雅鲁藏布江流域植被格局与NDVI分布的空间响应

雅鲁藏布江流域海拔高差约达7 000 m,气候条件复杂、生态系统类型多样,植被格局空间变化显著.笔者基于1:100万植被类型图、SPOT_VEGETATION NDVI数据集和数字高程模型(DEM),综合运用GIS空间分析技术,提取与定量分析了流域主要植被类型、空间分布特征,并结合海拔梯度、气候条件变化探讨了流域植被格局与NDVI空间变化的耦合关系.结果表明:(1) 雅鲁藏布江流域植被类型包括针叶林、阔叶林、灌丛、荒漠、草原、草丛、草甸、高山植被等11个植被型组,21个植被型,其中米林宽谷的植被型最多,自下游至上游的山南宽谷、日喀则宽谷及马泉河宽谷随着海拔梯度的变化,植被类型多样性总体呈下降趋势.(2) 随着海拔的增加,植被型组和植被型的个数均呈先增大后减小的趋势,以海拔3000～4 000 m和4 000～5 000 m最多,流域植被格局的垂直地带性显著.(3) 流域植被格局与NDVI变化表现出较好的空间一致性.针叶林、阔叶林和草丛等3个植被型组的NDVI值均以10-12月最大,其余8个植被型组的NDVI值均以7-9月最大、1-3月最小.海拔3 000 m是流域尺度植被格局变化的一个转折点.     

西藏拉萨市土地沙漠化现状与趋势

西藏拉萨市已有17万km²土地沦为沙漠化土地,沙漠化的危害涉及农牧业生产和人民生活的各个方面。研究表明,本区沙漠化过程是在干旱多风的气候条件下,以缓慢的自然沙漠化过程为基础,自然与人为因素共同作用,相互激发,相互促进所形成的人为加速与加剧过程,它是强度的不合理的土地利用与脆弱的生态环境和贫乏的自然资源之间不相协调的产物。在目前的土地利用方式和强度下,拉萨市沙漠化将会进一步加剧。预计到2000年和2025年,拉萨市沙漠化土地面积将分别增加到19.6万km²和28.9万km²,比目前的沙漠化土地面积扩大3.0万km²和11.9万km²,使人类的生存环境进一步恶化。     

雅鲁藏布江中游沙地植物区系与阿拉善荒漠植物区系的比较

从区系地理成分来看,雅鲁藏布江中游沙地植物区系以东亚成分(中国-喜马拉雅成分)占绝对优势,而阿拉善荒漠则以亚洲中部荒漠成分(即戈壁成分)占主导地位。另外,两个地区的地方特有成分的优势都十分明显,但雅鲁藏布江中游沙地中的特有种大都是在青藏高原隆起以后由于特殊的生境(高寒、干旱)而分化出来的新种,而阿拉善荒漠中的特有种大都是些古老的单种属和寡种属。雅鲁藏布江中游沙地植物区系和阿拉善荒漠植物区系都具有古老的热带起源,然而前者是在古老的第三纪热带植物区系的基础上,在高原隆升过程中经过山区特化和气候特化形成的新的年轻的区系,而后者则较多地保留了热带起源特别是古地中海起源的古老性。     

油蒿在沙坡头地区沙地植被演替中的地位

本文通过对沙坡头地区铁路两侧人工植被和天然植被演替过程的调查研究,应用定性和定量分析方法,论述了油蒿在沙坡头地区沙地植被演替中的地位及稳定性。油蒿是沙生演替系列中的前期植物种之-,随着沙面表土层的增厚及生境的旱化,如果不受人为干扰,最终将被其它植物群落所代替。然而,只要地表保持沙质疏松,油蒿就能很顺利地天然更新,并以其发达的浅层根系对水分的竞争能力,使其在群落中占据着优势地位。因此,在人畜对地表的长期扰动下,油蒿在本地区可以形成-种相对稳定的群落或偏途演替顶极。     

Multi-Fractal Analy is of Daily Air Temperature Time Series in Coastal Areas of China

In this article, the Multi-Fractal Detrended Fluctuation Analysis (MF-DFA) method is adopted to study the temperature, i. e., the maximum temperature (Tmax), mean temperature (Tavg) and minimum (Tmin) air temperature, multifractal characteristics and their formation mechanism, in the typical temperature zones in the coastal regions in Guangdong, Jiangsu and Liaoning Provinces. Following are some terms and concepts used in the present study. Multifractality is defined as a term that characterizes the complexity and self-similarity of objects, and fractal characteristics depict the distribution of probability over the whole set caused by different local conditions or different levels in the process of evolution. Fractality strength denotes the fluctuation range of the data set, and long-range correlation (LRC) measures the stability of the climate system and the trend of climate change in the future. In this research, it is found that the internal stability and feedback mechanism of climate systems in different regions show regional differences. Furthermore, the research also proves that the Tavg, Tmax and Tmin of the above three provinces are highly multifractal. The temperature series multifractality of each province decreases in the order of temperature series multifractality of Liaoning > temperature series multifractality of Guangdong > temperature series multifractality of Jiangsu, and the corresponding long-range correlations follow the same order. It reveals that the most stable temperature series is that of Liaoning, followed by the temperature series of Guangdong, and the most unstable one is that of Jiangsu. Liaoning has the most stable climate system, and it will thus be less responsive to the future climate warming. The stability of the climate system in Jiangsu is the weakest, and its temperature fluctuation will continue to increase in the future, which will probably result in the meteorological disasters of high temperature and heat wave there. Guangdong possesses the strongest degree of multifractal strength, which indicates that its internal temperature series fluctuation is the largest among the three regions. The Tmax multifractal strength of Jiangsu is stronger than that of Liaoning, while the Tavg and Tmin multifractal strength of Jiangsu is weaker than that of Liaoning, showing that Jiangsu has a larger internal Tmax fluctuation than Liaoning does, while it has a smaller fluctuation of Tavg and Tmin than Liaoning does. Guangdong and Liaoning both show the strongest Tmin multifractal strength, followed by Tavg multifractal strength, and the weakest Tmax multifractal strength. However, Jiangsu has the strongest Tmax, followed by Tavg, and the weakest Tmin. The research findings show that these phenomena are closely related to solar radiation, monsoon strength, topography and some other factors. In addition, the multifractality of the temperature time series results from the negative power-law distribution and long-range correlation, in which the long-range correlation influence of temperature series itself plays the dominant role. With the backdrop of global climate change, this research can provide a theoretical basis for the prediction of the spatial-temporal air temperature variation in the eastern coastal areas of China and help us understand its characteristics and causes, and thus the present study will be significant for the environmental protection of coastal areas.     

基于光谱特征的雅鲁藏布江源区草地类型识别

以雅鲁藏布江源区为研究对象,以Landsat5 TM图像为数据源,根据不同草地类型的波段组合特征,结合源区1∶100万植被类型图、DEM和NDVI数据,构建草地判别规则,利用决策树分类法对雅鲁藏布江源区草地类型进行遥感识别。研究结果表明:①不同类型草地因其生境不同,利用不同波段组合特征进行草地类型识别能够达到较好的效果;②与传统的监督分类法相比,基于波段组合特征的决策树分类法具有较高的识别精度(总体精度提高了15.4%,Kappa系数提高了0.225);③在海拔4 400～5 000 m区域内,固沙草草原面积最大,其次为矮嵩草和小嵩草混生草甸,再次为变色锦鸡儿和金露梅灌丛,藏北嵩草草甸面积最小。     

基于TM数据的雅鲁藏布江源区草地植被盖度估测

为了查明雅鲁藏布江源区的草地植被盖度,采用Landsat5 TM数据,以其派生数据NDVI,RVI,VI3,PVI,DVI,MSAVI,SAVI,TM4/TM5为主要分析因子,结合野外植被样地调查数据,选取相关性最高的因子与植被盖度实测值建立回归模型,然后利用该模型反演源区的植被盖度。研究结果表明:①和其他几种遥感评价指标相比,TM4/TM5的比值与草地植被盖度的相关性最高,与草地植被盖度实测值的变化趋势一致,在光谱特征上增强了不同退化程度草地植被的光谱反射值差异,最适宜用于草地植被盖度建模;②雅鲁藏布江源区植被盖度的10个分级中,植被盖度为10%～20%的分布地区最多,面积达到4 322.15 km2,占全部草地面积的49.27%;其次是植被盖度为0～10%和20%～30%的地区,面积分别达到2 238.53 km2和1 397.87 km2,分别占全部草地面积的25.52%和15.94%;植被盖度高于50%的草地面积为195.96 km2,仅占源区草地总面积的2.23%;③植被盖度大于50%的高覆盖度植被区主要分布于4 426～4 800 m高程范围内,面积达到186.25 km2,占整个源区高覆盖度草地面积的95.04%,这与源区的水分分布条件相关。     

水利设施对淮河水域生态环境的影响

该文从水情、水生生物、水环境质量等方面分析水利设施对淮河水域生态环境的影响。研究结果表明,由于水利设施的调节作用,大部分湖泊平均水位提高,水位变化趋于平缓,但部分河道型湖泊情况正好相反,水位下降且变化幅度较以前增大。同时,闸坝的存在阻碍了水生物的洄游通道,降低了河道水流流速,导致水生生物数量和种类发生较大变化,水体自净能力下降,加之闸坝的不合理调度,常常诱发突发性水污染事故。     

雅鲁藏布江中部流域沙地植的分类和排离

选用模糊聚为分析方法，利用群落组种的相对盖度作为优势度指标，将该区沙地植被划分为２２个群系，群系下又划出了不同的群丛。同时，采用主分量分析了（ＰＣＡ）模型，对１０个常见的群落类型进行了二维排序，比较直观和起初地反映了不同群落类型在土壤砾石化程度和土壤潜在肥力２个环境梯度上的分布情况。