Using spectral vegetation indices to estimate rangeland productivity

Richardson, A. J.; Everitt, J. H.

doi:10.1080/10106049209354353

Cited by 153 publications

(71 citation statements)

References 9 publications

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“…Indeed, the NIR-RED band difference index, as proposed by Richardson & Everitt (1992), also showed similar performance as VB-FAH (Figs. 12, 13), suggesting that NIR-RED may be another reliable index for the purpose of detecting and quantifying macroalgae.…”

Section: Application Of Vb-fah To Other Sensorsmentioning

confidence: 69%

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Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data: Application of a virtual baseline reflectance height technique

Xing

2016

Remote Sensing of Environment

139

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Several methods have been proposed in previous studies to map macroalgal blooms (MABs) in the Yellow Sea (YS) and East China Sea (ECS), yet some of the required spectral bands are not available on the new HJ-1 satellite sensors although they do provide optimal resolution and temporal coverage (30-m resolution with 2-day revisit frequency) for bloom mapping. In this study, an index of Virtual-Baseline Floating macroAlgae Height (VB-FAH) is proposed to use the green and red bands as the baseline to measure the height of the near-infrared (NIR) reflectance. Cross-sensor comparison with Landsat TM and ETM + data suggests that for several images evaluated here VB-FAH appears to be comparable to the previously proposed Floating Algae Index (FAI) even in the absence of a shortwave-infrared (SWIR) band. VB-FAH is applied to 30-m resolution TM and ETM + data for the YS during 1995-2006 and to HJ-1 data for the ECS during 2009-2014 to map MABs. Results show bloom history in the YS back to 1999 and early bloom occurrence in the ECS in winter and spring (e.g., February and March 2013). MABs are also found to extend to the ECS as far south as 26°N near Fujian Province and Taiwan, and as far east as the Kuroshio Current. These new findings provide important information for exploring the origins, causes, and consequences of MABs in the YS and ECS.

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Section: Application Of Vb-fah To Other Sensorsmentioning

confidence: 69%

“…Variations in sun glint might be one of major sources causing uncertainties in using the FAI, VB-FAH, NDVI and NIR-RED (the vertical height extracted by the NIR and red bands, or the band difference, Richardson & Everitt, 1992) indexes to classify macroalgae pixels.…”

Section: Sensitivity Of Vb-fah To Variations In Sea Surface Glint Andmentioning

confidence: 99%

Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data: Application of a virtual baseline reflectance height technique

Xing

2016

Remote Sensing of Environment

139

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“…With the use of two spectral bands red (RED) and near-infrared (NIR), NDVI computes total vegetation coverage area of the basin. This approach of using spectral data improves the utility of predictive change relations for vegetation (Richardson and Everitt 1992). Also, it demonstrates the best vegetation change detection (Lyon et al 1998).…”

Section: Methodsmentioning

confidence: 99%

“…NDVI is the ratio of difference between the near-infrared and red wavelength bands and sum of those two bands. It is expressed as follows (Richardson and Everitt 1992): where NDVI is Normalized Difference Vegetation Index, NIR is Near Infra Red colour spectral band and RED is Red colour spectral band. This is the most commonly used VI; it has the value in the range of -1 to 1.…”

Section: Methodsmentioning

confidence: 99%

Climate change impact on forest cover and vegetation in Betwa Basin, India

et al. 2014

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This paper evaluates the effect of climate change (described in terms of temperature and rainfall) on forest cover and vegetation (described in terms of Normalized Difference Vegetation Index) in the Betwa river basin, a tributary of River Yamuna in Central India. Temperature and rainfall data of 18 stations, forest cover and vegetation (derived using 5 years data from Landsat images employing ERDAS Imagine and ArcGIS) were used in the analysis. The effect of climate change was studied for both the premonsoon and post-monsoon seasons. In this study, the simple regression method was used to evaluate their relationship. In pre-monsoon season, temperature and forest cover analysis shows regression coefficient value of 0.6876 and, temperature and vegetation analysis shows regression coefficient value of 0.5751. Further, in post-monsoon season analysis rainfall and forest cover shows regression coefficient value of 0.8417 and, temperature and vegetation analysis shows regression coefficient value of 0.6854. The study reveals that, in pre-monsoon season temperature was significantly related with forest cover and vegetation. In postmonsoon season rainfall exhibited positive response to forest cover and, temperature exhibited negative response to vegetation in the Betwa river basin.

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“…The best known index is the NDVI (Normalized Difference Vegetation Index [8]), which is widely used among researchers. Besides, there are many indices reported in several papers which aim, for example, to reduce the effect of soils' reflectance on vegetation indices (SAVI, TSAVI, PVI) [9,10], to identify water (NDWI) [11] and to determine the water content of vegetation [12] or its chlorophyll content [13]. Finding the best indices becomes complicated as increasing number of bands are involved, i.e.…”

Section: Introductionmentioning

confidence: 99%

An interactive tool for semi-automatic feature extraction of hyperspectral data

Kovács

Szabó

2016

Open Geosciences

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Abstract:The spectral reflectance of the surface provides valuable information about the environment, which can be used to identify objects (e.g. land cover classification) or to estimate quantities of substances (e.g. biomass). We aimed to develop an MS Excel add-in -Hyperspectral Data Analyst (HypDA) -for a multipurpose quantitative analysis of spectral data in VBA programming language. HypDA was designed to calculate spectral indices from spectral data with user defined formulas (in all possible combinations involving a maximum of 4 bands) and to find the best correlations between the quantitative attribute data of the same object. Different types of regression models reveal the relationships, and the best results are saved in a worksheet. Qualitative variables can also be involved in the analysis carried out with separability and hypothesis testing; i.e. to find the wavelengths responsible for separating data into predefined groups. HypDA can be used both with hyperspectral imagery and spectrometer measurements. This bivariate approach requires significantly fewer observations than popular multivariate methods; it can therefore be applied to a wide range of research areas.

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Using spectral vegetation indices to estimate rangeland productivity

Cited by 153 publications

References 9 publications

Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data: Application of a virtual baseline reflectance height technique

Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data: Application of a virtual baseline reflectance height technique

Climate change impact on forest cover and vegetation in Betwa Basin, India

An interactive tool for semi-automatic feature extraction of hyperspectral data

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