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

Quite a few criteria have been defined in the literature to evaluate the performance of a model (Wang and Elhag, 2007). These criteria include the sum of squared error (SSE), mean square error (MSE), mean absolute error (MAE), root mean squared error (RMSE), absolute percentage error (APE), root mean square percentage error (RMSPE), correlation coefficient (RY), and so on. Among of them, RMSE, APE and R? are the most widely used performance evaluation criteria and will be used in this study. They are defined as follows: 2 R= E 6 55) (9m 3) i=] i=1 : (2) 2 2 n n G7») 3o») =] i=l (3) 18S bs x«l APE - 21009 4 2 ni x : e Where yu; Ya: - y, are the predicated value, measured value, average measured value and average predicated value, respectively, n is the sample number. 4. RESULTS 4.1 TIN inversion model 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 According to situ measured TIN data and matched four input parameters, a multiple regression model was established. Namely, y=0.685-0.004x, +0.018x, +1.930x, +0.137x, (5) Where y is TIN, X, , X , X4 , X, are VH,HV,Band4 and NDSI, respectively. The summary statistics for the above models of TIN (at the 9594 confidence level) are as following: R°=0.774, F-value-20.48, constant, X, , X,, X, and X,of T-value are 5.74,1.12,4.37,1.40 and 2.22, respectively. In addition, According to the above three parameters for assessing the performance of model, RMSE=0.063, APE=8.651%, it indicated that the model had relative low RMSE, APE and high R° value. 4.2 Spatial distribution of TIN According to the above multiple regression model based on situ measured data, the model established was applied to calculate TIN of area covered by two images in the sea. Figure 4 shows the spatial distribution of TIN in sea surface of study area. As seen in Figure 4, TIN of sea ranged from 0 to 0.30. The majority of study area had a low value of TIN with ranging from 0.05 to 0.10. However, the area adjoining to land had a high value of TIN above 0.20. It inferred that the area was nearly influenced by land matter. It still agrees well with the actual distribution of TIN in sea. Based on the above analysis, the multiple regression model for predicting TIN in sea surface performed well. It confirmed that the fusion of optical data and SAR data was effective. TIN Gus L) zo 0. 29-0. 30 9. 15-ü. 20 9. 10-0. 03 ü. 05-0. [0 Figure 4. Distribution of TIN in sea surface of study area 5. CONCLUSION RADARSAT-2 quad-polarization image and HJ-1 CCD image have been used to estimate TIN of sea surface. Based on the situ measured data, four parameters were selected as sensitive factors. Moreover, the multiple regression model, which interprets the variation of TIN as a function of sensitive factors. According to the models and correlation analysis, the results can be summarized in three points: 1) Band4 reflectance and NDSI are relative sensitive to TIN of sea surface with R° above 0.3. 2) Compared with HH and VV, VH and HV has a better correlation with the change of TIN of sea surface, implying the advantages of cross-polarization radar backscatter in sea biochemistry monitoring. 3) Fusion of optical data and SAR data can improve the accuracy for estimating TIN in sea surface. It is because different image data sources can get more subtle information for oceanic biochemistry from different view.

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