洞庭湖植被对降水的响应_英文_

J. Geogr. Sci. 2011, 21(1): 135-149DOI: 10.1007/s11442-011-0834-9© 2011 Science Press Springer-VerlagAnalysis of vegetation response to rainfall with satellite images in Dongting LakeJIANG Weiguo1,2, *HOU Peng1,3, ZHU Xiaohua4, CAO Guangzhen5,LIU Xiaoman3,4, CAO Ruyin11. State Key Laboratory of Earth Process and Resource Ecology, Beijing Normal University, Beijing 100875, China;2. Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing100875, China;3. Satellite Environment Center, MEP, Beijing 100094; China;4. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;5. Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China MeteorologicalAdministration, Beijing 100081, ChinaAbstract: We analyzed the Normalized Difference Vegetation Index (NDVI) from satellite images and precipitation data from meteorological stations from 1998 to 2007 in the Dongting Lake wetland watershed to better understand the eco-hydrological effect of atmospheric pre-cipitation and its relationship with vegetation. First, we analyzed its general spatio-temporal distribution using its mean, standard deviation and linear trend. Then, we used the Empirical Orthogonal Functions (EOF) method to decompose the NDVI and precipitation data into spa-tial and temporal modes. We selected four leading modes based on North and Scree test rules and analyzed the synchronous seasonal and inter-annual variability between the vegetation index and precipitation, distinguishing time-lagged correlations between EOF modes with the correlative degree analysis method. According to our detailed analyses, the vegetation index and precipitation exhibit a prominent correlation in spatial distribution and seasonal variation. At the 90% confidence level, the time lag is around 110 to 140 days, which matches well with the seasonal variation.Keywords: eco-hydrology; precipitation; vegetation; remote sensing; wetland; Dongting Lake1 IntroductionGrowth state of vegetation is very important to the health of terrestrial ecosystem and bio-sphere, which is close to the rainfall. The relationship between precipitation and vegetation has been paid more attention to by many researchers from different disciplines (Woodell et al., 1969; Sala et al., 1988; Sun et al., 2001; Cui et al., 2009), such as eco-hydrology, ecol-ogy, meteorology. In general, precipitation affects the floristic composition (Sala et al., 1988) Received: 2010-08-05 Accepted: 2010-09-20Foundation: National Natural Science Foundation of China, No.40701172; No.40671122; The International Program for Cooperation in Science and Technology, No.2007DFA20640; National Science and Technology SupportingItem, No.2008BAC34B01; The Beijing Municipal Science and Technology Plan, No.D08040600580801 Author: Jiang Weiguo (1976–), Ph.D, specialized in wetland ecology and flood remote sensing, E-mail: jwg@*Corresponding author: Hou Peng (1978–), Ph.D, specialized in environment remote sensing, E-mail: houpcy@ /content/1009-637X136 Journal of Geographical Sciences and distribution (Woodell et al., 1969) on an annual scale. However, vegetation growth fea-tures, including germination, survival, and leaf area, are often related to seasonal and monthly variations of rainfall (Beatley, 1967; Ackerman, 1979; Specht et al., 1989; Bertiller et al., 1991; Lázaro R, 2001). Although the relationship between precipitation and vegeta-tion is well understood globally, it varies from region to region and in different kinds of ecosystems (Chandrasekar et al., 2006). The NDVI, as one type of vegetation index, is a credible indicator reflecting the vigor of vegetation and can be obtained from many satellite platforms, including NOAA/AVHRR (1981-), EOS/MODIS (1999-), SPOT (1998-), and FY (2008-). The importance of spatial and temporal scales in ecology makes remote sensing become one of the key avenues to research their relationship. The wide spatial coverage and high quality available through satellite imagery allows this data to perform well in large scale ecological studies. Many researchers are trying to determine their relationship and in-teractions with the help of the NDVI (Normalized Difference Vegetation Index) in different disciplines (Sharon et al., 1990; Nezlin et al., 2005; Chandrasekar et al., 2006; Anup et al., 2007). However, no such study has been undertaken in humid and wetland regions of central China. As one of the most important ecosystems, the wetlands form a major feature of the landscape in almost all parts of the world, and humid is its remarkable features. So that, we collected NDVI remote sensing images and atmospheric precipitation in the Dongting Lake Wetland watershed from 1998 to 2007 in this research. The eco-hydrological effect of pre-cipitation and vegetation response to rainfall was analyzed with the EOF (Empirical Orthogo-nal Functions) and correlation analysis. The spatial distribution shows general similar patterns. In the temporal correlation, the time lag effect seems to be about 110 to 140 days. The eco-hydrological effect of atmospheric precipitation clearly matches the seasonal variation.2 Study siteDongting Lake is one of the most important international wetlands; its watershed covers 262,800 km2 in the middle reaches of Changjiang River, China. It consists of three parts: East, South and West Dongting Lake, which were on the List of Wetlands of International Importance in 1992, 2002, and 2002, respectively, and is thought of as a tremendous biology gene depot by many international and Chinese nature protection organizations. In this wa-tershed, about 211,800 km2 (80.98%) belong to Hunan Province, so that Dongting Lake wa-tershed in Hunan Province is selected as the study region. This region has a continental monsoon humid subtropical climate, with about 1200–1700 mm of precipitation per year. The annual fluctuation of precipitation is less than the seasonal change. Most of the rainfall occurs in the summer and is up to 40%–60% of the whole year. The high rainfall results in flourishing vegetation. In this watershed, the main type of ecosystem is wetlands, and its total acreage reaches 56,000 km2, which is 26.44% of the region. The eco-hydrological rela-tionship between vegetation and atmospheric precipitation is significant to wetland man-agement in this region. The sketch of the study region is shown in Figure 1.3 Data collection and methodology3.1 Empirical Orthogonal Functions (EOF) methodThe primary remote sensing data we used is the NDVI product of SPOT VEGETATION,JIANG Weiguo et al.:Analysis of vegetation response to rainfall with satellite images in Dongting Lake 137Figure 1 Dongting Lake wetland watershed regionwith a spatial resolution of 1 km, higher than the AVHRR NDVI (with a spatial resolution of8 km). The product is maximum value composite syntheses, based on daily images with the highest NDVI of an individual pixel for any given geographic coordinate, which helps to ensure the coverage of all landmasses worldwide with a minimum cloud cover effect. On the other hand, the narrower band width in the red and NIR (Near Infrared) wavelengths of SPOT helps to enhance vegetation discrimination. In this research, we selected NDVI re-mote sensing images from April 1998 to October 2007. Real NDVI were retrieved from the image, according to the equation: y =ax + b, where y is the NDVI value, x is the digital number of the image, and a and b are coefficients equal to 0.004 and –0.1, respectively. Pre-cipitation data observed with rain gauges in the Dongting Lake wetland watershed were ob-tained from the CMA (China Meteorological Administration) and were interpolated to raster layers with the IDW (Inverse Distance Weighted) method in GIS before analysis, with a spatial resolution of 1 km. In this region, there are about 25 important weather stations.To analyze the spatio-temporal variability of the NDVI and precipitation data, the EOF analysis method was used. EOF was invented by Lorenz (1956) and has been proved to be a good tool to extract a compact or simplified but “optimal” representation of data with both space and time dependence. EOF can be regarded as a kind of aligned eigenvectors, leading EOF modes to describe the spatially coherent pattern. It can convert successive data in spaceinto temporal and spatial modes. Thus, NDVI and a rainfall matrix X consisting of M linesand N columns, with each grid value x being the NDVI or rainfall data, can be decomposedinto space modes and a time index (Figure 2). M is the number of spatial sample points, andN is the number of time sample points.According to the principles of EOF, all sequence data distribution characters can be dele-gated with the principal components. How many modes should be retained for analysis? Onetest rule is to consider only those modes with eigenvalues over 1. In this research, we con-ducted a significance test by the North method (North 1982) and Scree test rules (Cattell,138Journal of Geographical Sciences11121121222211121212221212121112121(observation data matrix )(spatial function matrix )j n j n m m i i ij in m m mm m m mj mn n x x x x x x x x v v v v v v X V x x x x v v v x x x x X V t t t t T ⎡⎤⎢⎥⎡⎤⎢⎥⎢⎥⎢⎥⎢⎥⎢⎥==⎢⎥⎢⎥⎢⎥⎢⎥⎢⎥⎣⎦⎢⎥⎢⎥⎣⎦=⇒⇓""""""####""###"####"""22212(temporal cofficient matrix )n m m mn t t t t t ⎡⎤⎢⎥⎢⎥⎢⎥⎢⎥⎢⎥⎣⎦"###"Figure 2 Principle of empirical orthogonal functions1966) to recognize which mode has physical meaning in all modes. North thinks that if there are N samples, only when the difference of the eigenvalue λ between two adjacent modes is greater than λ(2/n)0.5, can the adjacent modes be considered as significant and valuable. Cattell was the first to use the Scree test rule. If all eigenvalues of samples are plotted in decreasing order, the plot will look like the side of a mountain, and “scree” refers to the de-bris fallen from a mountain and lying at its base. The Scree test proposes to stop analysis at the point where the mountain ends and the debris (error) begins, namely, at the point where the declining trend stops. The correlation function between different EOF modes of NDVI and precipitation are estimated to analyze the time-lag.3.2 Spatio-temporal variation character of vegetation and precipitationThe original precipitation and vegetation index consists of many observation stations and grid data. EOF can effectively extract the spatial distribution of the variable field, which can describe the original variable field with the least and irrelated typical spatial distribution modes. All of the modes include as much information as possible about the original field, which can help to keep the variable field consist of modes similar to the original field. The spatio-temporal variation character of vegetation and precipitation can therefore be analyzed by the EOF method. The spatial modes of the vegetation index and precipitation are the typical distribution, explaining the variation frequency in spatial distribution. If the values are the same sign, there is the same trend; otherwise, there is an opposite trend. The tempo-ral indices of the vegetation index and precipitation illustrate the temporal variation.The EOF method has been used to decompose the NDVI and precipitation into a spatial mode and temporal function. We first calculated the eigenvalues of the rainfall EOF modes and NDVI EOF modes, and selected the typical modes. The 30 leading space modes were selected to be plotted on a curve (Figure 3), because the eigenvalues of the remaining modes are less than them. According to the North and Scree test rules, the four leading eigenvalues were selected to explain the spatio-temporal variation of vegetation and precipitation; their cumulative contributions of deviation are 92.94% and 51.50% for the NDVI and precipita-tion, respectively.JIANG Weiguo et al .: Analysis of vegetation response to rainfall with satellite images in Dongting Lake 139Figure 3 Rainfall eigenvalue (×104) and NDVI eigenvalue (×104) of the 30 leading EOF modes4 Results and discussion4.1 General spatio-temporal distribution of NDVI and precipitationWe analyzed the general spatio-temporal variability distribution of the vegetation index and precipitation. It is important to become acquainted with and comprehensively analyze the relationship between vegetation and precipitation. To match the temporal cycle of NDVI, the precipitation was calculated every 10 days by summing up the 10-day daily precipitation from 1998 to 2007. The statistical character of NDVI and precipitation, including the aver-age, standard deviation and linear trend, was calculated from April 1981–October 2007 (Figures 4–6). Obviously, there is more rain in the northwest, east and south areas than the middle regions; these zones correspond to zones of high NDVI values in general.As Figure 4 shows, it is easy to find the general spatial distribution and change the char-acter of the temporal cycle of atmospheric precipitation every 10 days from 1998 to 2007. In Figure 4a, the mean varies from 34.48mm to 59.83mm. The maximum mean rainfall reaches59.83; it is located in the middle-east, at one “point,” and does not coincide with a highFigure 4 General spatio-temporal variability of precipitation140 Journal of Geographical SciencesFigure 5 General spatio-temporal variability of NDVIFigure 6 Seasonal variations of NDVI and precipitation average per 10 days from 1998 to 2007JIANG Weiguo et al.:Analysis of vegetation response to rainfall with satellite images in Dongting Lake 141 vegetation index. This is because the high density of human activity in this zone results in a lower vegetation coverage. The rainfall is about 25 mm less than the maximum, located inthe middle area. Figure 4b illustrates the spatial distribution of precipitation variability and reflects the spatial distribution of total values, changing from 30.20 to 56.39.Figure 5 shows the general distribution characters of the NDVI. Figure 5a indicates the general spatial distribution of NDVI averaged over the whole period, varying from –0.05 to0.62. It is good to know that where the NDVI is less than or close to zero, where there is no vegetation coverage. Otherwise, the bigger value is, the higher the vegetation coverage ratiois. Therefore, the bigger NDVI values in the east, the northwest of the middle part, and the north and west of the study area, presents dense vegetation cover in these zones, as can be seen in Figure 5a. In addition, small NDVI values around Dongting Lake, in the south of the middle part, explain the sparse vegetation. The NDVI values along Xiangjiang River arealso smaller. Figures 5b and 5c quantitatively illustrate the spatio-temporal variability of vegetation coverage by the standard deviation and linear change tendency of the NDVI. The former mainly shows the change intensity throughout the period, while the latter mainly presents the trend of vegetation growth from 1998 to 2007. In general, the coverage regionsare consistent in the two pictures, and decrease regions are bigger than increase regions in Figure 5c. The minimum standard deviation appears in the northeast of the areas around Dongting Lake (Figure 5b), which means that the vegetation changes little inter-seasonallyand inter-annually. However, Figure 5c shows an obvious vegetation decrease trend in this area.Figure 6 illustrates the seasonal cycles of atmospheric precipitation and NDVI averagedfor every 10 days. The minimum precipitation occurs on the first and the last 10 days of January and September. Rainfall gradually increases to the maximum in May and June, then decreases to the minimum in September; after that, there is a small increase. The minimum NDVI appears in the last 10 days of February. With the increase in precipitation, the NDVI gradually increases and reaches the maximum in July and August. We note that the increase speed of rainfall and NDVI are similar, however, the decrease speed of NDVI is less thanthat of precipitation. After June, it is easy to find the time lag of NDVI compared to precipi-tation.Figures 7 and 8 show the spatial variation distribution and temporal variation of the four leading atmospheric precipitation EOF modes. In general, there are similar variation trendsin the northwest and in the southeast, with opposite trends in these two zones. The first modehas the most obvious global variation feature; this feature becomes smaller and smaller from mode 1 to 4 because more and more “point” features appear.The first precipitation EOF mode (Figure 7a) illustrates 26.01% of the total variability. There is a similar variation trend in the northwest. There are opposite distribution features between the northwest and the east and northeast. The maximum absolute value appears around Dongting Lake, where the center is. The temporal variability of precipitation mode 1is shown in Figure 8a, and the value gradually climbs to the maximum in 2002. The valuewas at its minimum in 2003, and reached a second maximum in 2006. In 2007, there was a dramatic decrease.The second precipitation EOF mode (Figure 7b) contains 10.88% of the spatial distribu-tion information for the precipitation over the years. The spatial distribution with negative142 Journal of Geographical SciencesFigure 7Spatial distribution of the four leading EOF modes of atmospheric precipitationvalues was discontinuous, because there are few positive points with similar trends to the northwest zones. The maximum absolute value appears in some places along the Xiangjiang River, that is, there are several centers. Regarding its temporal variability (Figure 8b), there was a gradual decrease from 1998 to 2001 and a dramatic increase in 2002, which was the maximum. After that, there was a decrease along a wave line that went to its minimum in 2007. Its total variety is similar to the EOF mode 1 of precipitation and is bigger than modes 3 or 4.The third precipitation EOF mode (Figure 7c) explains 8.77% of the total variability. The centers with the highest absolute values are more obvious, around Dongting Lake andJIANG Weiguo et al .: Analysis of vegetation response to rainfall with satellite images in Dongting Lake 143Xiangjiang River. However the centers with positive values in the east zones in Figure 7b are weaker. The spatial distribution zone with positive values in the northwest is smaller than that of modes 1 and 2. The 10-day and annual increasing trend are both low; the highest values appeared in 2007 (Figure 8c).The fourth precipitation EOF mode (Figure 7d) indicates 5.84% of the total variability. A comparison of precipitation EOF modes 1, 2 and 3 shows that the center with the highest value is most obvious. However, the centers with positive values are separated, and the cen-ters with negative values are centralized. The highest value appears in 2000 (Figure 8d).Figure 8 Temporal variations of the four leading EOF modes of atmospheric precipitation. The dashed line and the equation are trendsFigures 9 and 10 are the spatial variation distribution and temporal variability of the four leading NDVI EOF modes, explaining 89.36%, 1.77%, 1.40% and 0.75% of the total vari-ability. It is clear that the spatial distribution and scope are different in different modes. Temporal variability is well matched with the seasonal cycle.The first NDVI EOF mode (Figure 9a) is the most important one due to its highest ratio (up to 89.36%). A comparison of Figures 9a and 7a shows that there are good matches along Xiangjiang River, around Dongting Lake, in the northwest zones and south of the middle, which explains the eco-hydrological effect. In general, the linear trend of every 10 days of temporal variability (Figure 10a) is almost zero. The minima were in 1998 and 2000; after that, the value increased significantly until 2001 and 2002, up to the maximum. After 2002, the change was small and stable. Positive values dominated; only 1998 and 2000 were dominated by negative EOF values.The second NDVI EOF mode (Figure 9b) indicates a simple distribution of vegetation. It is associated with the opposite spatial variation feature between the northwest and southeast. In the northwest and middle zones, it matches well with the rainfall mode 2, and some144 Journal of Geographical SciencesFigure 9 Spatial distribution of the four leading NDVI EOF modes“point” features are also well matched, which may be explained by the response of the vegetation variation to that of the rainfall. The temporal variability of the second mode of NDVI (Figure 10b) is less than the first mode, and in addition, the linear trend of each 10 days is close to zero and has a decreasing trend. The maximum EOF mode 2 appears in 1999, 2003, 2004 and 2006, and the minimum occurs in 2005.The third NDVI EOF mode (Figure 9c) illustrates that there are few similar distribution characteristics between it and the first EOF mode. The general variation feature is controlled by the negative values decided by the rainfall variation feature in Figure 7c. In general, the linear trend of every 10 days of temporal variability of the third mode (Figure 10c) is theFigure 10 Temporal variation of the four leading EOF modes of NDVI. The dashed line and the equation are trendssame as the second mode; both are decreasing. Beginning in 1998, there was a gradual in-crease that reached a maximum in 2002. After that, the value decreased to a minimum in 2005. From 2005 to 2007, another increase appeared.The fourth NDVI EOF mode (Figure 9d) explains that there is a larger area with negative values than with positive values. This mode matches with the rainfall EOF mode 4 in spatial distribution. The linear trend of every 10 days of temporal variability of the fourth mode (Figure 10d) is the same as the first mode, but is more obvious. The minimum occurs in 2001 and the maximum appears in 2003 and 2004. From 1998 to 2000 and 2001 to 2003, the value gradually increases. After that, it decreased in 2006 and increased in 2007.4.2 Time-lagged correlation between vegetation and precipitationIn addition to rainfall influencing the spatial distribution of vegetation, their temporal rela-tionship is very important and distinctly expresses the eco-hydrological effect of precipita-tion. Because most vegetation in this region has obvious phonological features and the leaf’s life cycle from bud to fall is not a full year, and the main vegetation growth period is only about half a year, we analyzed the time lag over a period not exceeding 180 days (about 6 months). The least temporal interval is about 10 days, because of SPOT’s vegetation cycle. To understand the response of vegetation to a variation in precipitation, we estimated time-lagged correlations by analyzing the correlative degree between four leading EOF modes of NDVI and precipitation (Figure 11). To recognize their degree of correlation, we calculated the 90% confidence level. Like in the following, a correlation coefficient that was bigger than the 90% confidence level was effective, and we selected the biggest one to ana-lyze the relationship between vegetation and atmospheric precipitation.As Figure 11 shows, the NDVI EOF mode 1 and precipitation EOF modes 1 and 4 arepositively correlated with a time lag of about 120 and 110 days, while NDVI EOF mode 2 and precipitation EOF modes 1, 3 and 4 are about 110, 140 and 110 days, and NDVI EOF mode 3 and precipitation EOF modes 2 and 4 are about 140 and 120 days. The positive cor-relation is about 3–4 months, with good similarity, which may result from the obvious sea-sonal patterns of precipitation (maximum in spring) and vegetation (maximum in summer) (Figure 6), as more precipitation in spring results in more vegetation growth during the sub-sequent summer. The NDVI EOF mode 4 and precipitation EOF mode 3 are positively cor-related with a time lag of 20 days, although it is difficult to explain this. We believe that, compared with other modes, they contribute less, and other environmental factors (tempera-ture, solar radiation, etc.) may cause the vegetation growth to become more notable, result-ing in this shorter time lag.Regarding the negative correlation, as Figure 11 shows, the NDVI EOF mode 1 and pre-cipitation EOF modes 1, 2, 3 and 4 have negative correlations with time lags of about 40, 20,Figure 11Correlation of the four leading EOF modes of NDVI and rainfall. The dashed lines show the 90% confidence level60 and 70 days, while NDVI EOF mode 2 and precipitation EOF modes 2, 3 and 4 have time lags of about 20, 60 and 70 days, resulting from positive NDVI zones coinciding with the negative precipitation zones in the east and middle region. This can be explained by the vegetation type: in the east region, there is mainly evergreen forest, such as pine and fir. Al-though there is low rainfall in the beginning, there is higher vegetation coverage. In all of the rainfall EOF modes, the negative value zone covers the eastern region of the area, which is evergreen, but from mode 1 to 4, more and more deciduous woods were included in the negative zone (Figure 9), which causes the percentage of evergreen to be less. As a result, the time lag of NDVI EOF mode 1 and rainfall modes is longer and longer in general (Fig-ures 11a-d).5 ConclusionsIn this paper, we analyzed the Normalized Difference Vegetation Index (NDVI) from satel-lite images and precipitation data from meteorological stations from 1998 to 2007 in the Dongting Lake wetland watershed to better understand the eco-hydrological effect of at-mospheric precipitation and its relationship with vegetation. According to the analyses, some main conclusions can be drawn:(1) For landscape-level studies, satellite data unquestionably provide a large scale view not possible using any other data collection technique. High frequency and large coverage of NDVI data from SPOT and other satellite sensors provide massive datasets for synoptic scale analyses of the ecology and vegetation growth monitoring. In this research, ten years of variation in the vegetation index (from 1998 to 2007) derived from satellites observed by the SPOT remote sensing were analyzed in conjunction with 10 years of precipitation data in the Dongting Lake watershed region. The mean, standard deviation and linear trendy change images can indicate the general distribution and variation in a long period (Figure 5). It is better to turn atmospheric precipitation data observed by traditional meteorological stations into raster images by interpolation using geographic information systems software, which is helpful to learn about its distribution. Atmospheric precipitation and the normalized differ-ence vegetation index in the study region exhibit prominent correlation in spatial distribution and seasonal variation. As Figure 6 shows, the maximum in vegetation index and precipita-tion occurred during the 140th-170th and 210th-260th days.(2) Notwithstanding rainfall is very important to the vegetation growth, there are different influences in different vegetation types. That is to say, the vegetation type is thus an impor-tant factor influencing the relationship between vegetation and atmospheric precipitation. Because there are many types of vegetation, including evergreen woods, deciduous woods, shrubs, grasses and crops, although a time lag is controlled by deciduous woods as the lead-ing vegetation, a short time lag from evergreen woods is clear. On the other hand, vegetation is influenced by the solar radiation, air temperature, too. If these factors and vegetation types are taken into account in this research, this work should be too different to analyze. In this paper, only when rainfall and vegetation are taken into account, their relationship is good. Detailed analysis with the EOF method shows that there was a wide range of time lags be-tween the precipitation and vegetation index, from about 10 to 180 days, which is because the EOF modes indicate more detailed information about the vegetation and rainfall. For the estimated 90% confidence level, the time lag effect is about 110 to 140 days. AcknowledgementsAll of the data for this study were provided by the Environmental and Ecological Science Data Center for West China (WESTDC) and China Meteorological Data Sharing Service System. The authors would like to express their sincere gratitude to these sources. ReferencesAckerman T L, 1979. Germination and survival of perennial plant species in the Mojave Desert. The Southwestern Naturalist, 24: 399–408.。