Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas

Barati, Susan; Rayegani, Behzad; Saati, Mehdi; Sharifi, Alireza; Nasri, M’barek

doi:10.1016/j.ejrs.2011.06.001

Cited by 105 publications

(62 citation statements)

References 32 publications

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“…The coefficient of determination between SMA and AGB was not significant in the high grazing intensity and low grazing intensity sites in the high rainfall zone but the coefficient of determination for the combined treatment in linear and non-linear relation was highly significant. This is similar to results of previous studies that found a highly significant correlation coefficients (Gamon et al 1995;Barati et al 2011).…”

Section: Discussionsupporting

confidence: 82%

Evaluating derived vegetation indices and cover fraction to estimate rangeland aboveground biomass in semi-arid environments

Fajji¹,

Palamuleni²,

Mlambo³

2017

SA J of Geomatics

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Section: Discussionsupporting

confidence: 82%

Evaluating derived vegetation indices and cover fraction to estimate rangeland aboveground biomass in semi-arid environments

Fajji¹,

Palamuleni²,

Mlambo³

2017

SA J of Geomatics

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“…VIs have a good proportional relation to biophysical parameters, which are often referred to as the "biomass index". Hundreds of remote-sensing-based VIs have been developed and used in various fields of geoscience (Barati et al, 2011). Regression equations are used to establish a functional relationship based on field survey samples and the VIs, using various linear and nonlinear equations.…”

Section: Model Developmentmentioning

confidence: 99%

Spatial dynamics of aboveground carbon stock in urban green space: a case study of Xi’an, China

et al. 2014

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Greenhouse gas emission of carbon dioxide (CO 2 ) is one of the major factors causing global climate change. Urban green space plays a key role in regulating the global carbon cycle and reducing atmospheric CO 2 . Quantifying the carbon stock, distribution and change of urban green space is vital to understanding the role of urban green space in the urban environment. Remote sensing is a valuable and effective tool for monitoring and estimating aboveground carbon (AGC) stock in large areas. In the present study, different remotely-sensed vegetation indices (VIs) were used to develop a regression equation between VI and AGC stock of urban green space, and the best fit model was then used to estimate the AGC stock of urban green space within the beltways of Xi'an city for the years 2004 and 2010. A map of changes in the spatial distribution patterns of AGC stock was plotted and the possible causes of these changes were analyzed. Results showed that Normalized Difference Vegetation Index (NDVI) correlated moderately well with AGC stock in urban green space. The Difference Vegetation Index (DVI), Ratio Vegetation Index (RVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI) and Renormalized Difference Vegetative Index (RDVI) were lower correlation coefficients than NDVI. The AGC stock in the urban green space of Xi'an in 2004 and 2010 was 73,843 and 126,621 t, respectively, with an average annual growth of 8,796 t and an average annual growth rate of 11.9%. The carbon densities in 2004 and 2010 were 1.62 and 2.77 t/hm 2 , respectively. Precipitation was not an important factor to influence the changes of AGC stock in the urban green space of Xi'an. Policy orientation, major ecological greening projects such as "transplanting big trees into the city" and the World Horticultural Exposition were found to have an important impact on changes in the spatiotemporal patterns of AGC stock.Citation: ZhengYang YAO, JianJun LIU, XiaoWen ZHAO, DongFeng LONG, Li WANG. 2015. Spatial dynamics of aboveground carbon stock in urban green space: a case study

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“…For these reasons, a recalibration of models performed on a field dataset collected on a longer time window appear to be convenient before an operational use of models. Furthermore, the use of different vegetation indices could also be exploited [Barati et al, 2011]. However, in this paper only NDVI was considered because additional MODIS spectral bands, required for the computation of most of these indices (typically blue or green bands), are not available at 250 m resolution.…”

Section: Models' Improvementmentioning

confidence: 99%

MODIS time series contribution for the estimation of nutritional properties of alpine grassland

Ranghetti

Bassano

Bogliani

et al. 2016

European Journal of Remote Sensing

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Despite the Normalised Difference Vegetation Index (NDVI) has been used to make predictions on forage quality, its relationship with bromatological field data has not been widely tested. This relationship was investigated in alpine grasslands of the Gran Paradiso National Park (Italian Alps). Predictive models were built using remotely sensed derived variables (NDVI and phenological information computed from MODIS) in combination with geo-morphometric data as predictors of measured biomass, crude protein, fibre and fibre digestibility, obtained from 142 grass samples collected within 19 experimental plots every two weeks during the whole 2012 growing season. The models were both crossvalidated and validated on an independent dataset (112 samples collected during 2013). A good predictability ability was found for the estimation of most of the bromatological measures, with a considerable relative importance of remotely sensed derived predictors; instead, a direct use of NDVI values as a proxy of bromatological variables appeared not to be supported.

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Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas

Cited by 105 publications

References 32 publications

Evaluating derived vegetation indices and cover fraction to estimate rangeland aboveground biomass in semi-arid environments

Evaluating derived vegetation indices and cover fraction to estimate rangeland aboveground biomass in semi-arid environments

Spatial dynamics of aboveground carbon stock in urban green space: a case study of Xi’an, China

MODIS time series contribution for the estimation of nutritional properties of alpine grassland

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