近岸Ⅱ类水体表层悬浮泥沙浓度遥感模式研究进展

因为具有明显的时间与空间分辨率优势,遥感数据成为近岸Ⅱ类水体悬浮泥沙浓度(SSC)信息提取研究的重要数据源之一.悬浮泥沙遥感信息提取的现状可归纳为:(1)建立近岸Ⅱ类水体SSC遥感模式的方法有三种类型,分别是基于地面光谱与SSC测量的反射率反演方法、基于图像信息法和基于大气辐射传输理论模型法;(2)基于地面测量的反射率反演方法属于理论与经验相结合的方法,也是目前用于SSC定量化遥感模式研究的常用方法.其数学表达形式包括线性关系式、对数关系式、负指数关系式、Gordon模式和综合模式等;(3)到目前为止已有的Ⅱ类水体SSC遥感模式适用性方面还不理想,远未达到与试验室分析相匹配的精度.文章认为:加强地面水文光谱实验研究,建立多光谱SSC定量模式,以高分辨率和高光谱遥感融合数据为基础的SSC定量遥感是今后该方向发展趋势.     

富营养化水体浮游植物遥感监测研究进展

随着湖泊流域经济的快速发展,蓝藻水华频繁暴发的现象越来越严重,水体富营养化已经成为国内外重大环境问题.浮游植物是水体的初级生产者,是衡量水体富营养化程度的主要指标之一,遥感技术则是探测水体浮游植物时空分布的重要手段.在收集整理近千篇国内外水体浮游植物遥感研究论文的基础上,从卫星数据源、研究内容及研究方法等角度,总结了遥感技术在富营养化湖泊浮游植物监测应用的历史进展、研究重点及发展趋势.研究表明,现有的富营养化水体浮游植物遥感研究,以湖泊蓝藻水华问题为切入点,研究视角由水体表层(藻华面积、色素浓度)转至水下三维(藻总量),研究方法从定性识别转向定量反演,研究内容从监测蓝藻水华推进到探测不同类群蓝藻,逐渐形成了以应用为导向,"MODIS/VIIRS大中型湖泊日常监测—GF/Sentinel2小型湖泊针对性监测—无人机应急监测"的浮游植物遥感综合监测体系.上述研究梳理了富营养化水体浮游植物遥感监测湖泊水环境学科的发展动向,以期为从事蓝藻水华生态灾害监测和预警人员提供重要的技术支撑和理论参考.     

环境一号卫星高光谱遥感数据的内陆水质监测适宜性——以巢湖为例

环境一号星座的A星(HJ-1A)上搭载了超光谱成像仪(HSI),它是具有高光谱分辨率的全新国产遥感数据源,为水质遥感特别是内陆水质遥感提供了新的高光谱数据.但HSI影像的处理方法尚不完善,在广泛应用于水质遥感前对其进行质量研究和评价非常必要.本文针对HSI数据在巢湖水质监测应用方面的适宜性,对信噪比和数据真实性、倾斜条...     

地震应急中制约遥感应用因素分析

遥感在汶川、玉树地震中的有效应用,突出了重大自然灾害对遥感技术的需求。针对地震灾害快速评估的特殊性,详细分析了地震发生在不同的地理环境下,产生不同的地震灾害,地震应急中监测目标的不同,需要采用最佳遥感数据进行灾情判别,从而更好地得到地震灾情的分布情况;对现有遥感数据源和地震应急实际案例进行综合分析,得到遥感在地震应急中的应用现状及制约因素,制约因素主要有两点:时间分辨率和图像处理技术;最后提出了遥感技术在地震应急应用中存在的一些问题和解决的方法。     

基于太湖气溶胶类型分区的环境一号卫星CCD大气校正

由两颗卫星组成的环境卫星星座系统所提供的CCD数据具有较高的时间分辨率,使其在内陆湖泊水环境遥感监测中具有较大的应用潜力,对其有效的大气校正方法的研究则是其定量化参数反演的前提.基于准同步的MODIS数据辅助,根据气溶胶的差异性,将太湖划分为北部湖区、其他湖区两块区域,利用辐射传输模型,研究太湖环境一号CCD数据大气校正的方法,并对2009年4月17、21、25日数据进行大气校正.研究结果表明,该大气校正方法直接使用较为成熟的MODIS各类产品,克服了传统大气校正中依赖于现场同步测量大气参数的缺陷,能够快速、有效地完成环境一号CCD数据的大气校正.基于气溶胶类型对太湖进行分区后,所求算的遥感反射率精度高于6S模型和暗像元等大气校正方法得到的结果.     

Scale effects of leaf area index inversion based on environmental and disaster monitoring satellite data

The spatial distribution of sub-pixel components has an impact on retrieval accuracy, and should be accounted for when inverting a three-dimensional adiative transfer model to retrieve leaf area index (LAI). To investigate this effect, we constructed three realistic scenarios with the same LAI values and other properties, except that the simulated plants had different distributions. We implemented the radiosity method to subsequently produce synthetic bidirectional reflectance factor (BRF) datasets based upon these simulated scenes. The inversion was conducted using these data, which showed that spatial distribution affects retrieval accuracy. The inversion was also conducted for LAI based on charge-coupled device (CCD) data from the Environment and Disaster Monitor Satellite (HJ-1), which depicted both forest and drought-resistant crop land cover. This showed that heterogeneity in coarse-resolution remote sensing data is the main error source in LAI inversion. The spatial distribution of global fractal dimension index, which can be used to describe the area of sub-pixel components and their spatial distribution modes, shows good consistency with the coarse resolution LAI inversion error.     

Scale effects of leaf area index inversion based on environmental and disaster monitoring satellite data

The spatial distribution of sub-pixel components has an impact on retrieval accuracy, and should be accounted for when inverting a three-dimensional adiative transfer model to retrieve leaf area index (LAI). To investigate this effect, we constructed three realistic scenarios with the same LAI values and other properties, except that the simulated plants had different distributions. We implemented the radiosity method to subsequently produce synthetic bidirectional reflectance factor (BRF) datasets based upon these simulated scenes. The inversion was conducted using these data, which showed that spatial distribution affects retrieval accuracy. The inversion was also conducted for LAI based on charge-coupled device (CCD) data from the Environment and Disaster Monitor Satellite (HJ-1), which depicted both forest and drought-resistant crop land cover. This showed that heterogeneity in coarse-resolution remote sensing data is the main error source in LAI inversion. The spatial distribution of global fractal dimension index, which can be used to describe the area of sub-pixel components and their spatial distribution modes, shows good consistency with the coarse resolution LAI inversion error.

     

卫星遥感技术在火山监测中的应用

作为一门新兴技术，卫星遥感已被有效地应用于火山活动监测、通过SAR(合成孔径雷达)和InSAR(合成孔径雷达干涉成像)资料可以监测火山地表形变、使用热辐射数据以及将遥感与其它技术相结合能够研究火山区的热活动和火山喷发物。在我国的火山监测中，广泛地应用卫星遥感技术是非常必要的。     

基于无人机和卫星遥感影像的升金湖草滩植被地上生物量反演

湿地植被地上生物量是衡量湿地生态系统健康状况的重要指标,对于珍稀水禽越冬繁殖、全球碳循环、生态净化具有重要意义,是生态学与遥感解译的研究热点之一.针对于地上生物量的测算,卫星遥感数据覆盖范围广但其空间分辨率较低,无人机遥感数据空间分辨率高但采集范围小,同时受湿地面积、观测系统及外界环境等条件的影响,使得遥感影像地上生物量反演更加复杂和困难.本研究基于无人机和高分一号数据对升金湖草滩植被地上生物量反演进行研究,结合升金湖保护区4个样区无人机可见光影像与相应样区实测样本数据,建立地上生物量与可见光波段、多种可见光植被指数的线性、幂函数、多项式、对数回归模型,并通过可决系数（R²）、平均绝对误差（MAE）和均方根误差（RMSE）对模型进行精度评价,选择最优模型对无人机影像进行地上生物量反演;通过可见光波段反演得到的生物量,与高分一号WFV归一化差分植被指数（Normalized Difference Vegetation Index,NDVI）影像相结合进行回归建模,获取整个升金湖草滩植被地上生物量分布.结果表明,利用无人机红光波段建立的多项式方程对地上生物量反演有着最高模拟精度,R²=0.86、预测精度MAE=111.33 g/m²、RMSE=145.42 g/m²,且红光波段生物量反演方法得到的结果与实际生物量分布一致性较高,高分一号WFV NDVI与无人机反演生物量构建的多项式模型为最优模型,R²为0.91.本研究利用无人机和高分一号数据进行生物量反演研究,整合多源遥感数据优点,以获取更加丰富和准确的信息,进而提高地上生物量反演精度,为湿地监测和湿地恢复管理提供数据和技术支撑,具有重要研究意义和应用价值.     

基于MODTRAN模型对高分五号卫星断层逸出气体探测能力的辐射模拟

地下流体监测数据和地表断层调查都显示构造活动强烈期和大地震前后活动断裂带会伴有大量气体逸出。 中国即将发射的高分五号(GF-5)卫星搭载的大气环境红外甚高光谱分辨率探测仪及全谱段光谱成像仪两个传感器, 主要以大气气体的探测为应用目标。 本文基于两个传感器的参数设置, 使用大气辐射传输模型, 对断层逸出气体中的水汽、 CH₄和CO₂三种气体在大气中的含量变化对卫星传感器的辐射影响进行了仿真模拟, 分析了两个传感器对水汽、 CH₄和CO₂气体异常的探测能力。 结果表明, GF-5卫星两个红外传感器特定的光谱通道对大气水汽、 CH₄和CO₂气体异常变化均有不同程度的敏感性, 可以期待发展具有较高精度的相关气体遥感反演模型, 用于地震的监测及预测。     

Monitoring turbidity with IRS-1A data

This study was undertaken to determine the application of Indian Remote Sensing Satellite 1A–Linear Imaging Self scanning (IRS-1A–LISS-I) spectral digital data for estimating turbidity in an inland water body. IRS-1A–LISS-I spectral digital data for 20 October 1988 for the Tawa Reservoir were analysed and compared with field measurements of turbidity and used to develop simple and multiple regression equations to estimate turbidity in the surface water. The turbidity values were positively correlated with increasing wavelength LISS-I bands, 1, 2 and 3 of the visible region of the spectrum. Simple linear regression analyses indicate that LISS-I band 3 (0·62–0·68 μm) is the best for prediction purpose. Multiple regression equations have higher correlations (r=0·91). For validation, the equations were tested using LISS-I data for 28 September 1988 for the Tawa Reservoir. IRS-1A–LISS-I data provide a solid foundation for further development of remote sensing as a practical tool in monitoring water quality. The regression technique has strong potential for the future application of IRS-1A–LISS-I data in monitoring water quality of inland water bodies and estuaries. © 1997 John Wiley & Sons, Ltd.     

Publications received by the Editor

Abstract

Remote sensing is the use of electromagnetic energy to measure the physical properties of distant objects. It includes photography and geophysical surveying as well as newer techniques that use other parts of the electromagnetic spectrum. The history of remote sensing begins with photography. The origin of other types of remote sensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors have been designed to operate in virtually all of the electromagnetic spectrum. Today a wide variety of remote sensing instruments are available for use in hydrological studies; satellite data, such as Skylab photographs and Landsat images are particularly suitable for regional problems and studies. Planned future satellites will provide a ground resolution of 10–80 m.

Remote sensing is currently used for hydrological applications in most countries of the world. The range of applications includes groundwater exploration determination of physical water quality, snowfield mapping, flood-inundation delineation, and making inventories of irrigated land. The use of remote sensing commonly results in considerable hydrological information at minimal cost. This information can be used to speed-up the development of water resources, to improve management practices, and to monitor environmental problems.     

Surveying earth resources by remote sensing from satellites

This paper reviews the techniques and recent results of orbital remote sensing, with emphasis on Landsat and Skylab imagery. Landsat (formerly ERTS) uses electronic sensors (scanners and television) for repetitive observations with moderate ground resolution. The Skylab flights used a wider range of electro-optical sensors and returned film cameras with moderate and high ground resolution. Data from these programs have been used successfully in many fields. For mineral resources, satellite observations have proven valuable in geologic mapping and in exploration for metal, oil, and gas deposits, generally as a guide for other (conventional) techniques. Water resource monitoring with satellite data has included hydrologic mapping, soil moisture studies, and snow surveys. Marine resources have been studied, with applications in the fishing industry and in ocean transportation. Agricultural applications, benefiting from the repetitive coverage possible with satellites, have been especially promising. Crop inventories are being conducted, as well as inventories of timber and rangeland. Overgrazing has been monitored in several areas. Finally, environmental quality has also proven susceptible to orbital remote sensing; several types of water pollution have been successfully monitored. The effects of mining and other activities on the land can also be studied. The future of orbital remote sensing in global monitoring of the Earth's resources seems assured. However, efforts to extend spectral range, increase resolution, and solve cloud-cover problems must be continued. Broad applications of computer analysis techniques are vital to handle the immense amount of information produced by satellite sensors.Abbreviations SSRERTS Symposium on Significant Results Obtained from the Earth Resources Technology Satellite - TERTSS Third Earth Resources Technology Satellite-1 Symposium National Academy of Sciences-National Research Council Research Associate at NASA Goddard Space Flight Center, 1974/75.Goddard Space Flight Center     

On MSDT inversion with multi-angle remote sensing data

With the wavelet transform, image of multi-angle remote sensing is decomposed into multi-resolution. With data of each resolution, we try target-based multi-stages inversion, taking the inversion result of coarse resolution as the prior information of the next inversion. The result gets finer and finer until the resolution of satellite observation. In this way, the target-based multi-stages inversion can be used in remote sensing inversion of large-scaled coverage. With MISR data, we inverse structure parameters of vegetation in semiarid grassland of the Inner Mongolia Autonomous Region. The result proves that this way is efficient.     

多参量联合分析的地震空间探测异常研究进展

卫星对地观测技术是全球范围内实时监测孕震区地震活动和震后应急救援的有力工具。过去的几十年里,不同种类的卫星观测数据被应用于地震遥感异常探测领域中,并逐渐开展了从单个震例分析,到普遍规律探索,再到内部机理研究的多方面探讨。但由于地球系统的复杂性和孕震过程的不确定性,地震遥感异常研究仍充满困难和挑战。本文利用文献计量分析方法,探究了现今国内外地震遥感异常研究的关注热点和趋势方向,认为目前对于地震遥感异常的研究已由单一参量发展为多参量联合分析阶段;通过3个前沿研究案例分析了常用的综合分析参量与异常分析方法,以及多参量之间的时空同步性关系,表明多参量遥感异常的结合研究和交叉验证是地震遥感异常研究中的重要手段,可以在未来地震前兆研究中发挥重要作用。      

MATLAB软件与ALOS遥感影像在震害建筑物自动识别中的应用——以都江堰市区为例

利用遥感技术进行震害建筑物的自动识别可为震害的快速评估与救灾决策提供科学可靠的依据.本文从震害建筑物在高分辨率遥感影像下灰度的特征入手,以5·12汶川特大地震后都江堰市区ALOS遥感影像为数据源,在MATLAB平台下对影像进行灰度增强处理、数学形态学重构以及连接、填充处理,并结合区域统计特性最后自动识别震害房屋.结果表明,利用ALOS影像丰富的纹理特征及空间结构信息与MATLAB在数学形态学处理中的优势能够准确有效地提取震害建筑物信息.     

Automatic mapping of snow cover depletion curves using optical remote sensing data under conditions of frequent cloud cover and temporary snow

Snow cover depletion curves are required for several water management applications of snow hydrology and are often difficult to obtain automatically using optical remote sensing data owing to both frequent cloud cover and temporary snow cover. This study develops a methodology to produce accurate snow cover depletion curves automatically using high temporal resolution optical remote sensing data (e.g. Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua MODIS or National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)) by snow cover change trajectory analysis. The method consists of four major steps. The first is to reclassify both cloud‐obscured land and snow into more distinct subclasses and to determine their snow cover status (seasonal snow cover or not) based on the snow cover change trajectories over the whole snowmelt season. The second step is to derive rules based on the analysis of snow cover change trajectories. These rules are subsequently used to determine for a given date, the snow cover status of a pixel based on snow cover maps from the beginning of the snowmelt season to that given date. The third step is to apply a decision‐tree‐like processing flow based on these rules to determine the snow cover status of a pixel for a given date and to create daily seasonal snow cover maps. The final step is to produce snow cover depletion curves using these maps. A case study using this method based on Terra MODIS snow cover map products (MOD10A1) was conducted in the lower and middle reaches of the Kaidu River Watershed (19 000 km²) in the Chinese Tien Shan, Xinjiang Uygur Autonomous Region, China. High resolution remote sensing data (charge coupled device (CCD) camera data with 19·5 m resolution of the China and Brazil Environmental and Resources Satellite (CBERS) data (19·5 m resolution), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data with 15 m resolution of the Terra) were used to validate the results. The study shows that the seasonal snow cover classification was consistent with that determined using a high spatial resolution dataset, with an accuracy of 87–91%. The snow cover depletion curves clearly reflected the impact of the variation of temperature and the appearance of temporary snow cover on seasonal snow cover. The findings from this case study suggest that the approach is successful in generating accurate snow cover depletion curves automatically under conditions of frequent cloud cover and temporary snow cover using high temporal resolution optical remote sensing data. Copyright © 2007 John Wiley & Sons, Ltd.     

卫星遥感热红外辐射与川滇强震探讨

为了使卫星空间监测技术在地震监测预报的应用研究由理论性、实验性阶段进入实用性阶段，进行了探索和尝试。按照地震前兆资料常规处理的格式和要求，对收集的部分卫星遥感热红外辐射观测资料进行了处理。处理过程中使用了包含数据格式转换、数据预处理、异常挑选等内容的计算机工作软件。在此基础上，根据卫星遥感数据资料所具有的二维均匀分布的特性，应用平面和立体的数据图像处理软件，对卫星遥感热红外辐射观测资料的异常场信息进行了研究。同时，开发出了使用卫星遥感热红外辐射观测资料的数据接口软件。卫星空间监测技术的应用将给地震监测预报带来全新的思维方式及更加快捷实用的数据采集、传送、处理和分析方法。     

On quantifying agricultural and water management practices from low spatial resolution RS data using genetic algorithms: A numerical study for mixed-pixel environment

In this paper, we present a genetic algorithm-based methodology to quantify agricultural and water management practices from remote sensing (RS) data in a mixed-pixel environment. First, we formulated a linear mixture model for low spatial resolution RS data where we considered three agricultural land uses as dominant inside the pixel—rainfed, irrigated with two, and three croppings a year; the mixing parameters we considered were the sowing dates, area fractions of agricultural land uses in the pixel, and their corresponding water management practices. Then, we carried out numerical experiments to evaluate the feasibility of the proposed approach. In the process, the mixing parameters were parameterized by data assimilation using evapotranspiration and leaf area index as conditioning criteria. The soil–water–atmosphere–plant system model SWAP was used to simulate the dynamics of these two biophysical variables in the pixel. The results of our numerical experiments showed that it is possible to derive some sub-pixel information from low spatial resolution data e.g. the existing agricultural and water management practices in a region, which are relevant for regional agricultural monitoring programs.     

利用卫星光谱分析技术进行断层逸出气体监测

在不少震例中,临震前地壳出现气体异常,震前地下岩石受力产生裂隙,隐藏在地层中的CO2、CH4等温室气体逸出地表或水面。利用卫星光谱分析技术对断层逸出气体组分含量和总量进行监测,筛选出能够反应地震孕育过程中地下逸出气体异常演化特征的参数,建立地震短期预测的动态演化方法。对于认识震前地表气体异常机理,了解流体震兆的演变规律与这些气体异常的动态关系,改善卫星遥感地震预测,是很必要的。