Evaluation of earth observation based long term vegetation trends — Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data

Fensholt, Rasmus; Rasmussen, Kjeld; Nielsen, Thomas Theis; Mbow, Cheikh

doi:10.1016/j.rse.2009.04.004

Cited by 470 publications

(306 citation statements)

References 52 publications

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“…NDVI of the growing season and different seasons were calculated as the mean of the NDVI values for the corresponding months. The method of leastsquares linear regression is characterized by simplicity and robustness (Peng et al 2012b), and the results are easy to interpret and compare between datasets and between pixels (Fensholt et al 2009). This statistical technique was therefore used to estimate linear time trends of NDVI and three climate variables (temperature, precipitation and sunshine duration) during given periods at regional and pixel scales, and the significance level (P) of these variables was determined using the F test.…”

Section: Methodsmentioning

confidence: 99%

“…Using a spatial average resampling method like Fensholt et al (2009) andFensholt et al (2012), the MODIS NDVI was resampled to a spatial resolution of 8 km 9 8 km to be consistent with the GIMMS NDVI datasets. Pixels with a mean growing season NDVI \0.10 were excluded in this study to reduce the influence of sparsely vegetated pixels on the NDVI trend following the lead of earlier related research studies (Mohammat et al 2013;Piao et al 2011a, b;Zhang et al 2013;Zhao et al 2011).…”

Section: Data Sources and Processingmentioning

confidence: 99%

“…To compare recent vegetation dynamics with historical vegetation dynamics, an extension of the GIMMS NDVI dataset time series is required (de Jong et al 2011;Mao et al 2012). Moderate resolution imaging spectroradiometer (MODIS) provides data that are improvements over GIMMS NDVI (Fensholt et al 2009). 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).…”

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: Methodsmentioning

confidence: 99%

Section: Data Sources and Processingmentioning

confidence: 99%

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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“…GIMMS AVHRR-NDVI was important model inputs for the calibration and validation. It was approved to be well-suited for vegetation studies of arid and semi-arid areas (Fensholt et al, 2009), which makes it helpful in building regional ET model. SPOT VEG-ETATION NDVI was used in model application before and after GFG project.…”

Section: Regional Database For Et Model Calibration Validation and Amentioning

confidence: 99%

“…SPOT VEG-ETATION NDVI was used in model application before and after GFG project. It was considered an improvement over AVHRR GIMMS especially in spatial resolution (Fensholt et al, 2009). The uncertainty caused by using different vegetation sensors was reduced by the following trend analysis of model application.…”

Section: Regional Database For Et Model Calibration Validation and Amentioning

confidence: 99%

Regional effects of vegetation restoration on water yield across the Loess Plateau, China

Feng

Sun

et al. 2012

Hydrol. Earth Syst. Sci.

181

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Abstract. The general relationships between vegetation and water yield under different climatic regimes are well established at a small watershed scale in the past century. However, applications of these basic theories to evaluate the regional effects of land cover change on water resources remain challenging due to the complex interactions of vegetation and climatic variability and hydrologic processes at the large scale. The objective of this study was to explore ways to examine the spatial and temporal effects of a large ecological restoration project on water yield across the Loess Plateau region in northern China. We estimated annual water yield as the difference between precipitation input and modelled actual evapotranspiration (ET) output. We constructed a monthly ET model using published ET data derived from eddy flux measurements and watershed streamflow data. We validated the ET models at a watershed and regional levels. The model was then applied to examine regional water yield under land cover change and climatic variability during the implementation of the Grain-for-Green (GFG) project during 1999-2007. We found that water yield in 38 % of the Loess Plateau area might have decreased (1-48 mm per year) as a result of land cover change alone. However, combined with climatic variability, 37 % of the study area might have seen a decrease in water yield with a range of 1-54 mm per year, and 35 % of the study area might have seen an increase with a range of 1-10 mm per year. Across the study region, climate variability masked or strengthened the water yield response to vegetation restoration. The absolute annual water yield change due to vegetation restoration varied with precipitation regimes with the highest in wet years, but the relative water yield changes were most pronounced in dry years. We concluded that the effects of land cover change associated with ecological restoration varied greatly over time and space and were strongly influenced by climatic variability in the arid region. The current regional vegetation restoration projects have variable effects on local water resources across the region. Land management planning must consider the influences of spatial climate variability and long-term climate change on water yield to be more effective for achieving environmental sustainability.

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The turning of ecological change in the Yellow River Basin

Gu,

Guan,

Huang

et al. 2023

Hydrological Processes

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The Yellow River Basin (YRB) as a source of the oldest world cultures, flows through vast arid and semi‐arid regions of China. Its fragile vegetation is suffering the obvious effect from regional climate change, and easily turns from a critical state into qualitatively different modes of change, which implies significant impacts on humans and ecosystems. Our results show that in the period of 1982–2015, the normalized difference vegetation index (NDVI) appeared significantly discontinuous changes by stagnant warming and strengthening wetting, especially in the central YRB (104.5°–111° E, 35.5°–38° N). The NDVI in this region shifted from slowly increasing (5.42 × 10−4 a−1) to rapidly increasing (5.13 × 10−3 a−1) around 2003, called the turning point (TP). Such shift change is mainly a result of increased moderate rain, with frequency trend changed from 0.083 times·a−1 to 0.324 times·a−1. Meanwhile, these changes on vegetation led to a reversal of the gross primary productivity (GPP) from decreasing to increasing. Such results indicated the vulnerable ecosystem of the central YRB has played a positive contribution to the carbon balance, and more sustainable management of vegetation is required for the ecological development and engaging adaptive strategy.

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Evaluation of earth observation based long term vegetation trends — Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data

Cited by 470 publications

References 52 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

Regional effects of vegetation restoration on water yield across the Loess Plateau, China

The turning of ecological change in the Yellow River Basin

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