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:: REMOTE-SENSING-BASED BIOPHYSICAL MODELS FOR ESTIMATING LAI OF IRRIGATED CROPS IN MURRY DARLING BASIN Indira Wittamperuma, Mohsin Hafeez, Mojtaba Pakparvar and John Louis

Write comment:: Textverlust im Original.

Document type:: Multivolume work

Structure type:: Chapter

Faram ZO0& LAE acid SR Measurement Fonts. * 3 sb spo aba ; AONO S OREO Bey à où Farm 2026 LAE and MER Data Measurement Points ve * os VUE * Sis cr CAE TIGR ACIER f Fasım 158 1A! and MSR Bieasurenent Fobimes Farms 38 LAI ard MSP Measurement Pants Figure 2: Spectral and LAI data collection sites at various CIA locations 2.3 Satellite measurements Landsat 5TM satellite images for the whole cropping period were downloaded and processed to extract NDVI data. Firstly the images were pre-processed and reflectance data (relative at sensor reflectance -Toar) was further processed and transformed into absolute ground/surface reflectance (Surf) using two atmospheric correction models: Mid Latitude Summer (MLS) and United States (US) standards in Grass 7.0 environment. Both the models can be used under Australian conditions where both models gave almost identical results. Using all forms of reflectance measurements, different NDVI maps were created for each crop. The first one was created using the reflectance which was not corrected for the atmospheric error (NDVIToar), the second by using the reflectance values which was corrected for atmospheric errors using MLS model (NDVIy;s), and the third one was created in the same way but using the US model (NDVIys).These maps were used to extract corresponding NDVI values at field sample points. 1.00 0.90 0.80 0.70 S 0.60 ê S 0.50 e 0.40 = 0.1653In(x) + 0.5475 R?= 0.8309 0.30 0.20 0.10 0.00 0 1 2 3 4 5 6 LAI The LAIg, NDVIg, NDVIyoar And NDVIgurr Values were used appropriately to develop different regression models and analysed the reliability of data sets and presented in following figures (3 to 10). Due consideration was given to phenological growth of crops in selecting the trend lines between different data sets regardless of best fit trend line. The shape of fitted lines is in the harmony with the crop growth phenology and shows that the NDVI saturates when crops are close to harvesting stage giving a good correlation. 3. RESULTS AND DISCUSSION Error! Reference source not found.Figure 3 shows the regression relationships between LAIg and NDVIToar for corn. The corresponding regression relationship between LAI; and NDVI; and LAIg and NDVIys is shown in Figure 4 and 5. It is clear that the atmospheric correction process has significantly improved the results of the relationship between LAI and NDVI for Landsat 5 TM. 1.00 0.90 0.80 0.70 2 0.60 2 0.50 0.40 0.30 0.20 0.10 0.00 s NDVI Figure 3: Relationship of LAIg and NDVIroar for corn crop Figure 4: Relationship of LAIg and NDVImzs for corn crop NDVI Toar NDVIiia Figui

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