国内外赤潮卫星遥感技术与应用进展

卫星遥感已成为赤潮探测与监测的重要技术手段之一。回顾了国内外赤潮卫星遥感技术与应用的进展,对国内外已开发的赤潮卫星遥感技术与方法进行了简要的评述,并给出了国内利用NOAA和SeaStar卫星探测与监测赤潮的3个实例。讨论了我国赤潮卫星遥感存在的问题,提出了发展我国赤潮卫星遥感技术与应用的建议。     

赤潮卫星遥感监测与应用研究进展

赤潮监测是海洋遥感应用的一个重要组成部分。近30 a来,国内外学者发展了许多基于卫星平台的赤潮遥感监测算法。通过回顾赤潮卫星遥感监测的发展历史,总结了各种传感器的赤潮卫星遥感监测算法,分析了各种算法的性能与适用范围。最后,对目前赤潮卫星遥感监测面临的问题进行了讨论,并对未来赤潮遥感监测的发展提出展望。     

应用MODIS进行赤潮遥感监测的研究进展

赤潮遥感监测是卫星遥感应用的重要领域。发射和应用时间相对较短的MODIS传感器具有相对的光谱分辨率和空间分辨率的优势,使得MODIS在赤潮遥感监测上发挥巨大的作用。分析总结了应用MODIS进行赤潮遥感监测的几种主要方法,并与其它几种主要传感器进行比较,分析了MODIS在赤潮遥感监测中的优势和不足。     

时间序列MODIS数据赤潮信息提取研究

MODIS数据在赤潮遥感监测中发挥着重要作用,其较高的光谱分辨率和时间分辨率在赤潮监测中有较大优势。本文利用2008年5月份多个时相MODIS数据对东海赤潮进行了研究,分别采用叶绿素浓度固定阈值法、月平均值差值法和海温及多光谱综合法对水体异常信息进行提取分析。通过对提取图像及其统计数据比较分析发现,固定阈值法所提取的信息会随阈值设置不同而变化,海温及多光谱综合法在有赤潮发生时提取信息与其他方法较一致,在未发生赤潮时则会发生多提误判。叶绿素浓度月平均值差值法所提取的时间序列赤潮信息则最客观地反映了赤潮变化趋势,能够很好地用于东海赤潮遥感监测。     

新型赤潮监测系统设计研究

基于电化学原理设计了赤潮监测系统,用于赤潮生消过程现场原位连续监测,为建立赤潮预测方法和研究赤潮发生机理提供必要的资料基础。在分析赤潮成因的基础上,选择温度、pH、H2S、DO、盐度、浊度作为赤潮监测的主要环境因子。赤潮监测系统由传感器组/调理电路、数据采集/处理模块和GPRS终端组成,文中给出了软硬件设计和调试结果。     

基于遥感技术的海岸带资源环境管理

海岸带是人口密集、资源丰富、开发程度较高,但生态环境又往往相对脆弱的地区。随着我国沿海地区经济高速发展,海岸带区域的环境状况发生了显著变化。这些变化信息依靠常规的调查手段难以及时获取,而卫星遥感技术则能够为监测海岸带及毗连海域资源环境变化提供有效手段。如何利用多源、多通道、多时相、主被动融合的星载遥感数据获取海岸带及毗连海域资源环境信息是我们亟需了解的问题。分别从海洋渔业资源调查、珊瑚礁及红树林等海岸生物资源的遥感、海洋水质监测、赤潮的监测与预警、溢油和海冰的监测等方面论述了遥感技术的特点与优势。并对遥感技术在海岸带资源环境管理中的应用做了展望。     

基于遥感技术的海岸带资源环境管理

海岸带是人口密集、资源丰富、开发程度较高,但生态环境又往往相对脆弱的地区.随着我国沿海地区经济高速发展,海岸带区域的环境状况发生了显著变化.这些变化信息依靠常规的调查手段难以及时获取,而卫星遥感技术则能够为监测海岸带及毗连海域资源环境变化提供有效手段.如何利用多源、多通道、多时相、主被动融合的星载遥感数据获取海岸带及毗连海域资源环境信息是我们亟需了解的问题.分别从海洋渔业资源调查、珊瑚礁及红树林等海岸生物资源的遥感、海洋水质监测、赤潮的监测与预警、溢油和海冰的监测等方面论述了遥感技术的特点与优势.并对遥感技术在海岸带资源环境管理中的应用做了展望.     

土壤养分遥感监测研究现状及展望

土壤肥力状况信息可以为精准农田管理提供响应依据,对近年来遥感技术在农田土壤养分监测方面的研究进行了归纳与分析,总结为直接监测方法和间接监测方法。直接监测方法主要利用土壤光谱特征监测土壤养分含量,而间接监测方法则通过不同土壤养分水平下的作物表现特征来估算土壤养分状况。在全面比较两种方法各自优缺点的基础上,指出两种方法各有优势并可互为补充,通过结合可以有效提高监测的精度与全面性,并对土壤养分遥感监测的发展进行了展望。     

遥感技术在区域水文地质调查中应用研究

在区域水文地质调查中应用遥感技术，可以获取地貌、第四纪地质、地质灾害、地质构造、水文地质要素等多方面的信息。为编制地质图，第四纪地质图及水文地质图提供基础资料，由于采用了遥感技术，提高了水文地质填图的效率，减少了野外地质工作量，节约了调查工作成本，提高了调查成果质量。本文以河北省1：20万区域水文地质普查遥感应用为例，总结了遥感技术应用的路线和方法，并对未来遥感水文地质应用进行了展望。     

秸秆焚烧遥感监测进展分析与展望

秸秆焚烧是生物质燃烧的重要组成部分,不仅导致秸秆资源浪费,而且还会对环境造成严重危害。传统秸秆焚烧监测方法以人工巡查为主,监测范围受限且人力物力资源耗费大。遥感技术作为新兴的地表信息监测手段,给秸秆焚烧大范围监测带来了发展契机。介绍了遥感技术在秸秆焚烧火点监测、过火面积估算和焚烧迹地监测3个方面的基本原理、监测方法和研究进展,并分析了遥感技术在秸秆焚烧监测应用中存在的不足。在此基础上,从多源数据融合互补、监测方法优化集成、监测信息深入挖掘和时空信息决策服务等4个方面对秸秆焚烧遥感监测的未来发展进行了展望。     

RTDNet：面向高分辨率卫星影像的赤潮探测网络

目的 赤潮是一种常见的海洋生态灾害,严重威胁海洋生态系统安全。及时准确获取赤潮的发生和分布信息可以为赤潮的预警和防治提供有力支撑。然而,受混合像元和水环境要素影响,赤潮分布精细探测仍是挑战。针对赤潮边缘探测的难点,结合赤潮边缘高频特征学习与位置语义,提出了一种计算量小、精度高的网络模型RTD-Net （red tide detection network）。方法 针对赤潮边缘探测不准确的问题,设计了基于RIR （residual-in-residual）结构的网络,以提取赤潮边缘水体的高频特征;利用多感受野结构和坐标注意力机制捕获赤潮水体的位置语义信息,增强赤潮边缘水体的细节信息并抑制无用的特征。结果 在GF1-WFV （Gaofen1 wide field of view）赤潮数据集上的实验结果表明,所提出的RTDNet模型赤潮探测效果不仅优于支持向量机（support vector machine,SVM）、U-Net、Deep-Labv3+及HRNet （high-resolution network）等通用机器学习和深度学习模型,而且也优于赤潮指数法GF1_RI （Gaofen1 red tide index ）以及赤潮探测专用深度学习模型RDU-Net （red tide detection U-Net）,赤潮误提取、漏提取现象明显减少,F1分数在两幅测试图像上分别达到了0.905和0.898,相较于性能第2的模型DeepLabv3+提升了2%以上。而且,所提出的模型参数量小,仅有2.65 MB,约为DeepLabv3+的13%。结论 面向赤潮探测提出一种基于RIR结构的赤潮深度学习探测模型,通过融合多感受野结构和注意力机制提升了赤潮边缘探测的精度和稳定性,同时有效降低了计算量。本文方法展现了较好的应用效果,可适用于不同高分辨率卫星影像的赤潮探测。     

Spectral unmixing of Landsat Thematic Mapper data

The observed spectral signature of pixels in remote sensing imagery in most cases is the result of the reflecting properties of a number of surface materials constituting the area of a pixel. Despite this knowledge most image classification techniques aim at labelling a pixel according to a singular surface category. An alternative product can be generated using spectral unmixing: a technique that strives to find the surface abundances of a number of spectral components together causing the observed spectral reflectance at a pixel. A stepwise approach to implement spectral unmixing in Landsat Thematic Mapper image analysis is proposed: (1) atmospheric calibration of the image data, (2) preselection of a large number of ‘candidate’ endmembers, (3) reduction to the most important spectral endmembers using spectral angle mapping, (4) finding the relative abundances of the endmembers through spectral unmixing analysis, (5) combining the abundance estimates into a final product comparable to a classified image, and (6) accuracy assessment. A Landsat Thematic Mapper image from southern Spain covering a large peridotite body with adjacent limestone and low-grade metamorphic rocks is used as an example to demonstrate the usefulness of unmixing.     

High-precision extraction of nearshore green tides using satellite remote sensing data of the Yellow Sea,China

Since 2007, a massive floating green tide has occurred repeatedly in the Yellow Sea. The work involved in monitoring and clearing such a phenomenon requires highly precise spatial information. Satellite remote sensing data can provide such detail but they can be limited by long revisit periods and small-scale observational coverage. Therefore, in operational monitoring of green tides, Moderate Resolution Imaging Spectroradiometer (MODIS) data have an important role because of their short revisit period (twice daily) and large-scale observational coverage (viewing swath width: 2330 km). However, errors exist in MODIS-derived areal detection of green tides because its coarse spatial resolution (250–1000 m) can lead to mixed-pixel problems (i.e. in many cases, the limit of spatial resolution results in single pixels covering multiple ground objects). In this study, a method for spectral unmixing was developed for remote green tide detection. This method is based on mathematical morphology, and it integrates both the spatial and spectral characteristics of the image in the process of endmember extraction. Experiments using simulated and actual MODIS images were undertaken to illustrate the advantages of the proposed method. Compared with traditional linear unmixing methods, the abundance and area of green tides extracted by the proposed method were closer to the ‘real’ condition. The average relative deviation of green tide area decreased from 84.0% to 21.0%. The results illustrate that the proposed method is able to work efficiently and that it can provide improved information on green tide abundance and area.     

基于EXCEL的高光谱影像光谱响应分析

高光谱遥感侧重于从光谱维角度对影像信息进行分析与处理。由于目前高光谱数据的处理技术跟不上数据获取技术,而已有的成熟的多光谱影像处理技术并不适合于处理高光谱数据,因此利用EXCEL软件展开了高光谱影像的地物光谱重建、光谱特征及其相关性分析、光谱微分计算、光谱向量相似性度量和信息提取等研究,并基于PHI（Pushbroom Hyperspectral Imager）航空高光谱影像像元光谱维矢量进行了光谱响应分析,实现信息监测和识别。     

An approach based on discrete wavelet transform to unsupervised change detection in multispectral images

Change vector analysis (CVA) and Spectral Angle Mapper (SAM) are widely used for change detection in multitemporal multispectral images. CVA and SAM describe the difference from the perspective of vector magnitude and spectral angle, respectively. It has been proved that three change categories may occur in a changed pixel; however, CVA or SAM alone can only detect two of the three change categories properly. Hence, we propose a novel approach integrating the advantages of them to acquire a better change map. This approach, based on discrete wavelet transform (ABDWT, i.e. approach based on discrete wavelet transform), obtains two difference images by using CVA and SAM, and then yields a novel difference image by fusing them in the coefficients domains of discrete wavelet transform. Experimental results from a simulated and two real data sets validate the effectiveness of the proposed approach. In the first real data set, the proposed approach can identify 14,916 changed pixels while the best result of other methods is 14,806. In the second real data set, the proposed approach detects 3203 changed pixels, while the maximum of other methods is 3189.     

赤潮遥感进展与算法研究

回顾了国内外赤潮遥感研究概况,介绍赤潮遥感的生物物理化学依据,并总结利用卫星光学遥感数据反演赤潮信息的算法研究成果。     

Wetland change mapping for the U.S. mid-Atlantic region using an outlier detection technique

Although the impacts of wetland loss are often felt at regional scales, effective planning and management require a comparative assessment of local needs, costs, and benefits. Satellite remote sensing can provide spatially explicit, synoptic land cover change information to support such an assessment. However, a common challenge in conventional remote sensing change detection is the difficulty of obtaining phenologically and radiometrically comparable data from the start and end of the time period of interest. An alternative approach is to use a prior land cover classification as a surrogate for historic satellite data and to examine the self-consistency of class spectral reflectances in recent imagery. We produced a 30-meter resolution wetland change probability map for the U.S. mid-Atlantic region by applying an outlier detection technique to a base classification provided by the National Wetlands Inventory (NWI). Outlier-resistant measures – the median and median absolute deviation – were used to represent spectral reflectance characteristics of wetland class populations, and formed the basis for the calculation of a pixel change likelihood index. The individual scene index values were merged into a consistent region-wide map and converted to pixel change probability using a logistic regression calibrated through interpretation of historic and recent aerial photography. The accuracy of a regional change/no-change map produced from the change probabilities was estimated at 89.6%, with a Kappa of 0.779. The change probabilities identify areas for closer inspection of change cause, impact, and mitigation potential. With additional work to resolve confusion resulting from natural spatial heterogeneity and variations in land use, automated updating of NWI maps and estimates of areal rates of wetland change may be possible. We also discuss extensions of the technique to address specific applications such as monitoring marsh degradation due to sea level rise and mapping of invasive species.