中国暖温带森林群落多样性若干问题的研究

中国暖温带落叶阔叶林区维管植物共158科，931属，近4000种（含亚种，变种和变型），种子植物l3l科，877属，3770余种。暖温带植物区系有很强的温带性质，各类温带成分共548属，而各类热带成分仅226属，热带成分与温带成分(R/T)的比率为0．31。运用TWINSPAN和DCA对全国34个植物区系进行了数量分类排序，结果反映了一个地区的植物区系性质主要取决于其所在地的地理位置，同时也受山地海拔高度的强烈影响这一植物区系的基本特征。 根据暖温带森林植物的特点，修订了Raunkiear生活型系统。暖温带森林植物以地面芽植物(H)占较大的优势，占暖温带全部种类的33.9%;其次是地下芽(G)植物，占l 9.7%;全部高位芽植物占27.5%，绝大部分为落叶阔叶高位芽植物。主要由这些生活型组成的暖温带植物生活型总谱基本反映了暖温带夏季温暖多雨、冬季寒冷干旱的中纬度地区地面芽植物群落气候特征。 暖温带森林植被类型主要有7个植被亚型，约50个群系。辽东栎群落是典型的地带性森林群落。应用TWINSPAN和DCA程序将68块暖温带部分地区辽东栎群落样地和83块北京山区辽东栎群落样地分别划分为1 5个和14个群落类型。用物种丰富度指数、Simpson指数、多样性奇测法、Shannon-Wiene r指数、Pielou均匀度指数，Heip均匀度指数、AIatalo均匀度指数等常用的多样性测度方法，分别对暖温带和北京山区辽东栎群落的多样性进行了测度，结果发现，多样性作为一个整体与DCA第1轴有很大的相关关系：暖温带辽东栎群落多样性指数与DCA第1轴的复相关系数为0.7左右，北京山区较高，为0.8左右。多样性的空间特征为：随海拔的升高和纬度的降低，多样性指数呈上升趋势，反映了水热条件在辽东栎水平分布范围内、人类活动和水分因子在辽东栎垂直分布范围内对群落多样性的影响;不同群落之间多样性指数由低到高的顺序为：灌丛、辽东栎萌生丛、辽东栎林、辽东栎纯林、混交林，符合群落演替过程中多样性的动态规律。 对秦岭主峰太白山海拔1400-1600m之间植被类型和物种多样性进行了研究，在划分的1 5种群落类型中，以位于海拔1500-2300m之间的落叶阅叶混交林和栎类混交林的群落多样性最高，在海拔2300-3600m之间，群落多样性趋于单调下降，反映了热量的不足在这一海拔高度范围成为多样性的主要限制因子。

人工神经网络在植被遥感影像分类中的应用研究

比起传统的统计方法，人工神经网络具有很好的非线性处理和并行计算能力，在植被遥感信息处理中得到广泛的应用。本研究系统地介绍了人工神经网络理论及其在植被遥感信息处理中的应用现状。并就如何提高人工神经网络的相干被遥感影像的分类能力进行了详细研究。首次提出了结合植被指数和组成分分析的神经网络分类方法。过去这方面的研究工作大都集中在通过选择一个合适的神经网络模型来提高植被分类精度，而我们认为：根据植被遥感自身的规律，结合统计方法，确定合适的网络输入模式的特征变量，也可以提高分类精度。 研究结果表明，尽管一般的神经网络分类器不需要对输入的模式做明显的特征提取，网络的隐层就具有特征提取的功能。但对TM影像七个波段和常用的五个植被指数（PVI、NDVI、WDVI、PVI、MSAVI2），分别做主成分分析，从而获得人工神经网络输入的特征变量，使用这样一种结合VI、PCA的神经网络对遥感TM多波段影像进行植被分类，能大大提高分类的精度。

Land-cover classification of China: integrated analysis of AVHRR imagery and geophysical data

Over last two decades, numerous studies have used remotely sensed data from the Advanced Very High Resolution Radiometer (AVHRR) sensors to map land use and land cover at large spatial scales, but achieved only limited success. In this paper, we employed an approach that combines both AVHRR images and geophysical datasets (e.g. climate, elevation). Three geophysical datasets are used in this study: annual mean temperature, annual precipitation, and elevation. We first divide China into nine bio-climatic regions, using the long-term mean climate data. For each of nine regions, the three geophysical data layers are stacked together with AVHRR data and AVHRR-derived vegetation index (Normalized Difference Vegetation Index) data, and the resultant multi-source datasets were then analysed to generate land-cover maps for individual regions, using supervised classification algorithms. The nine land-cover maps for individual regions were assembled together for China. The existing land-cover dataset derived from Landsat Thematic Mapper (TM) images was used to assess the accuracy of the classification that is based on AVHRR and geophysical data. Accuracy of individual regions varies from 73% to 89%, with an overall accuracy of 81% for China. The results showed that the methodology used in this study is, in general, feasible for large-scale land-cover mapping in China.

A ground hydrologic model with inclusion of a layer of vegetation canopy that can interact with general-circulation model

In this paper, a ground hydrologic model(GHM) is presented in which the vapor, heat and momentum exchanges between ground surface covers (including vegetation canopy) and atmosphere is described more realistically. The model is used to simulate three sets of field data and results from the numerical simulation agree with the field data well. GHM has been tested using input data generated by general circulation model (GCM) runs for both the North American regions and the Chinese regions, The results from GHM are quite different from those of GHMs in GCMs. It shows that a more active concerted effort on the land surface process study to provide a physically realistic GHM for predicting the exchange between land and atmosphere is important and necessary.