基于CBERS-02卫星数据的参数定量反演算法及软件设计

针对中巴资源(CBERS-02)卫星数据的定量化处理技术进行比较概括的阐述.在中国卫星应用中心已有的产品体系基础上,考虑CBERS卫星多波段摄像机、宽视场成像仪及红外多光谱扫描仪数据定量化的特点,建立了CBERS-02卫星数据定量化产品体系.并对其中的主要定量化过程与产品反演算法进行描述,具体包括CBERS-02数据的场地辐射定量标和交叉辐射定标结果、云监测算法、大气校正算法、地表反射率和反照率以及以此为基础的得到的植被指数等相关定量化产品算法.介绍了定量化处理CBERS-02卫星数据的软件,为CBERS-02数据的应用提供了技术支撑.     

高分四号遥感数据在环境监测中的应用研究

高分四号遥感卫星能够获取全色、多光谱以及中波红外谱段的卫星影像.农作废弃物焚烧较大程度上影响了空气的质量,威胁人们的健康.农业废弃物焚烧所产生的雾霾、粉尘等汇聚到空气中形成气溶胶.气溶胶的厚度是测量空气质量的一项重要参数.高分四号遥感卫星所具有的空间范围大、获取卫星影像高效以及中波红外火点敏感的特点可较好地解决当前农业废弃物焚烧与雾霾监测能力不足的情况.本文对高分四号卫星数据进行分析与处理,进行了火点提取及气溶胶反演.     

BDS-2/BDS-3伪距单点定位精度分析

针对目前BDS-3的定位性能,本文分析了BDS-2、BDS-3以及BDS-2/BDS-3组合下的数据质量以及伪距单点定位精度.研究发现,BDS-3卫星数据质量较优,相比于BDS-2卫星数据质量有了提升,BDS-3卫星的加入有效地提升了BDS卫星可见数,改善了卫星空间分布结构;BDS-2、BDS-3单频伪距与双频伪距单点定位精度相当,二者结合的伪距单点定位精度相比于单一系统有了较大提升.     

卫星激光测距仪光学接收系统的改造和设计

北京房山人卫站的卫星激光测距系统长期存在着接收敏度低,并由此产生的卫星激光测距数据精度不高、稳定性差以及数据采用率低的问题。针对该卫星激光测距系统的结构现状和接收探测器C-SPAD的特点,我们重新设计了光学接收小系统,对微光电视监视系统也进行了改造。从国际网反馈回来的数据公报可以看出,这些改造大大提高了北京房山人卫站卫星激光测距系统的接收灵敏度,从而观测数据的稳定性、精度以及数据的采用率也得到了提升。     

TM对NOAA面积校正模型的研究

为了实现NOAA卫星遥感影像在提供丰富的宏观信息的同时 ,能够具有较高的面积估计精度这一目标 ,以吉林省为例 ,根据 1998年的NOAA卫星遥感影像数据以及最新的TM卫星遥感影像数据 ,构建了高分辨率卫星影像数据对低分辨率卫星影像数据的面积精度校正模型 ,并引入分形理论对影像空间结构特征进行深入研究     

浅谈像素工厂下"吉林一号"卫星数据的处理

随着卫星遥感技术的不断发展,卫星影像成图精度越来越高,实际生产中接收到的数据类型也经常变化.由于每种类型卫星数据的特点不同,所以在处理卫星数据时一种软件往往满足不了需求,且常常出现卫星数据处理精度总是不能满足要求的情况.对于这种情况就需要我们探索其他的处理卫星数据的软件及方法.本文对像素工厂下处理"吉林一号"卫星数据的方法进行了探讨,对处理其他卫星数据类型也有重要的参考价值.     

遥感卫星数据产品分类分级规则研究

随着遥感卫星应用的发展,多源、多时相、多尺度以及不同类型遥感卫星的数据综合集成应用以及与地面观测信息集成,对遥感信息产品进行深度开发利用正在成为遥感应用的重要趋势。目前不同国家和地区、不同系列的遥感卫星数据产品分别采用不同的分类分级方案,难以适应多源、多系列地理空间信息整合应用的需求。本文从遥感应用对遥感卫星数据产品分类分级的需求出发,分析了目前主要遥感卫星的产品系列及其数据产品分类分级规则,参考了正在研究的相关国际标准,遵循系统性、科学性、完整性、兼容性、可操作性和可扩充性原则,研究提出了中国遥感卫星数据产品分类分级的规则。该规则以卫星载荷采用的遥感探测光谱特征及其数据获取方式作为分类依据,以卫星遥感数据的处理水平作为分级依据,建立统一的遥感卫星数据产品的分类分级体系。该规则与正在研究制订的相关国际标准保持一致,不但覆盖目前广泛应用的各类遥感卫星数据产品,而且能方便地建立与现有遥感卫星数据产品分类分级方案的映射关系,并且对未来的数据产品的分类分级留有比较充分的可扩充性,为遥感卫星数据产品分类分级指标体系的研究和相关国家标准的研制提供了依据。     

GRACE星载GPS非差观测值简化动力学定轨

利用GRACE卫星的实测数据研究了重力卫星精密定轨问题;针对简化动力学精密定轨方法给出了一种有效的星载数据编辑、处理策略.编制了相应的软件,并利用该软件处理了GRACE-B卫星3 d的实测数据;通过与JPL公布的轨道导航解比较,以及激光观测值检验的方式分析了卫星轨道的精度.结果显示,利用简化动力学定轨方法解算的轨道精度在6 cm以内,能够满足重力场反演对轨道精度的要求.     

ASTRON—G卫星定轨精度和地面跟踪效率分析

结合最新数据,探讨了GNSS系统对最新的空间VLBI计划(VSOP2)--ASTRON-G卫星定轨情况、地面跟踪网对卫星的跟踪效率,以及地面观测站与卫星实现联合观测射电源的情况等,并给出了分析结果.     

GRACE重力卫星在陆地水储量变化监测中的应用

介绍了GRACE重力卫星,并对GRACE重力卫星数据在陆地水储量变化中的应用现状进行分析,总结了GRACE重力卫星数据在陆地水储量变化检测中的数据获取、计算方法和精度分析,以及Grace数据在不同区域尺度陆地水储量变化估算中的应用情况,最后,指出GRACE在水储量应用中的不足和未来的研究方向。     

Detection and mapping of maize streak virus using RapidEye satellite imagery

The aim of this study was to detect and map MSV using RapidEye multispectral sensor in Ofcolaco farm. To achieve this objective, the acquired RapidEye sensor was classified using the robust Random Forest algorithm. Furthermore, the variable importance technique was used to determine the influence of each spectral band and indices on the mapping accuracy. For better performance of image data, the value of the commonly used vegetation indices in improving the classification accuracy was tested. The results revealed that the use of RapidEye spectral bands in detection and mapping of MSV yielded good classification results with an overall accuracy of 82.75%. The inclusion of vegetation indices computed from RapidEye sensor improved the classification accuracies by 3.4%. The most important RapidEye spectral bands in classifying MSV were near infrared, blue and red-edge. On the other hand, the most important vegetation indices were the Soil adjusted vegetation index, Enhanced vegetation index, Red index and Normalized Vegetation Index. The current study recommends future studies to assess the importance of multi-temporal remote sensing applications in detecting and monitoring the spread of MSV.     

RapidEye卫星影像在1∶50000矿山开发遥感调查中的应用研究

在矿产资源开发多目标遥感调查与监测基础上,以红格钒钛磁铁矿—拉拉铜矿区RapidEye卫星影像为例,深入探讨了RapidEye卫星影像在1∶50 000矿山开发遥感调查中的可行性。从RapidEye图像的几何纠正精度、合成处理及遥感解译标志来看,RapidEye影像可以满足1∶50 000矿山开发遥感调查的需要,大大提高1∶50 000矿山开发遥感调查的实时性。     

RapidEye影像正射影像图生产工艺探讨

介绍了在正射影像生产中一些关键环节,如用Photoshop对有薄云的RapidEye影像进行去薄云融合处理;对山区影像用小面元微分纠正等。总结了针对RapidEye影像去薄云和山区纠正困难的一套快速有效生产工艺。     

基于天绘一号卫星影像的水体信息提取对比研究

针对天绘一号卫星高分辨率影像,采用面向对象分类方法对怀柔水库区域进行水体信息提取,在多尺度分割的基础上,统计地物的光谱信息、形状因子和亮度均值等,建立水体信息的特征集,充分利用高分辨率的特点提取水体信息,同时选取了参数相近的SPOT和RapidEye两幅国外高分影像进行对比研究,使用相同方法进行水体提取,对实验过程和结果进行了对比分析。针对提取结果,采用野外采样和矢量图分析两种方法综合进行精度评价,根据采样数据得到的精度分别为96.97%,95.45%,92.42%,分析实验结果的矢量图,其中天绘影像水体提取面积为5 537 412.5㎡,SPOT影像为5 398 225㎡,RapidEye影像为5 053 262.5㎡,对实际水域的面积覆盖分别达到了101.40%,98.85%,92.54%,天绘影像的整体精度较高,但在细节表现上较为模糊,主要误差来自于对湿地的误分。实验制定了适用于天绘影像的水体提取方法和规则,分析不同因素对分割与分类结果的影响,同时,我们比较了天绘影像与国外同级别高分影像的优劣性,为天绘影像的进一步应用提供了参考。     

Predicting stem borer density in maize using RapidEye data and generalized linear models

Average maize yield in eastern Africa is 2.03 t ha⁻¹ as compared to global average of 6.06 t ha⁻¹ due to biotic and abiotic constraints. Amongst the biotic production constraints in Africa, stem borers are the most injurious. In eastern Africa, maize yield losses due to stem borers are currently estimated between 12% and 21% of the total production. The objective of the present study was to explore the possibility of RapidEye spectral data to assess stem borer larva densities in maize fields in two study sites in Kenya. RapidEye images were acquired for the Bomet (western Kenya) test site on the 9th of December 2014 and on 27th of January 2015, and for Machakos (eastern Kenya) a RapidEye image was acquired on the 3rd of January 2015. Five RapidEye spectral bands as well as 30 spectral vegetation indices (SVIs) were utilized to predict per field maize stem borer larva densities using generalized linear models (GLMs), assuming Poisson (‘Po’) and negative binomial (‘NB’) distributions. Root mean square error (RMSE) and ratio prediction to deviation (RPD) statistics were used to assess the models performance using a leave-one-out cross-validation approach. The Zero-inflated NB (‘ZINB’) models outperformed the ‘NB’ models and stem borer larva densities could only be predicted during the mid growing season in December and early January in both study sites, respectively (RMSE = 0.69–1.06 and RPD = 8.25–19.57). Overall, all models performed similar when all the 30 SVIs (non-nested) and only the significant (nested) SVIs were used. The models developed could improve decision making regarding controlling maize stem borers within integrated pest management (IPM) interventions.     

Testing the capability of spectral resolution of the new multispectral sensors on detecting the severity of grey leaf spot disease in maize crop

Abstract

In this study, we tested whether GLS field symptoms on maize can be detected using hyperspectral data re-sampled to WorldView-2, Quickbird, RapidEye and Sentinel-2 resolutions. To achieve this objective, Random Forest algorithm was used to classify the 2013 re-sampled spectra to represent the three identified disease severity categories. Results showed that Sentinel-2, with 13 spectral bands, achieved the highest overall accuracy and kappa value of 84% and 0.76, respectively, while the WorldView-2, with eight spectral bands, yielded the second highest overall accuracy and kappa value of 82% and 0.73, respectively. Results also showed that the 705 and 710 nm red edge bands were the most valuable in detecting the GLS for Sentinel-2 and RapidEye, respectively. On the re-sampled WorldView 2 and Quickbird sensor resolutions, the respective 608 and 660 nm in the yellow and red bands were identified as the most valuable for discriminating all categories of infection.     

Investigation of terrain illumination effects on vegetation indices and VI-derived phenological metrics in subtropical deciduous forests

We used RapidEye and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra data to study terrain illumination effects on 3 vegetation indices (VIs) and 11 phenological metrics over seasonal deciduous forests in southern Brazil. We applied TIMESAT for the analysis of the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) derived from the MOD13Q1 product to calculate phenological metrics. We related the VIs with the cosine of the incidence angle i (Cos i) and inspected percentage changes in VIs before and after topographic C-correction. The results showed that the EVI was more sensitive to seasonal changes in canopy biophysical attributes than the NDVI and Red-Edge NDVI, as indicated by analysis of non-topographically corrected RapidEye images from the summer and winter. On the other hand, the EVI was more sensitive to terrain illumination, presenting higher correlation coefficients with Cos i that decreased with reduction in the canopy background L factor. After C-correction, the RapidEye Red-Edge NDVI, NDVI, and EVI decreased 2%, 1%, and 13% over sunlit surfaces and increased up to 5%, 14%, and 89% over shaded surfaces, respectively. The EVI-related phenological metrics were also much more affected by topographic effects than the NDVI-derived metrics. From the set of 11 metrics, the 2 that described the period of lower photosynthetic activity and seasonal VI amplitude presented the largest correlation coefficients with Cos i. The results showed that terrain illumination is a factor of spectral variability in the seasonal analysis of phenological metrics, especially for VIs that are not spectrally normalized.     

基于多源多时相遥感数据的秋季菜田监测与分析——以北京市大兴区为例

根据北方蔬菜种植生育期特点,选择7~8月份的高分1号(GF1)融合影像、10月份的16 m宽覆盖的GF1影像以及8月份Rapid Eye融合影像,结合耕地本底数据开展提取秋季菜田信息的技术流程研究,并对2013年和2014年大兴区秋季菜田进行了动态监测,同时结合气象等数据分析其时空分布规律和变化原因。     

Monitoring canopy growth and grain yield of paddy rice in South Korea by using the GRAMI model and high spatial resolution imagery

Monitoring crop conditions and forecasting crop yields are both important for assessing crop production and for determining appropriate agricultural management practices; however, remote sensing is limited by the resolution, timing, and coverage of satellite images, and crop modeling is limited in its application at regional scales. To resolve these issues, the Gramineae (GRAMI)-rice model, which utilizes remote sensing data, was used in an effort to combine the complementary techniques of remote sensing and crop modeling. The model was then investigated for its capability to monitor canopy growth and estimate the grain yield of rice (Oryza sativa), at both the field and the regional scales, by using remote sensing images with high spatial resolution. The field scale investigation was performed using unmanned aerial vehicle (UAV) images, and the regional-scale investigation was performed using RapidEye satellite images. Simulated grain yields at the field scale were not significantly different (p = 0.45, p = 0.27, and p = 0.52) from the corresponding measured grain yields according to paired t-tests (α = 0.05). The model’s projections of grain yield at the regional scale represented the spatial grain yield variation of the corresponding field conditions to within ±1 standard deviation. Therefore, based on mapping the growth and grain yield of rice at both field and regional scales of interest within coverages of a UAV or the RapidEye satellite, our results demonstrate the applicability of the GRAMI-rice model to the monitoring and prediction of rice growth and grain yield at different spatial scales. In addition, the GRAMI-rice model is capable of reproducing seasonal variations in rice growth and grain yield at different spatial scales.     

Optimization of spectral indices and long-term separability analysis for classification of cereal crops using multi-spectral RapidEye imagery

Crop monitoring using remotely sensed image data provides valuable input for a large variety of applications in environmental and agricultural research. However, method development for discrimination between spectrally highly similar crop species remains a challenge in remote sensing. Calculation of vegetation indices is a frequently applied option to amplify the most distinctive parts of a spectrum. Since no vegetation index exist, that is universally best-performing, a method is presented that finds an index that is optimized for the classification of a specific satellite data set to separate two cereal crop types. The η² (eta-squared) measure of association – presented as novel spectral separability indicator – was used for the evaluation of the numerous tested indices. The approach is first applied on a RapidEye satellite image for the separation of winter wheat and winter barley in a Central German test site. The determined optimized index allows a more accurate classification (97%) than several well-established vegetation indices like NDVI and EVI (<87%). Furthermore, the approach was applied on a RapidEye multi-spectral image time series covering the years 2010–2014. The optimized index for the spectral separation of winter barley and winter wheat for each acquisition date was calculated and its ability to distinct the two classes was assessed. The results indicate that the calculated optimized indices perform better than the standard indices for most seasonal parts of the time series. The red edge spectral region proved to be of high significance for crop classification. Additionally, a time frame of best spectral separability of wheat and barley could be detected in early to mid-summer.