Optical–Biophysical Relationships of Vegetation Spectra without Background Contamination

Large portions of Earth's terrestrial surface are arid or semiarid. As in these regions the hydrological cycle and the vegetation dynamics are tightly interconnected, a coupled modelling of these two systems is needed to fully reproduce the ecosystem behaviour and to predict possible responses to climate change. In this paper, the performance of two parsimonious dynamic EVI is reported in literature to be highly correlated with leaf area index so it is compared with modelled LAI mod (r WUE-model =0.45; r LUE-model =0.57). In contrast, NDVI appears highly linked to soil moisture, through the control exerted by this variable on chlorophyll production, and is therefore used to analyze LAI* mod , models' output corrected by plant water-stress (r WUE-model =0.62; r LUE-model =0.59).MODIS LAI and ET are found to be unrealistic in the studied area. The performance of both models in this semiarid region is found to be reasonable.However, the LUE-model presents the advantages of a better performance, the possibility to be used in a wider range of climates and to have been extensively tested in literature.

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“…This suggests that EVI is responding to variations in LAI, as have been reported elsewhere (Gao et al, 2000;Huete et al, 2002).…”

Section: Sensitivity Analysissupporting

confidence: 77%

“…NDVI is sensitive to green leaf biomass (Tucker, 1979) while EVI has been found to be responsive to both LAI and canopy structure (Gao et al, 2000). For these reasons, these two vegetation indices provide complementary information, extremely valuable when studying vegetation by means of remote sensing.…”

Section: Satellite Datamentioning

confidence: 99%

Comparing two approaches for parsimonious vegetation modelling in semiarid regions using satellite data

et al. 2014

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“…In fact, a major problem in the use of these VIs arises from the fact that canopy reflectance, in the visible and near infrared, strongly depends on both structural (LAI, leaf orientation, canopy architecture) and biochemical properties (e.g., photosynthetic pigments content) (Asner, 1998;Gao et al, 2000;Haboudane et al, 2004). It is difficult to uncouple the combined effect of the two influences (red and NIR spectral regions) and, consequently, to develop a "unique" VI exclusively sensitive to a single vegetation property, as Agapiou et al (2012) point out.…”

Section: Methods Of Vegetation Monitoring Using Remote Sensing Datamentioning

confidence: 99%

“…These facts allow the remote sensing of plant phenology. Vegetation reflectance is primarily a function of the optical properties of leaves, but also of other canopy elements (nonphotosynthetic elements such as branches and trunks), canopy architecture (leaf and stem orientation, foliage clumping), background reflectance, illumination conditions, viewing geometry and atmospheric influence (Baret and Guyot, 1991;Asner, 1998;Huete et al, 1999;Gao et al, 2000;Homolová et al, 2013). Even when leaf spectral properties remain constant, the spectral signature of vegetation varies as the architectural arrangement of plant components changes and also the proportion of soil and plants (Pinter et al, 2003).…”

Section: Phenologymentioning

confidence: 99%

Remote sensing in food production and #8211; a review

Calvão

Pessoa

2015

Emir. J. Food Agric

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FAO's most recent assessments indicate that, globally, in 2011-13, about one in eight people in the world are likely to have suffered from chronic hunger, not having adequate food supplies for an active and healthy life. Food security crises are now caused, almost exclusively, by problems in access to food, not absolute food availability, but, monitoring agricultural production remains fundamental. Traditional ground-based systems of production estimation have many limitations which have restricted their use. However, remotely sensed satellite data offer timely, objective, economical, and synoptic information for crop monitoring. The objective of this paper is to review the contribution of remote sensing techniques in the classification, monitoring of crop phenology and condition and estimation of production.

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“…Huete et al (1997) desenvolveram o EVI, um índice que minimiza os efeitos de solos e da atmosfera e que é mais sensível às variações na estrutura do dossel (Gao et al, 2000). Seu cálculo é dado por (Equação 2):…”

Section: Materiais E Métodosunclassified

Séries Temporais De Índices De Vegetação (Ndvi E Evi) Do Sensor Modis Para Detecção De Desmatamentos No Bioma Cerrado

Bayma

Sano

2015

Bol. Ciênc. Geod.

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Resumo:O Cerrado é um bioma caracterizado por uma ampla variedade de formações vegetacionais, pela forte sazonalidade climática e pela acentuada pressão antrópica. Este estudo analisou o uso de séries temporais (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) do MODIS EVI e MODIS NDVI para detectar desmatamentos no Cerrado. As áreas de estudo corresponderam aos municípios de Jataí/GO, Luís Eduardo Magalhães/BA, Mateiros/TO e São Miguel do Araguaia/GO. As séries temporais foram suavizadas pelo filtro logística dupla, disponível no programa TIMESAT. Foi utilizada a estatística de Kruskal-Wallis para definir se as assinaturas temporais representativas das formações florestais de Jataí e São Miguel do Araguaia e das formações savânicas dos quatro municípios eram estatisticamente iguais. Em seguida, foram definidos os limiares de desmatamento para cada formação vegetacional (valores de EVI e NDVI abaixo dos quais são considerados desmatamentos). Resultados indicaram que não é possível definir um limiar único de desmatamento para cada formação vegetacional, porém, é possível detectar desmatamentos em formações florestais e savânicas. O desempenho do NDVI foi superior ao EVI no sentido de que a redução nos valores de NDVI durante o evento de desmatamento é mais acentuada do que nos valores de EVI.Palavras-chave: Sensoriamento Remoto; Assinaturas Temporais; Savana Tropical; Imagens de Satélite.

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Optical–Biophysical Relationships of Vegetation Spectra without Background Contamination

Cited by 530 publications

References 43 publications

Comparing two approaches for parsimonious vegetation modelling in semiarid regions using satellite data

Comparing two approaches for parsimonious vegetation modelling in semiarid regions using satellite data

Remote sensing in food production and #8211; a review

Séries Temporais De Índices De Vegetação (Ndvi E Evi) Do Sensor Modis Para Detecção De Desmatamentos No Bioma Cerrado

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