被动微波遥感数据反演地表温度研究进展

被动微波遥感具有穿透云层( 甚至雨区) 获取地表辐射信息的能力, 在处理云的问题上, 明 显优于可见光、红外遥感, 可更好地服务于相关领域研究。由于微波信号受多种因素的影响, 目前 被动微波反演地表温度的算法还不成熟。根据建模手段和方法的不同, 从经验反演模型和物理反 演模型两方面入手, 系统回顾国内外被动微波数据反演地表温度的算法并加以评述, 指出今后应 加强尺度转换、物理机理以及多数据源结合方面的研究。     

区域尺度蒸散发遥感估算——反演与数据同化研究进展

遥感技术近年来在估算区域尺度蒸散发中应用广泛。不同方法在驱动数据、模型机理和适用范围往往存在很大差别。鉴于此,阐述了基于传统方法空间尺度扩展的遥感模型,经验统计公式,特征空间法,单源、双源垂向能量平衡余项法等几类的遥感蒸散发反演方法,简要介绍了三温模型、非参数化模型、半经验模型、集成模型等常用模型。同时,分析了遥感数据同化实现连续估算区域蒸散发的主要思路,综述了基于能量平衡和基于复杂过程模型的数据同化的原理、方法演进及常用同化算法等。最后,探讨了各类区域蒸散发遥感方法的优劣、展望了模型机理完善、不确定性研究、结果验证等与蒸散发直接反演和数据同化相关的研究方向。     

Coupling numerical simulation with remotely sensed information for the study of frozen soil dynamics

The acquisition of spatial-temporal information of frozen soil is fundamental for the study of frozen soil dynamics and its feedback to climate change in cold regions. With advancement of remote sensing and better understanding of frozen soil dynamics, discrimination of freeze and thaw status of surface soil based on passive microwave remote sensing and numerical simulation of frozen soil processes under water and heat transfer principles provides valuable means for regional and global frozen soil dynamic monitoring and systematic spatial-temporal responses to global change. However, as an important data source of frozen soil processes, remotely sensed information has not yet been fully utilized in the numerical simulation of frozen soil processes. Although great progress has been made in remote sensing and frozen soil physics, yet few frozen soil research has been done on the application of remotely sensed information in association with the numerical model for frozen soil process studies. In the present study, a distributed numerical model for frozen soil dynamic studies based on coupled water-heat transferring theory in association with remotely sensed frozen soil datasets was developed. In order to reduce the uncertainty of the simulation, the remotely sensed frozen soil information was used to monitor and modify relevant parameters in the process of model simulation. The remotely sensed information and numerically simulated spatial-temporal frozen soil processes were validated by in-situ field observations in cold regions near the town of Naqu on the East-Central Tibetan Plateau. The results suggest that the overall accuracy of the algorithm for discriminating freeze and thaw status of surface soil based on passive microwave remote sensing was more than 95%. These results provided an accurate initial freeze and thaw status of surface soil for coupling and calibrating the numerical model of this study. The numerically simulated frozen soil processes demonstrated good performance of the distributed numerical model based on the coupled water-heat transferring theory. The relatively larger uncertainties of the numerical model were found in alternating periods between freezing and thawing of surface soil. The average accuracy increased by about 5% after integrating remotely sensed information on the surface soil. The simulation accuracy was significantly improved, especially in transition periods between freezing and thawing of the surface soil.     

土壤盐渍化遥感应用研究进展

文章从地面数据的调查、盐渍土影象的目视判读特征、光谱特征和土壤盐渍化区域的植被特征以及多光谱、高光谱遥感技术等方面综述国内外应用遥感数据探测土壤盐渍化程度及其制图的研究。利用数字图象并结合野外调查数据进行目视解译和计算机自动解译、图象变换提取盐渍土信息;结合G IS方法在分类中加入非遥感数据来提高分类精度;在研究盐渍土的光谱特征的基础上应用高光谱技术定量或半定量地提取盐渍土信息。这都是制定综合治理措施、决定土地利用方向的关键,也是进行区域土壤盐渍化动态预报的重要依据。     

极地记录冰川和达尔克冰川流速的遥感监测研究

测定南极冰川的流速 ,对于研究南极冰雪物质平衡、南极环境变化及其对全球环境的影响有重大意义。本文论述了利用多时相卫星遥感影像监测冰川变化的原理和方法 ,通过对英格里特 .克里斯泰森海岸不同时期的遥感影像进行几何纠正和配准及叠加处理 ,测量和计算出极地记录冰川和达尔克冰川的平均流速 ,并对它们的变化特点进行了初步的分析和评价。     

应用“北京一号”遥感数据计算官厅水库库滨带植被覆盖度

“北京一号”卫星是拥有多方面技术优势的一颗对地观测小卫星。本文在运用“北京一号”、SPOT5、QuickBird遥感图像对官厅水库库滨带植被覆盖度进行综合监测的基础上,对三种不同分辨率的遥感图像进行基于统计的尺度转换,并应用尺度转换的结果修正了“北京一号”图像提取的植被覆盖度。经检验,运用SPOT5和QuickBird图像对“北京一号”图像进行像元分解,将统计结果与“北京一号”图像的提取信息建立统计模型,应用该统计模型可以有效地提高“北京一号”图像提取植被覆盖度的精度。对湿生植被进行样方调查,结果证明运用像元分解和统计模型的方法使“北京一号”提取植被覆盖度的精度较运用植被指数转换模型的计算精度提高了22.7%。应用该方法可以更有效地运用“北京一号”遥感数据进行连续、大面积的植被监测。     

定量遥感尺度转换方法研究进展

首先对遥感科学和气象学、水文学、生态学、地理学和的尺度概念及其转换方法进行了区分。其次对遥感数据空间尺度转换方法的国内外研究进展情况进行了系统梳理,重点比对了目前空间尺度转换常用6种转换方法：统计转换法、分类转换法、数据融合转换方法、分形分析法、基于局域动态模型的转换方法和基于物理意义尺度转换方法及其各自所属方法的优点和缺点。再次以遥感时间尺度转换应用最为广泛的地表蒸散发和农业旱情监测等2个领域为例,对遥感时间尺度转换方法进行了总结。最后预测了今后定量遥感尺度转换研究可能的研究重点,以期为今后更好地开展尺度效应和尺度转换研究工作提供参考。     

地学应用中的遥感图像处理若干问题的分析

遥感技术在地理学应用中是如何从遥感影像上直观、准确的得到所需的信息,为本专业服务。文章从地学应用部门在进行遥感影像处理时遇到的几个问题:多光谱数据的选取与合成;多源信息的复合;新型图像分类器的应用;专题提取的精度等方面的进展作了分析。     

SAMPLING REMOTELY SENSED IMAGERY FOR STORAGE,RETRIEVAL, AND RECONSTRUCTION*

A major difficulty in remote sensing is handling the many data from sensors aboard aircraft and satellites. In this paper we identify an optimal procedure for sampling remotely sensed data before their storage or on their retrieval. The procedure depends on spatial correlation in the scene and uses kriging to estimate values that have been lost. An example in which data from an airborne multispectral scanner could be diminished to only about one tenth without serious loss of precision illustrates the method.     

青海湖流域草地产草量与MODIS植被指数的相关性分析

借鉴国内外基于遥感手段监测植被的方法,利用MODIS-NDVI遥感影像数据,探讨如何对草地变化进行监测。提取青海湖流域典型草原遥感影像植被指数,并与野外调查获得的79个样方生物量数据进行对比分析,建立了多个回归模型用于研究植被指数与草地产草量的相关关系。结果表明,植被指数与草地产草量之间存在较好的相关性,但是不同的回归模型与草地产草量相关性程度存在一定的差别,非线性回归模型优于一元线性回归模型,该方法的提出有助于监测青海湖流域草地变化情况。     

Evaluation of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing

The transformation of land cover, in particular coniferous forest, constitutes one of the most notable agents of regional-to-global-scale environmental change. Remote sensing provides an excellent opportunity for providing forest cover information at appropriate spatial and temporal scales. The optimal exploitation of remote sensing relies on the link between known forest cover and the remotely sensed dataset. This paper explores the accuracy of three methods – vegetation indices, regression analysis and neural networks – for estimating coniferous forest cover across the United States Pacific Northwest. All methods achieved a similar accuracy of forest cover estimation. However, in view of the benefits and limitations of each, the neural network approach is recommended for future consideration.     

基于遥感的城市热岛效应研究

热红外遥感和气候学的交叉融合为城市热岛效应提供了新的研究方法。从城市热岛的空间分布与土地利用类型、植被覆盖关系、人为热关系以及城市热岛效应演变4方面分析热红外遥感在城市热岛研究中的应用现状;探讨目前存在的两个主要问题:城市地物真实温度难以获取,遥感和气候模型结合不密切;提出遥感和气候模型结合以及多平台、多尺度、多角度遥感的综合应用等是城市热岛效应遥感研究的发展趋势。     

基于多尺度空间ANN - CA模型的遥感影像超分辨率制图方法研究

为获取遥感影像混合像元中各组分的空间分布状况,提出一种新的遥感影像超分辨率制图方法,用于继混合像元分解之后的亚像元定位。将元胞自动机理论移植到不同空间尺度的演化上,建立基于神经网络的多尺度元胞自动机模型(ANN-CA),并利用该模型提取北京市海淀区城镇用地超分辨率信息。结果表明,该方法能有效表达图像像元之间的空间自相关性。     

遥感在土壤碳储量估算中的应用

土壤碳库是陆地生态系统碳库的主体,在全球碳平衡中具有重要作用。从20世纪70年代早期Landsat-MSS数据的使用开始,各种传感器的卫星多光谱测量开始广泛应用于土壤调查中。首先,分析了利用遥感方法估算土壤碳储量的可行性。之后,系统分析总结了国内外在土壤碳储量研究中的常用遥感方法,即遥感影像直接估算法、植被指数法和光谱测定分析法。最后,对遥感在土壤碳储量估算研究中的发展趋势做出展望。     

海洋悬浮泥沙二元特征参数MODIS遥感反演模型研究

本文提出了一种采用海面离水辐射率和泥沙粒径二元特征参数来反演研究海区(渤海)海洋表层悬浮泥沙浓度的新的遥感反演算法,以此为基础分别建立了基于MODIS遥感数据和泥沙粒径二元特征参数的主成分和神经网络两种泥沙浓度反演模型,并对比分析了两类模型的反演精度以及泥沙粒径因子对模型的影响。分析结果表明,新建立的二元特征参数反演算法在采用主成分模型和神经网络模型时的检验误差分别为0.256和0.244,而忽略泥沙粒径因子贡献的主成分模型和神经网络模型的检验误差分别为0.384和0.390,因此可以认为,在泥沙浓度反演模型中加入粒径因子时,模型的预测精度和模型稳定性均比只考虑浓度对反射率贡献的模型有显著改善。     

国际草地资源遥感研究新进展

系统地评论了国际上有关草地资源遥感研究的最近文献。首先,分析了草地遥感的可行性,确定了监测和评估草地资源最有效的光谱波段,阐述了各种遥感影像在草地资源调查与评估中的实用价值。之后,介绍了草地资源遥感的常用方法,即植被指数和其他指数法,对比和分析了各种指数在草地资源遥感应用中的有效性及应用范围。这些草地资源遥感的应用包括草地盖度监测与制图、生物量估算、草地退化监测及草地资源定量分析。最后,探讨了草地资源遥感的发展趋势,包括摄像遥感、高分辨率影像(如CASI,AVIRIS和IKONOS)和GPS的运用。此外,GIS的引入及其与数字影像处理的集合会使草地资源遥感由简单监测逐渐向动态预报和模拟过渡。这些研究新动向对国内相关科学家在选题时具有极大的参考意义。     

河流水情要素遥感研究进展

河流是促进地貌形成和演化的重要因素,也是地球上最重要的淡水资源之一。河流的水体范围、水位/水深、流量、水质和冰情等水情要素在水资源动态监测和水文生态环境保护等研究中具有至关重要的作用。通过水文站观测河流可获得站点上的水情信息,但该方式在财力、物力方面均耗费巨大。同时,对于日益增长的河流水情信息需求,迫切需要一种能够快捷、准确补充河流实测数据的方法。遥感凭借全方位、多时相的对地观测能力极大地提升了河流水情信息获取的效率,已广泛应用于多尺度水情监测、无资料流域水文模拟等多个方面,取得了丰硕的研究成果。因此,有必要对近年来遥感应用于河流水情要素反演所取得的进展进行归纳、总结和展望,以期进一步促进遥感数据和方法在该领域的应用。论文以“河流水情要素遥感”为主题,系统归纳了河流水体遥感提取的常用传感器及方法、水位/水深遥感反演方法、河道流量遥感估算、河流水质及河冰遥感监测的研究进展,详细归纳了利用光学/微波遥感等不同类型的遥感手段获取河流水体范围、水位、水深的优势与不足,总结得出了河流水情要素遥感研究的数据、方法及应用方面的主要结论：① 新型遥感数据在河流水情要素监测中的应用愈加广泛,在空间分辨率、时间分辨率或光谱分辨率等方面不断突破,进一步丰富了河流水情要素研究的数据来源;② 多数中低空间分辨率光学影像依旧面临混合像元影响水体提取精度的问题,合成孔径雷达影像也存在数据处理算法开发难度大的问题,细小河流及非开阔水体的提取方法仍需探索;③ 大数据和云计算技术的发展为实现大尺度、长时序的河流水情要素高时空分辨率遥感监测提供了高效手段。     

Modelling patterned ground distribution in Finnish Lapland: an integration of topographical, ground and remote sensing information

New data technologies and modelling methods have gained more attention in the field of periglacial geomorphology during the last decade. In this paper we present a new modelling approach that integrates topographical, ground and remote sensing information in predictive geomorphological mapping using generalized additive modelling (GAM) . First, we explored the roles of different environmental variable groups in determining the occurrence of non‐sorted and sorted patterned ground in a fell region of 100 km2 at the resolution of 1 ha in northern Finland. Second, we compared the predictive accuracy of ground‐topography‐ and remote‐sensing‐based models. The results indicate that non‐sorted patterned ground is more common at lower altitudes where the ground moisture and vegetation abundance is relatively high, whereas sorted patterned ground is dominant at higher altitudes with relatively high slope angle and sparse vegetation cover. All modelling results were from good to excellent in model evaluation data using the area under the curve (AUC) values, derived from receiver operating characteristic (ROC) plots. Generally, models built with remotely sensed data were better than ground‐topography‐based models and combination of all environmental variables improved the predictive ability of the models. This paper confirms the potential utility of remote sensing information for modelling patterned ground distribution in subarctic landscapes.     

EFFECT OF VIEWING GEOMETRY ON REMOTELY SENSED NET RADIATION MEASUREMENTS FOR VEGETATED AND NON-VEGETATED SURFACES

Increased use of remotely sensed data for estimation of land-surface climatological variables such as the radiation and energy balance has underscored the need to understand errors inherent in remote sensing systems. The effects of viewing geometry on the signal received at the sensor have been examined for a variety of surface types, but systematic study of how sensor viewing geometry may introduce error into remote measurements of surface climate parameters is lacking. In this paper, close-range remote sensing is used to assess error caused by viewing geometry in remote measurements of net radiation, an important surface climate variable. Estimates of net radiation were made by inserting remotely sensed values of albedo and surface temperature into the surface radiation budget equation. Two contrasting surfaces, one vegetated and one bare, were evaluated at seven view angles and five azimuth angles. The remote net radiation measurements for each combination of view and azimuth angle then were compared to reference measurements to assess error. Results indicate that, under cloud-free conditions, view angle can affect measurement accuracy substantially, and that off-nadir view angles ranging from 15° to 30° in the forward-scatter direction are optimal. The potential impact of these findings on studies of the spatial variation of the surface radiation budget from spaceborne remote sensing platforms is discussed. [Key words: land-surface climatology, remote sensing, radiation budget, net radiation.]     

A rough set approach to the discovery of classification rules in spatial data

This paper proposes a novel rough set approach to discover classification rules in real‐valued spatial data in general and remotely sensed data in particular. A knowledge induction process is formulated to select optimal decision rules with a minimal set of features necessary and sufficient for a remote sensing classification task. The approach first converts a real‐valued or integer‐valued decision system into an interval‐valued information system. A knowledge induction procedure is then formulated to discover all classification rules hidden in the information system. Two real‐life applications are made to verify and substantiate the conceptual arguments. It demonstrates that the proposed approach can effectively discover in remotely sensed data the optimal spectral bands and optimal rule set for a classification task. It is also capable of unraveling critical spectral band(s) discerning certain classes. The framework paves the road for data mining in mixed spatial databases consisting of qualitative and quantitative data.