The implications of serial correlation and time-lag effects for the impact study of climate change on vegetation dynamics – a case study with Hulunber meadow steppe, Inner Mongolia

Lin, Ying-Ren; Xin, Xiaoping; Zhang, Hongbin; Wang, Xu

doi:10.1080/01431161.2015.1093196

Cited by 18 publications

(15 citation statements)

References 18 publications

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“…Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI) has been used to assess, monitor or model land cover changes for various applications ranging from drought early warning systems, health and epidemiology, to biodiversity monitoring and conservation [ 17 – 19 ]. Remote sensing is an indispensable tool for mapping spatial distribution and modelling malaria transmission risks.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of vegetation to climate variability and its implications for malaria risk in Baringo, Kenya

et al. 2018

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The global increase in vector borne diseases has been linked to climate change. Seasonal vegetation changes are known to influence disease vector population. However, the relationship is more theoretical than quantitatively defined. There is a growing demand for understanding and prediction of climate sensitive vector borne disease risks especially in regions where meteorological data are lacking. This study aimed at analyzing and quantitatively assessing the seasonal and year-to-year association between climatic factors (rainfall and temperature) and vegetation cover, and its implications for malaria risks in Baringo County, Kenya. Remotely sensed temperature, rainfall, and vegetation data for the period 2004–2015 were used. Poisson regression was used to model the association between malaria cases and climatic and environmental factors for the period 2009–2012, this being the period for which all datasets overlapped. A strong positive relationship was observed between the Normalized Difference Vegetation Index (NDVI) and monthly total precipitation. There was a strong negative relationship between NDVI and minimum temperature. The total monthly rainfall (between 94 -181mm), average monthly minimum temperatures (between 16–21°C) and mean monthly NDVI values lower than 0.35 were significantly associated with malaria incidence rates. Results suggests that a combination of climatic and vegetation greenness thresholds need to be met for malaria incidence to be significantly increased in the county. Planning for malaria control can therefore be enhanced by incorporating these factors in malaria risk mapping.

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Section: Introductionmentioning

confidence: 99%

Sensitivity of vegetation to climate variability and its implications for malaria risk in Baringo, Kenya

et al. 2018

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“…China is located in the monsoon region of eastern Asia, with complex climate types and rich vegetation types. There are also distinct differences in the correlation between vegetation changes and hydrothermal factors in different regions [25][26][27], as well as the time lag of response to climatic factors [13,28,29].…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Characteristics of EVI and Its Response to Climatic Factors in Recent 16 years Based on Grey Relational Analysis in Inner Mongolia Autonomous Region, China

Zhang

et al. 2018

Remote Sensing

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Abstract:The Inner Mongolia Autonomous Region (IMAR) is a major source of rivers, catchment areas, and ecological barriers in the northeast of China, related to the nation's ecological security and improvement of the ecological environment. Therefore, studying the response of vegetation to climate change has become an important part of current global change research. Since existing studies lack detailed descriptions of the response of vegetation to different climatic factors using the method of grey correlation analysis based on pixel, the temporal and spatial patterns and trends of enhanced vegetation index (EVI) are analyzed in the growing season in IMAR from 2000 to 2015 based on moderate resolution imaging spectroradiometer (MODIS) EVI data. Combined with the data of air temperature, relative humidity, and precipitation in the study area, the grey relational analysis (GRA) method is used to study the time lag of EVI to climate change, and the study area is finally zoned into different parts according to the driving climatic factors for EVI on the basis of lag analysis. The driving zones quantitatively show the characteristics of temporal and spatial differences in response to different climatic factors for EVI. The results show that: (1) The value of EVI generally features in spatial distribution, increasing from the west to the east and the south to the north. The rate of change is 0.22/10 • E from the west to the east, 0.28/10 • N from the south to the north; (2) During 2000-2015, the EVI in IMAR showed a slightly upward trend with a growth rate of 0.021/10a. Among them, the areas with slight and significant improvement accounted for 21.1% and 7.5% of the total area respectively, ones with slight and significant degradation being 24.6% and 4.3%; (3) The time lag analysis of climatic factors for EVI indicates that vegetation growth in the study area lags behind air temperature by 1-2 months, relative humidity by 1-2 months, and precipitation by one month respectively; (4) During the growing season, the EVI of precipitation driving zone (21.8%) in IMAR is much larger than that in the air temperature driving zone (8%) and the relative humidity driving zone (11.6%). The growth of vegetation in IMAR generally has the closest relationship with precipitation. The growth of vegetation does not depend on the change of a single climatic factor. Instead, it is the result of the combined action of multiple climatic factors and human activities.

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“…The methodology for estimating the phenological parameters is developing and different settings of thresholds and filtering window size may produce different results of starting, ending and the length of growing season (Gong et al 2015, Lin et al 2015, Ren et al 2017. This study employed the TIMESAT and revealed the duration of the plant growing period is approximately equal to the non-freezing period (TMN0°C) and comparable with a study in North China (Hou et al 2014).…”

Section: Trends and Controlling Factors Of Gslmentioning

confidence: 64%

“…Shinoda and Nandintsetseg (2011) investigated the soil moisture and plant phenology in Mongolian grassland and highlighted the availability of water in controlling vegetation activities. In contrast, Lin et al (2015) reported the insignificant relationship between precipitation and vegetation dynamics. To provide further controversy, Mohammat et al (2013) concluded temperature variability was the dominant factor in controlling the vegetation growth in northeast Asia, which complicates our understanding of the relationship between climate change and vegetation response.…”

Section: Introductionmentioning

confidence: 88%

“…The upper stream of Amur River is a typical region in north-eastern Asia characterized as both cold and dry (Simonov and Egidarev 2017). Vegetation growth and phenological change are vulnerable to climate variability (Shinoda and Nandintsetseg 2011, Mohammat et al 2013, Lin et al 2015. Besides the controversy about the driving factors, previous studies chose political borders as their study boundaries disregarding the natural watershed and basin boundaries.…”

Section: Introductionmentioning

confidence: 99%

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Inter-annual Climate Variability and Vegetation Dynamic in the Upper Amur (Heilongjiang) River Basin in Northeast Asia

You

Arain

Wang

et al. 2020

Environ. Res. Commun.

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Long-term (1982Long-term ( -2013 datasets of climate variables and Normalized Difference Vegetation Index (NDVI) were collected from Climate Research Union (CRU) and GIMMS NDVI3g. By setting the NDVI values below the threshold of 0.2 as 0, NDVI_0.2 was created to eliminate the noise caused by changes of surface albedo during non-growing period. TimeSat was employed to estimate the growing season length (GSL) from the seasonal variation of NDVI. Statistical analyses were conducted to reveal the mechanisms of climate-vegetation interactions in the cold and semi-arid Upper Amur River Basin of Northeast Asia. The results showed that the regional climate change can be summarized as warming and drying. Annual mean air temperature (T) increased at a rate of 0.13°C per decade. Annual precipitation (P) declined at a rate of 18.22 mm per decade. NDVI had an insignificantly negative trend, whereas, NDVI_0.2 displayed a significantly positive trend (MK test, p<0.05) over the past three decades. GSL had a significantly positive rate of approximately 2.9 days per decade. Correlation analysis revealed that, NDVI was significantly correlated with amount of P, whereas, GSL was highly correlated with warmth index (WMI), accumulation of monthly T above the threshold of 5°C. Principal regression analysis revealed that the inter-annual variations of NDVI, NDVI_0.2 and GSL were mostly contributed by WMI. Spatially, NDVI in grassland was more sensitive to P, whereas, T was more important in areas of high elevation. GSL in most of the areas displayed high sensitivity to T. This study examined the different roles of climate variables in controlling the vegetation activities. Further studies are needed to reveal the impact of extended GSL on the regional water balance and the water level of regional lakes, providing the habitats for the migratory birds and endangered species.

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The implications of serial correlation and time-lag effects for the impact study of climate change on vegetation dynamics – a case study with Hulunber meadow steppe, Inner Mongolia

Cited by 18 publications

References 18 publications

Sensitivity of vegetation to climate variability and its implications for malaria risk in Baringo, Kenya

Sensitivity of vegetation to climate variability and its implications for malaria risk in Baringo, Kenya

Temporal and Spatial Characteristics of EVI and Its Response to Climatic Factors in Recent 16 years Based on Grey Relational Analysis in Inner Mongolia Autonomous Region, China

Inter-annual Climate Variability and Vegetation Dynamic in the Upper Amur (Heilongjiang) River Basin in Northeast Asia

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