Land cover mapping using time series HJ-1/CCD data

It is very difficult to have remote sensing data with both high spatial resolution and high temporal frequency; thus, two categories of land-use mapping methodology have been developed separately for coarser resolution and finer resolution data. The first category uses time series of data to retrieve the variation of land surface for classification, which are usually used for coarser resolution data with high temporal frequency. The second category uses fine spatial resolution data to classify different land surface. With the launch of Chinese satellite constellation HJ-1in 2008, four 30 m spatial resolution CCDs with about 360 km coverage for each one onboard two satellites made a revisit period of two days, which brought a new type of data with both high spatial resolution and high temporal frequency. Therefore, by taking the spatiotemporal advantage of HJ-1/CCD data we propose a new method for finer resolution land cover mapping using the time series HJ-1/CCD data, which can greatly improve the land cover mapping accuracy. In our two study areas, the very high resolution remote sensing data within Google Earth are used to validate the land cover mapping results, which shows a very high mapping accuracy of 95.76% and 83.78% and a high Kappa coefficient of 0.9423 and 0.8165 in the Dahuofang area of Liaoning Province and the Heiquan area of Gansu Province respectively.     

Remote sensing application system for water environments developed for Environment Satellite 1

Remote sensing data collected by the Environment Satellite I are characterized by high temporal resolution, high spectral resolution and mid-high spatial resolution. We designed the Remote Sensing Application System for Water Environments (RSASWE) to create an integrated platform for remote sensing data processing, parameter information extraction and thematic mapping using both remote sensing and GIS technologies. This system provides support for regional water environmental monitoring, and prediction and warning of water pollution. Developed to process and apply data collected by Environment Satellite I, this system has automated procedures including clipping, observation geometry computation, radiometric calibration, 6S atmospheric correction and water quality parameter inversion. RSASWE consists of six subsystems: remote sensing image processing, basic parameter inversion, water environment remote sensing thematic outputs, application outputs, automated water environment outputs and a non-point source pollution monitoring subsystem. At present RSASWE plays an important role in operations at the Satellite Environment Center.     

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.     

The potential information in the temperature difference between shadow and sunlit of surfaces and a new way of retrieving the soil moisture

The thermal inertia and plant water stress index are often adopted to estimate soil moisture available for crops or plants. However, it is not very easy to obtain two temporal temperatures for thermal inertia model and air temperature for the plant water stress mode. Shadows of ground objects are often referred to noise on visible and near infrared remote sensing. But the difference of temperature between shadows and sunlit contains rich information concerning with heat-water status for soil. This paper presented a new way to excavate just by temperature difference usually between shadow and sunlit surface. Experiments validated the ideal. We can adopt thermal camera to measure the differences in the field measurements. However, we must use inversion based on multianglar thermal infrared remote sensing data in airborne and spaceborne. An inverting model was also presented by using Monte-Carlo and the least square method. Results show that this way is feasible.     

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.     

Information flow and controlling in regularization inversion of quantitative remote sensing

In order to minimize uncertainty of the inversed parameters to the largest extent by making full use of the limited information in remote sensing data, it is necessary to understand what the information flow in quantitative remote sensing model inversion is, thus control the information flow. Aiming at this, the paper takes the linear kernel-driven model inversion as an example. At first, the information flow in different inversion methods is calculated and analyzed, then the effect of information flow controlled by multi-stage inversion strategy is studied, finally, an information matrix based on USM is defined to control information flow in inversion. It shows that using Shannon entropy decrease of the inversed parameters can express information flow more properly. Changing the weight of a priori knowledge in inversion or fixing parameters and partitioning datasets in multi-stage inversion strategy can control information flow. In regularization inversion of remote sensing, information matrix based on     

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.     

基于高分辨率遥感影像的震后重点搜救目标判定模型初步研究

随着高分辨率卫星遥感技术的发展,高分遥感影像在灾害应急评估和紧急救援方面得到了越来越多的应用.本文首先总结了应急搜救目标选取和遥感影像判识两方面工作,然后分析了震后重点搜救区域和目标判定的基本原则,研究了面向搜救的高分辨率影像基本特征,确定了基于高分辨率影像判定重点搜救目标的方法.在此基础上给出了基于高分辨率遥感影像的...     

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.

     

Review of snow water equivalent microwave remote sensing

Accurate quantitative global scale snow water equivalent information is crucial for meteorology, hydrology, water cycle and global change studies, and is of great importance for snow melt-runoff forecast, water resources management and flood control. With land surface process model and snow process model, the snow water equivalent can be simulated with certain accuracy, with the forcing data as input. However, the snow water equivalent simulated using the snow process models has large uncertainties spatially and temporally, and it may be far from the needs of practical applications. Thus, the large scale snow water equivalent information is mainly from remote sensing. Beginning with the launch of Nimbus-7 satellite, the research on microwave snow water equivalent remote sensing has developed for more than 30 years, researchers have made progress in many aspects, including the electromagnetic scattering and emission modeling, ground and airborne experiments, and inversion algorithms for future global high resolution snow water equivalent remote sensing program. In this paper, the research and progress in the aspects of electromagnetic scattering/emission modeling over snow covered terrain and snow water equivalent inversion algorithm will be summarized.     

面向对象遥感分类方法在汶川地震震害提取中的应用

震后城市建筑物震害的自动识别与分类, 是遥感震害调查中的关键步骤, 其精度直接影响损失评估的结果. 而随着高分辨率遥感影像的发展, 传统基于像元的分类技术已不能满足需求, 引入面向对象的信息提取技术, 充分挖掘影像对象的纹理、形状和相互关系等信息, 能够有效的提高震害的分类精度. 该文阐述了面向对象的遥感震害提取思路和方法, 并应用汶川地震震后高分辨率航空遥感数据, 针对建筑物震害进行面向对象的快速提取与自动分类. 结果表明, 与基于像元分类比较, 面向对象的建筑物震害分类能够显著改善分类效果.     

Application of ensemble kalman filter to geophysical parameters retrieval in remote sensing:A case study of kernel-driven BRDF model inversion

Remote sensing can provide multi-spatial resolution, multi-temporal resolution multi-spectral band and multi-angular data for the observation of land surface. At present, one of research focuses is how to make the best of these data to retrieve geophysical parameters in conjunction with their a priori knowledge and simul-taneously consider the influence of data uncertainties on inversion results[1-5]. The essence of remote sensing lies in inversion. It is difficult to precisely retrieve parame…     

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.     

Lg波衰减模型中建模误差的定量评估

对Lg波衰减模型中建模误差的统计特征进行了系统研究,并建立了地壳二维Lg波衰减模型。由于Lg波振幅可能受到几何扩散函数的强烈影响,合理评估反演过程的误差对于能否使用最小二乘意义下的反演非常重要。通过在川滇及其邻近地区收集的建模误差样本,使用K-S数值检验方法、Q-Q图和正态分布图形检验方法对Lg波衰减层析成像反演的输入数据中建模误差的分布特征进行了统计分析。采用奇异值分解(SVD)和反投影方法,分别获得了川滇地区的Q_Lg模型,定量计算模型的协方差矩阵和分辨率矩阵,定量评估了Q_Lg模型中每个格点的分辨率和误差。结果表明：在一阶近似条件下建模误差服从正态分布;通过开发的数据筛选程序,可以产生一个接近完美正态分布的数据集;与反投影方法相比,利用SVD方法获得的地壳Q值的分辨率更高;在射线覆盖较好的区域,Q_Lg模型的分辨率达到100 km,相对误差小于3%。     

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。     

面向地震应急的遥感影像快速定位研究

为实现地震应急期间非常规手段获取的遥感影像的快速定位, 提出了建立序列影像航线方程实现影像快速定位的思路和方法, 开发了影像快速自动定位程序。 以玉树地震应急期间获取的多景无地理参考信息的高分辨率航空遥感影像为实验对象, 在人工交互方式对初始影像进行粗定位的基础上, 利用本方法实现了系列影像的快速定位。 通过随机抽样点验证定位精度, 二次函数相对于一次函数拟合精度较好, 表明在损失一定精度的同时可大大提高影像快速定位的效率, 满足地震应急的需求。     

电磁传播电阻率测井的二维全参数反演方法研究

Baker Hughes INTEQ的MPR是应用较广的传播电阻率测井仪器,它有4条相位差和4条幅度比测井曲线,本文以它在垂直井中的测井响应为例开展电磁传播电阻率资料的二维反演方法研究.由于其测井响应对地层纵向边界的探测灵敏度比对其他地层参数的大得多,在其资料反演中必须重视地层纵向边界位置的提取和反演精度.本文首先应用特征识别方法由分辨率最好的测井曲线提取地层纵向边界,接着对浅、深探测的两条相位差曲线分别做纵向一维反演,利用此结果来建立二维反演的初始地层模型,采用改进的阻尼型正则化高斯 牛顿优化算法,实现了MPR资料的二维全参数反演.对含23个纵向分层的两种典型地层模型的数值模拟测井资料的二维反演得到了令人满意的结果,它可显著提高MPR的资料解释水平.发现只用它的5条测井曲线就可得到与8条测井曲线几乎相同的反演效果.     

活动断裂调查中的高分辨率遥感技术应用方法研究

本文系统分析了高分辨率遥感在活动断裂调查中应用的技术现状、工作流程,梳理了各类遥感数据的要求、适用条件和处理方法,总结了活动断裂的遥感解译方法、解译要素和测量参数,并通过实例解析了一些典型的断错地貌,给出了相应的遥感特征. 基于资源三号卫星的立体像对和影像,判读了大青山活动断裂的几何特征和活动特性. 结果表明: 人工改造较大的地区宜收集早期遥感影像,利用不同波段间地物光谱的差异来增强隐伏活动断裂的信息,使用空间增强方法来识别断层陡坎等线性构造;雷达数据多极化分解是检测隐伏构造信息的有效方法;由宏观信息向局部信息追踪是活动断裂解译的有效途径;将遥感影像与数字高程模型(DEM)联合可进行活动断层参数的高精度测量. 本文结果可为活动断裂大比例尺、定量调查提供参考.      

Real time 2D inversion of induction logging data

Real time 2D inversion for an induction logging instrument may be achieved using a fast forward modeling and special inversion strategy. The fast forward modeling employs a low-frequency approximation of an induction response known as Doll's geometric factor. Modeling geometric factors is much faster than modeling the electromagnetic field in the frequency domain. To transform real data into the Doll's limit, multi-frequency skin-effect correction is applied. The correction technique involves an asymptotic theory of the integral equation for a 2D boundary value problem. The inversion is based on separating the parameter space into subspaces of lower dimension. Initially, adaptive overlapping windows split logging data into manageable portions. Each window consists of three subwindows: the predictor, corrector and upgrader. Further separation of parameters is introduced by Doll's approximation: the low-frequency response is linear with respect to formation conductivity. This allows us to split inversion for conductivity and geometric parameters. The next level of splitting inversion is achieved by independently determining parameters of the near borehole zone and remote formation areas. This is done by utilizing different subsets of sensors. The inversion does not require initial guess: layers are introduced dynamically, if necessary. The resolution is improved in sequential iterations by adding finer details to the previously obtained models. The final selection of parameters satisfies a variety of a priori constraints formulated as target resistivity distributions. The technique for imposing constraints is based on the analysis of data mapping into the model space. Interpretation of synthetic and real data confirms the viability of the method.     

A PRELIMINARY STUDY ON THE METHOD OF SEISMIC INTENSITY ASSESSMENT BASED ON RESIDENTIAL BUILDING DATA AND HIGH RESOLUTION REMOTE SENSING IMAGES

After destructive earthquakes, the assessment result of seismic intensity is an important decision-making basis for emergency rescue, recovery and reconstruction. This job requires higher timeliness by government and society. Because remote sensing technology is not affected by the terrible traffic conditions on the ground after the earthquake, large-scale seismic damage information in the earthquake area can be collected in a short time by the remote sensing image. The remote sensing technique plays a more and more important role in rapid acquisition of seismic damage information, emergency rescue decision-making, seismic intensity assessment and other work. On the basis of previous studies, this paper proposes a new method to assess seismic intensity by using remote sensing image, i.e. to interpret the building collapse rate of a residential quarter after an earthquake by high-resolution remote sensing images. If there already are detailed building data and building structure vulnerability matrix data of a residential area, we can calculate the building collapse rate under any intensity values in this residential area by using the theory of earthquake damage prediction. Assuming that the building collapse rate interpreted by remote sensing is equal to the building collapse rate predicted by using the existing data, it will be easy to calculate the actual seismic intensity of the residential area in this earthquake event. Based on this idea, according to the relevant standard specifications issued by China Earthquake Administration, this paper puts forward some functional models, such as the calculation model of building collapse rate based on remote sensing, the data matrix model of residential building structure, the prediction function matrix model of residential building collapse rate and the prediction model of residential building collapse rate. A formula for calculating seismic intensity by using remote sensing interpretation of collapse rate is also proposed. To test and verify the proposed method, this paper takes two neighboring blocks of Jiegu Town after the Yushu M7.1 earthquake in Qinghai Province as an example. The building structure matrix of the study block was constructed by using pre-earthquake 0.6m resolution satellite remote sensing image(QuickBird, acquired on November 6, 2004), post-earthquake 0.2m aerial remote sensing image(acquired by National Bureau of Surveying and Mapping, April 15, 2010) and some field investigation data. The building collapse rate in the two blocks was calculated by using the interpretation results of seismic damage from the Remote Sensing Technology Coordinating Group of China Seismological Bureau. The seismic damage matrix of building structures in Yushu area is constructed by using the abundant scientific data of the scientific investigation team of the project “Comprehensive Scientific Investigation of the Yushu M7.1 Earthquake in Qinghai Province” of China Seismological Bureau. On this basis, the collapse rate prediction function of different structures in Yushu area is constructed. According to the prediction function of collapse rate and the building structure matrix of the two blocks, the building collapse rate under different intensity values is predicted, and the curve of intensity-collapse rate function is drawn. By comparing the building collapse rate interpreted by remote sensing and the intensity-collapse rate function curve of this two blocks, the seismic intensity of both blocks are calculated to be the same value: Ⅸ degree, which is consistent with the results of the field scientific investigation of the earthquake. The validation shows that the method proposed in this paper can effectively avoid the influence caused by the difference of seismic performance of buildings and accurately evaluate seismic intensity when using remote sensing technique. The method has certain application value for earthquake emergency work.