Applicability of VI in arid vegetation delineation using shadow-affected SPOT imagery

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Abstract

GDVI3, GDVI2, NDVI, MSAVI and SAVI were evaluated for their dynamic ranges, the class accuracy of the Vegetation Index (VI) classifications, the effects of shadow delineation on the other land use classes and their applicability in vegetation delineation in Al-Qara Mountains, Oman. Supervised classifications of a SPOT scene by Support Vector Machines (SVM) algorithm were employed. GDVI3 showed the widest dynamic range in all land use types, while GDVI2 also exhibited evidently wider dynamic ranges for arid to semi-arid Al-Qara than NDVI, MSAVI and SAVI. GDVI3 reported the highest accuracies in delineating natural vegetation (dense – 74.80 %, medium-dense- 43.19 %), except for low-dense vegetation (40.51 %). It also performs the best in delineating bare soil and dry grass with over 80 % and 60 % accuracies. The attenuated reflectance created by the shadows results in VI signals in the range of dry grass to bare soil, enabling us to neglect the shadow effect on natural vegetation delineation due to below 9.50 % omissions from the shadows class. GDVI3 also limits shadow delineation better than the other indices, which will enable us to analyze spectral information recovery by the VI with the help of ground truth information under the shadows. For applications such as land degradation assessments, GDVI3 has better prospects over the other indices explored. Saturation at high-vigor vegetation is an issue in GDVI3, GDVI2 and NDVI. Our study also points to a dependency of a VI’s capability to weaken shadows on the number of training data pixels to be utilized in a supervised classification.

Original languageEnglish
Article number454
JournalEnvironmental Monitoring and Assessment
Volume187
Issue number7
DOIs
Publication statusPublished - Jul 22 2015

Fingerprint

SPOT
vegetation index
imagery
NDVI
vegetation
image classification
bare soil
grass
land use
Land use
land degradation
vigor
reflectance
Soils
pixel
saturation
mountain
Support vector machines
Pixels
Recovery

Keywords

  • Class accuracy
  • Dynamic range
  • GDVI
  • Salalah
  • Shadow effect
  • SVM classification

ASJC Scopus subject areas

  • Environmental Science(all)
  • Management, Monitoring, Policy and Law
  • Pollution

Cite this

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title = "Applicability of VI in arid vegetation delineation using shadow-affected SPOT imagery",
abstract = "GDVI3, GDVI2, NDVI, MSAVI and SAVI were evaluated for their dynamic ranges, the class accuracy of the Vegetation Index (VI) classifications, the effects of shadow delineation on the other land use classes and their applicability in vegetation delineation in Al-Qara Mountains, Oman. Supervised classifications of a SPOT scene by Support Vector Machines (SVM) algorithm were employed. GDVI3 showed the widest dynamic range in all land use types, while GDVI2 also exhibited evidently wider dynamic ranges for arid to semi-arid Al-Qara than NDVI, MSAVI and SAVI. GDVI3 reported the highest accuracies in delineating natural vegetation (dense – 74.80 {\%}, medium-dense- 43.19 {\%}), except for low-dense vegetation (40.51 {\%}). It also performs the best in delineating bare soil and dry grass with over 80 {\%} and 60 {\%} accuracies. The attenuated reflectance created by the shadows results in VI signals in the range of dry grass to bare soil, enabling us to neglect the shadow effect on natural vegetation delineation due to below 9.50 {\%} omissions from the shadows class. GDVI3 also limits shadow delineation better than the other indices, which will enable us to analyze spectral information recovery by the VI with the help of ground truth information under the shadows. For applications such as land degradation assessments, GDVI3 has better prospects over the other indices explored. Saturation at high-vigor vegetation is an issue in GDVI3, GDVI2 and NDVI. Our study also points to a dependency of a VI’s capability to weaken shadows on the number of training data pixels to be utilized in a supervised classification.",
keywords = "Class accuracy, Dynamic range, GDVI, Salalah, Shadow effect, SVM classification",
author = "Gunasekara, {N. K.} and Al-Wardy, {M. M.} and Al-Rawas, {G. A.} and Y. Charabi",
year = "2015",
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T1 - Applicability of VI in arid vegetation delineation using shadow-affected SPOT imagery

AU - Gunasekara, N. K.

AU - Al-Wardy, M. M.

AU - Al-Rawas, G. A.

AU - Charabi, Y.

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N2 - GDVI3, GDVI2, NDVI, MSAVI and SAVI were evaluated for their dynamic ranges, the class accuracy of the Vegetation Index (VI) classifications, the effects of shadow delineation on the other land use classes and their applicability in vegetation delineation in Al-Qara Mountains, Oman. Supervised classifications of a SPOT scene by Support Vector Machines (SVM) algorithm were employed. GDVI3 showed the widest dynamic range in all land use types, while GDVI2 also exhibited evidently wider dynamic ranges for arid to semi-arid Al-Qara than NDVI, MSAVI and SAVI. GDVI3 reported the highest accuracies in delineating natural vegetation (dense – 74.80 %, medium-dense- 43.19 %), except for low-dense vegetation (40.51 %). It also performs the best in delineating bare soil and dry grass with over 80 % and 60 % accuracies. The attenuated reflectance created by the shadows results in VI signals in the range of dry grass to bare soil, enabling us to neglect the shadow effect on natural vegetation delineation due to below 9.50 % omissions from the shadows class. GDVI3 also limits shadow delineation better than the other indices, which will enable us to analyze spectral information recovery by the VI with the help of ground truth information under the shadows. For applications such as land degradation assessments, GDVI3 has better prospects over the other indices explored. Saturation at high-vigor vegetation is an issue in GDVI3, GDVI2 and NDVI. Our study also points to a dependency of a VI’s capability to weaken shadows on the number of training data pixels to be utilized in a supervised classification.

AB - GDVI3, GDVI2, NDVI, MSAVI and SAVI were evaluated for their dynamic ranges, the class accuracy of the Vegetation Index (VI) classifications, the effects of shadow delineation on the other land use classes and their applicability in vegetation delineation in Al-Qara Mountains, Oman. Supervised classifications of a SPOT scene by Support Vector Machines (SVM) algorithm were employed. GDVI3 showed the widest dynamic range in all land use types, while GDVI2 also exhibited evidently wider dynamic ranges for arid to semi-arid Al-Qara than NDVI, MSAVI and SAVI. GDVI3 reported the highest accuracies in delineating natural vegetation (dense – 74.80 %, medium-dense- 43.19 %), except for low-dense vegetation (40.51 %). It also performs the best in delineating bare soil and dry grass with over 80 % and 60 % accuracies. The attenuated reflectance created by the shadows results in VI signals in the range of dry grass to bare soil, enabling us to neglect the shadow effect on natural vegetation delineation due to below 9.50 % omissions from the shadows class. GDVI3 also limits shadow delineation better than the other indices, which will enable us to analyze spectral information recovery by the VI with the help of ground truth information under the shadows. For applications such as land degradation assessments, GDVI3 has better prospects over the other indices explored. Saturation at high-vigor vegetation is an issue in GDVI3, GDVI2 and NDVI. Our study also points to a dependency of a VI’s capability to weaken shadows on the number of training data pixels to be utilized in a supervised classification.

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