Integrating AVHRR and MODIS data to monitor NDVI changes and their relationships with climatic parameters in Northeast China

Mao, Dehua; Wang, Zongming; Liu, Luo; Ren, Chunying

doi:10.1016/j.jag.2011.10.007

Cited by 240 publications

(184 citation statements)

References 22 publications

(30 reference statements)

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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: 76%

“…Hence, the combined use of the two data sets requires initial testing for continuity and consistency (Brown et al 2006;Mao et al 2012;Steven et al 2003). Data comparison is an effective method for evaluating the continuity of data collected from different sensors (Fontana et al 2012;Steven et al 2003).…”

Section: Discussion Integrated Use Of Gimms and Modis Ndvi Datasetsmentioning

confidence: 99%

“…There are significant differences between the NDVI value from GIMMS and MODIS, so the GIMMS and MODIS NDVI cannot be directly combined to evaluate the trend of vegetation activity (de Jong et al 2011;Ding et al 2010;He et al 2012;Mao et al 2012). Checking the continuity and consistency of the two NDVI datasets is necessary before using them together.…”

Section: Methodsmentioning

confidence: 99%

“…The most widely used GIMMS NDVI datasets are of the time series from 1981 to 2006. 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).…”

Section: Introductionmentioning

confidence: 99%

“…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%

See 4 more Smart Citations

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: Discussion Integrated Use Of Gimms and Modis Ndvi Datasetsmentioning

confidence: 76%

Section: Discussion Integrated Use Of Gimms and Modis Ndvi Datasetsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Past and future effects of climate change on spatially heterogeneous vegetation activity in China

Gao

Jiao

et al. 2017

Earth's Future

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Climate change is a major driver of vegetation activity but its complex ecological relationships impede research efforts. In this study, the spatial distribution and dynamic characteristics of climate change effects on vegetation activity in China from the 1980s to the 2010s and from 2021 to 2050 were investigated using a geographically weighted regression (GWR) model. The GWR model was based on combined datasets of satellite vegetation index, climate observation and projection, and future vegetation productivity simulation. Our results revealed that the significantly positive precipitation–vegetation relationship was and will be mostly distributed in North China. However, the regions with temperature‐dominated distribution of vegetation activity were and will be mainly located in South China. Due to the varying climate features and vegetation cover, the spatial correlation between vegetation activity and climate change may be altered. There will be different dominant climatic factors for vegetation activity distribution in some regions such as Northwest China, and even opposite correlations in Northeast China. Additionally, the response of vegetation activity to precipitation will move southward in the next three decades. In contrast, although the high warming rate will restrain the vegetation activity, precipitation variability could modify hydrothermal conditions for vegetation activity. This observation is exemplified in the projected future enhancement of vegetation activity in the Tibetan Plateau and weakened vegetation activity in East and Middle China. Furthermore, the vegetation in most parts of North China may adapt to an arid environment, whereas in many southern areas, vegetation will be repressed by water shortage in the future.

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Unraveling the effects of snow cover change on vegetation productivity: Insights from underlying surface types

Wang,

Xiao,

Zhang

et al. 2024

Ecosphere

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Changes in snow cover have an important effect on vegetation gross primary productivity (GPP), but the role of underlying surface types in this effect still remains unclear. The underlying surface refers to the surface of the Earth and is located at the bottom of the snow cover during the snow season. Here, we focus on the influence of snow cover change on GPP in Northeast China from 1982 to 2015, revealing the various dependencies of the association between snow cover and GPP on forest, grassland, and dryland. The results show that the increases in GPP in dryland and grassland are primarily influenced by the increase in snow water equivalent, while the variations in GPP in forest are primarily influenced by snow phenological indicators, with snow cover end date and snow cover days dominating GPP in the early and peak growing seasons, respectively. Additionally, snow cover can have a rather strong time‐lagged effect on GPP by changing the soil temperature in dryland. Our findings emphasize the importance of considering underlying surface types in the snow–GPP relation and provide insight into how climate change may affect ecosystems in regions with comparable underlying surface types.

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Integrating AVHRR and MODIS data to monitor NDVI changes and their relationships with climatic parameters in Northeast China

Cited by 240 publications

References 22 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

Past and future effects of climate change on spatially heterogeneous vegetation activity in China

Unraveling the effects of snow cover change on vegetation productivity: Insights from underlying surface types

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