基于GOCI影像和水体光学分类的内陆湖泊叶绿素a浓度遥感估算

叶绿素a作为水质参数之一,常用来作为衡量水体富营养化程度的指示标准.利用从太湖及洞庭湖获取的326个实测数据,基于实测遥感反射率对水体光谱进行光学分类,结果表明所采集的样点可分为3种水体类型.结合GOCI的波段设置,建立了不同类型水体的叶绿素a浓度反演模型.水体类型一可以利用490 nm(3波段)和555 nm(4波段)来反演,水体类型二可利用660 nm(5波段)和443 nm(2波段),水体类型三利用745 nm(7波段)和680 nm(6波段).精度分析表明,分类后的平均相对误差明显下降,类型一为38.91%、类型二为24.19%、类型三为22.90%;类型一均方根误差为4.87μg·L-1、类型二为8.13μg·L-1、类型三为11.66μg·L-1;分类前后的总体平均相对误差由49.78%降低到29.59%,总体均方根误差由14.10μg·L-1降低到9.29μg·L-1,分类后反演精度得到了显著提高.利用2013年5月13日8景GOCI影像反演了太湖的叶绿素a浓度,结果表明,2013年5月13日太湖叶绿素a浓度日变化显著,高值区主要集中在竺山湾、梅梁湾、贡湖湾,低值区主要集中在湖心区以及南部区域,10:00以后太湖西南部沿岸的叶绿素a浓度显著降低.这种先分类后反演的方法对于二类水体的模型反演精度的提高具有重要作用.     

牡丹江地区退耕还林前后土地利用景观格局变化分析

选用2000年和2008年TM遥感影像进行牡丹江地区退耕还林前后土地利用景观格局变化分析。研究结果表明：退耕还林后研究区生态环境有很大程度改善,景观格局指数反映出研究区景观异质性上升,结构趋于均衡化,稳定性增强,受人类干扰程度在加大,自然和半自然植被的高功能景观的生态环境基底作用不断增强。     

常用卫星图像数据光谱变换新方法

阐述了卫星图像提取数据信息的光谱变换法的基本原理和方法,并详细介绍了各种卫星图像的数据变换方程。     

基于机器学习遥感算法的大型漂浮藻面积提取方法研究

采用基于机器学习的多层感知机算法,利用GOCI(Geostationary Ocean Color Imager)传感器获取的瑞利校正反射率数据,对东中国海大型漂浮藻进行遥感自动识别,采用线性混合像元分解来计算大型漂浮藻的覆盖面积,并利用膨胀和侵蚀法进行大型漂浮藻的分布面积计算。利用L8/OLI(Landsat 8/Operational Land Imager)高空间分辨率资料进行验证,结果表明,基于机器学习遥感算法针对GOCI提取的大型漂浮藻覆盖面积,与L8/OLI结果十分接近,R2达到0. 959,平均绝对误差和平均相对误差分别为39. 32 km2和18. 15%。     

典型暴雨泥石流堆积扇危害范围演变规律

以小江流域蒋家沟、大白泥沟为研究区,利用1987~2014年陆地卫星影像数据,采用面向对象自动解译和人工目视解译相结合的方法,生成两条泥石流沟不同时期堆积扇危害范围数据集产品;结合地形资料,分析不同发育期泥石流堆积扇危害范围变化情况及其影响因素。分析表明:(1)1987~2014年,蒋家沟、大白泥沟泥石流堆积扇危害面积出现退缩趋势。(2)处于壮年期的大白泥沟流域堆积扇退缩幅度小,处于老年期的蒋家沟泥石流堆积扇退缩幅度大,且已趋于稳定。(3)水源、物源和人类活动是蒋家沟、大白泥沟堆积扇危害范围变化的主要影响因素。     

基于RS和GIS技术的重庆市主城区扩展分析

采用重庆市主城区2000年和2010年两个时相的TM影像提取城市扩展信息,对影像数据的归一化建筑指数（NDBI）、归一化植被指数（NDVI）和归一化水体指数（NDWI）进行RGB组合增强,再进行决策树分类和减法运算获取研究区扩展信息,发现建筑区面积增加了1.62倍,大部分是由植被区转变而来。其扩张方向受地理影响,南北方向为外延型,东西方向为飞地型。借助统计资料进行精度验证和城市扩展的驱动因素分析发现,区域发展政策、经济增长和人口扩张是主要的影响因素,探讨了对经济增长、城市扩张和人口增长的关系。研究结果表明,以遥感手段监测与研究城市发展变化的趋势是可行和高效的。     

用于研究土壤孔隙三维结构的连续数字图像的制备

主要介绍了一种用于分析土壤孔隙三维结构的连续数字图像的制备方法,内容包括土样的处理、不同格式图像之间的转换、孔隙结构分析指标等;对一类具体土壤的研究结果表明,在2cm的间距内,土壤孔隙结构仍存在较为明显的异质性。     

Application of remote and in situ information to the management of wetlands in Poland

The protection and regeneration of wetlands has been of crucial importance as a goal in ecological research and in nature conservation for some time and is more important than ever now. Knowledge about the biophysical properties of wetlands' vegetation retrieved from satellite images enables us to improve the monitoring of these unique areas, which are otherwise very often impenetrable and therefore difficult to examine, analyze and assess by means of site visits. The Biebrza Wetlands are situated in the North-East part of Poland and are one of the largest areas made up of marshes and swamps in the entire EU. This is still one of the wildest areas and one of the least destroyed, damaged or changed by human impact. However, in the recent decades there have been attempts made to intensify and overexploit the natural resources of the region and implement new agriculture practices in the area. In this period, drainage canals have been built, and a good deal of the area has been drained. The area of this precious ecosystem covers 25 494 ha. This valuable area of peat with unique vegetation species and with very special birds is one of the most valuable areas in Europe and in 1995 was added to the list of Ramsar sites. The investigation of wetlands in the Biebrza River Valley has been carried out at ground level by taking measurements of soil moisture, evapotranspiration, Leaf Area Index, wet and dry biomass and the levels of ground water and meteorological parameters. Also examined were radiative temperature, detailed vegetation mapping, and APAR. For some years the deterioration of peat lands has been noticed due to the drying out of the area and the frequent outbreak of fires. The consequence is the succession of new vegetation and the appearance of new ecosystems. The Remote Sensing Centre in the Institute of Geodesy and Cartography has undertaken the investigation by applying ERS-2.SAR and ENVISAT ASAR of IS2 and IS4 and VV, HH, HV polarization for the purpose of modeling soil moisture and humidity changes of the area under investigation. The investigation also aimed at finding the best biophysical properties of wetlands' vegetation to characterize marshland habitats and its changes. At the same time as registering the microwave data, the optical data from Landsat ETM+, SPOT VEGETATION, ERS-2.ATSR, ENVISAT MERIS, and NOAA/AVHRR have been registered and information about the biomass and heat fluxes as sensible and latent heat has also been calculated. The vegetation indices are calculated from EO satellite data taking into account jointly the features of vegetation responsible for reflection in various bands and combining this information from several spectral bands. Also, the changes in the humidity of the area have been examined by extracting the backscattering coefficients from two SAR images that were taken at a similar period of the year but with a gap of 5 years. The information about soil moisture as retention, soil moisture changes, heat fluxes and evapotranspiration are all very important for estimates of CO(2) sequestration. The ENVISAT images have been obtained for the ESA AO-ID122 project. Also the SMOS and ALOS data will be applied for the Biebrza Wetlands in the future.     

论文化意象的翻译

文化意象是一种文化符号,承载着独特、广阔、深沉的文化内涵,不同民族的文化差异导致的文化意象的空缺与错位往往使译者处于两难的境地.本文阐述了翻译文化意象应遵循的首要原则,即采取异化的方法直接移植原文文化意象.但这一原则的使用又必须受到译文读者审美意识的制约.     

基于洪水过程的农业洪灾变化遥感快速评估模型及其应用

洪涝灾情的准确测度需要同时兼顾淹没区的面积大小和淹水时长信息。利用淹没区内由水和作物等多种地物所组成的"复合水体"不同于水体的波谱时间变化特性,将不同洪灾时期的水体指数和植被指数进行信息复合,以有效凸显水体和洪涝淹没区之间的影像差异,据此进行了灾初期、峰期和中后期等3个时次受淹范围的有效识别。在此基础上,根据洪涝灾情随着淹没时长而加重以及灾区内淹水时长非均匀分布的特性,建立基于淹没时长的受淹面积不等权参与的洪灾扩展动态度指数(Variation Index of Flood,VIF)和区域灾情比较指数(Comparison Index of Flood Disaster,CIFD)两种模型,并将模型应用于鄱阳湖区2016年夏季农业洪涝灾害的时空变化遥感监测。结果显示,应用上述两种模型不仅可以准确获取鄱阳湖区本次农业洪涝灾情的演变趋势,而且能够方便地对比分析区域内不同地方的受灾程度。鄱阳湖区在2016年6月23日~7月25日期间的洪涝灾情具有由弱增强再趋弱的特征,其VIF指数由初始阶段(6月23日~7月9日)的3.75降至后续阶段(7月9日~7月23日)的1.29;鄱阳县是研究区内受灾最严重的区域,其CIFD指数值居于研究区内各受灾县市之首,该县受灾总面积以及多次被淹的灾区面积均高于其他县市。