XXII ISPRS Congress 2012: Technical Commission VII

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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:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

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:: [VII/3, VII/6, III/2, V/3: INTEGRATION OF HYPERSPECTRAL AND LIDAR DATA]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: FUSION OF OPTICAL DATA AND SAR DATA FOR THE ESTIMATION OF NITROGEN CONCENTRATION IN PEARL RIVER ESTUARY HONG KONG SEAS, CHINA Xiangnan Liu, Meiling Liu, Ling Wu

Document type:: Multivolume work

Structure type:: Chapter

n), (b) correlation to TIN, R? ctral Index (1) band and Our results 3and 4 was NDSI has is the input 3.0 Fusion of RADARSAT-2 image and HJ-1 CCD image In order to accurately estimate the TIN in continuous sea surface on a large scale, the fusion of RADARSAT-2 image and HJ-1 CCD image was adopted. In this study, the RADARSAT-2 image in the study area was converted into 30 x 30 m grids by nearest neighbor algorithm to keep consistency with HJ-1 CCD image in the spatial resolution. The operation was conducted using a routine written in MATLAB in combination with ENVI 4.8. Two types of image data sources were corrected to WGS-84 (World Geodetic System 1984) Geographic datum and Geographic Lat/Lon projection system. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 3.3 Spatial distribution of input variables from image Sea surface backscattering coefficients were converted to 30 x 30 m grids for RADARSAT-2 images. Figure 3 shows the distribution VH, HV, Band4 and NDSI input variables in the same spatial resolution. From Figure3, the backscattering coefficient of HV ranged from —45 to 9 dB, whereas VH ranged from —44 to 9 dB. HV and VH had the similar range, and the former had a little higher range than the latter. Band4 reflectance had a low value with ranging from 0 to 0.012, while NDSI ranged from -0.8 to 0.4. Detail statistics of four input variables were calculated in Table 2. (b) 0. 012 Figure 3. Distribution of four input variables in the same spatial resolution (a) HV, (b) VH, (c) Band4, (d) NDSI Input variables Min Max Mean Stdev HV -44.08 7.98 -32.62 4.72 VH -43.86 8.07 -32.53 4.71 Band4 0 0.012 0.0011 0.0008 NDSI -0.79 0.36 -0.66 0.092 Table 2. Statistics of input variables in study area 3.4 Establishment of model À series of HJ-1 CCD reflectance and SAR backscattering coefficient were chose in the study area, in addition, they kept the same geographical coordinates of situ measured TIN data. The statistical model was created to establish the relationship between the above four input parameters and TIN. Of all statistical models, multiple regression analyses method was most popular (Singh et al., 2009). Regression analysis is widely used for predicting and forecasting, and it is also used to understand which among the independent variables are related to the dependent variable. Therefore, in this study, multiple regression analysis was used to establish the relationship between four input parameters and TIN. 3.5 Evaluation of model's performance

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