satellite detected broad-scale vegetation change in china, 1982–1999

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SATELLITE DETECTED BROAD-SCALE VEGETATIONCHANGE IN CHINA, 1982–1999Stephen S. YOUNG aa Department of Geography , Salem State CollegePublished online: 03 May 2011.

To cite this article: Stephen S. YOUNG (2003) SATELLITE DETECTED BROAD-SCALE VEGETATION CHANGE IN CHINA,1982–1999, Asian Geographer, 22:1-2, 123-142, DOI: 10.1080/10225706.2003.9684103

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Young 123

SATELLITE DETECTED BROAD-SCALE VEGETATION CHANGE IN CHINA, 1982-1999

Stephen S. YOUNG Department of Geography Salem State College

Abstract: This paper analyzes vegetation change throughout China between 1982 and 1999 using global scale AVHRR satellite data. It analyzes changes in NDVI and links these changes to associated land covers. Two time series (1982 to 1993 and 1995 to 1999) were analyzed using Principal Components Analysis, Simple Differencing, and Temporal Profiling. In both periods more areas increased in NDVI than decreased, with some grasslands and croplands continually increasing. Fewer areas decreased in NDVI with forest areas decreasing the most. The North China Plain region experienced strong NDVI increases in the 1980's and then strong decreases in the late 1990's.

Keywords: vegetation change; AVHRR; China; Principal Components Analysis.

Introduction

China is the world's third largest country by land area and the most populated. It now has one of the largest economies and is responsible for significant environmental changes at the local and global scales (He, 1991; WRI, 1994). For the past two decades China has experienced extensive modernization and economic expansion, often marked with annual double-digit growth rates. As a result, the land cover of China has been changing. The extent and magnitude of these changes, however, is uncertain. There are a variety of land cover data sets for China, from international United Nations data sets to internal government statistics. Some of these data sets are strongly influenced by political issues and may be suspect (Moser, 1996). Satellite imagery has been proposed as an alternative to official government statistics (Smil, 1999). There currently are a number of satellite-derived data sets available and a variety of methodologies to extract land cover information. The main satellite-based sensor system used for regional, long-term, land-cover studies is the Advanced Very High Resolution Radiometer (AVHRR) (Ehrlich et al., 1994).

To gain a broad perspective of change in China, this study used the AVHRR satellite data to analyze changes in photosynthesis across China between 1982 and 1999. To approximate photosynthesis, a Normalized Difference Vegetation Index (NDVI) was used. This paper does not attempt to classify the different land covers, but rather shows where photosynthetic activity was found to be increasing and where it was decreasing. There are a variety of sources of change that include natural factors, such as varying weather patterns, as well as human activity, such as the intensification of agriculture.

China is an immense country with a wide range of climates, vegetation types, and human activity. It's topography ranges from low coastal plains to rugged mountains, with elevations ranging from 200 m below sea level in the Turpan Depression to over 8800 m on top of Mount Qomolungma (Everest) (Ren et al., 1985). China's dominant

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124 Vegetation Change in China

topographic feature is that of an undulating surface with mountains, hills and plateaus that cover more than two-thirds of the country (CHS, 1983). Climates range from humid tropical on Hainan Island to cold deserts in Xizang (Ren et al., 1985). Ecosystem types range from tropical rain forest in southern Yunnan to tundra in the Himalayas (Hou, 1983).

A number of authors have described the distribution of China's vegetation cover (Wang, 1961; Wu, 1980; FAO, 1982; Hou, 1983; Richardson, 1990; Institute of Geography et al., 1994). Concerning land cover-change analysis in China, cultivated land and forest cover are two very important and dynamic land covers. Intensive agriculture tends to be located on the plains, basins and river valleys, primarily in the eastern portion of the country, though there are some inland areas such as the Sichuan Basin, the Wei River Valley and the western oases where intensive agriculture occurs as well. Major agricultural activity in the east occurs on the Northeast China Plain, North China Plain, the lower Changjiang, and the Zhujiang Delta. The most important and dynamic forest areas include the Northeast, especially in the Greater and Lesser Hinggan and Changbai mountains, the Southwest, especially in Yunnan, western Sichuan and parts of Xizang, and the Southeast, especially Fujian, Guangdong, Jiangxi, and Zhejiang (Richardson, 1990; Xu et al., 1991; Institute of Geography et al., 1994).

A number of studies have used satellite imagery to analyze land cover and land-cover change in China (Gao et al., 2001; Wang et al., 2001). Most of these studies, however, have focused on small portions of China and specific land cover types. Changes in urban areas (Ji et al., 2001; Weng, 2001; Zhang, 2001), cropland (Frolking et al., 1999; Xu et al., 2000), and forest cover (Zheng et al., 1997) are probably the most common land covers studied in China with remote sensing. Most of the studies have used medium spatial resolution imagery such as Landsat MSS and TM (Hathout and Smil, 1985; Huang et al., 1998; Kaufmann and Seto, 2001). There have been a few studies using the more course resolution AVHRR data (Hasegawa et al., 1998). Some of the AVHRR studies have shown a high correlation between AVHRR NDVI and vegetation dynamics or climate in China (Chen et al., 2001; Zhao et al., 2001; Li et al., 2002). There have been few studies that look at land cover change for all of China (Quan et al., 2000; Young and Wang, 2001), with the most comprehensive one currently being undertaken by the Institute of Remote Sensing Applications (Zhuang et al., 1999).

Methodology

Overview

Remote sensing is able to detect changes in land cover because any change on the surface of the earth will result in changes in energy being reflected (or emitted) by the earth. To identify these changes with remote sensing there are a number of techniques (Eastman and McKendry, 1991), of which this study uses three: standardized Principal Components Analysis (PCA), simple differencing and temporal profiling.

PCA is a statistical method that uses a linear transformation of a set of image bands to create a new set which is uncorrelated and ordered according to the amount of variance explained, from most to least (Singh and Harrison, 1985). The result of the PCA is a set of Principal Components (PC) where the first PC explains the maximum variance in the original data and subsequent PCs are created through a rotation of original images

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where the new axes are orthogonal to each other, thus uncorrelated, and decrease in the amount of variance explained (Singh and Harrison, 1985). When using the PCA methodology on a time series data set, the first PC shows the average state over the course of the time series, and each of the following PCs pull out changes to the average with greater changes first and subtler changes in later PCs. Some of the earliest studies using the global-scale AVHRR data utilized the PCA methodology (Tucker et al., 1985). There have been a number of subsequent studies that have shown the ability of the PCA methodology to pull out meaningful land cover change information (Singh and Harrison, 1985; Fung and LeDrew, 1987; Eastman and Fulk, 1993; Anyamba and Eastman, 1996; Young and Anyamba, 1999; Young and Wang, 2001). Each PC has two outputs, a loadings chart that shows how it changed through the time series and an image that shows the correlation between land covers and the changes in the loadings.

Simple differencing, or univariate image differencing, is one of the most widely used change detection techniques and has been used for a variety of land covers with a variety of satellites (Singh, 1989). This method determines the difference between two dates where one satellite image is subtracted (differenced from) the other image. This method shows pixels that have experienced: no change, a positive change, or a negative change. This method can be used with NDVI data to show how photosynthesis changes over time (Jensen, 1996). Temporal profiling is a technique used to show how the value of a pixel (or group of pixels) changes throughout the time series (Eastman, 1995). The output of this method is a graph where the value of the same group of pixels is graphed for each of the images in the time series.

Specific methods

This study used monthly Maximum Value Composites (MVC) from the NOAAINASA Pathfinder AVHRR Land (PAL) data set (James and Kalluri, 1994) to detect vegetation change in China between 1982 and 1999. For the simple differencing, temporal profiling, and PCA methodologies, yearly averages were used to minimize seasonal variations. The 12 monthly MVC NDVI files for each year (January - December) were added together and then divided by 12 creating an annual average image. This process reduces a variety of external factors. First, the data are in an NDVI format that reduce a number of sun angle and atmospheric problems (Kidwell, 1994). Monthly maximum value composites (the base data of the annual averages) also reduces atmospheric problems such as cloud cover (Holben, 1986). The data in an annual average composite captures average productivity over the course of the entire year and thus reduces the problem of capturing vegetation at different times of its phenological cycle. In addition, an annual average, as opposed to a growing season average, was important because some parts of China have agriculture throughout the year, as well as capturing winter productivity from evergreen trees.

For the simple differencing technique, the annual average years of 1982 and 1983 were added together and divided by two creating a 1982183 average composite. In the same fashion, the years 1992 and 1993, 1995 and 1996, 1998 and 1999 were added together and divided by two creating 1992193, 1995196, and 1998199 average composites. The data were averaged in this fashion to further remove any extreme events, such as floods. The 1982183 average composite was then subtracted from the 1992193 average composite creating a differencing image where positive values indicate increases in NDVI during the time period and negative values indicate decreases. A similar process


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was undertaken where the 1995196 average composite was subtracted from the 1998199 average composite.

To determine the percent change in NDVI between 1982183 and 1992193, the difference image (1992193 minus 1982183) was divided by the 1982183 average image creating a 'percent change' image. This captures the magnitude of change because a 10-point positive change in NDVI for an area with a value of 140 in 1982183 would be a 7% change; while for an area with a value of 200 would be a 5% change. To show the direction and magnitude of change, a threshold image was created where the percent change image was value-sliced into 5 categories based on the standard deviations (std) of the percent change image: no change (1.5 std to -1.5 std), moderate NDVI increase (1.5 std to 3 std), intensive NDVI increase (greater than 3 std), moderate NDVI decrease (-1.5 to -3 std) and intensive NDVI decrease (greater than -3 std).

Standardized PCAs were run using the twelve annual average images from 1982 to 1993, and the five annual average images from 1995 to 1999. They were processed in the IDRISI GIS software program (Eastman, 1995) using the Time Series Analysis Module that is based on standardized PCA. Temporal profiling was undertaken to evaluate the data. For each year, the NDVI values for all of China were averaged and then graphed. In addition, a region of barren land in the Taklimakan desert was windowed out and then the desert values were averaged for each year and graphed.

Data

Base data

The base data for the NOAAINASA PAL data set are from the AVHRR sensor that is on-board NOAA's Polar Orbiting Environmental Satellite (POES) series which has been in operation since 1978 (Hastings and Emery, 1992). The AVHRR sensor on- board the NOAA satellite series is able to image the earth daily, thus the data have a fine temporal resolution. However, the spatial resolution is not as fine. At its best, the AVHRR has a pixel resolution of 1.1 km by 1.1 km at nadir at the Equator. The 1.1 km resolution data are not stored on the satellite, but are sent directly to the nearest receiving station. Therefore, in order to have global coverage, receiving stations throughout the world need to store and share the data. Some global scale 1.1 km data sets have been created from data collected in the 1990's, but there are no long-term 1.1 km data sets (Hansen and Reed, 2000).

In order to gain global coverage, a daily Global Area Coverage (GAC) data set has been established with a resolution of 4.4 km by 4.4 km from re-sampled 1.1 km AVHRR data (Goward et al., 1993). These data are stored on-board the satellite and later downloaded to a central receiving station. There is now a long-term, global-scale data set that stretches back to 1981. The GAC data have been re-sampled and calibrated by a number of users to even coarser scales such as 8 km and 16 km. The AVHRR-derived data sets have been especially useful for vegetation studies because the sensor records information in near infrared light as well as in visible light. Plant leaves produce a clear signal because chlorophyll absorbs strongly in portions of the visible spectrum and the structure of leaves reflect highly in near infrared light (Gates et al., 1965). Therefore, a number of "greenness" indexes have been established using


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near infrared and visible light (Lillesand and Kiefer, 2000). The most widely used greenness index is NDVI (near infrared - visible / near infrared + visible) (Tucker, 1979). AVHRR-derived NDVI data have been correlated with a variety of vegetational parameters such as: seasonal variation in vegetation (Justice et al., 1985); net primary productivity (Goward and Dye, 1987); biomass burning (Malingreau et al., 1985); large-scale climatic effects on vegetation (Eastman and Fulk, 1993); photosynthetic activity (Dye, 1996); land cover classification (Townshend et al., 1991); biomass (Box et al., 1989) among other vegetation attributes (Thomas et al., 1989).

Cloud cover has been a major problem with satellite-derived studies of land cover (Skole and Tucker, 1993). To diminish the problem of cloud cover, a Maximum Value Composite (MVC) procedure has been developed by using a number of continuous days (such as 7 days), where the maximum value for each pixel is extracted from the data (Holben, 1986). The MVC is created on a pixel-by-pixel basis where each pixel's NDVI value is the highest value over the multi-day period. Clouds decrease the NDVI value, so if there is one day without cloud cover, the data for that day will be the maximum and will be used.

With more studies using AVHRR-derived global data sets, it has become apparent that there are spatial, temporal and radiometric problems in the data (Goward et al., 1993; Kdwell, 1994; Young and Anyamba, 1999). To overcome these known problems, the PAL data have been reformatted from the original GAC data with new algorithms to remove the known problems in the GAC data (Agbu and James, 1994). Gutman and Ignatov (1995) analyzed the PAL data set and found that the new calibrations used in the PAL data have removed both the drift in the NOAA -9 data and the discontinuity at the NOAA -1 1 launch. Prince and Goward (1996) along with Smith et al. (1997) have found that the new calibrations in the PAL data have decreased the sensor discontinuities between satellites. Despite these problems, quantitative correlations have been made between AVHRR data and productivity (Box et al., 1989; Goward et al., 1993).

The PAL data used in this study are monthly MVC NDVI data from January of 1982 to December of 1999, with a spatial resolution of 8 km. There were satellite problems in 1994 and therefore no data from 1994 were used in this study. As noted above, the twelve monthly composites for each year were averaged to create annual average NDVI images. This paper focuses on long-term, inter-annual variation in photosynthesis, and therefore annual variation in the data (i.e. the cyclical greening up and browning down of vegetation) was removed by creating annual averages. The NDVI data were scaled to 0 - 255. The data were re-projected from the Goode's Interrupted Homolosine Projection to a latitude-longitude projection and China was windowed out of the global data set using the China Country Border vector file (Lam, 1989). The 'Percent Forest Cover Map' from The National Economic Atlas of China (Institute of Geography et al., 1994) was digitized at the county level, using the China Country Border vector file as a base map. The resulting digital map of forest cover was in the same projection as the PAL data and was geo-referenced to the China PAL data. Non-digital maps and atlases used for land cover references include: The Vegetation of China (Wu, 1980), Vegetation Map of China (Hou, 1983), Atlas of Forestry in China (Xu et al., 1991), and The National Economic Atlas of China (Institute of Geography et al., 1994).


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Data evaluation

The original intent of this project was to analyze changes in NDVI in China from 1982 to 1999, using the PAL data set as a single time series. However, soon after analyzing the entire data set it became evident that there were some potential problems with the data. Initial studies showed that all of China was increasing in NDVI, including barren areas. After running an annual temporal profile of the PAL data for all of China and for selected pixels from the Taklimakan desert, it became clear that there were problems in the data for China (Figure 1).

125 - - - , 120

0% 0 06 00 9F' 9% 96 90

Years: 1982 to 1999

1. Annual average data is plotted for each year. 2. There were no data available for 1994. 3. "All China" consists of all pixels for China in the data set (96,482). 4. The desert category is made up of 1,423 pixels from the Taklimakan desert. 5. NDVI has been scaled: 0 to 255.

Figure 1. Temporcll profile o f China times series data (1982-1999).

The PAL data set is continuous from 1982 until 1994. There were satellite problems in 1994 and as a result there are data missing for the months of October, November and December, and therefore an annual average could not be created for 1994. A new satellite carrying the AVHRR sensor was used in 1995 and was used for the subsequent data as well (1995-1999). Based on the temporal profile of the China data (Figure l), it appeared that the 1995 data (and the years to follow) were not correctly calibrated to the pre-1994 data. In addition to analyzing the China data, a sub-region of barren land from the Taklimakan desert was windowed out and profiled. There is a clear shift upwards in both the "All China" data and the desert pixels. Desert pixels are often used to check the calibration of time series data sets because they have the least amount of photosynthesis of all ecosystems (except perhaps glaciers), and therefore they show very little change in NDVI throughout the year and from year to year. NDVI for desert pixels should remain stable, and as Figure 1 shows, NDVI remained relatively stable from 1982 to 1993 and from 1995 to 1999, but there was a large shift from 1993 to Asian Geographer 22(1-2): 123-142 (2003)

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1995. The "All China" data show a similar shift. Because of this potential problem in the data, this study used two time series, one from 1982 to 1993 and the other from 1995 to 1999. This study first looks at the overall distribution of NDVI throughout China, followed by an analysis of change from 1982 to 1993 and from 1995 to 1999. Results

Overall NDVI

As indicated by NDVI, most of the photosynthetic activity in China occurred in the eastern half of the country, especially in the southern portion of the eastern half. Figure 2 shows the distribution of the top 5, 10, 25, and 50% of NDVI across China, based on an average of NDVI from 1982 to 1993. Yunnan Province in the south along with parts of Sichuan, Xizang, Hainan, Fujian and Taiwan had the highest cluster of NDVI at the top 5% level. In fact, Yunnan Province alone had over 46% of the pixels that were in the top 5% of NDVI. Yunnan's extensive forest cover, along with its tropical and subtropical climate gives the province high levels of annual NDVI. As the top percent of NDVI increased (i.e. moves from 5 to 10, to 25, to 50%) more of the south, central and northeastern portions of the country greened up (Figure 2). Throughout the country areas of forest cover were highly correlated with greater amounts of NDVI (Table 1).

Table 1. Levels of NDVI and forest cover.

% Forest cover' >60% 50% 40% 30% 20% 10% <5%

% NDVI' Top 5% 60% 16% 13% 6% 5% < 1% < I % Top 10% 55% 18% 13% 6% 8% <1% < I % Top 25% 38% 16% 13% 9% 14% 5% 5% Top 50% 25% 10% 11% 9% 17% 14% 14% Bottom 50% <1% < l % < I % <1% 4% 6% 89% China all' 13% 5% 6% 5% 12% 13% 46%

1. Percent Forest Cover based on the map 'Percent Forest Cover Map' from The National Economic Atlas of China (Institute o f Geography et al., 1994).

2. Based on the digital data from the images in Figure 2. 3. 'China all' indicates how much o f China is classified in the different forest cover classes based on

the map 'Percent Forest Cover Map' from The Nntionnl Economic Atlas of China (Institute o f Geography et al., 1994).

Concerning areas with the lowest levels of NDVI (Figure 2), the Taklimakan desert and other areas of the arid west appeared first, then followed by the Gobi and the Qinghai- Tibetan Plateau. At the 50% level some of the agricultural areas, such as those in the Sichuan Basin, Northeast China Plain and the North China Plain appeared in the lower half of average NDVI.

Changes in NDVI: All China (1982-1993)

A standardized PCA was run on the time series data from 1982 to 1993 (the end of the continuous data). As noted in the methodology section above, the PCA methodology has been proven to pull out changes in time series satellite data. For this 12-year


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130 Vegetation Clzange in China

series, the first component shows the average distribution of NDVI over the period. The second and third components show problems in the calibration of the data. These are known problems in the data that become apparent in the AVHRR time series data, but do not affect the long-term analysis of land cover change (Young and Anyamba, 1999), unlike the major break between 1993 and 1995. The fourth component shows the primary change in NDVI for China from 1982 to 1993. As seen in Figure 3, the loadings for component four show a general increase in NDVI from 1983 to 1992, with a slight drop from 82 to 83 and from 92 to 93. Therefore, the primary change occurring to China's vegetation for this period was a general increase in NDVI.

A. Top 5% E. Bottom 5%

B. Top 10%

C. Top 25%

D. Top 50%

F. Bottom 10%

G. Bottom 25%

H. Bottom 50%

Annual average NDVI composites for the years 1982 to 1993 were averaged together and then the top and bottom 5%. lo%, 25%, and 50% o f NDVI levels were extracted from the data and displayed in a gray scale. White areas indicate the top and bottom percents.

Figure 2. Top and bottom 5, 10, 25, and 50% of annual average NDVI.

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The image for the fourth component shows the pixels that are positively and negatively correlated to the loadings (Figure 4). The higher the positive correlation, the more closely those pixels mimic changes in NDVI from 1982 to 1993 as depicted by the loadings. The more negatively correlated, the more inversely related the pixels are. Therefore positively correlated pixels experienced an increase in NDVI from 1982 to 1993, and those negatively correlated experienced a decrease in NDVI from 1982 to 1993. Most of the positive change in NDVI has occurred in agricultural areas, such as the North China Plain and western oases, as well as in grasslands, such as those on the Qinghai-Tibetan Plateau and in the Tien Shan Mountains. The negative change has occurred primarily in forested areas such as in Xishuangbanna, Yunnan and the Greater Hinggan Mountains in Inner Mongolia.

Component I Component 2 0 03

0 025 0 02

0 015 0 01

0 005 0

-0 005 -0 01

-0015 -0 02

82 83 84 85 87 88 89 90 91 92 93

Component 3 Component 4

1 . Loadings for the first four components from the standardized Principal Components Analysis, 1982- 1993.

2. The loadings show how NDVI changes throughout the time series for that particular component.

Fig~lre 3. Loading clzarts for principal coml~onerzts 1-4 (time series 1982-1993).

To further analyze change, a simple differencing was undertaken between 1993 and 1982. As noted in the methodology section, the images for 1992 and 93, as well as 1982 and 83, were averaged together and then the resulting images were differenced (1992193 minus 1982183). The resulting image shows areas that have increased in NDVI and areas that have decreased in NDVI. To determine the magnitude of the change, the resulting image was divided by the 1982183 image. Figure 4 shows where the magnitude of change occurred. Between 1982 and 1993 China experienced an overall increase in NDVI, especially in agricultural areas such as in the North China Plain, along the Huang He, and in western oases as well as in a variety of grasslands.

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There were fewer areas that experienced major declines in NDVI, though some of the forested areas in the south and northeast did experience declines.

A. CMP 4 - Negative Strong E. Simple Differencing Negative Strong

B. CMP 4 - Negative Mild

C. CMP 4 - Positive Strong

D. CMP 4 - Positive Mild

F. Simple Diffcrcncing Negative Mild

G. Simple Differencing Positive Mild

H. Simple Differencing Positive Mild

1. The pixels for the PCA are the raw pixels that are positively and negatively correlated with the loadings for Component 4.

2. The pixels for the Simple Differencing image are based on the magnitude of change, [(I993192 - 1983182) 1 19831821.

3. Negative (positive) STRONG are pixels with a standard deviation of greater than 3. 4. Negative (positive) MILD are pixels with a standard deviation of greater than 1.5 and less than 3.

Figure 4. Change analysis for 1982- 1993: principal components analysis and simple drfferencing.

Table 2 shows the number of pixels that increased and decreased based on the PCA and simple differencing analyses. When cross-tabulating the simple differencing results with component four, over 90% of the simple differencing pixels of increasing value (both mild and strong increase) were also the increasing pixels of component four, while over 90% of the negative pixels (mild and strong) were the same for component

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four. This indicated that these two methodologies were capturing the same phenomenon. There is a very high correlation between negative pixels and forest cover as well as positive pixels and lack of forest cover (Table 3).

Table 2. Percent of China increasing and decreasing in NDVI based on PCA and image differencing (1982-1993).

PCA Component 4 Simple Differencing Results Number I Percent of total Number I Percent of total

All china1 96,482 100.0% 96,482 100.0% Negative ~ i l d ' 10,895 11.3% 1,803 1.9% Negative strong3 2,602 2.7% 787 0.8% Positive Mild 10,672 11.1% 10,480 10.9% Positive Strong 1.303 1.4% 4.974 5.2% I . All China shows the total number of pixels in the data set. 2 . Negative (positive) Mild is all pixels negatively (positively) correlated with Component 4 loadings (Simple

Differencing Results) between I .5 and 3 standard deviations. 3. Negative (positive) Strong is all pixels negatively (positively) correlated with Component 4 loadings (Simple

Differencing Results) greater than 3 standard deviations. 4. Negative pixels are those where NDVI decreased between 1983182 to 1993192. 5. Positive pixels are those where NDVI ~ncreased between 1983182 to 1993192.

Table 3. 1982-1993 PCA component 4 and forest cover.

~ e ~ a t i v e ' positive2 Percent Forest cover3 Pixel ~umber" /~ercent~ Pixel NumberIPercent 60% 4,521 33% 480 4% 50% 1,709 13% 278 2% 40% 1,649 12% 415 3 % 30% 1,046 8% 670 6% 20% 2,371 18% 1,475 13% 10% 1,145 8 % 1,979 16% 5 % 1.056 8 % 6.678 56% Total 13,497 100% 1 1,975 100%

1. Both strongly and mildly negative pixels were combined. 2 . Both strongly and mildly positive pixels were combined. 3. Based on the 'Percent Forest Cover Map' from The Narioi~al Ecot7oit1ic Atlas of China (Institute of Geography

et al., 1994). 4. Raw number of negative (positive) pixels. 5. Percent of the total number of negative (positive) pixels that fall into the "Percent Forest Cover" category.

Changes in NDVI: All China (1995-1999)

A standardized PCA was run on the time series data from 1995 to 1999. In this case, there were no major sensor-related issues and therefore, the primary change occurring to land cover in China appeared in the second component (Figure 5). Similar to component four in the 1982 to 1993 PCA, it indicated an increase in NDVI during the period. One difference, however, was that some agricultural areas that were increasing in NDVI in the 1980's experienced a decline of NDVI. In this case, the negative areas of change were agricultural areas and some forested areas, while the areas of positive change continued to be areas of grasslands along with some agricultural areas (Figure 6).


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Vegetation Change in China

-0 02 4 95 96 97 98 99

Years The loadings show how NDVI changes throughout the time series for Component 2.

Figure 5. Loadings chart for PCA Component 2 (time series 1995-1999).

A simple differencing between 1999 and 1995 (1998199 minus 1995196, resulting image divided by 1995196) indicates the same areas of change as found in component two (Figure 6). In this time period, agricultural and forested areas appear to be decreasing in NDVI, while grassland areas, along with some forested and agricultural areas, were increasing in NDVI. Similar to the 1980's, however, the major thrust of the change has been an overall increase in NDVI (Table 4).

Regional analysis

Most areas in China experienced similar changes in the late 90's as they did in the 80 ' s except for one major region, the North China Plain. This area experienced some of the strongest growth in NDVI between 1982 and 1993 (Figure 4), but then experienced some of the strongest declines between 1995 and 1999 (Figure 6). To examine this area in more detail, the North China Plain and surrounding areas were windowed out of the data set and using the simple differencing methodology, areas of increasing and decreasing NDVI were extracted (Figure 7). Again, the results were based on percent change from the level of NDVI at the beginning of the time period and the results were value-sliced based on standard deviations. The thrust of the 1980's was a broad increase in NDVI, while the late 1990's showed a broad decline in NDVI.

Discussion

Concerning the overall distribution of NDVI (Figure 2), major factors influencing areas with high levels of annual NDVI includes: high levels of precipitation, mild winter temperatures, and extensive forest cover. Concerning the influence of water, the image showing the 50% division (Figure 2) remarkably shows the humid vs. dry regions of China, similar to the 400 mm annual precipitation line in China (Ren et al., 1985). As expected, the highest levels of NDVI are found in the more humid east and lowest values in the arid west, except for some oases and selected areas in the Tien Shan Mountains. In the humid east, sections of the Northeast China Plain do not appear in the top 50% of NDVI because some portions of the plain are semi-arid and other parts are under single crop agriculture with a low annual average NDVI. Concerning the


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mild winter temperatures, the southern part of China greens up before the North, though some of the agricultural areas like the Sichuan Basin and the North China Plain green up after the forests of the northeast. Concerning the influence of forests, Table 1 shows that the greater the NDVI, the higher the percent of forest cover. For example, in the top 5% of annual average NDVI for China, more than half of the pixels fall into areas with a greater than 60% forest cover.

(:. (.'MI1 2 - Iheitivc Strong

11. ( !MI1 2 - I'ositiue MIM

E. Simple Diffcrmcing Nc~atlvc Strong

F. Sirnplc f>iffcrcnc~ng Ncgarivc Mild

G. SuupIe Dflerer~ci i~ Positrve Strong

1. The pixels for the PCA are the raw pixels that are positively and negatively correlated with the loadings for Component 2.

2. The pixels for the Simple Differencing image are based on the magnitude of change, [(I999198 - 1996195) I 19961951.


Figure 6. Change analysis for 1995-1999: principal components analysis and simple dijjerencing.


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Table 4. Percent of China increasing and decreasing in NDVI based on PCA and image differencing (19951999).

P C A Component 2 Simple Differencing Results

Number / Percent of total Number / Percent of total

All c h i n a ' 96,482 100.0% 96,482 100.0%

Negative ~ i l d ' 3 ,440 3.6% Negative strong3 4,530 4.7%

Positive Mild 8,158 8.5% Positive Strong 5.771 6.0% 1. All China shows the total number of pixels in the data set. 2. Negative (positive) Mild is all pixels negatively (positively) correlated with Component 2 loadings (Simple

Differencing Results) between 1.5 and 3 standard deviations. 3. Negative (positive) Strong is all pixels negatively (positively) correlated with Component 2 loadings (Simple

Differencing Results) greater than 3 standard deviations. 4. Negative pixels are those where NDVI decreased between 1995196 to 1999198. 5. Positive pixels are those where NDVI increased between 1995196 to 1999198.

The factors of humidity, temperature and forest cover also play a role in the distribution of the low levels of NDVI. The most important factor being aridity and as seen in Figure 2, the dry deserts appear in the lowest levels of NDVI. The cold north appears in the low levels of NDVI as well. Concerning forest cover, there is a clear inverse relationship between forest cover and NDVI (Table 1). An interesting point about the high levels and the low levels of NDVI is that the pixels remain clustered together and are not spread out. It appears that in China, the physical factors that enhance or inhibit photosynthesis exert themselves over homogeneous areas, unlike how change occurs.

The images showing changes in NDVI from 1982 to 1993 and from 1995 to 1999 (Figures 4 and 5) show a much more diffuse pattern, though distinct regions of change can be discerned. Change in photosynthesis is a phenomenon that exerts itself across a broad area. The two time series (1982 to 1993 and 1995 to 1999) created an interesting study in that it showed some trends occurred throughout the entire time period while others reversed. The main trend throughout both periods was a greater increase in NDVI overall where more pixels experienced an increase than a decrease. This was especially true when looking at the simple differencing data (Tables 2 and 4). Based on simple differencing, between 1995 and 1999 there were three and a half times as many pixels increasing in NDVI than decreasing, at the one and a half standard deviation level. Between 1982 and 1993 there were six times as many pixels increasing in NDVI than decreasing. One major factor bringing about this broad trend might be global warming, and in particular milder winters and earlier green up periods, which have been noted in East Asia and many parts of the world (Zhou et al., 2001). Myneni et al. (1997) show regions in the northern hemisphere which are increasing in photosynthetic activity, and the regions they note in China are similar to the regions depicted in this study. Throughout both periods, many grasslands have been experiencing this increase, followed by agricultural areas. Areas of decreasing NDVI are fewer and not as clear, though it does appear that areas with a higher forest cover tend to be decreasing at a greater rate than areas with less forest cover.

When running a cross-tabulation between the two times series to determine if any regions experienced a change of trajectory (going from increasing to decreasing NDVI or visa versa), one particular area became apparent. The North China Plain and its


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surrounding region showed a strong increase in NDVI through the 1980's and early 1990's, but then showed a mild to strong decrease in NDVI through the late 1990's. Windowing out this region and analyzing it in greater detail confirms that this region did indeed see a reversal in NDVI. One potential explanation was that this area saw an increase during the 1980's due to economic reforms that focused on agricultural reform. The increased use in fertilizers and the increasing economic return made this region more productive and thus increased NDVI (FAO, 1993; Ash and Edmonds, 1998). In the coastal regions during the 1990's, however, some farm fields were being abandoned and urban expansion decreased the amount of cropland, thus deceasing photosynthesis (Yang and Li, 2000).

A. 1993 - 82 Positive C. 1999 - 95 Positive

B. 1993 - 82 Negative D. 1999 - 95 Negative

1. The pixels for the Simple Differencing image are based on the magnitude of change, [(1993/92- 1983182) 1 19831821.


Figure 7. Change analysis ( 1 995- 99 and 1982- 93) for the North China Plain region.


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This study has shown some of the broad changes that have occurred to vegetation cover in China from 1982 to 1999. It has demonstrated that there has been a general overall increase in NDVI, especially in grasslands and agricultural regions. There are also areas, such as forested regions, that have experienced declining NDVI, though the decline has been relatively mild compared to the overall increase. Although most of the trends seen in the 80's were continued in the late 90's, there were some places, especially the North China Plain, where trends were reversed. Now that the broad trends and regions have been identified, a more detailed regional approach needs to be undertaken in the areas where continuous increases or decreases of NDVI were observed, as well as in areas where the trends reversed themselves. Through a more detailed regional approach, we can begin to understand some of the driving forces creating vegetation change in China. The use of satellite imagery will continue to be critical in identifying where changes in vegetation are occurring in China.

Acknowledgements

The author wishes to thank the Goddard Distributed Active Archive Center for the PAL data. The author also appreciates the advice from the anonymous reviews, and Qihao Weng for coordinating the article. [Please note: since the writing of this article the Goddard DAAC has reprocessed the PAL data making the entire data set (1982 - 1999) comparable.]

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