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

box below, and spatial nployed in veen urban gether with alWeighted | : on (GWR) i ysis on surface statistical | analysis is ban surface üch can be :R. Table 1 dels. Under important an surface e endent variable pende nt variables 'SIS a of all of ch in Hong ler stations ogical data ese stations LO) for this ats. Within ing to 957 where the with the isive urban sparsely in ii, Sha Tin, ly focusing which are study area. long Kong fish ponds ea includes a few industrial development areas located in Tuen Mun, Tsuen Wan, Tai Po and Yuen Long. Together with the mountains topography, the diverse land use in the study area provides practical meaning for the study of local effect on surface thermal anomalies under the subtropical climate. The study period ranges from 2003 to 2006 when the ASTER data used for this site study is available. The ASTER images of these years are obtained from the achieved database in the Land Process Distributed Active Archive Center through the Earth Observing System Data Gateway (EDG), which would be demonstrated and discussed in the subsequent part of this section. Since Hong Kong is a highly developed city with mature urban infrastructure. During this study period from 2003 to 2006, Hong Kong experienced no pervasive urban development but with a few percentages of urban sprawl recognized. C ma dm Là * Sheu cut * lox Far Sinh dura Park S. Figure 2. Study area with weather stations In this research ASTER LIB data and Land Surface Temperature (LST) product AST_08 data were both acquired for this research. ASTER L1B data is used to derive some of the ecological parameters. The surface temperature of AST 08 can be regarded as valid for cloud-free pixels, the reported accuracy of surface temperature estimation achieved proved to be within 1.5K. To facilitate further analysis, the surface temperature of AST 08 data in units of degrees Kelvin has been converted to degrees Celsius (°C). The primary descriptive statistics of surface temperature during these observation periods are given in the Table 2 to provide some background information about the surface temperature distribution within the observation periods. DATE | TIMEOFDAY ] MIN MAX 1 MEAN | STD 2005-04-17 | 16075 18.15 44.75 | 26.74 238 2005-10-23 11:02:39.38 1715 41.85 | 26.60 3.18 2005-10-01 22:34:53.98 2215 53.85 | 28.27 150 2004-11-21 | 11:02:10.94 14.95 42.85 | 25.57 334 2004-1005 | 241137 1195 225 | 19.95 236 2008-11-03 | 11:03:35.52 22325 | 49.45 | 3235 3.46 | 2003-10-28 | 22:35:49.75 1485 | 2225 | 2103 182 Table 2. Descriptive statistics of surface temperature for the images used in this research For the analysis of the relationship between local surface temperature and urban environmental measures by linear regression analysis, there were 1070 sample points with systematic sampling covering the whole study area chosen as the observations for correlation analysis which was strategically located within the whole study area. All the values of each correlation variables corresponding to each sample point have been calculated. 4. RESULTS AND DISCUSSIONS Geographical Weighted Regression (GWR) analysis is chosen to measure the possible spatial varying relationship between the referred local environmental factors and surface temperature with the use of a software package GWR 3.0 (Fotheringham et al, 2002). In this calculation, the significance of potential spatial variation in relationship is measured and verified with a Monte Carlo statistical test. P- value for each variable is given and compared to offer a formal evaluation about the significance of spatial variation within each influential factor coefficient. Because the adaptive kernel is more capable to reflect the scale of local variation resulting from the equal amount of sample data at each regression point as compared to the fixed kernel (Su et al, 2005), the adaptive kernel with AIC (Akaike Information Criterion) minimization bandwidth selection is adopted for GWR analysis in this study. On the other hand it would provide one valued reference indicating the grain size of local varying patterns existed and the further inference of local fragmentation in the relationship pattern within whole study area can be made. The Monte Carlo significance test of spatial variation corresponding to each variable is summarized in Table 3, with the grey color rows showing the nighttime models, others corresponding with the daytime models. The goodness-of-fit of GWR models within each image time period can be evaluated with the adjusted R-square in this table. In general, the R-square has greatly improved to over 70%. The highest value of adjusted R-square (0.772) appears in daytime model of 11-21-2004. On the other hand, the R- square value of daytime models is consistently higher than the values of nighttime models, which implies that the daytime surface temperature can be better modeled with current environmental variables than nighttime surface temperature. In summary the F values of GWR models at each time show that the relationships between urban environmental factors and local surface temperature are significant. When comparing the calculated bandwidth of each GWR model between daytime and nighttime, it can be found that the bandwidths of nighttime models is larger than the ones of daytime models, this also confirmed the relative stable surface heating pattern during nighttime than during daytime. It is noted that the bandwidth of GWR model during 10/01/2005 is the biggest (184) among the daytime and nighttime models. This may be due to the strong “smoothing” effect of even ‘noise’ introduced by the stripped LST image and the highest relative humidity (89.1%) at this image time.The standard residual distribution of the models confirmed that spatial association has been successfully removed with GWR model, During daytime the Intercept and Building square footage demonstrate spatial association correlation with local surface temperature. While during nighttime these spatial variations of parameter coefficients tend to be less obvious. Except these factors referred above, no significant spatial association

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