Pasture Land Cover in Eastern Australia from NOAA-AVHRR NDVI and Classified Landsat TM

Hill, Michael J.; Vickery, P. J.; Furnival, EP; Donald, G. E.

doi:10.1016/s0034-4257(98)00075-3

Cited by 44 publications

(28 citation statements)

References 21 publications

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“…Conversely, it is closely related to fractional green cover (0-1; Carlson & Ripley, 1997;Lu et al, 2003) and to the fraction of Photosynthetically Active Radiation absorbed by vegetation (fPAR, 0-0.95; Asrar et al, 1984). It has been used to make inferences about vegetation characteristics such as Leaf Area Index (e.g., McVicar et al, 1996;Pierce et al, 1993), canopy structure (e.g., Berry & Roderick, 2002), phenology (e.g., DeFries et al, 1995;Jolly et al, 2005;Guerschman et al, 2003), biomass (e.g., Baret et al, 1989), and vegetation type (e.g., Hill et al, 1999). The NDVI has also been related to vegetation-related phenomena-for example, landscape condition (Boer & Puigdefabregas, 2003;Holm et al, 2003); drought severity (McVicar & Jupp, 1998); biodiversity (Gould, 2000); carbon assimilation (Asrar et al, 1984); water availability (Wellens, 1997), and catchment energy and water balances (Szilagyi, 2000;Donohue et al, 2007a).…”

Section: Traditional Approaches To Correcting Avhrr Reflectance Datamentioning

confidence: 99%

Deriving consistent long-term vegetation information from AVHRR reflectance data using a cover-triangle-based framework

Donohue

Roderick

McVicar

2008

Remote Sensing of Environment

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Long-term vegetation dynamics associated with climatic changes can be assessed using Advanced Very High Resolution Radiometer (AVHRR) red and near-infrared reflectance data provided that the data have been processed to remove the effects of non-target signal variability, such as atmospheric and sensor calibration effects. Here we present a new method that performs a relative calibration of reflectance data to produce consistent long-term vegetation information. It is based on a simple biological framework that assumes that the position of the vegetation cover triangle is invariant in reflectance space. This assumption is in fact an intrinsic assumption behind the commonly used Normalised Difference Vegetation Index (NDVI) and is violated when the NDVI is calculated from inadequately corrected reflectance data. In this new method, any temporal variability in the position of the cover triangle is removed by geometrically transforming the observed reflectance data such that two features of the triangle-the soil line and the dark point-are stationary in reflectance space. The fraction of Photosynthetically Active Radiation absorbed by vegetation (fPAR; 0.0-0.95) is then calculated, via the NDVI, from calibrated reflectances. This method was tested using two distinct, monthly AVHRR products for Australia: (i) the coarse-resolution, fully calibrated, partially atmospherically corrected PAL data (1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994); and (ii) the fine-resolution, fully calibrated, nonatmospherically corrected HRPT data (1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004). Results show that, in the 20-month period when the two datasets overlap (1992)(1993)(1994), the Australia-wide, root mean square difference between the two datasets improved from 0.098 to 0.027 fPAR units. The calibrations have produced two approximately equivalent datasets that can be combined as a single input into time-series analyses. The application of this method is limited to areas that have a wide-enough variety of land-cover types so that the soil line and dark point are evident in the cover triangle in every image of the time-series. Another limitation is that the methodology performs only bulk, relative calibrations and does not remove the absolute effects of observation uncertainties. The simplicity of the method means that the calibration procedure can be easily incorporated into near-real-time operational remote-sensing environments. Vegetation information produced using this invariant-cover-triangle method is expected to be well suited to the analysis of longterm vegetation dynamics and change.

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Section: Traditional Approaches To Correcting Avhrr Reflectance Datamentioning

confidence: 99%

Deriving consistent long-term vegetation information from AVHRR reflectance data using a cover-triangle-based framework

Donohue

Roderick

McVicar

2008

Remote Sensing of Environment

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“…Therefore, methodologies for the reduction, or selection, of the data used in the former can be transferred to the optimisation of data acquired over a period of time. Hyper-temporal data are generated from a platform which offers a high temporal resolution and a sufficiently long time-series such as AVHRR (Hill et al 1999;Hermance et al, 2007), MERIS (Zurita-Milla et al, 2009;Carrão et al, 2010;O'Connor et al, 2012) or MODIS (Lunetta et al, 2006;Carrão et al, 2008;Wardlow and Egbert, 2008;Pringle et al, 2012). Typically, Normalized Difference Vegetation Index (NDVI) or other sensor-specific vegetation indices (VI) serve as the base for land-cover and crop type classification or phenological analysis from regional to global scales.…”

Section: Introductionmentioning

confidence: 99%

Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series

Nitze

Barrett

Cawkwell

2015

International Journal of Applied Earth Observation and Geoinfor

120

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Abstract:The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types, the ability to discriminate different land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may limit their application. The optimisation of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Forest, Water, Settlement, Peatland) and grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland on a 9-year time-series of MODIS Terra 16 day composite data (MOD13Q1). Feature Importances for each acquisition period of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) were calculated for both classification scenarios. In the general land cover classification, the months December and January showed the highest, and July and August the lowest separability for both VIs over the entire nine-year period. This temporal separability was reflected in the classification accuracies, where the optimal choice of image dates outperformed the worst image date by 13% using NDVI and 5% using EVI on a mono-temporal analysis. With the addition of the next best image periods to the data input the classification accuracies converged quickly to their limit at around 8 to 10 images. The binary classification schemes, using two classes only, showed a stronger seasonal dependency with a higher intra-annual, but lower inter-annual variation. Nonetheless anomalous weather conditions, such as the cold winter of 2009/2010 can alter the temporal separability pattern significantly. Due to the extensive use of the NDVI for land cover discrimination, the findings of this study should be transferrable to data from other optical sensors. However, the high impact of outliers from the general climatic pattern highlights the limitation of spatial transferability to locations with different climatic and land cover conditions. The use of high-temporal, moderate resolution data such as MODIS in conjunction with machine-learning techniques proved to be a good base for the prediction of image acquisition timing for optimal land cover classification results.2

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“…Apesar de ocorrerem em menor proporção, estas áreas são de fundamental importância para a pecuária extensiva, pois representam uma alternativa de pastoreio para o gado no período de seca (transumância). Segundo Hill et al (1999), estas espécies são mais resistentes às interferências climáticas, com menores reduções de biomassa durante os períodos de estiagem. quartizarênicos, de baixa fertilidade natural, distribuídos espacialmente em faixas contínuas, além das "Áreas Urbanas", representadas principalmente pelas sedes dos municípios de Aruanã, Britânia, Matrinchã, Santa Fé de Goiás, Jussara, Itapirapuã, Novo Brasil, Fazenda Nova, Buriti de Goiás e Goiás.…”

Section: Características Gerais Sobre a Paisagemunclassified

Análise Descritiva Dos Aspectos Biofísicos Associados Aos Padrões De Cobertura E Uso Da Terra Na Bacia Hidrográfica Do Rio Vermelho, Bioma Cerrado

Santos

Ferreira

2017

GeoFocus

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RESUMOO presente trabalho, ao utilizar a bacia hidrográfica do rio Vermelho como unidade experimental do bioma Cerrado, teve como objetivo principal realizar uma análise ampla das respostas biofísicas, frente aos processos históricos que condicionaram as transformações na paisagem, considerando o período entre 2001 a 2011 e uma ampla base de dados satelitária. Algumas características edáficas, à exemplo de solos e relevo, foram fatores determinantes na organização espacial da paisagem. Fatores climáticos, à exemplo da sazonalidade, tem uma influência direta na biomassa das principais formações vegetais, principalmente àquelas que apresentam sistemas radiculares superficiais (ex. pastagens cultivadas), com reduções nos valores dos índices de vegetação de até 50 % durante o período de seca, a partir da observação e correlação comprovada entre os valores de P e EVI. De forma significativa, as áreas de vegetação natural, comparadas às áreas de pastagens cultivadas, apresentaram comportamentos distintos do ponto de vista biofísico (p < 0,05). Em fato, dois macroambientes puderam ser definidos para a área de estudo, i.e. "Região 1" e "Região 2", onde predominam a ocupação antrópica e a vegetação natural remanescente, respectivamente.Palavras chave: Dinâmica da Paisagem, Sensoriamento Remoto, Parâmetros Biofísicos.

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Pasture Land Cover in Eastern Australia from NOAA-AVHRR NDVI and Classified Landsat TM

Cited by 44 publications

References 21 publications

Deriving consistent long-term vegetation information from AVHRR reflectance data using a cover-triangle-based framework

Deriving consistent long-term vegetation information from AVHRR reflectance data using a cover-triangle-based framework

Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series

Análise Descritiva Dos Aspectos Biofísicos Associados Aos Padrões De Cobertura E Uso Da Terra Na Bacia Hidrográfica Do Rio Vermelho, Bioma Cerrado

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