基于直射辐射遥感图象的地物双向反射特性研究(英文)

该文讨论了一种在ＧＩＳ支持下利用直射辐射遥感图象研究地物双向反射特性的原理和方法。其主要依据是象元地面的坡度和坡向的不同，同时改变了象元地面与太阳之间、象元地面与卫星传感器之间的相对位置，并且造成了同类象元太阳直射辐射遥感分量的差异。而正是这种差异体现了该类地物双向反射特性。在ＧＩＳ的支持下利用卫星直射辐射遥感数字图象，研究卫片象元同类地物的这种差异的规律以及它们与对应坡面上的太阳位置、卫星位置的关系，便可进行该类地物的双向反射特性研究。     

基于两种无人机航拍影像的林窗和林冠提取分析

森林冠层和林窗的结构及其时空变化是理解森林生态系统格局、动态变化过程的重要基础。在当前生物多样性监测倍受关注的契机下,如何以合适的手段准确描述林窗面积、分布等特征,并与森林固定样地监测数据有效地结合,更好地回答群落构建的理论问题,使森林群落物种多样性维持机制得到更全面的认识,是目前亟待解决的问题。以鼎湖山南亚热带常绿阔叶林20hm2固定监测样地为研究对象,基于不同遥感影像提取方法对其林窗和林冠表层数据进行提取分析。结果表明:基于监督分类的提取方法适合RGB波段航片林窗的提取,在林窗分类中,应首先确定林窗高度、边界木与最小面积,不同分类方法差异主要表现在林冠分类中,林窗分类生产者精度和用户精度表现都较为一致。无人机航拍识别率受地形因素影响较大,在地形复杂林地应按坡度分区域进行飞行以降低误差。相对于地面调查,MD4-1000无人机航片的林窗识别率为98.7%;大疆Phantom4无人机航片的林窗识别率为72.3%,影像后期处理数据量小,同样适用于森林林窗定量研究,符合生态学、林业等从业人员对大型样地林窗长期监测的要求。无人机航拍南亚热带森林物种识别难度较大,基于MD4-1000无人机搭载的高分辨率相机,在地势平缓区域优选的4 hm2样地中可识别林冠表层物种数17种,共2 706个个体。搭载高分辨率无人机在降低飞行高度的基础上可进行部分物种识别。应用无人机近地面遥感对森林固定样地进行林冠监测,可为后期群落构建研究提供数据基础,有望从新的研究角度探讨森林群落物种多样性维持机制。     

Exploring uncertainty in remotely sensed data with parallel coordinate plots

The existence of uncertainty in classified remotely sensed data necessitates the application of enhanced techniques for identifying and visualizing the various degrees of uncertainty. This paper, therefore, applies the multidimensional graphical data analysis technique of parallel coordinate plots (PCP) to visualize the uncertainty in Landsat Thematic Mapper (TM) data classified by the Maximum Likelihood Classifier (MLC) and Fuzzy C-Means (FCM). The Landsat TM data are from the Yellow River Delta, Shandong Province, China. Image classification with MLC and FCM provides the probability vector and fuzzy membership vector of each pixel. Based on these vectors, the Shannon's entropy (S.E.) of each pixel is calculated. PCPs are then produced for each classification output. The PCP axes denote the posterior probability vector and fuzzy membership vector and two additional axes represent S.E. and the associated degree of uncertainty. The PCPs highlight the distribution of probability values of different land cover types for each pixel, and also reflect the status of pixels with different degrees of uncertainty. Brushing functionality is then added to PCP visualization in order to highlight selected pixels of interest. This not only reduces the visualization uncertainty, but also provides invaluable information on the positional and spectral characteristics of targeted pixels.     

Vision-based tracking with projective mapping for parameter identification of remotely operated vehicles

Parameter identification of a remotely operated vehicle (ROV) is often based on the dynamic responses collected by its onboard sensors. However, for commercial ROVs, the required data for identification may not be available due to the absence of suitable sensors or limitations in accessing onboard sensor data. Therefore, this study proposes a vision-based tracking system to measure the dynamic response of an ROV. The tracking system is independent of the ROV, and is able to localize an ROV to a high degree of precision by means of projective mapping. The validity of the proposed tracking system is demonstrated through identification of a commercial ROV. A simplified nonlinear ROV dynamic model with six degrees of freedom (DOF) is used for identification. Uncoupled motions, including surge, sway, and yaw, are obtained from the ROV dynamic model, and the corresponding experiments are carried out for each DOF. Hydrodynamic parameters are then estimated with numerical optimizations by comparing the measured ROV responses with the output of the mathematical model. The experimental results show that the vision-based tracking system can accurately measure the transient and steady-state responses of an ROV. Additionally, the simulations of the ROV dynamic model, with the optimal parameter estimates, give results in agreement with the measured data.     

A Comparison between a Modified Counter Propagation Network and an Extended Self-Organizing Map in Remotely Sensed Data Classification

A modified counter propagation network model and an extended self-organizing map model have the same three-layer network architecture while employing slightly different learning rules. Their network architecture comprises an input layer, a Kohonen layer and an output layer. The neurons between two neighboring layers are fully connected and the neighboring neurons within the Kohonen layer also have neighborhood connections. The modified counter propagation network model employs the Kohonen algorithm to train the Kohonen layer while using the Widrow–Hoff rule to train the output layer. However, the extended self-organizing map model applies a modified Kohonen’s learning rule to train both the Kohonen layer and the output layer. This paper compares the performances of these two models in supervised classification of remotely sensed data. The training results show that compared to the extended self-organizing map model, the modified counter propagation model has faster learning speed but larger output errors. The classification results indicate that the extended self-organizing map model has a faster classification speed and a much higher classification precision than the modified counter propagation model.     

基于神经网络的遥感影像分类方法研究

本文在研究BP神经网络和模糊理论的基础上,提出了传统BP算法的一种改进方法和基于模糊系统的神经网络遥感影像分类方法。通过试验表明:基于模糊技术的神经网络分类方法要优于BP神经网络方法,取得了令人满意的效果。     

自主技术无人机航空物探(磁/放)综合站研发进展

无人机航空物探技术是航空物探技术的一个新兴分支。文章首先阐述了无人机航空物探技术的国内外研发情况,然后介绍了基于国产彩虹3无人机的国内首套应用于地球物理勘查的无人机航空物探(磁/放)综合站的研究进展。基于国产彩虹3无人机平台,开展了专用飞机改装、超低空地形跟随飞控、远距离遥控遥测等关键技术攻关,集成研发了自主知识产权的无人机航空物探(磁/放)综合站样机,成功开展了面积性应用试验,综合站样机功能、性能等达到了世界先进水平。无人机航空物探(磁/放)综合站研发取得关键进展。     

Quantifying the variability and allocation patterns of aboveground carbon stocks across plantation forest types,structural attributes and age in sub-tropical coastal region of KwaZulu Natal,South Africa using remote sensing

Quantifying the variability and allocation patterns of aboveground carbon stocks across plantation forests is central in deriving accurate and reliable knowledge and understanding of the extent to which these species contribute to the global carbon cycle and towards minimizing climate change effects. The principal objective of this study was to quantify the variability and allocation patterns of aboveground carbon stocks across Pinus and Eucalyptus plantation forests, tree-structural attributes (i.e. stems, barks, branches and leaves) and age groups, using models developed based on remotely sensed data. The results of this study demonstrate that aboveground carbon stocks significantly (α = 0.05) vary across different plantation forest species types, structural attributes and age. Pinus taeda and Eucalyptus grandis species contained aboveground carbon stocks above 110 t C ha⁻¹, and Eucalyptus dunii had 20 t C ha⁻¹. Across plantation forest tree structural attributes, stems contained the highest aboveground carbon stocks, when compared to barks, branches and leaves. Aboveground carbon stock estimates also varied significantly (α = 0.05) with stand age. Mature plantation forest species (i.e. between 7 and 20 years) contained the highest aboveground carbon stock estimates of approximately 120 t C ha⁻¹, when compared to younger species (i.e. between 3 and 6 years), which had approximately 20 t C ha⁻¹. The map of aboveground carbon stocks showed distinct spatial patterns across the entire study area. The findings of this study are important for understanding the contribution of different plantation forest species, structural attributes and age in the global carbon cycle and possible climate change moderation measures. Also, this study demonstrates that data on vital tree structural attributes, previously difficult to obtain, can now be easily derived from cheap and readily-available satellite data for inventorying carbon stocks variability.     

野马河—大雪山断裂肃北段晚第四纪活动特征研究

前人对野马河—大雪山断裂肃北段的晚第四纪活动特征研究相对薄弱,缺少有关断层最新活动的证据。本文在高分辨率遥感解译的基础上,对肃北断裂开展全段1∶5 000的地质地貌填图,通过探槽开挖、放射性碳(~(14)C)测年以及低空无人机摄影测量(small Unmanned Aerial Vehicles,简称sUAV)等工作,对该断裂晚第四纪活动性进行系统研究。发现肃北断裂为一条全新世的逆冲活动断裂,晚第四纪以来具有多期活动,西水沟探槽剖面揭示出2次古地震事件,古地震离逝时间为(4 192.5±97.5)a B.P.之后。