Journal of Geographical Sciences © 2007
Science in China Press
Springer-Verlag
DOI: 10.1007/s11442-007-0259-7
The relationship between NDVI and precipitation on the Tibetan Plateau DING Mingjun, *ZHANG Yili, LIU Linshan, ZHANG Wei, WANG Zhaofeng, BAI Wanqi Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
Abstract: The temporal and spatial changes of NDVI on the Tibetan Plateau, as well as the relationship between NDVI and precipitation, were discussed in this paper, by using 8-km resolution multi-temporal NOAA AVHRR-NDVI data from 1982 to 1999. Monthly maximum NDVI and monthly rainfall were used to analyze the seasonal changes, and annual maximum NDVI, annual effective precipitation and growing season precipitation (from April to August) were used to discuss the interannual changes. The dynamic change of NDVI and the correlation coefficients between NDVI and rainfall were computed for each pixel. The results are as follows: (1) The NDVI reached the peak in growing season (from July to September) on the Tibetan Plateau. In the northern and western parts of the plateau, the growing season was very short (about two or three months); but in the southern, vegetation grew almost all the year round. The correlation of monthly maximum NDVI and monthly rainfall varied in different areas. It was weak in the western, northern and southern parts, but strong in the central and eastern parts. (2) The spatial distribution of NDVI interannual dynamic change was different too. The increase areas were mainly distributed in southern Tibet montane shrub-steppe zone, western part of western Sichuan-eastern Tibet montane coniferous forest zone, western part of northern slopes of Kunlun montane desert zone and southeastern part of southern slopes of Himalaya montane evergreen broad-leaved forest zone; the decrease areas were mainly distributed in the Qaidam montane desert zone, the western and northern parts of eastern Qinghai-Qilian montane steppe zone, southern Qinghai high cold meadow steppe zone and Ngari montane desert-steppe and desert zone. The spatial distribution of correlation coefficient between annual effective rainfall and annual maximum NDVI was similar to the growing season rainfall and annual maximum NDVI, and there was good relationship between NDVI and rainfall in the meadow and grassland with medium vegetation cover, and the effect of rainfall on vegetation was small in the forest and desert area. Keywords: Tibetan Plateau; land cover change; NDVI; precipitation; correlation
Received: 2007-02-10 Accepted: 2007-03-29 Foundation: National Basic Research Program of China, No.2005CB422006; National Natural Science Foundation of China, No.40331006; No.90202012 Author: Ding Mingjun (1979–), Ph.D. Candidate, specialized in land-use/land-cover change and physical geography. E-mail:
[email protected] *Corresponding author: Zhang Yili, E-mail:
[email protected]
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Introduction
With the development of remote sensing, it is possible to get more information from multi-date and multi-spectral remote sensing data, which provide effective methods to study the vegetation distribution and interannual and seasonal changes. Among all the remote sensing data, NOAA/AVHRR data are widely used to study vegetation on global and regional scale because of long series, wide extent of observation and short cycle (Defnies et al., 1994) The vegetation indexes are very significant to reflect more information of vegetation. Among them, the normalized difference vegetation index (NDVI, NDVI=(NIR–Red) /(NIR+Red) NIR denoted the reflection of near infrared, red denoted the reflection of red light from visible light) is widely used, and it is the perfect indicator of growth status, spatial density distribution (Sun et al., 1998; Purevdorj et al., 1998; Liu et al., 1999) and phenology of plant (Defnies et al., 1994; Derrein et al., 1992). No matter physical or cultivated vegetation, their growth processes are all affected by temperature, precipitation, etc. (Li et al., 2000; Zhang et al., 2003; Li et al., 2000; Nicholson et al., 1990; Schmidt et al., 2000). To further study the relationship between vegetation and climate and to get the spatial variation will boost the research on forecasting of growth status. With the influence of complicated topography and southwest monsoon, southeast monsoon, Siberian high, Qinghai-Tibet high etc., the change of vegetation on the Tibetan Plateau is typical and representative on high altitude region, which is indispensable for the research about global change. In this research, the NOAA/AVHRR data and precipitation data, with long series, were used to study the spatio-temporal change characteristics of NDVI on the Tibetan Plateau, and analyze the relationship between NDVI and precipitation. The study will offer some information or evidences to exploit the resources on the Tibetan Plateau and to study the global vegetation change.
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The study area
The study area extends from 26º00′12″N to 39º46′50″N and 73º18′52″E to 104º46′59″E and includes low area of the southeastern Himalaya Mountains (Mêdog, Zayü and Cona counties), that’s the Tibetan Plateau in China. The total area is about 2.61×106 km2 (Zhang et al., 2002) and the average altitude is about 4380 m. In winter, the climate of Tibetan Plateau is controlled by Siberian high and Mongolian high, and it is very cold and arid, and has very little rain and more strong winds. In summer, the climate is controlled by Indian low there, and it is warm and moist (Zhao, 1998). All kinds of habitats offer favorable condition for most plants. From southeast to northwest, there are seven types of vegetation. They are subtropical evergreen broad-leaved forest, deciduous broad-leaved forest, alpine brush, alpine meadow, alpine grassland and high and cold desert (Chen et al., 1999).
3 3.1
Data and methods Data
NOAA/AVHRR-NDVI data with an 8-km resolution of every ten days from 1982 to 1999
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from NASA was used in this research (Agbu et al., 1994). It is the third generation product with values ranging from 0 to 254. The formula (NDVI=0.8(I)/254–0.1) can transmit the value to normalize difference vegetation indexes with a value ranging from –1 to 1. To eliminate the effect of clouds, the Maximum Value Composite was used in data processing which assumes that the position of clouds often change in a period of time, such as ten days, and any point has a day without cloud covering. The value of NDVI in this day at the point is higher than those of the cloudy days, and this study selects this value as the value of the point in ten days, then it selects the highest value of the point as value of this month. The primitive projection of these images is Goode Interrupted Homolsine Projection. In this study, we convert it into Albers Projection and select the area of the Tibetan Plateau. Because the annual maximum NDVI value can reflect the vegetation well, so in this study, we used the Maximum Value Composite method to get the annual maximum NDVI. Meanwhile we count the mean monthly maximum NDVI from 1982 to 1999 to analyze the seasonal change. The data of temperature and precipitation are provided by Dr. Tao (2005), and his research report utilizes spline function interpolation to obtain spatial interpolation data. In the process of interpolation, the effects of latitude and longitude were discussed here, meanwhile the DEM data with 0.1 degree was utilized to fit the surface data from the above method. The data quality is very good through the test. Considering the growing season ends at the end of September and precipitation after September has no influence on the vegetation NDVI of the current year, but has effects on vegetation of the next season, so we counted the amount of precipitation from October of the last year to September of the ensuing year that we called annual effective precipitation and precipitation from April to August that we called growing season precipitation respectively. 3.2
Methods
We calculated the linear regression coefficients of annual maximum NDVI by means of Least Square Method (Micael, 2000). To find the natural fluctuation range of NDVI, 13 areas were selected–acreage of each is 200 km2–including lakes, frozen grounds, deserts, and salinas on the Tibetan Plateau (Ding et al., 2006). There was no vegetation in these areas. Through analysis and statistics, 97% of the coefficients in these areas were distributed between −0.2 and 0.2, so it was regarded as a range of no vegetation change. Based on the above analysis, all the coefficients are divided into three grades, decrease: less than -0.2; no change: −0.2 to 0.2; and increase: more than 0.2. The change of precipitation was divided by the standard deviation, more than the standard deviation was considered the precipitation was increasing, but less than the negative standard deviation was thought the precipitation was decreasing. The correlation between precipitation and NDVI was adapted to linear correlation. All the processes were done by the software ARCGIS. In order to analyze the spatial difference of vegetation seasonal change, we selected 12 areas with 3×3 pixels around the weather stations along longitude and latitude respectively (Figure 1) and utilized the precipitation from the weather stations and the monthly maximum NDVI value extracted from NOAA/AVHRR-NDVI by ARCGIS software to analyze the relationship between precipitation and vegetation in one year.
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Figure 1
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The location of the weather stations and research region
Results and analyses
4.1 Spatial distribution pattern of mean annual maximum NDVI value and annual effective precipitation from 1982 to 1999 Because of controlled by the southwest warm and humid air flow, the southeast monsoon and Siberian high, the amount of precipitation decreases from southeast to northwest (Figure 21). Precipitation can reach a range from 700 mm to 800 mm in the southeastern part of the Tibetan Plateau, from 150 mm to 200 mm in the northwestern part and less than 100 mm in the Qaidam Basin. Alike precipitation, the NDVI value decreases gradually from southeast to northwest (Figure 3). In the eastern and southern parts of the plateau, NDVI reaches the highest, but in the western and northern, it is very low. From Figures 2 and 3 and previous studies (Chen et 1
Based on the map of system of physico-geographical regions of Qinghai-Tibet Plateau (Zheng, 1996), we digitalized it (Though the edge of the physico-geographical regions is a transitional belt, in order to analyze conveniently in this study, we assumed that it was a line and digitalized it. The name of the physico-geographical regions are as follows: IB1 Golog-Nagqu high-cold shrub-meadow zone; IC1 Southern Qinghai high-cold meadow steppe zone; IC2 Qangtang high-cold steppe zone; ID1 Kunlun high-cold desert zone; IIAB1 Western Sichuan-eastern Tibet montane coniferous forest zone; IIC1 Southern Tibet montane shrub-steppe zone; IIC2 Eastern Qinghai-Qilian montane steppe zone; IID1 Ngari montane desert-steppe and desert zone; IID2 Qaidam montane desert zone; IID3 Northern slopes of Kunlun montane desert zone; OA1 Southern slopes of Himalaya montane evergreen broad-leaved forest zone.
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al., 1999), we can find that evergreen broad-leaved forest and coniferous forest are distributed in places with precipitation more than 600 mm and NDVI value more than 200; that brush and meadow in places with precipitation from 400 mm to 600 mm and NDVI between 180 and 200; meadow grasslands, alpine grassland and montane grassland in places with precipitation from 200 to 400 and NDVI from 150 to 180; and alpine desert and montane desert with precipitation lower than 200 mm and NDVI under 150.
Figure 2 Spatial distribution of mean annual precipitation on the Tibetan Plateau from 1982 to 1999
4.2
Figure 3 Spatial distribution of mean annual maximum NDVI on the Tibetan Plateau from 1982 to 1999
The relationship between monthly maximum NDVI and monthly precipitation
Figure 41 shows spatial difference of the rainy season. From north to south along the longitude, the rainy season becomes longer gradually. In the north, there is no apparent rainy season and only has little rain in July. In the central part, the rainy season may last from the beginning of April to October, but most of the precipitation is concentrated in July and August. In the south, there is no apparent rainy season and two rainfall peaks exist. The growing season is in accord well with the rainy season (Figure 5). In the north, there is no evident growing season. In the central part, the growing season begins in May and ends in October, it lags behind rainy season. In the south, the vegetation has no apparent growing season and grows all the year round. From west to east along the latitude, extent of rainy season gets longer (Figure 62). In the west, there is only a little rain in July, August and September. In the east, the rainy season almost accords with the central part. The growing season is also in accord well with the rainy season (Figure 7). In the west, the growing season is very short, but the growing season in the east is long and lasts from mid-April to the end of October. Results show that monthly NDVI and monthly precipitation have weak correlation in the west and strong correlation in the mid-east along the latitude (Table 1). It indicates that precipitation is a key factor for vegetation growth in the mid-east and not a key factor for vegetation growth in the west. 1
The weather stations along longitude from 1 to 12 are Lenghu, Da Qaidam, Xiaozaohuo, Golmud, Qumarlêb, Zadoi, Nangqên, Qamdo, Baxoi, Zogang, Dêqên and Gongshan in proper order. 2 The weather stations along latitude from 1 to 12 are Shiquanhe, Gêrzê, Xainza, Amdo, Tuotuoheyan, Zhidoi, Qingshuihe, Madoi, Maqên, Golog, Henan and Hezuo in proper order.
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Along the longitude, the north and south have weaker correlation between monthly NDVI and monthly precipitation than that in the central part. At some special weather stations, such as Lenghu and Gongshan, the correlations are negative. The reason is that the increasing precipitation may lead to reduction of the NDVI value of bare soil with very low coverage at Lenghu station, but at Gongshan, the reason was not clear and further study is needed. From the above analysis, vegetation responded to precipitation very well in the central-eastern Tibetan Plateau.
Figure 4
Monthly mean precipitation along longitude
Figure 5 Monthly mean NDVI along longitude
Figure 6
Monthly mean precipitation along latitude
Figure 7
Monthly mean NDVI along latitude
Table 1 The correlation between monthly NDVI and precipitation Station along latitude Correlation coefficient Station along longitude Shiquanhe 0.47 Lenghu Gêrzê 0.16 Da Qaidam Xainza 0.87 Xiaozaohuo Amdo 0.89 Golmud Tuotuoheyan 0.86 Qumarlêb Zhidoi 0.90 Zadoi Qingshuihe 0.92 Nangqên Madoi 0.90 Qamdo Maqên 0.87 Baxoi Golog 0.90 Zogang Henan 0.93 Dêqên Hezuo 0.91 Gongshan
Correlation coefficient -0.67 0.71 0.94 0.55 0.91 0.93 0.90 0.91 0.81 0.72 0.36 -0.24
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The changes of vegetation types
In order to study the effect of precipitation on vegetation, we calculated the correlation between annual maximum NDVI and precipitation (annual effective precipitation and growing season precipitation) (Figure 8a). Significance level (p
