Greenness in semi-arid areas across the globe 1981–2007 — an Earth Observing Satellite based analysis of trends and drivers

Fensholt, Rasmus; Langanke, Tobias; Rasmussen, Kjeld; Reenberg, Anette; Prince, Stephen D.; Tucker, Compton J.; Scholes, Robert J.; Le, Quang Bao; Bondeau, Alberte; Eastman, Ronald; Epstein, Howard E.; Gaughan, Andrea E.; Helldén, Ulf; Mbow, Cheikh; Olsson, Lennart; Paruelo, José M.; Schweitzer, Christian; Seaquist, Jonathan; Wessels, Konrad J

doi:10.1016/j.rse.2012.01.017

Cited by 628 publications

(449 citation statements)

References 55 publications

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“…Of the areas with an extended LGS, 76.53% showed a positive correlation with precipitation, but only 58.9% positively correlated with temperature. This result confirms again that precipitation is one of the key factors in changing the vegetation phenology in water-limited environments (Yu, 2003;Fensholt et al 2012).…”

Section: Discussionsupporting

confidence: 83%

Driving force and changing trends of vegetation phenology in the Loess Plateau of China from 2000 to 2010

Wang

et al. 2016

J. Mt. Sci.

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Section: Discussionsupporting

confidence: 83%

Driving force and changing trends of vegetation phenology in the Loess Plateau of China from 2000 to 2010

Wang

et al. 2016

J. Mt. Sci.

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“…A previous study (Du et al 2014) on independently assessing the performance of GIMMS and MODIS NDVI using Landsat samples indicated that spatial patterns and dynamic trends of GIMMS NDVI were in overall acceptable agreement with MODIS NDVI. Correlations and differences between the two datasets on the TP are both superior to those from corresponding results in other arid regions of the world (Fensholt et al 2012), and in northeast China (Mao et al 2012). This indicates that the GIMMS and MODIS NDVI datasets show relatively high agreement on the TP and that the use of the MODIS to extend the time series of the GIMMS is appropriate.…”

Section: Discussion Integrated Use Of Gimms and Modis Ndvi Datasetsmentioning

confidence: 77%

“…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%

“…The Normalized Difference Vegetation Index (NDVI) is a vegetation index used to measure vegetation greenness and it is one of the best indicators for visualizing temporal changes of vegetation growth. Several studies have demonstrated that the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI dataset performs best in detecting long-term vegetation trends Beck and Goetz 2011;Fensholt et al 2012;Jeong et al 2011;Peng et al 2011;Zhao et al 2011). The most widely used GIMMS NDVI datasets are of the time series from 1981 to 2006.…”

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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“…Because of the low spatial resolution and wide swath, these datasets are typically used for vegetation analysis at the global, national, and regional scales (Rigina and Rasmussen 2003;Ma et al 2006;Fensholt et al 2012). These data, however, are unable to discern the spatial heterogeneity of land cover within the urban areas Zhou and Troy 2008).…”

Section: Pros and Cons Of Low Medium And High Resolution Datamentioning

confidence: 99%

Quantifying spatiotemporal pattern of urban greenspace: new insights from high resolution data

et al. 2015

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Context Urban greenspace provides myriad ecosystem services. To fully understand the ecological services provided by urban greenspace, it is crucial to first accurately quantify the composition and configuration, and change of urban greenspace. Objectives (1) Quantify the dynamics of greenspace in highly urbanized areas. (2) Compare and evaluate the efficacy of medium and high resolution image data on quantifying urban greenspace dynamics. Methods Using two very well-developed urban districts of Beijing, we compare and contrast the changes in urban greenspace from 2005 to 2009 based on two different datasets, the most commonly used Landsat TM data with 30 m resolution, and 2.5 m high spatial resolution imagery.Results We found urban greenspace in the two welldeveloped urban districts of Beijing to be very dynamic, in contrast to findings from previous research that greenspace in inner cities tends to remain largely unchanged. Such dynamics, however, could only be revealed by high spatial resolution imagery because medium resolution data, such as TM data greatly underestimated the percent cover of greenspace. The underestimate neglects smaller elements of greenspace as well as changes in configuration of larger patches, limiting the ability of TM data to detect such changes. Conclusions Our results underscore the importance and necessity of using high spatial resolution data to adequately quantify the distribution of urban greenspace and its change. Results from this study have important implications for urban greenspace management and planning. In addition, the dynamics revealed support emerging conceptions of urban areas as hierarchical patch mosaics.

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Greenness in semi-arid areas across the globe 1981–2007 — an Earth Observing Satellite based analysis of trends and drivers

Cited by 628 publications

References 55 publications

Driving force and changing trends of vegetation phenology in the Loess Plateau of China from 2000 to 2010

Driving force and changing trends of vegetation phenology in the Loess Plateau of China from 2000 to 2010

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

Quantifying spatiotemporal pattern of urban greenspace: new insights from high resolution data

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