类脂化合物单体碳稳定同位素在古气候环境研究中的意义

类脂化合物极大地扩展了全球变化研究的深度，尤其是单体碳稳定同位素技术在古气候、古环境重建上有着重要作用。基于近些年来国内外的最新研究成果，就类脂化合物单体碳同位素在古气候环境方面的应用研究进行了较为全面的评述。已有研究表明利用类脂化合物单体碳同位素组成可以追溯到海洋和湖泊环境中陆源有机质的物源，其母源区历史时期 C3、C4植被的变迁以及区域性的古气候、古环境重建。     

MRI分割的几何活动轮廓模型的半隐式AOS迭代方法

几何活动轮廓模型以其计算稳定等优点，被广泛用于图像分割。给出一种基于AOS(additive operator splitting)算法的快速活动轮廓模型，将AOS方法与几何活动轮廓模型相结合构造一种半隐式迭代模型，它对迭代步长没有限制，从而可以选择较大的迭代步长，提高了模型的时间效率，并使用区域信息构造系数矩阵，使模型具有全局性。对MR(magnetic resonance)图像分割的实验表明，该模型可以得到较好的分割结果，同时时间效率也有大幅提高，方便了实时应用。     

基于小波统计特性的遥感图像像素与特征联合最优融合方法

遥感影像的IHS融合方法由于匹配误差导致光谱畸变和退化,而小波变换在变换域具有良好的分频特性,小波系数的统计特性反映了遥感影像的边缘、线和区域等显著特征。提出了基于小波统计特性的遥感影像的像素和特征联合最优融合方法,在IHS空间,对强度分量I的高频部分利用多分辨率小波融合方法进行影像的高频细节特征融合,低频部分选取光谱信息和空间分辨率评价指标作为融合权系数求优指标,进行像素级最优融合,实验结果证明了该方法的有效性。     

国土资源行业“3S”技术集成研究及实用技术体系构建

本文面向国土资源信息化建设的实际需求,通过关键技术研究和技术集成,建立基于“3S”的国土资源数据获取、更新、管理、交换与应用的技术体系,通过应用示范研究,形成实用、可推广的技术流程和应用软件。研究结果表明,基于“3S”的技术体系,以其精确的空间定位、快速准确的数据获取、强大的数据管理能力,能够满足国土资源信息化建设的实际需要。     

近20年全球总云量变化趋势分析

利用ISCCP月平均云气候资料集的总云量资料, 采用趋势分析的方法, 得到1983年7月至2001年9月近20年来全球平均总云量的变化趋势, 并分析云量变化的可能原因。近20年全球增温幅度加快, 研究这期间云的变化, 对气候研究和模拟具有重要的意义。结果表明:平均大气环流决定总云量的分布;全球平均总云量的变化趋势在20世纪80年代末发生逆转, 即由增加转为减少;全球平均云量呈减少的变化趋势, 2000年与1987年相比, 减少量约占平均总云量的4%;从地理位置上看, 云量的变化存在区域性差异, 热带和中纬度地区的总云量减少较多, 高纬度地区云量略有增加, 其中南极大陆云量增加较多。     

温度变化对夏季降温耗能的影响

该文采用降温度日数作为评估夏季降温耗能的指标 ,分析了我国夏季降温度日分布和长期变化特征。温度与降温耗能相关分析表明 ,两者相关密切 ,相关程度随气温升高而增加 ;降温耗能的 1℃效应量 ,北方大于南方。文章最后 ,利用气温距平与降温度日变率建立了夏季降温耗能评估模型。     

Exploring the spatial and temporal dynamics of land use in Xizhuang watershed of Yunnan, southwest China

Over the last two decades, China has introduced a series of agricultural and forestland use reforms, aiming to feed the largest population in the world and maintain ecological services locally and nationally. This paper studies the impacts of local government-driven reforestation on land use and land cover change, as well as its further impacts on livelihoods of upland farmers in Xizhuang watershed. An analysis of aerial photographs and ASTER satellite imagery from 1987 to 2002, respectively, showed that the forest has significantly increased at the expense of decreasing farmland. However, the monoculture reforestation of pine has caused both biophysical and socio-economic consequences. This case study also shows forestry decentralization in China remains incomplete. Land use and land cover change is also a political economic issue. Some of the reforms designed to protect forest resources have had a negative impact on rural livelihoods.     

A multidisciplinary multi-scale framework for assessing vulnerabilities to global change

Terrestrial ecosystems provide a number of vital services for people and society, such as food, fibre, water resources, carbon sequestration, and recreation. The future capability of ecosystems to provide these services is determined by changes in socio-economic factors, land use, atmospheric composition, and climate. Most impact assessments do not quantify the vulnerability of ecosystems and ecosystem services under such environmental change. They cannot answer important policy-relevant questions such as 'Which are the main regions or sectors that are most vulnerable to global change?’ 'How do the vulnerabilities of two regions compare?’ 'Which scenario is the least harmful for a sector?’This paper describes a new approach to vulnerability assessment developed by the Advanced Terrestrial Ecosystem Analysis and Modelling (ATEAM) project. Different ecosystem models, covering biodiversity, agriculture, forestry, hydrology, and carbon sequestration are fed with the same Intergovernmental Panel on Climate Change (IPCC) scenarios based on the Special Report on Emissions Scenarios (SRES). Each model gives insights into specific ecosystems, as in traditional impact assessments. Moreover, by integrating the results in a vulnerability assessment, the policy-relevant questions listed above can also be addressed. A statistically derived European environmental stratification forms a key element in the vulnerability assessment. By linking it to other quantitative environmental stratifications, comparisons can be made using data from different assessments and spatial scales.     

Land-use classification using ASTER data and self-organized neutral networks

Operationally AVHRR and TM/TM+ data were used and a supervised maximum likelihood classification (MLH) was applied to depict land use changes in Beijing, providing basic maps for planning and development. With rapid growth of the city these are helpful to deal with higher resolution data, whereas new classification algorithms produce land use maps more accurate. In the paper, new sensor ASTER data and the Kohonen self-organized neural network feature map (KSOM) were tested.The TSOM classified 7% more accurately than the maximum likelihood algorithm in general, and 50% more accurately for the classes ‘residential area’ and ‘roads’. The results suggest that ASTER data and the Kohonen self-organized neural network classification can be used as an alternative data and method in a land use update operational system.     

Development of a remotely sensed,historical land-cover change database for rural Chihuahua,Mexico

The purpose of this article is to describe the development of a remotely sensed, historical land-cover change database for the northwestern quarter of Chihuahua, Mexico, The database consists of multi-temporal land-cover classifications and change detection images. The database is developed to facilitate future investigations that examine urban–rural linkages as possible drivers of rural land-use and land-cover changes. To develop the needed land-cover change database, this study uses the North American Landsat Characterization (NALC) MSS triplicates because of their temporal depth and spatial breadth. Challenges exist, however, to effective classification and change detection using the NALC triplicates, including illumination differences across multiple scenes and periods caused by topographic and solar variations and the lack of ground reference data for historic periods. Therefore, creation of the database is a four step process. First, extensive pre-processing is performed to enhance comparability of multi-date images. Pre-processing includes topographic correction, mosaic creation and multi-date radiance normalization. Second, ancillary sources of land-cover data are combined with visual interpretations of enhanced images to define reference pixels used to classify the images using the maximum likelihood algorithm. Third, classification accuracy is assessed. Fourth, post-classification change detection is performed. Results indicate significant image improvements after pre-processing that permit very good overall classification (86.26% classified correctly) and change detection. To conclude, findings are presented that indicate significant changes to arid grasslands/shrublands and forest resources in mountainous regions.