Exploring Spectral Index Band and Vegetation Indices for Estimating Vegetation Area

Visual analysis and transformation of vegetation indices have been widely applied in studies of vegetation density using remote sensing data. However, visual analysis is time intensive compared to index transformation. On the other hand, the index transformation from medium resolution imagery is not...

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Bibliographic Details
Published in:The Indonesian journal of geography 2018-12, Vol.50 (2), p.211-221
Main Authors: Hidayati, Iswari Nur, Suharyadi, R., Danoedoro, Projo
Format: Article
Language:English
Subjects:
Accuracy
Biomass
Climate change
Data analysis
Data processing
Density
Genetic transformation
Image classification
Image resolution
Random sampling
Remote sensing
Sampling
Semantics
Spatial discrimination
Spatial resolution
Spectra
Statistical sampling
Urban areas
Urban studies
Vegetation
vegetation density, spectral index, normalized index
Visualization
Wavelengths
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Summary:Visual analysis and transformation of vegetation indices have been widely applied in studies of vegetation density using remote sensing data. However, visual analysis is time intensive compared to index transformation. On the other hand, the index transformation from medium resolution imagery is not fully representative for urban vegetation studies. Meanwhile, the spectral range of high-resolution imagery is usually limited to visible wavelengths for the image transformation. Worldview-2 imagery provides a new breakthrough with a high spatial resolution and supports various spectral resolutions. This study aims to explore the spectral value of the Worldview-2 image index for estimation of vegetation density. Normalized indices were made for 56 band combinations and Otsu thresholding was implemented for the threshold selection to separate vegetation and non-vegetation areas. This thresholding was done by minimizing classes’ variances between two groups of pixels which are distinguished by system or classification. The image binarization process was performed to differentiate between vegetation and non-vegetation. For the accuracy testing, a total of 250 samples was produced by a stratified random sampling method. Our results show that the combination of indices from red channel, red-edge, NIR-1, and NIR-2 provides the best accuracy for semantic accuracy. Vegetation area extracted from the index was then compared with the results of the visual analysis. Although the index results in area difference of 2.32 m2 compared to visual analysis, the combination of NIR-2 and red bands can give an accuracy of 96.29 %.
ISSN:0024-9521
2354-9114
DOI:10.22146/ijg.38981