A ratio vegetation index adjusted for soil brightness

Major, D. J.; Baret, Frédéric; Guyot, G.

doi:10.1080/01431169008955053

Cited by 289 publications

(105 citation statements)

References 12 publications

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“…ASTER band 2 was used as a red band, band 3 as a near-infrared band and band 4 as a shortwave-infrared band in the equations. The parameters of the soil line were determined from the scatterplot of the red and near-infrared ASTER reflectances (figure 2) to calculate SAVI2 (Major et al 1990). The soil line was fitted to the reflectance of the nonvegetated sandbars and stony areas.…”

Section: Spectral Feature Extractionmentioning

confidence: 99%

“…However, the empirical relationships are affected by various factors, including canopy closure, understory vegetation and background reflectance (Spanner et al 1990). A set of soil-adjusted vegetation indices have been developed to reduce the effects of the soil background reflectance (Huete 1988, Major et al 1990, Qi et al 1994, Rondeaux et al 1996. Furthermore, some studies have stated that the inclusion of the shortwave infrared (SWIR) into the SVIs would unify different cover types and reduce the background effects (Nemani et al 1993, Brown et al 2000.…”

Section: Introductionmentioning

confidence: 99%

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Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data

Heiskanen

2006

International Journal of Remote Sensing

162

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Biomass and leaf area index (LAI) are important variables in many ecological and environmental applications. In this study, the suitability of visible to shortwave infrared advanced spaceborne thermal emission and reflection radiometer (ASTER) data for estimating aboveground tree and LAI in the treeline mountain birch forests was tested in northernmost Finland. The biomass and LAI of the 128 plots were surveyed, and the empirical relationships between forest variables and ASTER data were studied using correlation analysis and linear and non-linear regression analysis. The studied spectral features also included several spectral vegetation indices (SVI) and canonical correlation analysis (CCA) transformed reflectances. The results indicate significant relationships between the biomass, LAI and ASTER data. The variables were predicted most accurately by CCA transformed reflectances, the approach corresponding to the multiple regression analysis. The lowest RMSEs were 3.45 t ha 21 (41.0%) and 0.28 m 2 m 22 (37.0%) for biomass and LAI respectively. The red band was the band with the strongest correlation against the biomass and LAI. SR and NDVI were the SVIs with the strongest linear and non-linear relationships. Although the best models explained about 85% of the variation in biomass and LAI, the undergrowth vegetation and background reflectance are likely to affect the observed relationships.

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Section: Spectral Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data

Heiskanen

2006

International Journal of Remote Sensing

162

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“…Most VIs are computed from radiance, re ectance, or at least apparent re ectance values in the red and the near-infrared (NIR) bands (Bannari et al 1995) The most commonly applied vegetation index for agricultural and ecological applications is the Normalized DiVerence Vegetation Index (NDVI, table 1), which has been shown to be widely usable for vegetation analysis (Rouse et al 1974). Previous studies have shown that multi-temporal NDVI images are useful for analys- Major et al (1990) ing spatial vegetation patterns and for assessing vegetation dynamics (e.g. Justice et al 1985, Townshend andJustice 1986).…”

Section: Introductionmentioning

confidence: 99%

“…TSAVI takes into account the soil parameters in the PVI concept and does not apply the correction factor L in the SAVI formula. Furthermore, a new version of the SAVI was developed by Major et al (1990); this is referred to as SAVI 2 (table 1), which considers whether the soil is either wet or dry, and incorporates the soil line parameter into the calculation.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of vegetation indices to substrate brightness in hyper-arid environment: The Makhtesh Ramon Crater (Israel) case study

Schmidt¹,

Karnieli²

2001

International Journal of Remote Sensing

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Abstract. The in uence of soil background on most vegetation indices ( VIs) derived from remotely sensed imagery is a well known phenomenon, and has generated interest in the development of indices that would be less sensitive to this in uence. Several such indices have been developed thus far. This paper focuses on testing and comparing the sensitivity of seven intensively used, Landsat Thematic Mapper (TM) derived, VIs (NDVI, SAVI, MSAVI, PVI, WDVI, SAVI 2 and TSAVI) to bare surface variation with almost no vegetation signal. The study was conducted on a terrain composed of a high variety of bare surface materials of which basalt and gypsum are two extremely dark and bright substrates respectively. It was found that SAVI and MSAVI respond to bare surface material very similarly. Such close similarity was also found between PVI and WDVI, and between SAVI 2 and TSAVI. NDVI tends to be overestimated on dark surfaces, while SAVI, PVI and TSAVI show more sensitivity to bright surfaces. Comparison between D VI (the diVerence between pairs of VIs) and the brightness of the diVerent surface materials showed a high correlation in each case, which underlines the fact that the response of diVerent VIs to bare surface variation is mainly related to the surface brightness.

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“…Especially, the signal contribution from canopy/soil background is known to be one major source of variations in VI values. Numerous studies have been conducted to minimize canopy/soil background influences on VI values [5][6][7][8][9][10][11]. A systematic investigation and theoretical formulation on the impact of canopy/soil background has yet to be investigated and would be a critical and meaningful contribution to operational monitoring of terrestrial vegetation with satellite VI time series [7,[12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Derivation of Soil Line Influence on Two-Band Vegetation Indices and Vegetation Isolines

et al. 2009

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This paper introduces derivations of soil line influences on two-band vegetation indices (VIs) and vegetation isolines in the red and near infra-red reflectance space. Soil line variations are described as changes in the soil line parameters (slope and offset) and the red reflectance of the soil surface. A general form of a VI model equation written as a ratio of two linear functions (e.g., NDVI and SAVI) was assumed. It was found that relative VI variations can be approximated by a linear combination of the three soil parameters. The derived expressions imply the possibility of estimating and correcting for soil-induced bias errors in VIs and their derived biophysical parameters, caused by the assumption of a general soil line, through the use of external data sources such as regional soil maps.

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A ratio vegetation index adjusted for soil brightness

Cited by 289 publications

References 12 publications

Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data

Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data

Sensitivity of vegetation indices to substrate brightness in hyper-arid environment: The Makhtesh Ramon Crater (Israel) case study

Derivation of Soil Line Influence on Two-Band Vegetation Indices and Vegetation Isolines

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