Vegetation Indices Correlation Of Different Calibration Stages Of The Hyperion And Landsat 8 Imagery (CROSBI ID 677137)
Prilog sa skupa u zborniku | sažetak izlaganja sa skupa | međunarodna recenzija
Podaci o odgovornosti
Ivelja, Tamara ; Brook, Anna
engleski
Vegetation Indices Correlation Of Different Calibration Stages Of The Hyperion And Landsat 8 Imagery
This paper presents the analysis of two types of remote sensing data ; hyperspectral Hyperion (EC-1) and multispectral LANDSAT 8 (OLI). The main aim is to assess the influences of atsensor radiometric re-calibration and atmospheric correction on Vegetation Indices (VI) performances by estimating its values and credibility. The proposed examination is corresponding to different spectral resolution, as well as bands wavelengths and its distance between the band center and the edge of the band (FWHM). On the selected data sets preprocessing and processing procedures were applied. In the preprocessing stage, the Hyperion at-sensor DN data is first radiometrically calibrated by a standard protocol EO-1 User Guide v. 2.3 (Beck, 2003). Next stage is radiometric recalibration and atmospheric correction via Supervised Vicarious Calibration (SVC) method (Brook and Ben-Dor, 2011). The SVC method is based on a mission-by-mission approach of the data of interest. Data quality is evaluated before and during the calibration procedure and involves multiple stages of correction depending on the quality of the analyzed data. While for LANDSAT imagery a simpler calibration procedure was applied using coefficients reported in its inherent MTL file. Following processing procedures included calculation of the three commonly used VIs ; Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and Simple Ration Index (SRI). Aforementioned VIs were calculated for every correction stage of both calibration procedures. The VIs values were normalized for further comparison. Determining how much different data type VIs results are affected by the atmosphere and sensors radiometric performance was done by applying spatial correlation analysis. Also, for comparison, structural similarity index (SSIM) was applied. The procedure was done for all normalized VIs that were derived in all calibration stages in order to assess in what scope the calibration procedures and steps are needed to have reliable results. Beck, R., 2003. EO-1 user guide v. 2.3. Department of Geography University of Cincinnati. Brook, A. and Dor, E.B., 2011. Supervised vicarious calibration (SVC) of hyperspectral remote-sensing data. Remote Sensing of Environment, 115(6), pp.1543- 1555.
Hyperion, Landsat 8, Vegetation Indices, Calibration
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Podaci o prilogu
32-32.
2017.
objavljeno
Podaci o matičnoj publikaciji
37th EARSeL Symposium - Smart Future with Remote Sensing - Abstract Book
Halounová, Lena
Prag: Cultural and Natural Heritage - EARSeL
978-80-01-06192-3
Podaci o skupu
37th EARSeL Symposium
poster
27.06.2017-30.06.2017
Prag, Češka Republika