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March, 2014 Journal of Resources and Ecology J. Resour. Ecol. 2014 5 (1) 053-059 DOI:10.5814/j.issn.1674-764x.2014.01.006 www.jorae.cn Vol.5 No.1 Article The Three-North Shelterbelt Program and Dynamic Changes in Vegetation Cover WANG Qiang1, ZHANG Bo2*, ZHANG Zhiqiang1, ZHANG Xifeng3 and DAI Shengpei4 1 Scientific Information Center for Resources and Environment, Lanzhou Branch of the National Science Library, CAS, Lanzhou 730000, China; 2 College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China; 3 Center for Dryland Water Resources Research and Watershed Science, Key Laboratory of West China’s Environmental System (Ministry of Education), Lanzhou University, Lanzhou 730000, China; 4 Institute of Scientific and Technical Information, Chinese Academy of Tropical Agricultural Sciences, Danzhou 571737, China Abstract: The Shelterbelt Forest System Program in northeast, north and northwest China (the Three-North Shelterbelt Program, TNSP) is the largest ecological reforestation program in the world. TNSP vegetation research has important ecological meaning and profound social and economic significance. Here, spatio-temporal variation in vegetation cover under the TNSP was examined using the NDVI average method, major climatic factors such as temperature and precipitation, and linear regression trend analysis from 1982 to 2006. We found that in the past 25 years, NDVI vegetation in the study area has consistently risen at a rate of 0.007 per decade. Vegetation cover, temperature and precipitation are positively correlated. The area of vegetation associated with precipitation is larger than the area related to temperature; precipitation is the key factor affecting vegetation growth across the TNSP. From 1982 to 2006, regions with improved vegetation cover were found in the central and southern part of the Greater Khingan Mountains, central part of the Lesser Khingan Mountains, northeastern part of the Changbai Mountains, Yanshan Mountians, Western Liaoning Hilly Region, Altai Mountains, Tien Shan Mountains, eastern part of the Qilian Mountains, eastern part of the northwest desert as well as southern part of the Gully Region of the Loess Plateau. However, vegetation cover declined on both sides of the Greater Khingan Mountains, western part of the Hulun Buir Plateau, northern part of the Sanjiang Plain, southern part of Horqin Sandy Land, southern part of the northwest desert and northern part of the Gully Region of the Loess Plateau. Key words: NDVI; Three-North Shelterbelt Program (TNSP); spatio-temporal changes; correlation analysis; dynamic study; northern China 1 Introduction As the main component of the terrestrial ecosystem, plants are sensitive indicators of climate and human factors, and tie together the soil, air and water. Plants play a regulation and indicator role in energy balance, biological chemical cycles and water circulation. Changes in plants are the product of the earth’s interior (e.g. soil parent material and soil types) and external (e.g. temperature, precipitation and human activity) environments (Kaufman and Tanre 1992; Carlson and Ripley 1997; Ichii et al. 2002; Jason and Roland 2004; Pettorell et al. 2005; Song and Ma 2008). At a large scale and mesoscale, remote sensing is an effective means for monitoring global and regional vegetation (Adams and Arkin 1997; Shi et al. 2006). The normalized difference vegetation index (NDVI) is the most widely used vegetation monitoring data via remote sensing, and indicates changes in vegetation cover, biomass and ecological systems (Tucker and Townshend 1985; Defries and Townshond 1994; Tucker et al. 2005; Lasaponara 2006; Brown et al. 2008; Wang et al. 2011). NDVI is a ratio parameter of near infrared band and infrared band using the formula: NDVI = (NIR-VIS) / (NIR+VIS) Received: 2013-06-09 Accepted: 2014-03-03 Foundation: National Natural Science Foundation of China (41171116; 40961038). * Corresponding author: ZHANG Bo. Email: [email protected]. 54 Journal of Resources and Ecology Vol.5 No.1, 2014 where, NIR indicates near infrared band, and VIS indicates infrared band. NDVI can accurately reflect vegetation greenness, photosynthesis intensity, vegetation metabolic intensity and its seasonal and interannual variability. The Three-North Shelterbelt Program (TNSP) is a major ecological construction project underway in China. This project will transform nature at the largest scale and over the longest time span in human history. Vegetation dynamics resulting from the TNSP will have important ecological environmental significance and profound social and ecological significance. Due to the dramatic effect of long-term human activities and global climate warming, problems such as flood disasters, desertification and soil erosion are becoming more serious in northern China. For example, the area affected by desertification has reached 1.49 million km2 (Zhu et al. 2004). Based on a 1982-2006 GIMMS NDVI data set, statistical methods and vegetation cover mapping, we analyzed temporal and spatial variation and characteristics of vegetation under the TNSP. 3 Data acquisition and methodology 2 Study area Monthly NDVI was obtained by 15-day composed NDVI data with maximum value composed (MVC), i.e. the monthly NDVI image pixel was replaced by the maximum NDVI values of two 15-day composed NDVI values, which can reduce the negative influence of atmospheric clouds, particles, shadows, perspective and solar altitude (Stow et al. 2007). Then, the average method was used to composite the annual average NDVI, which was representative of the most abundant vegetation period within a year. 75˚0΄0˝E 90˚0΄0˝E 105˚0΄0˝E 120˚0΄0˝E 135˚0΄0˝E N GIMMS NDVI data from 1 January 1982 to 12 December 2006 was based on NOAA meteorological satellite global 8 km × 8 km data issued formally by NASA and widely used in global vegetation change research. The 15-day NDVI maximum synthetic data are generated by radiation correction, geometric correction, atmospheric correction and reflectivity calculation; spatial resolution is 8 km with Albert equal-area conic projection. The DN value is converted to NDVI using the formula NDVI = 0.008×(DN– 128) by ArcGIS 9.2 and the NDVI time series for 1982– 2006 was clipped by boundary vector data. Land cover data from the global land cover data subset of China was developed by CLC2000. Precipitation data was provided by the National Meteorological Library. 3.2 Methodology 3.2.1 Maximum value composited of yearly NDVI Y = 1 25 25 ∑ NDVI , i=1, 2, …, 25 where, Y is composited the total annual average NDVI; NDVI is the annual average NDVI from 1982 to 2006; i is 1 for 1982, 2 for 1983 and so on. 3.2.2 Trend line analysis Simulating the trend of each raster grid reflects the spatial 40˚0΄0˝N Provincial region I Sandy area II Northwest desert area III Gully region Loss Plateau IV Northeast North China plain 30˚0΄0˝N 30˚0΄0˝N 40˚0΄0˝N Legend 90˚0΄0˝E 105˚0΄0˝E 120˚0΄0˝E (1) i =1 50˚0΄0˝N 50˚0΄0˝N Construction of the Three-North Shelterbelt started in 1978 and is predicted to take 73 years to complete. Altogether, eight stages will be needed to build a forest shelterbelt along the Great Wall of China. The objective of the TNSP is to control sand and wind erosion, harness soil and water losses, improve ecological environments and produce multiple forest products. The project is acclaimed as China’s Green Great Wall and World’s Best Ecological Project both nationally and internationally (Li 1994). The area covers 551 counties and 13 provinces (73°26′E to 137°50′E; 33°30′N to 50°12′N), covering an area of 4.069×106 km2 or 42.4% of China (Fig. 1). The west is higher than the east, and the altitude ranges from 100–5000 m a.s.l. According to the natural climate, geomorphic features, fragile ecological environment and regional otherness of economic levels, the area can be divided into four areas: the Sand, Northeastern, North China Plains and Northwestern Desert Areas. 3.1 Data acquisition Fig. 1 The Three-North Shelterbelt Program area. 55 WANG Qiang, et al.: The Three-North Shelterbelt Program and Dynamic Changes in Vegetation Cover characteristics of changes in vegetation cover (Stow et al. 2003; Ma et al. 2006). The change in trend can be calculated for a certain period and respective pixel by the linear regression of one variable, y=ax+b. Here, x is the year number from 1 to 10, and y is the MNDVIi value for each year. We can use (2) to calculate the slope of the linear time trend by OLS (ordinary least squares) estimation: n× θslop= n ∑i× M i =1 NDVI i − n n ∑ i∑ M i =1 i =1 n n∑ i − ∑ i i =1 i =1 n NDVI i (2) 2 2 where, n is cumulative number of monitoring years; and θ slope is the slope of the linear regression of one variable equation. It is the average annual increase (or decrease) of NDVI from 1982 to 2006. 3.2.3 Correlation analysis of vegetation and climate factors Correlation analysis is commonly applied to analyze the relationship between inter-annual vegetation changes and climatic factors. Here, study area NDVI, annual mean temperature and annual precipitation for each pixel was N Legend 0 500 1000 km High: 1 Low: -1 Fig. 2 Spatial distribution of NDVI in the Three-North Region of China from 1982 to 2006. N used in spatial correlation analyses to reflect the correlation between climatic factors and NDVI sequences using ArcGIS. The correlation coefficient between –1 and 1, was calculated as follows: ∑ (x n rxy = i i =1 ∑ (x n i =1 i −x −x )(y − y ) i ) ∑ (y − y ) 2 n i =1 (3) 2 i where, n is the cumulative number of monitoring years; x and y are two variables related to the analysis; and x and y are the variable sample mean value. 4 Results and discussion 4.1 Spatial distribution characteristics of vegetation cover Distribution characteristics of the NDVI mean value show spatial variation. According to Fig. 2, the general characteristics of vegetation cover in the three-north region show the order IV area>III area>II area>I area; the vegetation cover status of the northeast and north plain regions is better than that over the northwest and sandy regions. The maximum NDVI value is in the IV area and the value in most areas is more than 0.75. The forest types are evergreen, deciduous coniferous forest broad-leaved deciduous forest and shrub located in northern Greater Khingan Range, southern-central Lesser Khingan Range, Yanshan Mountains, Changbai Mountains, Sungari River Basin and Haihe River Basin (Fig. 3). NDVI values of the III and I areas are second place, and the value in most of these areas is in the range of 0.5–0.75. The forest types are evergreen, deciduous coniferous forest and broad-leaved deciduous forest mostly distribute in south-central Greater Khingan Range, Yinshan Mountains, Qinling Mountains and the Yellow River Basin. The minimum NDVI value is in the II area and the value of most areas is less than 0.5. In a few regions the value is in the range of 0.6–0.8 and located in the Tien Shan Mountains, west-central Kunlun Mountains, natural oasis in Xinjiang, east of the Qilian Mountains, Qaidam Basin and Tarim Basin. The forest types are evergreen and deciduous coniferous forest. Forestland had the highest NDVI value. 4.2 Temporal characteristics of vegetation cover 0 500 1000 km Legend Deciduous needle-leaf forest Evergreen coniferous forest Deciduous broad leaved forest Shrub Fig. 3 Spatial distribution of forest in the Three- North Region of China. Analyzing the 25-year mean NDVI value curve we found that the minimum NDVI value was 0.584 appearing in 1982 and the maximum value was 0.634 occurring in 2003 (Fig.4). The annual average linear change trend was 0.007 per 10 years which suggests that the NDVI value has followed a general slow upward trend; the correlation coefficient of annual NDVI value and year is 0.40 (p < 0.05). Specifically, the minimum NDVI value in the sandy, northwest desert, Loess Plateau and northeast-north China plain areas was 56 Journal of Resources and Ecology Vol.5 No.1, 2014 I 0.95 II III y=0.0016x–2.2263 R2=0.8216 0.86 0.77 IV The study area y=0.0008x–0.9327 R2=0.1475 0.68 0.59 y=0.0025x–4.2955 R2=0.3859 y=–0.0002x–0.8489 R2=0.0103 y=0.0007x–0.8422 R2=0.177 0.50 0.41 1982 1985 1988 1991 1994 1997 2000 2003 2006 Fig. 4 Inter-annual variation of the NDVI in the TNSP. 0.636 in 1989, 0.428 in 1995, 0.928 in 1982 and 0.935 in 1982, respectively. The maximum NDVI value in the sandy, Loess Plateau, and northeast-north China plain areas was 0.748, 0.768 and 0.976, respectively, all in 2003. In the northwest desert area the maximum value was 0.479 in 1988. The NDVI value increased most in the sandy and northwest desert areas (p < 0.01) with a linear change trend of 0.025 and 0.016 per 10 years, respectively. The Loess Plateau (p < 0.05) had a linear change trend of 0.008 per 10 years. The northwest desert area showed a low decreasing trend (p < 0.05) with a linear change of –0.002 per 10 years. 4.3 Spatial change characteristics in vegetation cover Table 1 and Fig. 5 show vegetation cover patterns for 1982 to 2006, 1982 to 1991, 1991 to 2001, and 2002 to 2006. Vegetation cover change across the TNSP area is increasing. Table 1 NDVI changes in the Three-North Region of China. Rank 1982–1991 1992–2001 2002–2006 1982–2006 θ slope Change level Area (km2) Ratio (%) Area (km2) Ratio (%) Area (km2) Ratio (%) Area (km2) Ratio (%) < –0.005 Significant reduction 23 407 0.58 27 453 0.67 17 627 0.43 15 462 0.38 –0.005– –0.002 Slightly reduced 174 310 4.28 242 739 5.96 165 063 4.06 166 015 4.08 –0.002–0.002 Basically unchanged 3 398 289 83.52 3 347 776 82.28 3 451 114 84.81 3 338 207 82.04 0.002–0.005 Slightly increase 338 100 8.31 355 092 8.73 372 546 9.16 441 893 10.86 > 0.005 Significant increase 134 894 3.31 95 940 2.36 62 650 1.54 107 423 2.64 N 0 N a Legend Significant reduction Slightly reduced Basically unchanged Slightly increase Significant increase 500 1000 km N 0 b 500 1000 km N c d Legend 0 500 1000 km Significant reduction Slightly reduced Basically unchanged Slightly increase Significant increase Legend Significant reduction Slightly reduced Basically unchanged Slightly increase Significant increase 0 500 1000 km (a: 1982–2006; b: 1982–1991; c: 1992–2001; d: 2002–2006) Fig. 5 Spatial distributions of NDVI in the Three-North Region of China. Legend Significant reduction Slightly reduced Basically unchanged Slightly increase Significant increase WANG Qiang, et al.: The Three-North Shelterbelt Program and Dynamic Changes in Vegetation Cover Increased vegetation cover from 1982 to 1991, 1992 to 2001, and 2002 to 2006 was 472 994, 451 032 and 435 196 km2, accounting for 11.62%, 11.09% and 10.70% of the total study area. This was mainly the case in the central and southern Greater Khingan Range, central Lesser Khingan Range, Changbai Mountains in the northeast segment, Yanshan Mountains, low mountains upland region of western Liaoning Province, the Altai Mountains, Tien Shan Mountains, Qilian Mountains (Shi et al. 2006; Guo et al. 2008), eastern of the northwest dessert, and the southern Loess Plateau hilly-gully region. Vegetation here increased primarily because the climate is moderate and precipitation increased, and because vegetation construction projects such as the TNSP and Green for Grain facilitate vegetation recovery and growth (Zhu et al. 2004; Deng et al. 2006). A decline in vegetation cover of 197 717, 270 192 and 182 690 km2 was found, accounting for 4.86%, 6.63% and 4.49% of the total study area. These areas were mainly distributed on both sides of the Greater Khingan Range, western Hulun Buir Plateau (Zhang et al. 2009), northern Three River Plain, Horqin Sandy Land and southern the northwest dessert and northern Loess Plateau hillygully region. The human population has grown extremely fast in these areas, and combined with over grazing and land reclamation, vegetation cover has declined. Further, precipitation in these regions is low and its distribution is uneven. In the last 25 years, precipitation has decreased at the rate of 98.8 mm per 10 years, especially from 1992 to N 0 Temperatur 500 1000 N 0 Correlation coefficient km -0.81– -0.50 -0.50– -0.38 -0.38–0.38 0.38–0.50 0.50–0.75 km 2001 (rate of decline was 319.4 mm per 10 years). High evaporation and high wind with a long duration have aggravated drought in this region. As the climate continues to warm, climate volatility in these regions will increase and drought will be the main cause of vegetation cover decline. 4.4 Correlation between vegetation and climatic factors The relationship between NDVI and temperature is positive overall. A negative relationship between NDVI and precipitation was found except for differences in some regions (Fig. 6; Table 2). Regions with a negative correlation between NDVI and temperature account for approximately 17.74% of the total area and are mainly distributed in the Junggar Basin, eastern Tarim Basin, Hexi Corridor, western Qaidam Basin, Ulan Buh Sandy Land, and northern Loess Plateau. Most of these regions are located in arid or semiarid areas with high evaporation, high temperatures, low precipitation, and irrigation agriculture, leading to soil drying. On the contrary, the proportion of the TNSP area with a positive correlation between NDVI and temperature is 6.84%, and primarily distributed in the Altai Mountains, Tien Shan Mountains, western Kunlun Mountains, Qilian Mountains, eastern Qaidam Basin, Qinling Mountains, the central Inner Mongolia Plateau, southern Greater Khingan Range and Changbai Mountains. These areas are similar in that they have a high latitude and high altitude, suggesting that heat conditions are the dominant natural factor influencing changes in vegetation. Vegetation NDVI was negatively correlated with precipitation across 10.60% of the total area, mainly distributed in the Altun Mountains, western Alxa Plateau and northern central Greater Khingan Range. Vegetation NDVI was positively correlated with precipitation in 19.53% of the area, and this was found in the Tien Shan Mountains, eastern Kunlun Mountain, eastern Qinghai Province, Maowusu Sandy Land, Wulanchabu Plateau, Xilingol Plateau, Horqin Sandy Land, northeast of Chifeng, Songnen Plain, Liaohe River Plain and the southern part of the Lesser Khingan Range. The different acquisition ability of soil moisture results from the diversity of surface Table 2 Correlation coefficient between average annual NDVI, temperature and precipitation. Precipitation 500 1000 57 Correlation coefficient -0.90– -0.50 -0.50– -0.38 -0.38–0.38 0.38–0.50 0.50–0.79 Fig. 6 Correlation between NDVI and climate factors 1982–2006. Correlation coefficient distribution range (%) Precipitation (%) Temperature Significant negative –0.81– –0.50 6.44 –0.90– –0.50 0.88 correlation Low negative –0.50– –0.38 11.30 –0.50– –0.38 9.72 correlation Weak correlation –0.38–0.38 75.42 –0.38–0.38 69.87 Low positive 0.38–0.50 4.93 0.38–0.50 14.07 correlation Significant positive 0.50–0.75 1.91 0.50–0.79 5.46 correlation 58 vegetation cover and leads to differences in precipitation relevance (Li et al. 2005; Dai et al. 2010a, 2010b). In arid and semiarid areas precipitation is a crucial constraint on vegetation growth. 5 Conclusions Regarding the temporal pattern, NDVI values across TNSP region appear a slow upward trend during the last 25 years and the growth rate is 0.007 per 10 years. The growth rate of the wind-sand, northeast and north China plain areas is most obvious, indicating that construction of the TNSP is achieving and the trend of ecological degradation and vegetation cover loss in most regions has been curbed. It is necessary to make the TNSP sustainable and maintain effort to reach the stated goals of this project. There is a positive correlation between vegetation, precipitation and temperature across the TNSP. For example, for 17.74% of the region temperature was negatively correlated.However, 6.84% of the region it was positively correlated. Further, for 10.60% of the region precipitation was negatively correlated and for 19.53% of the region it was positively correlated. The area where vegetation is closely associated with precipitation is significantly larger than the area where temperature plays the major role, and therefore precipitation is the key driver of vegetation growth across the TNSP. During the periods 1982–1991, 1992–2001 and 2002– 2006, vegetation cover area increased 472 994, 451 032 and 435 196 km2 respectively. This was mainly in central and southern parts of the Greater Khingan Mountains, the central Lesser Khingan Mountains, northeast section of the Changbai Mountains, Yanshan Mountains, Western Liaoning Hilly Region, Altai Mountain, Tien Shan Mountains, east of the Qilian Mountains, east of the western desert area and south of the hilly-gully area of the Loess Plateau. However, in the same three periods, vegetation cover declined by 197 717, 270 192 and 182 690 km2, respectively, both sides of the Greater Khingan Mountains, west of the Hulun Buir Plateau, north of Sanjiang Plain, south of Horqin Sandy Land, south of the western desert area and north of the hilly-gully area of the Loess Plateau. Vegetation reduction is affected by human activities as zones with obvious reductions are places where the ecological environment is weak and interference from human activity is intense. With climate warming, climate fluctuations in these zones will increase so the arid climate is another reason for vegetation cover decline. These zones should be key management areas into the future. Acknowledgements The authors thank the Environmental & Ecological Science Data Center for West China and the Climatic Data Center, National Meteorological Information Center, China Meteorological Administration for data sets. Journal of Resources and Ecology Vol.5 No.1, 2014 References Adams J E, G F Arkin. 1997. A light interception method for measuring row crop ground cover. Soil Science Society of America Journal, 41 (4): 789792. Brown M E, D J Lary, A Vrieling, et al. 2008. Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS. International Journal of Remote Sensing, 29:7141-7158. Carlson T N, D A Ripley. 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3): 241-252. 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(in Chinese) Zhu J Z, Zhou X C, Hu J Z. 2004. Thoughts and views about the three north shelterbelt program. Journal of Natural Resources, 19(1): 79-85. (in Chinese) 基于GIMMS AVHRR NDVI数据的三北防护林工程区植被覆盖动态变化与影响因子分析研究 王 强1,张 勃2,张志强1,张喜风3,戴声佩4 1 中国科学院资源环境科学信息中心/中国科学院国家科学图书馆兰州分馆,兰州 730000; 2 西北师范大学地理与环境科学学院,兰州 730070; 3 兰州大学西部环境教育部重点实验室旱区流域科学与水资源研究中心,兰州 730000; 4 中国热带农业科学院科技信息研究所,儋州 571737 摘 要:本文基于1982-2006年连续25年的GIMMS AVHRR NDVI植被覆盖指数,采用了最大化NDVI均值法、与气温及 降水变化的相关性和一元线性回归趋势分析法,对中国三北防护林工程区连续25年的植被覆盖时空变化特征进行了动态变化 研究。结果表明:(1)近25年来,研究区植被NDVI平均值总体呈缓慢上升趋势,增速为每10年0.007;(2)研究区植被和气 温、降水整体呈正相关关系,植被与降水正相关面积明显大于植被与气温正相关面积,说明降水是研究区植被生长的关键因 子;(3)1982-2006年,研究区植被覆盖增加的区域主要分布在大兴安岭中、南部,小兴安岭中部,长白山东北段,燕山,辽 西低山丘陵区,阿尔泰山,天山,祁连山东段,西北荒漠区东部和黄土高原丘陵沟壑区南部等;植被覆盖减少的区域主要是在 大兴安岭两侧,呼伦贝尔高原西部,三江平原北部,科尔沁沙地南端,西北荒漠区南部和黄土高原丘陵沟壑区北部等。 关键词:NDVI;三北防护林工程区;时空变化;相关分析;动态研究;中国北部