWR analysis proved that the spatial variation in relationships between environmental setting and surface temperature was significant with Monte Carlo significance test and distinctive in day-night change. the site specific relation patterns related to each environmental parameter are mapped and compared in order to analyze the local impact of each factor on surface temperature variation .Through analysis, we found that the local dominant factor accounted for most to the site specific surface temperature variation was highly varied in space and time which prevented a general delineation of the relative association among environment factors to surface temperature disparities. This implied that the effective adaptive measures should be devised locally with reference to day-night needs in the identification of this feature. Comparatively, GWR facilitated the site specific investigation based on local statistical technique. The inference based on GWR model provided enriched information regarding the spatial variation of local environment effect on surface temperature variation which global model cannot approach. REFERENCES Brunsdon, C., Fotheringham, S. and Charlton, M., 1998. Geographically Weighted Regression-Modelling spatial non- stationarity. The Statistician, 47( 3),pp. 431-443. Fotheringham, A.S., Brunsdon, C. and Chartlon, M., 2002. Geographically Weighted Regression: the analysis of spatially varying relationships. John Wiley & Sons Ltd, England. Holt, J. B. and Lo, C. P., 2008. The geography of mortality in the Atlanta metropolitan area. Computers, Environment and Urban Systems, 32(2), pp.149-164. Huang, Y. , 2000. Regional economic development in yangtze river delta since 1978: Jiangsu province as a particular case. PHD thesis, CUHK. Huang, Y. and Leung, Y., 2002. Analyzing regional industrialization in Jiangsu province using geographically weighted regression. Journal of Geographical Systems, 4, pp.233-249. Miller, P. C., Stoner, W. A. and Tieszen, L. L. , 1976. A model of stand photosynthesis for the wet meadow tundra at Barrow, Alaska. Ecology, 57(3), pp.411-430. Oke, T.R., 1982. The energetic basis of the urban heat island. Quarterly Journal of Royal Meteorology Society, 108, pp.1- 24. Su, B. S., Chong, A. K., Moore, A. ,2005. Geostatistical Modeling, Analysis and Mapping of Epidemiology of Dengue Fever in Johor State, Malaysia. Paper Presented at SIRC 2005 — The 17th Annual Colloquium of the Spatial Information Research Centre, University of Otago, Dunedin, New Zealand, November 24th-25th 2005. Voogt, J.A. How Researchers Measure Urban Heat Islands. http://www.epa.gov/heatisland/resources/pdf/ EPA How to measure a UHI.pdf, | EPA presentation. Inter! Gl ? Surve ? 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