珠穆朗玛峰自然保护区植被变化分析

利用1981～2001 年美国NASA Pathfinder NOAA/NDVI 数据, 以1∶100 万植被图为基础, 结 合气温降水资料、DEM数据和2000 年人口空间化数据, 研究了珠穆朗玛峰自然保护区植被变化 空间格局和海拔梯度特征及其影响因素。结果表明: ①1981～2001 年珠峰自然保护区植被变化以 稳定为主, 有5.09%的区域发生严重退化, 13.34%的区域发生退化, 54.31%的区域保持稳定, 26.31%的区域变好以及0.95%的区域植被显著变好。退化和严重退化区域主要分布在保护区南 部, 国境沿线; 植被变好地区集中分布在保护区北部, 雅鲁藏布江南岸。稳定区域位于退化区域和 变好区域之间。植被退化区域主要分布在海拔2400m ~ 4000m 带上。②针叶林、针阔混交林和灌 丛构成了区域植被退化的主体。③从空间上看, 主要是气温变化对植被变化有影响。在海拔梯度 上, 气温变化和坡度共同影响植被变化。④在珠峰自然保护区内, 人类不合理的资源利用方式造 成了部分地区的植被退化。     

珠穆朗玛峰国家自然保护区南北坡植被覆盖变化

利用2000-2009年MODIS NDVI数据,基于每个像元的生长季NDVI峰值进行了像元水平的线性趋势分析,研究珠穆朗玛峰自然保护区南坡和北坡的植被覆盖的空间分布和变化特征.结果表明:①保护区内植被覆盖显著改善区域和轻微改善区域NDVI-Max的年平均增加率分别为3.06%和1.25%;显著退化区域和轻微退化区域NDVI-Max的年平均减少率分别为2.82%和1.09%a 22000-2009年,保护区南坡的植被覆盖整体上呈现上升趋势,22.59%的区域显著改善,19.05%的区域轻微改善,24.75%的区域保持稳定;北坡的植被覆盖整体上呈现下降趋势,19.5%的区域严重退化,24.43%的区域轻微退化,38.12%的区域保持稳定.③南坡有植被覆盖的8种土地利用类型中,山区旱地植被覆盖呈现退化趋势,其余土地利用类型都呈现上升趋势;北坡有植被覆盖的10种土地利用类型中,植被覆盖都呈现退化趋势.     

1982-2009 年珠穆朗玛峰自然保护区植被指数变化

植被指数是指示植被变化的重要指标, 本研究基于1982-2009 年珠穆朗玛峰自然保护区(简称珠峰地区)植被指数(NDVI)时间序列数据、土地覆被和野外调查等数据, 采用时序变化趋势和空间分析法, 对珠穆朗玛峰地区植被的时空变化过程及保护区成效进行了定量分析。研究表明:①珠峰地区NDVI分布的总特征是南部和北部高, 中部低。②1982-2009 年珠峰地区NDVI年际变化趋势和空间异质性十分明显:1982-1997 年, 珠峰地区NDVI总体上呈显著上升趋势, 北部地区增幅较大;1998-2009 年, NDVI总体下降(56.96%的NDVI呈下降趋势), 其中, 珠峰地区中部和北部的NDVI下降最为明显, 而南部核心保护区森林和灌丛的NDVI则呈显著上升趋势, 且变化幅度较大。③长时间序列植被指数变化的过程和空间差异性推断:1998 年以来, 天然林保护等生态工程促使珠峰地区保护效果更加明显。     

近30年珠穆朗玛峰国家自然保护区冰川变化的遥感监测

利用1976、1988和2006年的3期陆地卫星遥感数据,采用面向对象的解译方法并结合专家知识分类规则自动提取珠穆朗玛峰国家自然保护区(以下简称珠峰保护区)3个时期的冰川信息,并利用遥感、地理信息系统和图谱的方法对冰川时空分布特征和变化及其原因与不确定性进行了分析。结果如下:(1)2006年珠峰保护区内冰川面积为2710.17±0.011km²,为研究区总面积的7.41%,主要分布在研究区南部海拔4700～6800m的高山区;(2)1976-2006年,珠峰保护区冰川持续退缩明显,总面积减少501.91±0.035km²,冰湖扩张迅速(净增加36.88±0.035km²);研究区南坡子流域冰川退缩率(16.79%)高于北坡子流域(14.40%);珠峰保护区冰川以退缩为主,退缩冰川主要分布于海拔4700～6400m,退缩区上限海拔为6600～6700m;(3)1976年以来,气温显著上升和降水减少是冰川退缩的关键因素。     

2001-2015年喜马拉雅南麓地区植被变化遥感监测

本文应用喜马拉雅南麓地区MODIS NDVI 植被遥感数据和格点数据,采用趋势线分析、多元回归等方法分析了该研究区2001-2015 年植被 NDVI_max 时空变化特征,同时利用Person 相关分析探讨了植被 NDVI_max 时空变化特征与气候因子的响应关系。结果表明：（1）2001-2015 年,喜马拉雅南麓地区年内平均 NDVI_max 1~3 月份呈下降趋势,4~6 月份开始缓慢生长,6~9 月份进入植被生长高峰期,10 月份开始逐渐降低;植被 NDVI_max 平均值为0.59,植被覆盖度较高;空间上植被覆盖度总体呈东南高西北低,由东南向西北递减;平均 NDVI_max 随海拔变化表现出明显规律性,80%的植被主要分布在较低海拔区（<4 050 m）。（2）15 a 间,喜马拉雅南麓地区植被 NDVI_max 变化具有阶段性特征,年均 NDVI_max 呈三个变化阶段：2001-2006 年和2010-2015 年分别以0.003 9·a^-1、0.005 3·a^-1 的速率增长,而2006-2010 年以-0.007 0·a^-1 的速率减少。植被生长季 NDVI_max 呈4 个阶段：2001-2004 和2007-2010 年分别以-0.001 8·a^-1、-0.010 6·a^-1 的速率逐年减少,但2005、2006 两年（0.014 8·a^-1）快速增长至最大值,2010-2015 年（0.006 3 a^-1）波动增长。空间上大部分地区表现出不显著退化,但少部分地区表现出不显著改善（0.05< p<0.01）,而西段低海拔区表现出极显著改善。（3）喜马拉雅南麓地区植被的变化主要由温度和降水量共同影响,此外,高海拔区气温上升引起的冰川融水对植被生长起到一定的作用,中部低海拔区可能还受到人类活动的影响。     

1971-2000年青藏高原气候变化趋势

Trends of annual and monthly temperature, precipitation, potential evapotranspi- ration and aridity index were analyzed to understand climate change during the period 1971–2000 over the Tibetan Plateau which is one of the most special regions sensitive to global climate change. FAO56–Penmen–Monteith model was modified to calculate potential evapotranspiration which integrated many climatic elements including maximum and mini- mum temperatures, solar radiation, relative humidity and wind speed. Results indicate gen- erally warming trends of the annual averaged and monthly temperatures, increasing trends of precipitation except in April and September, decreasing trends of annual and monthly poten- tial evapotranspiration, and increasing aridity index except in September. It is not the isolated climatic elements that are important to moisture conditions, but their integrated and simulta- neous effect. Moreover, potential evapotranspiration often changes the effect of precipitation on moisture conditions. The climate trends suggest an important warm and humid tendency averaged over the southern plateau in annual period and in August. Moisture conditions would probably get drier at large area in the headwater region of the three rivers in annual average and months from April to November, and the northeast of the plateau from July to September. Complicated climatic trends over the Tibetan Plateau reveal that climatic factors have nonlinear relationships, and resulte in much uncertainty together with the scarcity of observation data. The results would enhance our understanding of the potential impact of climate change on environment in the Tibetan Plateau. Further research of the sensitivity and attribution of climate change to moisture conditions on the plateau is necessary.     

北京汉石桥湿地自然保护区植被分类与2003～2006年植被格局变化

采用样线法结合样方法对北京汉石桥湿地自然保护区的湿地植被进行调查和分类研究。汉石桥湿地共有湿地植被2个植被型组、4个植被型、22个群系组、31个群系。对研究区主要植物群系进行多样性分析发现,莲(Nelumbo nucifera)群系的Simpson多样性指数最高,而芦苇(Phragmites australis)群系的Shannon-Wiener多样性指数和群系物种数最高。并对该湿地核心区2003年和2006年的主要湿地植被分布情况进行分析,结果表明,该湿地在2004年进行恢复工程的前后各区域土地利用和植被分布有较大变化,人为干扰及恢复工程是汉石桥湿地植被的分布变化的主要原因。针对目前的状况,保护区应该采取保障水源,营造不同的生境类型和保护湿地动植物资源等方法,维持湿地物种多样性和生态功能的稳定。     

甘青地区中晚全新世植被变化与人类活动

史前时期人类对环境的影响是近几年来国际研究的热点。在甘青地区，全新世的孢粉资料比较多。但在中晚全新世，该区人类对环境的影响作用及其程度仍不清楚，许多孢粉分析资料中并没有充分考虑人类活动的影响。本文选取孢粉分辨率较高、代表性较好的青海湖、兰州、秦安大地湾三地的资料，着重研究了其中乔木成分的变化。分析发现，孢粉组合中乔木成分的变化与气候变化的趋势并不一致。通过对考古及历史资料的分析，笔者认为，该区的植被很早就受到人类活动的影响。自全新世中期，本区植被中的乔木成分波动下降。5000－3000a BP期间，史前农业对植被的影响较大。3000－2000a BP期间，植被略有恢复。2000a BP以后，人为影响加剧，植被中乔木成分迅速下降。研究者认为，依赖孢粉资料重建中晚全新世的气候变化历史应当慎重。     

2001-2015年间我国陆地植被覆盖度时空变化及驱动力分析

本文基于MODIS-NDVI遥感数据反演计算了我国陆地2001—2015年地表植被覆盖度的空间分布,讨论了植被覆盖度的时空变化规律,分析了影响植被覆盖度近十几年来动态变化的主要驱动因素。研究结果表明：我国陆地植被覆盖度从2001—2015年,植被覆盖度总体上呈增加趋势,其中淮河流域、华北平原地区、以及黄土高原地区增加趋势显著。根据植被覆盖度在时间序列上的变化特征,可将其变化类型分为持续增长型、先减小后增长等六种类型,其中农业种植区基本为一直增长型,而主要森林覆盖区,特别是西南地区的植被覆盖度在研究时段内表现出波动性的变化特征。降水是驱动华北平原北部,内蒙古,以及西北大部分区域植被覆盖度动态变化的重要因素,东北、青藏高原等地区植被覆盖度受温度的影响较大,而在中国东南沿海地区,光照条件是影响该区域植被覆盖度的主要因素。     

1982-2006年蒙古高原植被覆盖时空变化分析

在利用HANTS方法对GIMMS NDVI数据进行时间序列平滑处理的基础上,对蒙古高原1982-2006年植被覆盖时空动态变化进行了分析.结果表明:蒙古高原植被覆盖年平均上升趋势为0.0004·a-1,其中蒙古国的上升趋势(0.0005·a-1)比中国内蒙古(0.0003·a-1)更加显著,而内蒙古多年平均NDVI比蒙古国高0.0433;从不同类型植被NDVI变化趋势看,除森林和戈壁荒漠的NDVI变化趋势较平稳外,草地、农田和灌丛均呈显著的上升趋势;从空间分布趋势看,在过去的25年内植被覆盖呈增加趋势的地区主要分布在高原南部——内蒙古农牧交错区和蒙古国中、西和北部的山脉及其环抱的大湖盆地地区,而植被覆盖呈下降趋势的地区主要集中在高原中部的干旱地带和东部呼伦贝尔地区;从时间推移规律看,蒙古高原植被覆盖在4个不同研究时间段内(20世纪80年代、90年代、21世纪初和过去25年间)呈增加(包括显著增加)趋势的面积均大于呈下降趋势的面积,且其面积按20世纪80年代＜90年代＜21世纪初的顺序依次增加.     

Vegetation change in the Mt. Qomolangma Nature Reserve from 1981 to 2001

Based on the NOAA AVHRR-NDVI data from 1981 to 2001, the digitalized China Vegetation Map (1:1,000,000), DEM, temperature and precipitation data, and field investiga-tion, the spatial patterns and vertical characteristics of natural vegetation changes and their influencing factors in the Mt. Qomolangma Nature Reserve have been studied. The results show that: (1) There is remarkable spatial difference of natural vegetation changes in the Mt. Qomolangma Nature Reserve and stability is the most common status. There are 5.04% of the whole area being seriously degraded, 13.19% slightly degraded, 26.39% slightly im-proved, 0.97% significantly improved and 54.41% keeping stable. The seriously and slightly degraded areas, which mostly lie in the south of the reserve, are along the national bounda-ries. The areas of improved vegetation lie in the north of the reserve and the south side of the Yarlung Zangbo River. The stable areas lie between the improved and degraded areas. Degradation decreases with elevation. (2) Degeneration in the Mt. Qomolangma Nature Re-serve mostly affects shrubs, needle-leaved forests and mixed forests. (3) The temperature change affects the natural vegetation changes spatially while the integration of temperature changes, slopes and aspects affects the natural vegetation change along the altitude gradi-ents. (4) It is the overuse of resources that leads to the vegetation degeneration in some parts of the Mt. Qomolangma Nature Reserve.     

Glacial change in the vicinity of Mt. Qomolangma (Everest), central high Himalayas since 1976

Glaciers are one of the most important land covers in alpine regions and especially sensitive to global climate change. Remote sensing has proved to be the best method of investigating the extent of glacial variations in remote mountainous areas. Using Landsat thematic mapping (TM) and multi-spectral-scanner (MSS) images from Mt. Qomolangma (Everest) National Nature Preserve (QNNP), central high Himalayas for 1976, 1988 and 2006, we derived glacial extent for these three periods. A combination of object-oriented image interpretation methods, expert knowledge rules and field surveys were employed. Results showed that (1) the glacial area in 2006 was 2710.17 ± 0.011 km2 (about 7.41% of the whole study area), and located mainly to the south and between 4700 m to 6800 m above sea level; (2) from 1976 to 2006, glaciers reduced by 501.91 ± 0.035 km2 and glacial lakes expanded by 36.88 ± 0.035 km2; the rate of glacier retreat was higher in sub-basins on the southern slopes (16.79%) of the Himalayas than on the northern slopes (14.40%); most glaciers retreated, and mainly occurred at an elevation of 4700–6400 m, and the estimated upper limit of the retreat zone is between 6600 m and 6700 m; (3) increase in temperature and decrease in precipitation over the study period are the key factors driving retreat.     

Glacial change in the vicinity of Mt. Qomolangma （Everest）, central high Himalayas since 1976

Glaciers are one of the most important land covers in alpine regions and especially sensitive to global climate change. Remote sensing has proved to be the best method of investigating the extent of glacial variations in remote mountainous areas. Using Landsat thematic mapping (TM) and multi-spectral-scanner (MSS) images from Mt. Qomolangma (Everest) National Nature Preserve (QNNP), central high Himalayas for 1976, 1988 and 2006, we derived glacial extent for these three periods. A combination of object-oriented image interpretation methods, expert knowledge rules and field surveys were employed. Results showed that (1) the glacial area in 2006 was 2710.17 ± 0.011 km² (about 7.41% of the whole study area), and located mainly to the south and between 4700 m to 6800 m above sea level; (2) from 1976 to 2006, glaciers reduced by 501.91 ± 0.035 km² and glacial lakes expanded by 36.88 ± 0.035 km²; the rate of glacier retreat was higher in sub-basins on the southern slopes (16.79%) of the Himalayas than on the northern slopes (14.40%); most glaciers retreated, and mainly occurred at an elevation of 4700–6400 m, and the estimated upper limit of the retreat zone is between 6600 m and 6700 m; (3) increase in temperature and decrease in precipitation over the study period are the key factors driving retreat.     

Climate change in Mt. Qomolangma region since 1971

Using monthly average, maximum, minimum air temperature and monthly precipitation data from 5 weather stations in Mt. Qomolangma region in China from 1971 to 2004, climatic linear trend, moving average, low-pass filter and accumulated variance analysis methods, the spatial and temporal patterns of the climatic change in this region were analyzed. The main findings can be summarized as follows: (1) There is obvious ascending tendency for the interannual change of air temperature in Mt. Qomolangma region and the ascending tendency of Tingri, the highest station, is the most significant. The rate of increasing air temperature is 0.234 oC/decade in Mt. Qomolangma region, 0.302 oC/decade in Tingri. The air temperature increases more strongly in non-growing season. (2) Compared with China and the global average, the warming of Mt. Qomolangma region occurred early. The linear rates of temperature increase in Mt. Qomolangma region exceed those for China and the global average in the same period. This is attributed to the sensitivity of mountainous regions to climate change. (3) The southern and northern parts of Mt. Qomolangma region are quite different in precipitation changes. Stations in the northern part show increasing trends but are not statistically significant. Nyalam in the southern part shows a decreasing trend and the sudden decreasing of precipitation occurred in the early 1990s. (4) Compared with the previous studies, we find that the warming of Mt. Qomolangma high-elevation region is most significant in China in the same period. The highest automatic meteorological comprehensive observation station in the world set up at the base camp of Mt. Qomolangma with a height of 5032 m a.s.l will play an important role in monitoring the global climate change.     

1971年以来珠穆朗玛峰地区气候变化

Global climate change has profound influence on natural ecosystem and socioeconomic system and is a focus which governments, scientific societies as well as common people of various countries have paid much attention to. Observations indicate that there i…     

珠穆朗玛峰地区雪冰中重金属浓度与季节变化

The concentrations of heavy metals Ba, Pb, Cu, Zn and Co in snow pit collected in September, 2005 from the accumulation area of the East Rongbuk Glacier (6523 m a.s.l.), which lies on the northern slope of Mt. Qomolangma, were determined by inductively coupled plasma mass spectrometry (ICP-MS). Concentrations (pg/ml) of heavy metals are Ba2-227, Co2.8-15.7, Cu10-120, Zn29-4948 and Pb14-142, respectively. The δ¹⁸O was determined by MAT-252. The time period of the snow pit spans from autumn 2005 to summer 2004. Seasonal variations of the concentrations and δ¹⁸O are observed, of which Pb, Cu, Zn and Co are much lower in summer monsoon season than that in non summer monsoon season, suggesting that different sources of heavy metals contributed to the site. EF_c (crustal enrichment factors) is Co3.6, Cu27, Pb33 and Zn180, respectively. Higher EF_c values of Pb, Cu and Zn suggest that Pb, Cu especially Zn are mainly contributed by anthropogenic sources. Foundation: National Natural Science Foundation of China, No.40501014; No.40871058 Author: Duan Jianping (1981–), Ph.D. Candidate, specialized in climate and environmental change.     

Climate change on the southern slope of Mt. Qomolangma （Everest） Region in Nepal since 1971

Based on monthly mean, maximum, and minimum air temperature and monthly mean precipitation data from 10 meteorological stations on the southern slope of the Mt. Qomolangma region in Nepal between 1971 and 2009, the spatial and temporal characteristics of climatic change in this region were analyzed using climatic linear trend, Sen＇s Slope Estimates and Mann-Kendall Test analysis methods. This paper focuses only on the southern slope and attempts to compare the results with those from the northern slope to clarify the characteristics and trends of climatic change in the Mt. Qomolangma region. The results showed that： （1） between 1971 and 2009, the annual mean temperature in the study area was 20.0℃, the rising rate of annual mean temperature was 0.25℃/10a, and the temperature increases were highly influenced by the maximum temperature in this region. On the other hand, the temperature increases on the northern slope of Mt. Qomolangma region were highly influenced by the minimum temperature. In 1974 and 1992, the temperature rose noticeably in February and September in the southern region when the increment passed 0.9℃. （2） Precipitation had an asymmetric distribution; between 1971 and 2009, the annual precipitation was 1729.01 mm. In this region, precipitation showed an increasing trend of 4.27 mm/a, but this was not statistically significant. In addition, the increase in rainfall was mainly concentrated in the period from April to October, including the entire monsoon period （from June to September） when precipitation accounts for about 78.9% of the annual total. （3） The influence of altitude on climate warming was not clear in the southern region, whereas the trend of climate warming was obvious on the northern slope of Mt. Qomolangma. The annual mean precipitation in the southern region was much higher than that of the northern slope of the Mt. Qomolangma region. This shows the barrier effect of the Himalayas as a whole and Mt. Qomolangma in particular.     

Seasonal variations in heavy metals concentrations in Mt. Qomolangma Region snow

The concentrations of heavy metals Ba, Pb, Cu, Zn and Co in snow pit collected in September, 2005 from the accumulation area of the East Rongbuk Glacier (6523 m a.s.l.), which lies on the northern slope of Mt. Qomolangma, were determined by inductively coupled plasma mass spectrometry (ICP-MS). Concentrations (pg/ml) of heavy metals are Ba2-227, Co2.8-15.7, Cu10-120, Zn29-4948 and Pb14-142, respectively. The δ18O was determined by MAT-252. The time period of the snow pit spans from autumn 2005 to summer 2004. Seasonal variations of the concentrations and δ18O are observed, of which Pb, Cu, Zn and Co are much lower in summer monsoon season than that in non summer monsoon season, suggesting that different sources of heavy metals contributed to the site. EFc (crustal enrichment factors) is Co3.6, Cu27, Pb33 and Zn180, respectively. Higher EFc values of Pb, Cu and Zn suggest that Pb, Cu especially Zn are mainly contributed by anthropogenic sources.