Multi-sensor NDVI data continuity: Uncertainties and implications for vegetation monitoring applications

Leeuwen, Willem J. D. van; Orr, Barron J.; Marsh, Stuart E.; Herrmann, Stefanie M.

doi:10.1016/j.rse.2005.10.002

Cited by 222 publications

(132 citation statements)

References 42 publications

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“…MODIS instrumentation enhances spatial resolution and sensitivity to chlorophyll, excludes the interference of atmospheric and water vapor, modifies the synthesis method, and is a continuation and upgrade of GIMMS NDVI (Huete et al 2002). Studies comparing GIMMS and MODIS NDVI datasets and data interpolation have been conducted in many locations (Brown et al 2006;Tucker et al 2005;Chen et al 2011;Ding et al 2010;Gallo et al 2005;He et al 2012;Mao et al 2012;Peng et al 2012a;van Leeuwen et al 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Comparative analysis of results from multi-periods, changes in vegetation NDVI over the most recent years, and dynamics of NDVI trends are poorly studied. An increase in the number of remote sensors and the combined use of multi-sensor data to monitor current and historical vegetation dynamics will become more common in the future (Shen et al 2014;van Leeuwen et al 2006). For these reasons, GIMMS NDVI and MODIS NDVI data sets were combined in the present study, and these datasets were used to study changes in the TP vegetation over 14 periods : 1982-1999, 1982-2000,…, 1982-2012 …”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal changes of vegetation on the Tibetan Plateau and relationship to climatic variables during multiyear periods from 1982–2012

Zhao

Jian-min

et al. 2015

Environ Earth Sci

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Understanding the dynamics of vegetation system change is often limited by relatively brief data sequences and the shortage of comparable analyses of variation over longer periods. In this study, GIMMS NDVI and MODIS NDVI datasets were integrated to establish a consistent NDVI time series from 1982 to 2012 on vegetation of the Tibetan Plateau in China. The spatiotemporal patterns of change in seasonal NDVI and their linkage with climatic variables were analyzed at regional and pixel scales over 14 periods ranging from 18 to 31 years and beginning in 1982. On a regional scale, positive trends of growing season and seasonal NDVI were observed during the 14 periods, and the increases were statistically significant for growing season NDVI during all periods, and for summer and autumn NDVI during only the last four and the last two periods, respectively. The rates of NDVI increase in growing season and spring significantly decreased over the 14 periods. NDVI rates slightly decrease in summer and significantly increase in autumn. At a pixel scale, areas with significant greening or significant browning significantly increased over the 14 periods during the growth season and during all seasons except spring, in which the proportion of vegetated area with greening rapidly decreased. Temperature was the primary climatic driver for the observed vegetation changes during multiple periods while precipitation and sunshine duration had significant impacts on vegetation growth only in limited parts of the study area or during spring. Vegetation growth response to climate change varied across seasons and regions. Trend analysis during the multiple nested time series provides a better understanding of NDVI dynamics and may help to forecast future changes. Spring NDVI is likely to continue decreasing, autumn NDVI will continue increasing, and the magnitude of the NDVI increase during growing season will decline in the future.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal changes of vegetation on the Tibetan Plateau and relationship to climatic variables during multiyear periods from 1982–2012

Zhao

Jian-min

et al. 2015

Environ Earth Sci

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“…Estudos têm analisado as tendências dos índices de vegetação, especialmente do NDVI, para inúmeros fins, desde mudanças de uso e cobertura do solo a mudanças fenológicas (Leeuwen et al, 2006). Uma abordagem frequente é estabelecer tendências por meio de regressão linear do NDVI, calculado em períodos anuais ou sazonais (Jong & Bruin, 2012;Forkel et al, 2013).…”

Section: Introductionunclassified

Tendências temporais de índices de vegetação nos campos do Pampa do Brasil e do Uruguai

Wagner

Fontana

Fraisse³

et al. 2013

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi avaliar a redução do vigor vegetativo da cobertura vegetal do Pampa do Brasil e do Uruguai, por meio da identificação de tendências negativas em séries temporais de imagens. Utilizaram-se séries temporais de imagens de NDVI/EVI do sensor Modis, de 2000 a 2011; imagens de índices de umidade do solo do "climate forecast system reanalysis"; e dados de precipitação pluvial de estações meteorológicas. O estudo quantificou tendências lineares e não lineares nas séries de NDVI e EVI, em áreas de campos. Na tendência monotônica de Mann-Kendall, a 5% de probabilidade, 81,9% da área total estudada foi significativa com o NDVI, e 74,8%, com o EVI; no entanto, o EVI apresentou contraste superior na estimativa dos parâmetros. Os resultados mostraram maior sinal negativo a oeste, com valores médios de R 2 >0,15, r<-0,3 e τ <-0,15 na tendência dos índices de vegetação, e tendência decrescente para NDVI, EVI e precipitação pluvial, com menores valores médios de umidade do solo. A tendência negativa dos índices de vegetação, relacionada à combinação da ocorrência de deficit hídrico em solos rasos com o sobrepastoreio, indica alterações no padrão de cobertura vegetal do Pampa, com redução do vigor vegetativo.Termos para indexação: campos nativos, EVI, Modis, NDVI, séries temporais. Temporal trends of vegetation indices on Pampa grasslands in Brazil and UruguayAbstract -The objective of this work was to evaluate the reduction in the vegetative vigor of Pampa vegetation cover in Brazil and Uruguay, by identifying negative trends in images time series. The following were used: time series of NDVI/EVI images from the Modis sensor, from 2000 to 2011; images from soil moisture indices from the climate forecast system reanalysis; and precipitation data from meteorological stations. The study quantified linear and nonlinear trends in the NDVI and EVI series in grassland areas. With the MannKendall monotonic trend, at 5% probability, 81.9% of the total area studied was significant with NDVI, and 74.8% with EVI; however, EVI showed superior contrast in the estimation of parameters. The results showed: highest negative signal in the west, with average values of R 2 >0.15, r<-0.3, and τ <-0.15 in the tendency of the vegetation indices; and decreasing tendency for NDVI, EVI, and rainfall, with lower mean soil moisture values. The negative trend of the vegetation indices, related to the combination of drought occurrence in surface soils with excessive grazing, indicates changes in the pattern of Pampa vegetation cover, with reduction in vegetative vigor.

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“…NDVI is a specific tool that can separate vegetation from other classes precisely (van Leeuwen et al 2006). The study area is predominantly covered by vegetation; consequently, this index is appropriate for processing of the data (Pettorelli et al 2011).…”

Section: Methodsmentioning

confidence: 99%

Rule-based semi-automated approach for the detection of landslides induced by 18 September 2011 Sikkim, Himalaya, earthquake using IRS LISS3 satellite images

Siyahghalati

Saraf

Pradhan

et al. 2014

Geomatics, Natural Hazards and Risk

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Landslide is considered as one of the most devastating and most costly natural hazards in highlands, which is triggered mainly by rainfalls or earthquakes. In comparison with other methods, landslide mapping and monitoring via remote sensing data products are considered as the least expensive method of data collection. The current research attempts to detect landslides which occurred due to a 6.9 magnitude earthquake in Sikkim Himalaya, India, on 18 September 2011 and also to establish the spatial relationship between landslides and the slope of the terrain. To detect the landslides, decision tree method was applied on two Indian remote sensing satellites linear imaging self-scanning sensor (LISS III) images acquired from 2007 and 2011 which were taken before and after the earthquake. As the study area was relatively huge for identifying the landslides, the region was separated into two parts: "tested study area" and "real study area". The overall accuracy of landslide detection was 76%, and 75% for tested and real study area, respectively. Then, the spatial relationship between the landslides and the slope of the terrain was conducted using the digital elevation model. The results revealed that most of the landslides occurred between the slope of 25 and 45 covering 2.3 km 2 and no landslide recorded in the slope of 65 -90 in the real study area. The results obtained in this study may be useful for decision-making and policy support towards reconstruction effort after the landslide occurrence. In addition, the information can be useful for reducing the risk of potential damages to substructures and properties by developing new and efficient strategies.

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Multi-sensor NDVI data continuity: Uncertainties and implications for vegetation monitoring applications

Cited by 222 publications

References 42 publications

Spatiotemporal changes of vegetation on the Tibetan Plateau and relationship to climatic variables during multiyear periods from 1982–2012

Spatiotemporal changes of vegetation on the Tibetan Plateau and relationship to climatic variables during multiyear periods from 1982–2012

Tendências temporais de índices de vegetação nos campos do Pampa do Brasil e do Uruguai

Rule-based semi-automated approach for the detection of landslides induced by 18 September 2011 Sikkim, Himalaya, earthquake using IRS LISS3 satellite images

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