Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas
Quantitative estimation of canopy biophysical variables are very important in different studies such as meteorology, agriculture and ecology, so knowledge of the spatial and temporal distribution of these variables would be highly beneficial. Meanwhile, remote sensing is known as an important source...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Susan Barati [verfasserIn] Behzad Rayegani [verfasserIn] Mehdi Saati [verfasserIn] Alireza Sharifi [verfasserIn] Masoud Nasri [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Egyptian Journal of Remote Sensing and Space Sciences - Elsevier, 2016, 14(2011), 1, Seite 49-56 |
|---|---|
| Übergeordnetes Werk: |
volume:14 ; year:2011 ; number:1 ; pages:49-56 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.ejrs.2011.06.001 |
|---|
| Katalog-ID: |
DOAJ060576006 |
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| 520 | |a Quantitative estimation of canopy biophysical variables are very important in different studies such as meteorology, agriculture and ecology, so knowledge of the spatial and temporal distribution of these variables would be highly beneficial. Meanwhile, remote sensing is known as an important source of information to estimate fractional vegetation cover in large areas. Today spectral indices have been very popular in the remote sensing of vegetation features. But often reflections of soil and rocks are much more than reflections of sparse vegetation in these areas, that makes separation of plant signals difficult. So in this study measured fractional vegetation cover of a desert area were evaluated with 20 vegetation indices in five different categories as the most appropriate category, or indicator for desert vegetation to be identified. The five categories were including: (1) conventional ratio and differential indices such as NDVI; (2) indices corrected and derived from the traditional indicators such as NDVIc and GNDVI; (3) soil reflectance adjusted indices such as SAVI; (4) triangle indices based on three discreet bands in their equation (Green, Red and NIR) like TVI; and (5) non-conventional ratio and differential indices such as CI. According to the results of this research, DVI index with 0.668 the coefficient of determination (R2) showed the best fractional vegetation cover estimation. But according to the sparse vegetation in desert areas and the results of this research it seems none of these indicators alone can accurately estimate the percentage of vegetation cover, however, to do a proper estimation it is possible to enter data of these indices in a multivariate regression model. Using this method enabled us to increase the coefficient of determination of fractional vegetation cover estimation model up to 0.797. | ||
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Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas |
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Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas |
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Susan Barati |
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Egyptian Journal of Remote Sensing and Space Sciences |
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Susan Barati Behzad Rayegani Mehdi Saati Alireza Sharifi Masoud Nasri |
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10.1016/j.ejrs.2011.06.001 |
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comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas |
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Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas |
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Quantitative estimation of canopy biophysical variables are very important in different studies such as meteorology, agriculture and ecology, so knowledge of the spatial and temporal distribution of these variables would be highly beneficial. Meanwhile, remote sensing is known as an important source of information to estimate fractional vegetation cover in large areas. Today spectral indices have been very popular in the remote sensing of vegetation features. But often reflections of soil and rocks are much more than reflections of sparse vegetation in these areas, that makes separation of plant signals difficult. So in this study measured fractional vegetation cover of a desert area were evaluated with 20 vegetation indices in five different categories as the most appropriate category, or indicator for desert vegetation to be identified. The five categories were including: (1) conventional ratio and differential indices such as NDVI; (2) indices corrected and derived from the traditional indicators such as NDVIc and GNDVI; (3) soil reflectance adjusted indices such as SAVI; (4) triangle indices based on three discreet bands in their equation (Green, Red and NIR) like TVI; and (5) non-conventional ratio and differential indices such as CI. According to the results of this research, DVI index with 0.668 the coefficient of determination (R2) showed the best fractional vegetation cover estimation. But according to the sparse vegetation in desert areas and the results of this research it seems none of these indicators alone can accurately estimate the percentage of vegetation cover, however, to do a proper estimation it is possible to enter data of these indices in a multivariate regression model. Using this method enabled us to increase the coefficient of determination of fractional vegetation cover estimation model up to 0.797. |
| abstractGer |
Quantitative estimation of canopy biophysical variables are very important in different studies such as meteorology, agriculture and ecology, so knowledge of the spatial and temporal distribution of these variables would be highly beneficial. Meanwhile, remote sensing is known as an important source of information to estimate fractional vegetation cover in large areas. Today spectral indices have been very popular in the remote sensing of vegetation features. But often reflections of soil and rocks are much more than reflections of sparse vegetation in these areas, that makes separation of plant signals difficult. So in this study measured fractional vegetation cover of a desert area were evaluated with 20 vegetation indices in five different categories as the most appropriate category, or indicator for desert vegetation to be identified. The five categories were including: (1) conventional ratio and differential indices such as NDVI; (2) indices corrected and derived from the traditional indicators such as NDVIc and GNDVI; (3) soil reflectance adjusted indices such as SAVI; (4) triangle indices based on three discreet bands in their equation (Green, Red and NIR) like TVI; and (5) non-conventional ratio and differential indices such as CI. According to the results of this research, DVI index with 0.668 the coefficient of determination (R2) showed the best fractional vegetation cover estimation. But according to the sparse vegetation in desert areas and the results of this research it seems none of these indicators alone can accurately estimate the percentage of vegetation cover, however, to do a proper estimation it is possible to enter data of these indices in a multivariate regression model. Using this method enabled us to increase the coefficient of determination of fractional vegetation cover estimation model up to 0.797. |
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Quantitative estimation of canopy biophysical variables are very important in different studies such as meteorology, agriculture and ecology, so knowledge of the spatial and temporal distribution of these variables would be highly beneficial. Meanwhile, remote sensing is known as an important source of information to estimate fractional vegetation cover in large areas. Today spectral indices have been very popular in the remote sensing of vegetation features. But often reflections of soil and rocks are much more than reflections of sparse vegetation in these areas, that makes separation of plant signals difficult. So in this study measured fractional vegetation cover of a desert area were evaluated with 20 vegetation indices in five different categories as the most appropriate category, or indicator for desert vegetation to be identified. The five categories were including: (1) conventional ratio and differential indices such as NDVI; (2) indices corrected and derived from the traditional indicators such as NDVIc and GNDVI; (3) soil reflectance adjusted indices such as SAVI; (4) triangle indices based on three discreet bands in their equation (Green, Red and NIR) like TVI; and (5) non-conventional ratio and differential indices such as CI. According to the results of this research, DVI index with 0.668 the coefficient of determination (R2) showed the best fractional vegetation cover estimation. But according to the sparse vegetation in desert areas and the results of this research it seems none of these indicators alone can accurately estimate the percentage of vegetation cover, however, to do a proper estimation it is possible to enter data of these indices in a multivariate regression model. Using this method enabled us to increase the coefficient of determination of fractional vegetation cover estimation model up to 0.797. |
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