Annual and interannual (ENSO) variability of spatial scaling properties of a vegetation index (NDVI) in Amazonia

Poveda, Germán; Salazar, Luis

doi:10.1016/j.rse.2004.08.001

Cited by 70 publications

(51 citation statements)

References 70 publications

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“…This lag causes significant variation in regional vegetation's sensitivity to precipitation-thus contributing to heterogeneity and complexity in the response of vegetation growth to global change. In addition, the spatio-temporal pattern of global vegetation growth is also influenced by large-scale regional climate oscillation [72,73]. For example, regions with obvious NDVI dynamic change have been found to have certain spatial connections to regions where vegetation is sensitive to the impact of North Atlantic Oscillation [74].…”

Section: Discussionmentioning

confidence: 99%

Investigation on the Patterns of Global Vegetation Change Using a Satellite-Sensed Vegetation Index

Deng

Liang

et al. 2010

Remote Sensing

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Abstract:The pattern of vegetation change in response to global change still remains a controversial issue. A Normalized Difference Vegetation Index (NDVI) dataset compiled by the Global Inventory Modeling and Mapping Studies (GIMMS) was used for analysis. For the period 1982-2006, GIMMS-NDVI analysis indicated that monthly NDVI changes show homogenous trends in middle and high latitude areas in the northern hemisphere and within, or near, the Tropic of Cancer and Capricorn; with obvious spatio-temporal heterogeneity on a global scale over the past two decades. The former areas featured increasing vegetation activity during growth seasons, and the latter areas experienced an even greater amplitude in places where precipitation is adequate. The discussion suggests that one should be cautious of using the NDVI time-series to analyze local vegetation dynamics because of its coarse resolution and uncertainties.

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Section: Discussionmentioning

confidence: 99%

Investigation on the Patterns of Global Vegetation Change Using a Satellite-Sensed Vegetation Index

Deng

Liang

et al. 2010

Remote Sensing

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“…Also, the usefulness of NOAA-AVHRR NDVI series in the study of inter-annual variability produced by ENSO events has been demonstrated Seiler and Kogan, 2002;Gurgel and Ferreira, 2003;Poveda and Salazar, 2004;Barbosa et al, 2006). Moreover, in places where rainfall data are sparse, the use of NDVI series instead of rainfall data improved the correlations with ENSO indices, to the extent that using 13 years of NDVI data allowed to model high anomaly values of NDVI and ENSO indices to predict drought onset in Northeastern Brazil 4 months 1176 M. M. GONZÁLEZ LOYARTE ET AL. in advance with 68% success .…”

Section: Introductionmentioning

confidence: 99%

Modelling bioclimate by means of Fourier analysis of NOAA‐AVHRR NDVI time series in Western Argentina

Loyarte

Menenti²,

Diblasi

2007

Intl Journal of Climatology

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This study assessed whether the relationship of climate with foliar phenology is sufficiently robust to use a measure of foliar phenology to interpolate climate statistics in areas where observations are sparse. The normalized difference vegetation index (NDVI) was used to represent vegetation activity. As a measure of foliar phenology, we used parameters obtained by modelling NDVI time series with a Fast Fourier Transform (FFT) applied to a 9-year time series of monthly National Oceanographic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) NDVI global area coverage (GAC) images. The FFT decomposes the series into an average signal and to sinusoidal components. The selected FFT parameters were mean NDVI, and amplitude and phase for a 1-year period. Our specific objective was to relate the ratio of precipitation, P, over potential evapotranspiration, ETP, to the FFT parameters in two complementary ways. The first was to use them as attributes in a numerical classification to obtain a map of foliar isophenology, and then associate these classes with bioclimatic types, thus generating a bioclimatic map. The second was to fit a multiple linear regression model with P/ETP as predicted variable and the FFT parameters as predictive variables. The regression model was then applied to obtain a map of the ratio P/ETP. The latter gave a second bioclimatic map. Foliar isophenology classes show a north-south decrease in phase value and increase in amplitude and mean NDVI values, thus reflecting the transition in climate conditions from hotter and drier to wetter and cooler. The model explains 92% (p-value <10 −12 ) of the spatial variation in the P/ETP ratio. When using a single FFT parameter, no significant relationship was obtained. The three parameters provide complementary information to understand phenological variability in response to climate variability. Modelling bioclimate by means of monthly NDVI series summarized by Fourier analysis is an adequate tool to extend climate data where they are sparse.

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“…For more detail concerning the significant changes in the processing of the AVHRR data to create the GIMMS data set please see: Tucker et al, in press;Pinzon et al, 2004;Pinzon, 2002. The processing of the AVHRR data for the GIMMS data set has reduced inter-annual variability related to the PAL data (figure 3b (Poveda and Salazar, 2004;Jia et al, 2003;Lotsch et al, 2003;Nemani, et al, 2002).…”

Section: Data Qualitymentioning

confidence: 99%

Changing patterns of global‐scale vegetation photosynthesis, 1982–1999

Sunwoo

Harris

2005

International Journal of Remote Sensing

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Abstract. The primary objective of this research was to assess changes in global vegetation photosynthesis between 1982 and 1999. Global-scale AVHRR NDVI data from the PAL and GIMMS data sets were analysed for 96% of the nonAntarctic land area of the Earth. The results showed that over 30% of the Earth's surface increased and less than 5% decreased in annual average photosynthesis greater than 4%. Although both the PAL and GIMMS data sets produced broadly similar patterns of change, there were distinct differences between the two data sets. Changes in vegetation photosynthesis were occurring in spatial clusters across the globe and were being driven by climate change, ENSO events and human activity.

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Annual and interannual (ENSO) variability of spatial scaling properties of a vegetation index (NDVI) in Amazonia

Cited by 70 publications

References 70 publications

Investigation on the Patterns of Global Vegetation Change Using a Satellite-Sensed Vegetation Index

Investigation on the Patterns of Global Vegetation Change Using a Satellite-Sensed Vegetation Index

Modelling bioclimate by means of Fourier analysis of NOAA‐AVHRR NDVI time series in Western Argentina

Changing patterns of global‐scale vegetation photosynthesis, 1982–1999

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