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Journal of Geographical Sciences >

2017 , Vol. 27 >Issue 2: 143 - 160

DOI: https://doi.org/10.1007/s11442-017-1369-5

Research Articles

Correlation analysis between vegetation coverage and climate drought conditions in North China during 2001-2013

  • GONG Zhaoning ,
  • ZHAO Shuyi ,
  • GU Jinzhi
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  • Beijing Laboratory of Water Resources Security;Key Laboratory of 3D Information Acquisition and Application, Ministry of Education;Key Laboratory of Resources Environment and GIS of Beijing;College of Resource Environment & Tourism, Capital Normal University, Beijing 100048, China

Author: Gong Zhaoning (1976-), PhD and Associate Professor, specialized in remote sensing for natural resources and environment. E-mail:

Received date: 2016-08-26

  Accepted date: 2016-10-09

  Online published: 2017-04-10

Supported by

International Science & Technology Cooperation Program of China, No.2014DFA21620, The China Scholarship Fund

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Journal of Geographical Sciences, All Rights Reserved
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Abstract

Climate change is one of the most important factors that affect vegetation distribution in North China. Among all climatic factors, drought is considered to have the most significant effect on the environment. Based on previous studies, the climate drought index can be used to assess the evolutionary trend of the ecological environment under various arid climatic conditions. It is necessary for us to further explore the relationship between vegetation coverage (index) and climate drought conditions. Therefore, in this study, based on MODIS-NDVI products and meteorological observation data, the Palmer Drought Severity Index (PDSI) and vegetation coverage in North China were first calculated. Then, the interannual variations of PDSI and vegetation coverage during 2001-2013 were analyzed using a Theil-Sen slope estimator. Finally, an ecoregion perspective of the correlation between them was discussed. The experimental results demonstrated that the PDSI index and vegetation coverage value varied over different ecoregions. During the period 2001-2013, vegetation coverage increased in the southern and northern mountains of North China, while it showed a decreasing trend in the Beijing-Tianjin-Tangshan City Circle area and suburban agricultural zone located in Hebei Province and Henan Province). Over 13 years, the climate of the northeastern part of North China became more humid, while in the southern part of North China, it tended to be dry. According to the correlation analysis results, 73.37% of North China showed a positive correlation between the vegetation coverage and climate drought index. A negative correlation was observed mainly in urban and suburban areas of Beijing, Tianjin, Hebei Province, and Henan Province. In most parts of North China, drought conditions in summer and autumn had a strong influence on vegetation coverage.

Key words： North China; vegetation coverage; PDSI; ecoregion; correlation analysis

Cite this article

GONG Zhaoning , ZHAO Shuyi , GU Jinzhi . Correlation analysis between vegetation coverage and climate drought conditions in North China during 2001-2013[J]. Journal of Geographical Sciences, 2017 , 27(2) : 143 -160 . DOI: 10.1007/s11442-017-1369-5

1 Introduction

Vegetation coverage can be used as an indicator to show the growth status and tendency of vegetation and is also an important parameter that reflects the interactions among the atmosphere, hydrosphere, and lithosphere by mirroring the photosynthetic area and the extent of vegetation growth (Gan et al., 2001). Global climate change has led to significant vegetation changes in the past half century. Researchers have consistently selected vegetation coverage as an important index to evaluate the ecosystem health in view of the impact of global climate change. Drought is an abnormal climate phenomenon reflecting a continuous water deficit. It can synthetically reflect the action of all climate factors on the eco-environment. We define it as the drought index to comprehensively evaluate the extent of climate drought.
Researchers have consistently paid close attention to the relationship between the change in vegetation coverage and the climate drought condition. In the study of vegetation coverage in macroregions, some researchers have analyzed the evolution tendency of vegetation coverage using moderate or low resolution images; other researchers have analyzed the correlation between different climate factors and vegetation coverage. When conducting correlation analysis of the extent of vegetation coverage and meteorological factors or drought index over a time series, a linear correlation equation is generally used to obtain the correlation coefficient. In addition, significant test methods can be used to verify the significance of the correlation. In North China, many researchers have studied the correlation between vegetation cover and climate drought index or climatic factors. Sun et al. (2012) used Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) data from 1982 to 2006 to reflect the time series characteristics of vegetation coverage in North China. The relationship between vegetation coverage and climatic variations at yearly, seasonal, and monthly time scales were analyzed together with temperature and precipitation data from 85 meteorological sites. Wang et al. (2014) analyzed the interannual variability of vegetation NDVI, temperature, and precipitation using NOAA/AVHRR NDVI and 56 meteorological stations over the period from 1982 to 2006 in North China. The response of NDVI to temperature and precipitation at seasonal, monthly, and growing season time scales was also analyzed. Based on GIMMS NDVI data and basic meteorological data from 1981 to 2006, Liu et al. (2015) studied the temporal and spatial variation characteristics of vegetation coverage and explored the impact of human activities on vegetation coverage in North China using trend analysis, residual analysis, and relative role analysis. Mu et al. (2012) used MODIS-NDVI data and climate data such as precipitation and temperature to analyze vegetation dynamics and their relationship with climate change at different spatial (forest, grassland, and desert biome) and temporal (yearly and monthly) scales in Inner Mongolia from 2001 to 2010. The correlation analysis between vegetation coverage and climate showed that annual vegetation coverage was better correlated with precipitation, while the change in monthly vegetation coverage was consistent with changes in both temperature and precipitation, indicating that vegetation growth within a year is more sensitive to the combined function of hydrothermal factors rather than a single climate factor.
Based on the correlation analysis results between vegetation coverage and meteorological factors, drought index, and even the hydrological index, some studies suggest that the effects of different seasonal climatic conditions on vegetation also differ. In analyzing the vegetation coverage and drought index, we paid attention to the correlation between drought in different seasons and different types of vegetation. Wang et al. (2003) studied the correlation between NDVI and climatic factors, in particular precipitation and temperature in the central Great Plains, USA. The results showed that average growing season (March-October) NDVI values were highly correlated with precipitation received during the current growing season and seven preceding months (15-month duration). During the early and late stage, NDVI was positively correlated with temperature, but in the middle of the growing season, NDVI was negatively correlated with temperature. Based on NDVI data, Yu et al. (2003) studied the changes in NDVI in different seasons and discussed the response relationship between NDVI and climatic variability. Zheng et al. (2009) analyzed vegetation cover and its relationship with climate change factors in Guizhou from 1987 to 2003 and considered that NDVI and temperature in spring were significantly correlated on a seasonal scale, and the correlation coefficients in spring were higher than in other seasons. Li et al. (2012) believed that the correlation coefficient between NDVI and air temperature was higher than that between NDVI and precipitation in the East China region and its surrounding areas, and the NDVI and temperature were highly correlated in summer and autumn; NDVI and precipitation exhibited a high correlation in spring and autumn.
In large study areas, the ecological environment in response to the degree of surface drought climate is different because of the differences in the surface ecological environment. Therefore, climate change and vegetation distribution characteristics of various types of large intra-regional ecosystems need to be analyzed from a more detailed perspective. According to the guidelines of the ecological system partitioning, based on the difference between the natural surface topography, water and heat conditions, and vegetation cover types, large areas can be classified into different ecoregions. Thus, it is possible to analyze the impact of climate condition on the ecological environment from the perspective of ecoregions.
In this study, MODIS NDVI products were used to obtain vegetation coverage in North China from 2001 to 2013, and the drought index in North China was obtained by combining data from a basic meteorological weather station over the same period. The response of vegetation cover change to the degree of drought and the driving effect of climatic conditions on regional ecosystem changes in North China in different ecological areas were analyzed and discussed. In addition, the correlation between the drought degree and surface vegetation cover was also investigated.

2 Data and methods

2.1 Study area

According to the outline of the administrative division in North China, the study area contained five provinces or municipalities, including Beijing, Tianjin, Hebei, Henan, and Shandong (Figure 1).
Figure 1 The study area in North China and the distribution of meteorological stations
There is undulating terrain in northern North China, which slopes from south to north, most of which is higher than 1000 m above sea level, with an average annual temperature of -0.3 to 3.5°C, and annual rainfall of 340-450 mm. The climate of the central North China Plain is a warm temperate continental monsoon, with an average annual temperature of 5-13°C and an average annual precipitation of 500-800 mm. In the eastern part of North China, the landscape is dominated by low hills with an elevation of 500-1000 m, annual average temperature of 10-13°C, and annual precipitation of 600-900 mm. The hilly mountainous region in the south of Shandong Province is considerably hot; the hilly area in east of Shandong Province includes the entire Shandong Peninsula and extends southward to the southeast of the provincial boundary; the ocean climate is fairly mild in winter, rather cool, and experiences rainfall. The western Region includes the eastern side of Taihang Mountains, with an average annual temperature of 3-18°C, excellent annual rainfall 400-800 mm, and superior hydrothermal conditions; the Jixi Mountain elevation exceeds 1000 m; this area is distributed between the mountains in the northern mountainous small basins with an altitude of 300-400 m. The southwestern mountain region belongs to the east extension of Qinling Mountains including Xiao Mountain, Xiong’er Mountain, Funiu Mountain, etc.; the altitude 1500-2000 m, with an average annual temperature of 12-15°C and precipitation of 800-1000 mm. The mountainous southern region includes Tongbai Mountain and Dabie Mountain, at an altitude of about 800 m, with an average annual temperature of 13-15°C, and annual precipitation 1000-1300 mm; this is one of North China’s richest areas in terms of heat and water resources.
The study area includes four types of climate zones (Gu et al., 1991; Zheng et al., 2010) and three vegetation zones (Sun et al., 1998). The climate zones of North China include the temperate semi-arid region of the Inner Mongolian Plateau, the warm temperate humid area of the east Liaoning hilly region, the warm temperate semi-humid region of Yanshan Mountains, the North China Plain and east Shandong Province, the north subtropical humid zone of the Dabie Mountains and the Qinling-Daba Mountains. Subdivision vegetation zones include the adjacent portion of the Inner Mongolian Plateau in the northern temperate grassland and mountain areas in northern Hebei, the North China Plain, the western extension of the Taihang Mountains foothill, the Qinling Mountains and warm temperate deciduous broad-leaved forest zone, southwestern Henan, and the northern subtropical evergreen mountainous area south of Henan with a mixed deciduous broad-leaved forest zone.

2.2 Ecological zoning

According to the classification standard of Chinese ecological regions from the China Ecosystem Assessment and Ecological Security Database, the study area was divided into seven ecoregions (Figure 2), from north to south, as follows: East Central Inner Mongolian Plateau Typical Steppe Ecoregion, Yanshan-Taihang Mountains Deciduous Forest Ecoregion, Tianjin -Beijing-Tangshan Urban and Suburban Agricultural Ecoregion, North China Plain Agricultural Ecoregion, Liaodong-Shandong Hilly Deciduous Broad-leaved Forest Ecoregion, Qinling-Daba Mountains Deciduous and Evergreen Broad-leaved Forest Ecoregion, Huaiyang Hilly Evergreen Broad-leaved Forest Ecoregion. Among them, the area of North China Plain Agricultural Ecoregion accounted for the largest proportion, 34%; the most northern part of the East Central Inner Mongolian Plateau Typical Steppe Ecoregion only accounted for 3%.
Figure 2 The ecoregion regionalization map and the area proportion of each ecoregion in North China

2.3 Data sources and preprocessing

MODIS NDVI daily data products (16 days, 250 m) were used in this study. The data serial numbers in the global sinusoidal projection system are h26v04, h26v05, h27v04 and h27v05. In order to exclude the seasonal impact of crop over a year, this article focuses on the growth season from May to October; therefore, data from May 1 to October 31, 2001-2013 were selected. We used the maximum value from the ENVI synthesis (Maximum Synthesis Method, MVC) calendar NDVI data processing, after removing the influence of clouds, atmosphere, and solar elevation angle (Holben et al., 1986; Piao et al., 2001), to obtain the 13-year NDVI value used to calculate the vegetation coverage. The study area range vector interception was used to obtain annual North China and various ecoregion NDVI data.
The daily average air temperature, barometric pressure, relative humidity, wind speed, and sunshine hours, and basic monthly meteorological data from 1964 to 2013 were obtained from the China Meteorological Data Sharing Service System based on MODIS data, data quality and 100 weather stations located in North China and its surrounding areas (Figure 1).
Vector data for the ecoregions in North China were obtained from the Chinese Ecosystem Assessment and Ecological Security Database. Criteria for the Chinese ecoregions were primarily based on the Fundamental Data of the regionalization of ecological function, issued previously by the National Key Laboratory of Urban and District; reference was also made to the Ecological Function Zoning (interim) Regulations issued by the State Environmental Protection Administration.

2.4 Calculation of vegetation coverage

The Mixed Pixel Decomposition model is a common method of calculating the vegetation coverage. It assumes that a remote sensing image pixel is composed of two parts that contain information on pure vegetation and pure soil. The NDVI value of mixed pixels is the weighted average of the NDVI value of pure soil and pure vegetation (Li et al., 2010).
NDVI is calculated as follows:
NDVI=fv·NDVIveg+(1-fv)·NDVIsoil (1)
where NDVI is the vegetation index mixed pixel values; NDVIveg is the pure vegetation pixel vegetation index value; NDVIsoil is the pure soil vegetation index values as elements; and fv is the vegetation coverage.
Therefore,
Fv=(NDVI-NDVIsoil)/(NDVIveg-NDVIsoil) (2)
where NDVIsoil represents the pure vegetation pixelminimum for soil, which is theoretically close to zero; NDVIveg represent the pure vegetation pixel maximum, which is theoretically close to 1. In fact, due to weather conditions, vegetation types and distribution, seasonal changes and other factors, NDVIsoil and NDVIveg can vary to different degrees.
First, the vegetation index value of mixed pixels was obtained from the NDVI data after pre-processing. Then, focusing on the NDVI of the vegetation growing season (June-August) every year, NDVI values were calculated by MVC. A 0.5% confidence level was used to obtain the maximum and minimum NDVI threshold. NDVIveg is the average of the maximum 0.5% NDVI value, and NDVIsoil is the average of the minimum 0.5% NDVI value. Finally, vegetation coverage images were obtained using formula (2).

2.5 Calculation of the drought index

Faced with the choice of a variety of meteorology drought indices, Wei et al. (2003) and Han et al. (2009) compared the PDSI, surface humid index, precipitation anomaly percentage, and standardized precipitation index in China. These authors considered that the PDSI index, although only represented by a limited number of stations, surface structure, and level of information, could reasonably describe the characteristics of drought and the effect of weather conditions on the pre-drought degree and showed good spatial and temporal comparison (Liu et al., 2004). Thus, the PDSI is used here to represent climate drought.
Proposed and defined by Palmer (Palmer, 1965), PDSI is widely used in drought data analysis and the reconstruction and monitoring of drought sequences in the U.S. (Liu et al., 2004). Some researchers constructed the Palmer Drought Severity mode evapotranspiration model according to the climate and surface characteristics of China’s environmental features, and using a constant value for soil, proposed a suitable correction for the basic model in China (Liu et al., 2004; An et al., 1986; Ma et al., 1993; Liu et al., 1997).
The PDSI corrected for China can be calculated on the basis the following formula (Liu et al., 2004):
Xi=0.9331xi-1+zi/125.99 (3)
where xi is the drought index formula in the i-th month and zi is the moisture anomaly index in the i-th month.
On the basis of equation (4), the weight factor was modified by the meteorological data of other stations:
\[K^{‘}=1.28151g(\frac{\overline{PE}+\overline{R}+\overline{RO}}{(\overline{P}+\overline{L})\overline{D}})\ \ (4)\]
\[K=\frac{581.391}{\sum^{12}_1DK^{‘}DK^{‘}}\ \ (5)\]
where\(\overline{PE}\) is the average annual value of potential evapotranspiration, \(\overline{R}\) is the average annual value of the recharge water amount, \(\overline{RO}\) is the average annual value of the runoff, \(\overline{P}\) is the average annual value of precipitation, \(\overline{L}\) is the average annual value of the water loss amount, \(\overline{D}\) is the average of the absolute moisture anomalies, K and K’ are estimated values of the climatic characteristics.
The final calculated PDSI ranged between -10 to 10, where a value greater than 1 indicates a humid climate, a value less than -1 indicates drought, and a value between -1 and 1 indicates that the climate is in a relatively normal state (Table 1).
The modified Palmer Drought Severity Model, used here, was based on meteorological data from a total of 100 sites located in Beijing, Tianjin, Hebei, Henan, Shandong, Shanxi, Shaanxi, Inner Mongolia, Liaoning, Jiangsu, Anhui, and Hubei, and a total of 12 provinces and autonomous regions over the period 1964-2013 to generate the discrete distribution of PDSI for North China and the surrounding region.
The ArcGIS geostatistical analysis tool with Kriging spatial interpolation methods was used to generate the PDSI raster data. Conversion of the spatial coordinate system and clipping and vegetation coverage data matching the PDSI vector data was conducted in accordance with the scope of the study area.
Table 1 Palmer Drought Severity Index ranges and classes
PDSI Class PDSI Class
≥ 4.00 Extremely wet -0.50 to -0.99 Incipient drought
3.00 to 3.99 Very wet -1.00 to -1.99 Mild drought
2.00 to 2.99 Moderately wet -2.00 to -2.99 Moderate drought
1.00 to 1.99 Slightly wet -3.00 to -3.99 Severe drought
0.50 to 0.99 Incipient wet ≤-4.00 Extreme drought
0.49 to -0.49 Near normal

2.6 The trend analysis and correlation calculation of vegetation coverage and PDSI

The Theil-Sen slope estimation is a non-parametric estimation method commonly used for long-term trend analysis of vegetation coverage changes. In this paper, the Theil-Sen slope estimation method was used to obtain the vegetation coverage trend and PDSI changes in the study area from 2001 to 2013.
The Theil-Sen slope can be calculated using equation 6:
\[TS_{slope}=(\frac{x_j-x_i}{t_j-t_i})\ \ (6)\]
where median is the median function; xi and xj are sequence data that represent vegetation coverage or PDSI in different years; ti and tj are the time-series data corresponding to the sequence data; n is the sequence length; and i<j<n.
Analysis of the correlation between vegetation coverage in North China and the PDSI was conducted based on the pixel-scale correlation analysis of vegetation coverage and PDSI, using vegetation coverage and PDSI raster data.
The correlation coefficient of vegetation coverage and PDSI could be estimated using the following equation:
\[R=\frac{\sum^n_{i=1}(x_i-\overline{x})(y_i-\overline{y})}{\sqrt{\sum^n_{i=1}(x_i-\overline{x})^2\sum^n_{i=1}(y_i-\overline{y})^2}}\ \ (7)\]
where the correlation coefficient, between two variables x and y, xi is the vegetation coverage in the i-th year, the yi is the average annual PDSI in the i-th year.

3 Results and analysis

3.1 Average annual data of the spatial distribution of vegetation coverage in each ecoregion

According to the 2001 to 2013 vegetation coverage data, the distribution of vegetation coverage in various ecoregions of North China in presented in Figure 3.
The average vegetation coverage of grassy prairie plants in the temperate grassland zone, mainly in the eastern part of the typical steppe of the Inner Mongolian Plateau ecoregion, was low, only 0.61 over the 13 years.
The warm temperate, deciduous, broad-leaved forest zone is mainly located in the mountains around the northern, western, and southern of the North China Plain, and its vegetation coverage was high. The average vegetation coverage area of the north of Yanshan and Taihang Mountains ecoregions and the southwest region of Qinling-Daba Mountains was 0.77 and 0.82, respectively. In northern Hebei Province, mountain birch, aspen, and oak forests dominated the western distribution of the Yanshan Mountains pine oak forest; shrub steppe was widely distributed in the northwestern Hebei basin between the warm temperate, deciduous broad-leaved forest zone and the temperate steppe transition; the Taihang Mountains foothills were sparsely covered with deciduous broad-leaved forest and shrub bushes (Wu et al., 1980); in addition, the mountainous region of western Henan Province, was mainly covered with common vegetation of North China, including some regional vegetation of central and western North China (Liu et al., 1996).
The average vegetation coverage of agro-ecological zones in North China plains, eastern Liaoning-Shandong hilly deciduous forest ecoregions, and Tianjin-Tangshan urban and suburban areas was 0.80, 0.77 and 0.70, respectively. Agricultural crops in the North China area had high density and the highest vegetation coverage; the vegetation in the Tianjin-Tangshan area was mainly artificial garden, which accounted for the lowest extent of vegetation cover.
The average vegetation coverage in the Huaiyang evergreen broad-leaved forest ecosystem in the hilly area located in the north subtropical evergreen and deciduous broad-leaved mixed forest zone was 0.84, the highest regional vegetation coverage of the entire North China area, including deciduous and evergreen broad-leaved forest coniferous forest. The vegetation in Nanyang Basin in the western mountain area was mostly oak forest, aspen, and birch, including small amounts of low-altitude mountain evergreen trees and economic trees; deciduous oak forest dominated the south Henan mountainous region (Kuang et al., 1991).
Figure 3 The average vegetation coverage of each ecoregion in North China during 2001-2013
1) East Central Inner Mongolia Plateau Typical Steppe Ecoregion; 2) Yanshan-Taihang Mountain Deciduous Forest Ecoregion; 3) Tianjin-Beijing-Tangshan Urban and Suburban Agricultural Ecoregion; 4) North China Plain Agricultural Ecoregion; 5) Liaodong-Shandong Hilly Deciduous Broad-leaved Forest Ecoregion; 6) Qinling-Daba Mountains Deciduous and Evergreen Broad-leaved Forest Ecoregion; 7) Huaiyang Hilly Evergreen Broad-leaved Forest Ecoregion

3.2 Vegetation coverage and yearly PDSI trends

On an annual basis, this paper estimated the trends in the vegetation coverage data and PDSI data from 2001 to 2013.
According to the changes in vegetation cover (Figure 4a), the positive and negative trends in the regional study area covered about 2.99×105 km2 and 2.43×105 km2, respectively. Areas with increased vegetation coverage include Yanshan-Taihang Mountains in most parts, hilly evergreen broad-leaved forest ecoregions from the western Liaodong-Shandong hilly ecoregion, the ecoregion near the western mountainous Qinling-Daba regions, and the central part of Inner Mongolian grassland ecoregion. However, the vegetation coverage decreased significantly in the Beijing-Tianjin-Tangshan urban and suburban agro-ecological zones and the western part of the North China agro-ecological zones.
In addition to Beijing, Tianjin and the Inner Mongolian grassland ecoregion in downtown Chinatown, the vegetation coverage in other parts of this 13-year study exhibited a similar trend, i.e., a steady change as low vegetation coverage was evident in 2002, 2006, and 2009 (Figure 5). Over the 13 years, vegetation coverage in the grassland ecoregion of Inner Mongolia exhibited a general increasing trend from 2001 to 2013, increasing from 0.57 to 0.69; from 2005 to 2007, vegetation coverage showed an initial increasing trend followed by a decrease, but volatility from 2008 to 2010 was high and there was a sharp increase after the first decrease. Vegetation coverage in downtown Beijing and Tianjin Chinatown displayed a continuing downward trend, decreasing from 0.74 in 2001 to 0.66 in 2013.
Figure 4 The changing trend of annual vegetation coverage (a) and annual PDSI (b) in North China during 2001-2013
1) East Central Inner Mongolia Plateau Typical Steppe Ecoregion; 2) Yanshan-Taihang Mountain Deciduous Forest Ecoregion; 3) Tianjin-Beijing-Tangshan Urban and Suburban Agricultural Ecoregion; 4) North China Plain Agricultural Ecoregion; 5) Liaodong-Shandong Hilly Deciduous Broad-leaved Forest Ecoregion; 6) Qinling-Daba Mountains Deciduous and Evergreen Broad-leaved Forest Ecoregion; 7) Huaiyang Hilly Evergreen Broad-leaved Forest Ecoregion
The PDSI increasing trend (Figure 4b) in the Yanshan Mountains region, urban and suburban areas of the Tianjin-Tangshan region, the northern part of the North China Plain, and the agricultural region and eastern Shandong hilly area, was explained by the lessening of the meteorological drought conditions. The southern agricultural areas of the North China Plain, southern mountainous parts and western hilly areas of Henan Province displayed a decreasing PDSI trend, indicating an increase in the extent of the drought area.
The PDSI curves for each ecological region over the 13 years were quite different, but except mountainous Qinling-Daba ecoregion and hilly Huaiyang ecoregion, PDSI in the other regions exhibited a clear upward trend (Figure 6). The first decrease in the North China PDSI appeared in 2002, in 2002 and 2004, PDSI increased in most parts, while the second decreased appeared in 2006 and 2007, whereas a relatively uniform low value occurred in 2009. PDSI increased in the Inner Mongolian grassland areas and the Yanshan-Taihang Mountains area from 2001 (moderately dry) to 2013 (slightly moist), and in the Beijing-Tianjin-Tangshan region, a severe drought occurred in 2001 whereas 2013 was very wet. PDSI in the Huaiyang hilly evergreen broad-leaved forest ecoregion decreased since 2003, reaching a minimum in 2011, relative to other northern ecoregions that showed a significant lag. In other areas, the PDSI changed little under drought conditions, and drought occurred at a normal level.
Figure 5 The inter-annual change of vegetation coverage in each ecoregion in North China during 2001-2013
1) East Central Inner Mongolia Plateau Typical Steppe Ecoregion; 2) Yanshan-Taihang Mountain Deciduous Forest Ecoregion; 3) Tianjin-Beijing-Tangshan Urban and Suburban Agricultural Ecoregion; 4) North China Plain Agricultural Ecoregion; 5) Liaodong-Shandong Hilly Deciduous Broad-leaved Forest Ecoregion; 6) Qinling-Daba Mountains Deciduous and Evergreen Broad-leaved Forest Ecoregion; 7) Huaiyang Hilly Evergreen Broad-leaved Forest Ecoregion
Figure 6 The inter-annual change of each ecoregion’s PDSI in North China during 2001-2013
1) East Central Inner Mongolia Plateau Typical Steppe Ecoregion; 2) Yanshan-Taihang Mountain Deciduous Forest Ecoregion; 3) Tianjin-Beijing-Tangshan Urban and Suburban Agricultural Ecoregion; 4) North China Plain Agricultural Ecoregion; 5) Liaodong-Shandong Hilly Deciduous Broad-leaved Forest Ecoregion; 6) Qinling-Daba Mountains Deciduous and Evergreen Broad-leaved Forest Ecoregion; 7) Huaiyang Hilly Evergreen Broad-leaved Forest Ecoregion
According to PDSI and trends analysis based on vegetation coverage of these ecoregions in North China, over the 13 years, there were changes with respect to more unified point mutations, namely in 2002, 2006, and 2009. Therefore, the vegetation coverage in North China exhibits a certain degree of correlation with PDSI.

3.3 Relationship with the North China MODIS vegetation coverage

3.3.1 Correlation Analysis of the average annual PDSI and vegetation coverage in North China
Figure 7 shows the results of equation (7) used in conjunction with the 2001 to 2013 vegetation coverage data and the average annual PDSI data pixel-based correlation analysis. Orange areas indicate where vegetation coverage in North China and the PDSI are positively correlated; the blue areas represent negative correlations between the vegetation coverage and PDSI in North China.
Figure 7 Correlation coefficient between vegetation coverage and PDSI in North China during 2001-2013
1) East Central Inner Mongolia Plateau Typical Steppe Ecoregion; 2) Yanshan-Taihang Mountain Deciduous Forest Ecoregion; 3) Tianjin-Beijing-Tangshan Urban and Suburban Agricultural Ecoregion; 4) North China Plain Agricultural Ecoregion; 5) Liaodong-Shandong Hilly Deciduous Broad-leaved Forest Ecoregion; 6) Qinling-Daba Mountains Deciduous and Evergreen Broad-leaved Forest Ecoregion; 7) Huaiyang Hilly Evergreen Broad-leaved Forest Ecoregion
The average correlation coefficient was 0.20 throughout the study area, and the regional correlation coefficient for the positive and negative regions was 73.37% and 26.63%, respectively. In the areas with positive correlation, 21.60% and 10.06% of the region were significant at P<0.05 and P<0.01, respectively; in the regions with negative correlations, 8.60% and 2.78% were significant at P<0.05 and P<0.01, respectively. This shows that there is a relationship between arid climate conditions and vegetation coverage. The correlation coefficients of each ecoregion differ considerably, which needs to be discussed separately for the different regions.
The vegetation coverage area and PDSI were positively correlated for the vast majority of the eastern part of the Inner Mongolian Plateau Ecological typical steppe; a positive correlation coefficient was observed for 94.30% of the area of the entire region, and the regional average correlation coefficient was 0.41.
The vegetation coverage area and PDSI were positively correlated in most areas of the Yanshan-Taihang Mountains deciduous forest ecoregion; the average correlation coefficient was 0.24 for the entire region, of which the positive correlation accounted for 79.05% of the region, with 18.26% significant at P<0.05. Over the 13 years studied, the extent of the Yanshan-Taihang Mountains area drought lessened and a slightly wet trend was exhibited, but this was not significant, and the vegetation coverage did not change significantly. Stable climate and terrain conditions changed slightly with the extent of the vegetation cover in the area.
In the North China Plain agro-ecological zone, the vegetation coverage and PDSI exhibited a positive correlation coefficient accounting for 71.60% of the total area; a negative correlation coefficient accounted for 28.40% of the overall region, the regional average correlation coefficient was 0.18, and an average correlation coefficient of 0.33 was observed for positive regional trends. From the analysis, it was concluded that the extent of the drought had a relatively large impact on the vegetation coverage, mainly a positive correlation. Negative correlation areas were mainly concentrated in the Taihang Mountains foothill area in contact with the north-south strip, connected to the Tianjin-Tangshan urban and suburban agro-ecological zones.
The average correlation coefficient for the Beijing-Tianjin-Tangshan urban and suburban agro-ecological zones was -0.11, a negative correlation in this region accounted for 63.54%, of which 19.18% passed the P<0.05 test. The negative correlation exhibited similar characteristics as the Beijing-Tianjin-Tangshan region, including suburban areas of Baoding, Shijiazhuang, Xingtai, Handan in Hebei Province, Xuchang, Luohe, Zhumadian and other cities in Henan Province. Beijing and the Tianjin region with an arid climate showed a slight improvement compared with a decade ago; the internal urban vegetation coverage of Beijing, Tianjin, and other cities increased, but the city and surrounding suburban vegetation coverage decreased rapidly over the 13 years. Suburban area vegetation coverage exhibited a negative correlation with PDSI; whereas the internal urban vegetation coverage was positively correlated with PDSI, but neither was significant. This ecoregion is considered as urban sprawl and other artificial disturbances are severe, contributing to the low correlation.
The average correlation coefficient in the Liaodong-Shandong hilly deciduous forest ecoregion was 0.26. The region with a positive correlation coefficient accounted for 78.00% of the total area, of which 25.63% of the region was significant at P<0.05; the visible vegetation coverage and PDSI were closely linked, and most of the region presented a significant positive correlation. Over the 13 years studied, drought in the eastern Shandong Province gradually reduced, and that of the southern Shandong Province changed but was not obvious. With the decrease in the extent of drought in the eastern Shandong Province, western and coastal eastern areas of Shandong Peninsula, vegetation coverage decreased. There was a positive correlation coefficient in the southern Shandong Province hilly area, as well as agricultural areas in North China. An increasing number of areas exhibited negative correlations, including the entire Shandong Peninsula extending southward to the southeast of the provincial boundary, at the distribution of the bustling coastal city.
The mountainous deciduous and evergreen broad-leaved forest ecoregions and the Huaiyang hilly evergreen broad-leaved forest ecoregion are regions with the highest degree of vegetation coverage across the entire region of North China. The average correlation coefficient for the mountainous ecoregion was 0.20, a positive correlation coefficient accounted for 75.50%; the average correlation coefficient of the Huaiyang hilly ecoregion was 0.12, a positive correlation coefficient accounted for 65.77%. We found that the positive correlation between the PDSI and vegetation coverage of these two regions was not related.
3.3.2 Correlation Analysis of the PDSI of North China and vegetation coverage during each season
From a seasonal perspective, the four seasons of the year were set as follows: winter (December of the previous year - February), spring (March-May), summer (June-August) and autumn (September-November). The correlation coefficients of each season are shown in Figure 8.
Figure 8 Correlation coefficient between vegetation coverage and PDSI in different seasons in North China during 2001-2013
1) East Central Inner Mongolia Plateau Typical Steppe Ecoregion; 2) Yanshan-Taihang Mountain Deciduous Forest Ecoregion; 3) Tianjin-Beijing-Tangshan Urban and Suburban Agricultural Ecoregion; 4) North China Plain Agricultural Ecoregion; 5) Liaodong-Shandong Hilly Deciduous Broad-leaved Forest Ecoregion; 6) Qinling-Daba Mountains Deciduous and Evergreen Broad-leaved Forest Ecoregion; 7) Huaiyang Hilly Evergreen Broad-leaved Forest Ecoregion
According to the relevant analytical data for each ecological region (Figure 9), it is obvious that in summer and autumn, the drought in the North China region had the strongest effect on the vegetation coverage.
The strongest correlation coefficients were measured for the vegetation coverage of the typical steppe ecosystem of the eastern Inner Mongolian Plateau and the deciduous forests of the Yanshan-Taihang Mountains ecoregion with respect to the summer and autumn drought index; these values were as high as 0.45 and 0.42, and 0.25 and 0.24, respectively, and areas with negative correlations did not exceed 5% and 20%.
In the four ecoregions, i.e., the North China Plain agro-ecological zone, Liaoning- Shandong hilly deciduous forest ecoregion, mountainous deciduous and evergreen broad- leaved forest ecoregion and the Huaiyang hilly evergreen broad-leaved forest ecoregion, drought in spring, summer, and autumn were influenced by the degree of vegetation cover during the year. The drought of the first three ecoregions in spring, summer and autumn had a lower impact on some of the vegetation coverage; the correlation coefficient for the North China Plain agro-ecological zone in the spring, summer, and autumn did not differ considerably, which to some extent confirms the influence of the North China Plain drought on crops. The results show that in the Huaiyang hilly evergreen broad-leaved forest ecoregion, the greatest effect of the drought on vegetation coverage occurred in spring, followed by summer and autumn, as shown by the correlation coefficient.
The average correlation coefficient for towns in the Tianjin-Tangshan area and the AEZ suburbs was small and negative for all four seasons; a negative correlation was observed across 62%-69% of the area of this ecoregion. The impact of human activities in these areas is considerable, and arid conditions occur naturally; hence, the correlation is not obvious.
Figure 9 Average correlation between vegetation coverage and PDSI in different seasons in North China
1) East Central Inner Mongolia Plateau Typical Steppe Ecoregion; 2) Yanshan-Taihang Mountain Deciduous Forest Ecoregion; 3) Tianjin-Beijing-Tangshan Urban and Suburban Agricultural Ecoregion; 4) North China Plain Agricultural Ecoregion; 5) Liaodong-Shandong Hilly Deciduous Broad-leaved Forest Ecoregion; 6) Qinling-Daba Mountains Deciduous and Evergreen Broad-leaved Forest Ecoregion; 7) Huaiyang Hilly Evergreen Broad-leaved Forest Ecoregion

4 Conclusions and discussion

4.1 Conclusions

(1) Vegetation coverage in the southern part of the North China Plain was higher than that in the north. The vegetation coverage in the central region was higher than in the surrounding area. Among these areas, the highest degree of vegetation coverage occurred in the Huaiyang hilly evergreen broad-leaved forest ecoregion, the lowest occurred in the eastern region of the Inner Mongolian Plateau typical steppe ecoregion.
(2) From 2001 to 2013, the vegetation coverage in the North China Plain showed an overall upward trend. Around the North China Plain, to the north, west, and south and in the mountainous foothills, the vegetation coverage increased, while in the agricultural areas of the North China Plain, Beijing, Tianjin suburban areas, and coastal regions of Shandong Province, vegetation coverage decreased. Vegetation coverage exhibited an upward trend inside the city of Beijing, Tianjin and others.
(3) From 2001 to 2013, the northeastern region of North China became wet, and the southern region became arid. Drought conditions in the Inner Mongolian steppe region and North China Plain in the central, eastern, and southwestern Liaoning-Shandong hilly area did not change significantly.
(4) It can be seen from the results of the annual analysis that vegetation coverage and the extent of drought in North China were generally positively correlated, the most obvious being the northwestern North China areas, including the Inner Mongolian typical steppe area, agricultural area in the central North China Plain and the Liaodong-mountainous region in southwestern Shandong. Primary and secondary urban agglomerations in North China exhibited negative regional correlation coefficients; these regions have been severely disturbed by human activities, and there was a weak correlation between vegetation coverage and climate-related drought conditions.
(5) In most parts of the north, it is obvious that the extent of summer and autumn drought was related to vegetation coverage. The vegetation coverage response to the extent of the drought in spring, summer and autumn was relatively low, but in the southeastern North China Plain and agricultural areas in North China, correlation coefficients were lower in spring, summer, and autumn.

4.2 Discussion

Taking into account that during the vegetation growing season, the vegetation coverage and drought conditions showed a significant linear relationship (Mu et al., 2012), the monthly vegetation coverage and PDSI correlation analysis were not presented in this article.
This study analyzed vegetation coverage in the ecoregions of North China in response to the extent of drought conditions. Under natural conditions, the vegetation coverage in most parts was positively correlated with the change in drought. The correlations with vegetation cover types were high. The typical steppe responded most strongly to the drought conditions, followed by the northern forest areas, farmland, and the southern forest region.
The average temperature of the Inner Mongolian grassland ecoregion is low, precipitation is low and is concentrated in summer; this area is drought-prone, with low field capacity, and sufficient heat and rainfall are the main factors contributing to the drought (Mu et al., 2012). In this area, PDSI can better estimate drought conditions.
Surface drought in the North China Plain agricultural areas mainly depends on summer precipitation and artificial practices aimed to improve water availability. This study focused on the crop growing season from May to October; in the North China region, May to October is the cotton growing season and June to September is the summer crop growing season, e.g., corn. Summer crops are sown, and the abundant rainfall during this period together with the temperature increase result in the highest potential evapotranspiration of the year (Liu et al., 2010), which likely exceeds rainfall. In this agricultural area, although frequent irrigation is used, which affects the drought conditions, there is also strong correlation between the growth rate of a large number of crops and potential evapotranspiration and precipitation.
Human activities have a strong impact on vegetation coverage and surface drought conditions. The 13-year PDSI data from the urban and suburban areas were not positively correlated with vegetation coverage. As the city expanded, the city boundary blurred, farmland was transformed into building land, and the vegetation coverage of the suburban areas decreased rapidly. In contrast, because of the extent of the urban greening rate, the proportion of vegetation coverage in urban cities has begun to increase (Yang et al., 2014). Construction activity has affected the impact of drought conditions on the rate of vegetation growth has been greatly reduced, and the change in vegetation coverage is not the dominant factor causing the drought conditions. Therefore, to analyze the vegetation cover in North China in the future, we should consider the impact of human activities on vegetation cover and changes in vegetation trends.
Altitude, topography, and other factors also affect the response of vegetation to drought conditions. Under high vegetation coverage and the abundant rainfall and warm climate in the mountainous Qinling-Daba and hilly Huaiyang ecoregions, changes in drought conditions over the short period of vegetation growth did not affect the average correlation coefficient, which was small. However, in ecological areas such as high altitude mountain areas, e.g., the western Henan mountains, vegetation coverage and PDSI was mostly positively correlated. The effect of elevation, topography and other factors on the vegetation coverage in North China remains to be studied.

The authors have declared that no competing interests exist.

References

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[1]
An Shunqing, Xing Jiuxing, 1986. A Modified Palmer’s Drought Index.Journal of Academy of Meteorological Science, 1(1): 75-82. (in Chinese)

[2]
Cui Linli, Shi Jun, 2012. Characteristics of seasonal response of NDVI to variations in temperature and precipitation in East China and its surrounding areas.Resources Science, 34(1): 81-90. (in Chinese)

[3]
Gan Chunying, Wang Xizhi, Li Baosheng,et al.., 2011. Changes of vegetation coverage during recent 18 years in Lianjiang River watershed.Scientia Geographica Sinica, 31(8): 1019-1024. (in Chinese)With TM images as a data source,this paper calculates and analyses the characteristics of change and spatial distribution about the vegetation in Lianjiang watershed in 1988–2006 by using the dimidiate pixel model based on NDVI.Overlaying the vegetation coverage map and the sub-karst geological map,the impact on vegetation coverage exerted by geological structure is analyze.The results can be described in the following three aspects.1) Because of the effect of climate and human activities,the vegetation coverage in Lianjiang River watershed got a rise in 1988–2006.The area of higher and high vegetation coverage districts increased,while that of low,lower and middle vegetation coverage districts decreased.2) The quality of vegetation in non-karst district was better than that in karst district due to the influence of geological formation.3) The change of vegetation coverage's spatial distribution was significant in Lianjiang River watershed during the recent 18 years.

DOI

[4]
Gu Tingmin, Zhu Ruizhao, Lin Zhiguang et al., 1991. Climate of North China Plain. Beijing: China Meteorological Press, 1-2. (in Chinese)

[5]
Han Haitao, Hu Wenchao, Chen Xuejun,et al.., 2009. Application and comparison of three meteorological drought indices.Agricultural Research in the Arid Areas, 27(1): 237-241, 247. (in Chinese)Drought is a kind of meteorological disaster that has hurt severely agricultural production and affected economic development and people's living in China over a long period of time.Based on the research about drought and drought indices at home and abroad,this paper mainly compared and analyzed a few meteorological drought indices usually used in current domestic and international researches,and pointed out the merit and shortcoming as well as applicability of each drought index.The percentage of precipitation anomaly(Pa) has several characteristics,such as simple calculation,easy to obtain data,slow extent of response to the drought,and no reflection of the inherent mechanism of the drought.With sensitive response to drought,the standard precipitation index(SPI) can be applied to different time scales and stability calculation,while the Palmer drought index(PDSI) not only reflects the extent of drought but also includes the start and end time of drought.

[6]
Holben Brent N, 1986. Characteristics of maximum-value composite images from temporal AVHRR data.International Journal of Remote Sensing, 7(11): 1471-1434.

[7]
Kuang Shengshun, 1991. The horizontal zonation distribution of vegetation in Henan.Journal of Wuhan Botanical Research, (2): 153-160. (in Chinese)Henan is located in the eastern part of China, lies 31°23′—36°22′NL,110°21′—116°39′EL.It is about 167074 km~2 in area. The topography consists of plains, hills and mountains. The eastern part is the southern part of Huabei plains. The western part is mainly mountainous region which range from 1000m to 2000m above sea level. In summer, the climate of Henan is controlled by the summer monsoons coming from the Pacific Ocean. But in winter it is influenced by the cold air coming from Siberia. The vegetation of Henan hasa law of geographical distribution from north to south closely related to climate. The types of vegetation are different in different latitudinal zonations.The vegetation in the northern part belong to the temperate deciduous broadleaf forest region. The representative vegetations are the deciduous broadleaf forest and the evergreen needleleaf forest in the mountainous region. For example, Quercus variabulis forest,Quercus aliena var.acutiserrata forest and Pinus tabulaeformis forest, Pinus bugenana forest, etc. On the plains of eastern part, winter wheat, maize and rice are planted two crops a year, or winter wheat, cotton, maize, sesame and sweetpotato are planted three crops in two years. on the sandland, winter wheat, sweetpotato, peanut and sesame are planted oncea year. The mixed evergreen broadleaf forest and deciduous broadleaf forest which belongs to the subtropical evergreen broadleaf forest region cover the southern part of Henan. The representative vegetations are Quereus variabulis,Quercus aliena var.acutserrata and a few evergreen broadleaf trees, for example, Cyclobalanopsis glanca, Chclobanopsis myrsinaefolis, etc. The evergreen needleleaf forest consists of Pinus massoniana, Pinus taiwanensis (higher than 700m above sea level)and Cunnunghamia lanceolata etc. The climate in the eastern part of Henan is moister than that in the western part. In eastern part, the vegetation on the Dabie Mountain is characterized by Pinus taiwanensis and tea plantations. In the western part, however, there are no Pinus taiwanensis forest and tea plantations in Tongbai Mountain region.

[8]
Li Dengke, Fan Jianzhong, Wang Juan, 2010. Change characteristics and their causes of fractional vegetation coverage (FVC) in Shanxi Province.Chinese Journal of Applied Ecology, 21(11): 2896-2903. (in Chinese)Based on the sub-pixel analysis model,and by using 2000-2009 MODIS NDVI(250 m resolution),this paper quantitatively analyzed the spatiotemporal change characteristics and their causes of fractional vegetation coverage(FVC) in Shannxi Province.From 2000 to 2009,the FVC in the Province had a significant increasing trend,with the great magnitude of 35.0%.During that period,the vegetation coverage increased from 56.9% in 2000 to 68.9% in 2009 in the provincial scale,and the increment was much higher in northern Shannxi,being 21.6% in Yulin and 22.0% in Yan'an.Though the vegetation coverage had an overall increase,it was locally degraded in some areas.The areas with improved vegetation coverage at the significance levels of 0.01,and 0.05 were 37.8% and 11.9%,while those with non-improved and degraded vegetation coverage were 46.1% and 4.2%,respectively.The areas whose vegetation coverage had a change rate of 200%,200%-100%,100%-10%,10%-10%,and -10% occupied 12.2%,13.3%,38.8%,29.3%,and 6.4% of the total,respectively.During the study period,the structure of vegetation coverage in the Province also improved.The areas with high and normal vegetation density increased significantly by 10% and 8.4%,respectively,while the area with low vegetation density decreased significantly by 18.4%.The improvement of the FVC in Shaanxi Province was the interactive effect of natural factors and human activities,but the main cause was the implementation of a series of ecological construction projects such as closing hill for forestation and restoring farmland into forestland and grassland.

DOI PMID

[9]
Liu Bin, Sun Yanling, Wang Zhongliang,et al.., 2015. Analysis on the relative effects of vegetation cover change and its influence factors in North China.Journal of Natural Resources, 30(1): 12-23.

[10]
Liu Lian, Zhang Yuming, Wang Shouyi et al., 1996. Hebei Vegetation. Beijing: Science Press. (in Chinese)

[11]
Liu Weiwei, An Shunqing, Liu Gengshan,et al.., 2004. The farther modification of Palmer Drought Severity Model.Journal of Applied Meteorological Science, 15(2): 207-216. (in Chinese)To evaluate the ravages of a drought, a farther modified Meteorological Drought Severity Model for China was developed based on the Palmer Drought Severity Model (1965) and the modified Palmer Drought Severity Model corrected by An Shunqing etc.(1986). The monthly temperature and precipitation data at Jinan, Zhengzhou and Taiyuan (1961—2000) were used as basic data and relevant data gained at other 21 stations (Harbin, Jiamusi, Hohhot, Shenyang, Beijing, Guyuan, Xian, Hanzhong, Qingdao, Dezhou, Yuncheng, Changsha, Wuhan, Nanchang, Hangzhou, Fuzhou, Guangzhou, Kunming, Nanning, Chengdu and Guiyang) to obtain the weighing coefficient. The potential evapotranspiration for an individual month was computed by the Penman-Monteith equation commended by FAO. The available soil moisture in both layers (0—20 cm, 20—100 cm) was determined by the soil moisture data and the soil characters. The monthly Palmer Drought Index was computed at 139 stations of North China from 1961 to 2000. Comparing the computed Palmer Drought Index with the situation of drought and waterlog recorded in some documents, it is found that the farther modified Palmer Drought Severity Model can reflect the severity of meteorological drought or waterlogging comparatively and is applicable for China.

[12]
Liu Yu, Cai Lingen, 1997. Update definition and computation of reference evapotranspiration comparison with former method.Journal of Hydraulic Engineering, 6: 27-33. (in Chinese)react-text: 503 The assessment of present and future water consumptions and needs for irrigation is of prime importance for water resources planning and management at basin level. As river Ave basin is being used as a Portuguese case study, a model has been developed for assessing present situation: agrometeorological data and crop systems and yield data provides the estimation of real evapotranspiration... /react-text react-text: 504 /react-text [Show full abstract]

[13]
Liu Yuan, Wang Ying, Yang Xiaoguang, 2010.Trends in reference crop evapotranspiration and possible climatic factors in the North China Plain.Acta Ecologica Sinica, 30(4): 923-932. (in Chinese)Reference crop evapotranspiration(ET0) is the key factor to estimate crop water requirement and guide farming irrigation scheduling.Under the climate change background,daily reference crop evapotranspiration at typical stations across the North China Plain(NCP) during the period from 196 1 to 2007 were calculated by the Penman-Monteith approach.The seasonal and annual characteristics of ET0 were analyzed from the view of energy balance and dynamics.Using the mathematical statistics,primary and secondary climatic impact factors were studied for reference crop evapotranspiration and its constituents(ETrad and ETaero) respectively,for guiding farming water management more effective in the NCP.The results showed that the annual reference crop evapotranspiration and its constituents(ETrad and ETaero) were significantly declined,while annual mean temperature was significantly increased,annual mean sunshine hour,related humidity and wind speed were significantly decreased.The highest reference crop evapotranspiration and ETrad were in summer,and lowest in winter,while the spring ETaero value was highest across the NCP.The relationship between annual ETrad and ETaero was slightly negative,while was positive in spring and summer as well as negative in autumn and winter.The main climatic factors for the change of ETrad were sunshine hour,wind speed and mean temperature,and the impact of wind speed was negative to ETrad.However,the main climatic factors for the change of ETaero were wind speed,related humidity and mean temperature,and the impact of related humidity was negative.Therefore,the change of reference crop evapotranspiration was synthetically impacted by sunshine hour,relative humidity,difference of maximum and minimum temperature and wind speed.In addition,the correlation between rainfall and reference crop evapotranspiration was significantly negative,while the decline rate of rainfall was a little more than that of reference crop evapotranspiration.

DOI

[14]
Ma Zongjin, Fang Weiqing, Gao Qinghua et al., 1993. Major Natural Disasters and Countermeasures of Disaster Release in China (Theory). Beijing: Science Press. (in Chinese)

[15]
Meteorological Reference Room, National Meteorological Information Center. China Meteorological Data Sharing Service System. .

[16]
Mu Shaojie, Li Jianlong, Chen Yizhao,et al.., 2012. Spatial Differences of Variations of Vegetation Coverage in Inner Mongolia during 2001-2010.Acta Geographica Sinica, 67(9): 1255-1268. (in Chinese)Global climate change has led to significant vegetation changes in the past half century.Inner Mongolia,most of which was located in arid and semi-arid areas,is undergoing a process of prominent warming and drying.It is necessary to investigate the response of vegetation to the climatic variations(temperature and precipitation) for a better understanding of the accumulated consequence of climate change.Vegetation coverage,which is an important indicator for evaluating terrestrial environment,is used to monitor vegetation change.MODIS-NDVI data and climate data were used to analyze the vegetation dynamics and its relationship with climate change on different spatial(forest,grassland and desert biome) and temporal(yearly and monthly) scales in Inner Mongolia during 2001-2010.It was found that vegetation coverage increased from west to east across Inner Mongolia with a change rate of 0.2/10掳N.During 2001-2010,the mean vegetation coverage was 0.57,0.4 and 0.16 in forest,grassland and desert biome,respectively,exhibiting evident spatial heterogeneities.There is a slight increase of vegetation coverage over the study period.Across Inner Mongolia,the vegetation coverages with extremely significant and significant increase accounted for 11.25% and 29.13% of the total study area,respectively,while those with extremely significant and significant decrease were 7.65% and 26.61%,respectively.The correlation analysis between vegetation coverage and climate shows that annual vegetation coverage was better correlated with precipitation,while the change of monthly vegetation coverage is consistent with both the changes of temperature and precipitation,indicating that the vegetation growth within a year is more sensitive to the joint function of hydrothermal combination rather than either climate factor.The vegetation coverage of forest biome was mainly affected by temperature on both yearly and monthly scales,while that of desert biome was mainly influenced by precipitation on the two temporal scales.

DOI

[17]
Omuto C T, Vargas R R, Alim M S,et al.., 2010. Mixed-effects modelling of time series NDVI-rainfall relationship for detecting human-induced loss of vegetation cover in drylands. Journal of Arid Environments, 74(11): 1552-1563.Many researchers have used time-series analysis of remotely sensed images to gain understanding of the dynamics of loss of vegetation cover in drylands. However, complex interactions between vegetation and climate still mask the potential of remote sensing signals to detect human-induced loss of vegetation cover. This paper presents mixed-effect modelling method for time-series NDVI-rainfall relationship to account for the complex interaction between vegetation and climate. Mixed-effects method is a form of statistical modelling that can simultaneously model environmental relationships for a population and for different groups within the population. In this study, it was used to model the NDVI-rainfall relationship in Somalia and for different vegetation types in the country. Its time-series application removed the interaction between vegetation and rainfall and identified areas experiencing human-induced loss of vegetation cover in the country. On average, it gave an accurate relationship between rainfall and NDVI ( r 2 > 60%) and detected areas with human-induced loss of vegetation cover (kappa = 75%). Although the potential of mixed-effects was shown using vegetation types, other factors such as soil types and land use can also be included in the method to improve accuracy of time-series NDVI images in detecting human-induced loss of vegetation cover in the drylands.

DOI

[18]
Palmer W C. Meteorological Drought. Research Paper No.45. Washington DC: U.S. Department of Commerce Weather Bureau, 1965.

[19]
Piao Shilong, Fang Jingyun, 2001. Dynamic vegetation cover change over the last 18 years in China.Quaternary Sciences, 21(4): 294-302. (in Chinese)Dynamic changes in Chinese terrestrial vegetation over the last 18 years from 1982 to 1999 and their spatial patterns were explored, using NDVI (normalized difference vegetation index) data derived from remote sensing imagery (NOAA/AVHRR), along with the information of vegetation distribution and climatic records. As a result, the dynamic vegetation cover change was closely coupled with climatic fluctuation, and showed a remarkable regional difference. In the past 18 years, regions with significant decline in NDVI appeared in Northwest China and Tibetan Plateau whilest a marked increase occurred in eastern regions of the country. The trends in interannual NDVI change displayed a large discrepancy between 1980's and 1990's; areas with decline in NDVI in 1990's was considerably larger than in 1980's, especially for northwest arid region. Due to rapid urbanization, Zhujiang River delta and Changjiang River delta showed a sharp drop in NDVI during the past 18 years.

[20]
Research Center for Eco-Environmental Sciences, 2014. Chinese Academy of Sciences. China ecosystem assessment and ecological security database. .

[21]
Sun Shizhou, 1998. Concerning the Vegetation Chinese Regionalization Map as a part of in the Natural Geographical Atlas of the Peoples Republic of China.Chinese Journal of Plant Ecology, 22(6): 523-537. (in Chinese)This paper explicitly defines the principles,bases,indices and marks of Chinese vegetation regionalization.Chinese vegetation regionalization system was classified into 4 levels.The super level unit is vegetation biome.The whole country can be subdivided into forest biome,grasslands biome,and desert biome.The primary unit is vegetation region,having 14 ones in China.The secondary unit is vegetation zone and the tertiary unit is vegetation area.The above division scheme has fully absorbed the achievements of previous Chinese vegetation regionalization schemes.The borders of many regionalization units,were modified according to the updated materials.

[22]
Sun Yanling, Guo Peng, 2012. Spatiotemporal variation of vegetation coverage index in North China during the period from 1982 to 2006.Arid Zone Research, 29(2): 187-193. (in Chinese)In this paper,the spatiotemporal variation of normalized difference vegetation index(NDVI) values in north China was analyzed based on the GIMMS data during the period of 1982-2006.The preliminary results were as follows:(1) The average annual NDVI curves of vegetation were unidomal in north China.The NDVI peaks of vegetation occurred in summer,the NDVI peak in Beijing was the highest,then in Hebei Province,and it was the lowest in Inner Mongolia.The NDVI peak of forests was higher than that of grasslands and farmland;(2) In recent 25 years,the average annual NDVI in north China was increased.The average annual NDVI in Hebei Province was increased significantly,and the trend was the most significant(P0.01).The average annual NDVI in Beijing was also increased significantly.In different land cover types,the highest increase of average annual NDVI occurred over farmland,than over grasslands,and it was the slowest in forests;(3) Based on the spatial trend analysis,the proportion of NDVI increase in the study area was 15.96%,and that of NDVI decrease was 11.86%.In different provinces,the NDVI increase occurred mainly in Hebei Province,and the NDVI decrease occurred mainly in Inner Mongolia.In three land cover types,NDVI in farmland was increased more significantly than that in forests and grasslands;(4) Based on the background of warming and drying climate in north China,human activities were the main causes resulting in the NDVI variation.

[23]
Wang J, Rich P M, Price K P, 2003. Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA.International Journal of Remote Sensing, 24(11): 2345-2364.Normalized Difference Vegetation Index (NDVI) is generally recognized as a good indicator of terrestrial vegetation productivity. Understanding climatic influences, in particular precipitation and temperature, on NDVI enables prediction of productivity changes under different climatic scenarios. We examined temporal responses of remotely sensed NDVI to precipitation and temperature during a nine-year period (1989-97) in Kansas. Biweekly (every two weeks) and monthly precipitation data were derived from 410 weather stations and biweekly temperature data were derived from 17 weather stations inside and around the borders of Kansas. Biweekly and monthly climate maps were derived by interpolation. Biweekly growing season (March-October) NDVI values for Kansas were calculated using National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) NDVI images. Average growing season NDVI values were highly correlated with precipitation received during the current growing season and seven preceding months (15-month duration); biweekly NDVI values were correlated with precipitation received during 2-4 preceding biweekly periods; and response time of NDVI to a major precipitation event was typical 1-2 biweekly periods (2-4 weeks). Temperature was positively correlated with NDVI early and late in the growing season, and there was a weak negative correlation between temperature and NDVI in the mid growing season. Precipitation has the primary influence on NDVI and, by inference, on productivity. The relationship between precipitation and NDVI is strong and predictable when viewed at the appropriate spatial scale.

DOI

[24]
Wang Yi, Tan Yanjing, 2014. Research on the variation of vegetation ndvi and its relationship with climatic factors in northern china.Journal of Shanxi Agricultural Sciences, 42(8): 890-895. (in Chinese)Based on the remote sensing data, NOAA/AVHRR NDVI and meteorological data of 56 meteorological stations,temperature and precipitation, during 1982-2006 in the Northern China, the inter-annual variability of vegetation NDVI, temperature and precipitation and the response of NDVI to the temperature and precipitation were analyzed from monthly, seasonal and growing season time scales. From the monthly, the inter-annual change of vegetation NDVI and temperature were mainly increased, while the precipitation was mainly reduced. In view of the season, the spatial distribution of NDVI in spring was consistent with the distribution in autumn, but contrast with the distribution in summer. The time series NDVI in spring and autumn were increased, and the decreased trend in summer was weak. The rate of temperature increasing in spring was the fastest, and the summer followed. The precipitation in spring and autumn were induced, while the precipitation in summer was increased. In the growing season, the vegetation NDVI showed an increasing trend, the temperature increased 0.48 鈩 every 10 years(P0.01), while the trend of precipitation was not obvious. The vegetation NDVI in March, April and November was mainly affected by the temperature. The correlation coefficients between NDVI and temperature of the season and growing season were greater than the correlation coefficients between NDVI and precipitation. The order of the correlation coefficients between NDVI and temperature was: growing season spring autumn summer, while the order of the correlation coefficients between NDVI and precipitation was: springgrowing seasonsummer autumn.

[25]
Wei Jie, Ma Zhuguo, 2003. Comparison of Palmer Drought Severity Index, percentage of precipitation anomaly and surface humid index.Acta Geographica Sinica, 58(Suppl.1): 117-124. (in Chinese)The monthly PDSI, surface moisture index and percentage of precipitation anomaly are calculated for the past 49 years with data from 160 stations in China. The result shows that PDSI and surface moisture index can grasp rainfall, a decisive factor of drought. Moreover, they reflect the process of drought on a greater space and time scale. In the region with more actual evaporation, PDSI describes the severity of drought more precisely than percentage of precipitation anomaly. In North China, before the 1980s, the rainfall was more sufficient, the temperature was higher and the potential evaporation was lower. It was a relatively moist period. Since the 1980s, the rainfall has been less sufficient together with higher temperature, the drought in North China has become more and more severe.

DOI

[26]
Wei Jie, Tao Shiyan, Zhang Qingyun, 2003. Analysis of drought in northern China based on the Palmer Severity Drought Index.Acta Geographica Sinica, 58(Suppl.1): 91-99. (in Chinese)Drought during the period 1999-2000 in North China is studied by using monthly mean temperature and precipitation data from 160 stations covering January 1951 to October 2000. The revised Palmer drought severity index (PDSI) of the 160 stations covering the same period was also calculated. It is found that the protracted drought was a manifestation of an abnormal form of general circulation. The PDSI can characterize the temporal and spatial distributions of remarkable droughts and floods in China. During 1999-2000 there was a persistent negative PDSI in North China, the PDSI also shows that there is a marked interannual and interdecadal variation of drought in North China. From 1950-1970 there is a wet summer climate, while since 1970 there have been more drought years in North China.

[27]
Wu Zhengyi, Wang Xianpu, Liu Fangxun et al., 1980. China Vegetation. Beijing: Science Press. (in Chinese)

[28]
Yang Jun, Huang Conghong, Zhang Zhiyong,et al.., 2014. The temporal trend of urban green coverage in major Chinese cities between 1990 and 2010.Urban Forestry & Urban Greening, 13(1): 19-27. (in Chinese)Information on changes in urban green spaces and the causes of these changes is important for urban planning. In this study the trends of urban green coverage (UGC) between 1990 and 2010 in 30 major Chinese cities were studied using classified Landsat satellite images. Associations between the trends and natural and socio-economic variables were analyzed using the maximum information-based nonparametric exploration method. The results showed that, overall, the studied cities have become greener over the past two decades. Greening in old city districts and expanded built-up areas (BUAs) led to the increase of urban green coverage at a mean annual rate of 1.51%. However rapid urbanization also caused a dramatic turn-over in vegetation covers. On a regional scale, around 46.89% of original vegetation cover was converted to other land cover types. The trends of UGC cannot be attributed to any one of natural or socio-economic variables alone. The combined influences of economic growth, climate change, and urban greening policies are the most likely causes behind the detected trend. One lesson from this study is that the preservation of existing vegetation cover must be a priority in urban greening programs.

DOI

[29]
Yu Fangfang, Pricea Kevin P, Ellis James,et al.., 2003. Response of seasonal vegetation development to climatic variations in eastern central Asia.Remote Sensing of Environment, 87(1): 42-54.Meteorological records show that central Asia has experienced one of the strongest warming signals in the world over the last 30 years. The objective of this study was to examine the seasonal vegetation response to the recent climatic variation on the Mongolian steppes, the third largest grassland in the world. The onset date of green-up for central Asia was estimated using time-series analysis of advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) biweekly composite data collected between January 1982 and December 1991. Monthly precipitation and mean temperature data (1982–1990) were acquired from 19 meteorological stations throughout the grasslands of the eastern Mongolian steppes in China. Our results showed that while the taiga forest north of the Mongolian steppes (>50°N) experienced an earlier onset of green-up during the study period, a later onset was observed at the eastern and northern edges of the Gobi Desert (40°N–50°N). Responses of different vegetation types to climatic variability appeared to vary with vegetation characteristics and spring soil moisture availability of specific sites. Plant stress caused by drought was the most significant contributor to later vegetation green-up as observed from satellite imagery over the desert steppe. Areas with greater seasonal soil moisture greened up earlier in the growing season. Our results suggested that water budget limitations determine the pattern of vegetation responses to atmospheric warming.

DOI

[30]
Zhang Yichi, Wu Kai, Yu Jingjie,et al.., 2011. Characteristics of precipitation and air temperature variation during 1951-2009 in North China.Journal of Natural Resources, 26(11): 1930-1941. (in Chinese)Annual and monthly observed temperature and precipitation data at 64 weather stations in five provinces of North China are used to estimate the areal temperature and precipitation using the Thiessen polygon method.The regression equations between the observed temperature/precipitation at seven stations with long time-series records and areal magnitude from 1961 to 2009 were then built.The areal temperature/precipitation time series was extended back to 1951 through the seven stations with long time-series records.With this extended dataset,the temperature/precipitation trend for 1951-2009 was investigated with Mann-Kendall,Mann-Whitney and the moving average method.The results indicate that: 1) the temperature increased by 1.5 鈩 during the past 59 years and showed a statistically significant increasing trend with the magnitude of about 2.2% per decade;the time series of annual temperature appeared step change around 1993 and the temperature anomalies changed from negative periods to positive periods from 1989.2) The annual precipitation did not present a statistically significant trend and showed a decline of 1.3% per decade;the precipitation anomaly showed that the year 1976 is the turning point from wet to dry.The result of this paper can be used as a reference for further analysis of climate change and its impacts as well as the responses of water resources to climate change in North China.

DOI

[31]
Zheng Jingyun, Ying Yunhe, Li Bingyuan,et al.., 2010. A new scheme for climate regionalization in China.Acta Geographica Sinica, 65(1): 3-12. (in Chinese)A new scheme for climate regionalization in China was established based on the daily observations for 609 meteorological stations during the period 1971-2000.During regionalization,current basic theories,classification methodologies and criteria system were used,besides,five principles were taken into consideration,mainly included zonal and azonal integration,genetic unity and regional relative consistent climate integration,comprehensiveness and leading factors integration,bottom-up and top-down integration,spatial continuity and small patch omission.The new scheme consists of 12 temperature zones,24 moisture regions and 56 climatic sub-regions.

DOI

[32]
Zheng Youfei, Liu Hongju, Wu Rongjun,et al.., 2009. The change of NDVI in Guizhou Province and its relationship with major climatic factors. Chinese Journal of Ecology and Rural Environment, 25(1): 12-17. (in Chinese)

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