Detection of coffee berry necrosis by digital image processing of landsat 8 oli satellite imagery

Coffee berry necrosis is a fungal disease that, at a high level, significantly affects coffee productivity. With the advent of surface mapping satellites, it was possible to obtain information about the spectral signature of the crop on a time scale pertinent to the monitoring and detection of plant phenological changes. The objective of this paper was to define the best machine learning algorithm that is able to classify the incidence CBN as a function of Landsat 8 OLI images in different atmospheric correction methods. Landsat 8 OLI images were acquired at the dates closest to sampling anthracnose field data at three times corresponding to grain filling period and were submitted to atmospheric corrections by DOS, ATCOR, and 6SV methods. The images classified by the algorithms of machine learning, Random Forest, Multilayer Perceptron and Naive Bayes were tested 30 times in random sampling. Given the overall accuracy of each test, the algorithms were evaluated using the Friedman and Nemenyi tests to identify the statistical difference in the treatments. The obtained results indicated that the overall accuracy and the balanced accuracy index were on an average around 0.55 and 0.45, respectively, for the Naive Bayes and Multilayer Perceptron algorithms in the ATCOR atmospheric correction. According to the Friedman and Nemenyi tests, both algorithms were defined as the best classifiers. These results demonstrate that Landsat 8 OLI images were able to identify an incidence of the coffee berry necrosis by means of machine learning techniques, a fact that cannot be observed by the Pearson correlation.     

基于像元二分模型的地表植被覆盖度估算模块化设计与实现

地表植被覆盖度是的一种应用广泛的定量遥感产品,在水文、生态、区域变化等方面都具有重要的意义。像元二分模型是应用最多的一种遥感估算地表覆盖度的方法。目前,用遥感的方法进行地表植被覆盖度估算没有完整、系统的工具,用户只能逐步进行操作,效率低下,鉴于上述情况,本文运用IDL交互式数据语言,基于ENVI二次开发了一个植被覆盖度估算程序,取得了一定的成效,对遥感定量产品的生产、应用具有一定意义。     

DNQ3型能见度仪故障分析及校准方法研究

为有效提高国家地面气象观测站能见度仪观测数据准确性和设备运行稳定性,对能见度仪开展日常维护、核查校准、数据质控和故障修复等工作显得尤为重要。该文通过对DNQ3型前向散射能见度仪硬件结构和基本工作原理的介绍,以及对一些典型故障进行分析,总结出能见度仪的检测维修流程,归纳了对应的故障解决措施,凝练出该型能见度仪的核查校准和疑误数据质控方法,为一线业务人员开展能见度仪常规维护、故障排查、运行监控等提供借鉴。     

空间视图在图形数据更新中的应用研究

在研究Geodatabase数据模型的基础上,给出了空间视图的定义,探讨了基于空间视图的图形数据更新过程,设计了更新的结构图和活动图。     

An accessible strong-motion dataset (PGA,PGV, and site vS30) of 2022 MS6.8 Luding,China Earthquake

A M_S6.8 earthquake occurred on 5th September 2022 in Luding county, Sichuan, China, at 12: 52 Beijing Time(4:52 UTC). We complied a dataset of PGA, PGV, and site v_S30 of 73 accelerometers and 791 Micro-Electro-Mechanical System(MEMS)sensors within 300 km of the epicenter. The inferred v_S30 of 820 recording sites were validated. The study results show that:(1)The maximum horizontal PGA and PGV reaches 634.1 Gal and 71.1 cm/s respectively.(2) Over 80% of records ar...     

天文卫星下传数据处理系统的规划

天文卫星获取的数据需要经过卫星下传数据处理系统的一系列加工,生成可以分析的数据产品,这些产品及相应软件要发布给国内外用户,同时数据处理系统还要监测载荷状态、数据质量及天体源爆发等。这样,下传数据处理系统的建设直接关系到物理成果的获取,规划该系统就变得非常有意义。从数据产品定义、子系统规划、数据流程等方面介绍卫星下传数据处理系统的规划,并提出以模块化开发方式从整体上协调各个子系统的开发,使各个系统及其软件相互配合,共同促进科学产出。     

国土资源卫星遥感数据应用评估系统构建

从国土资源应用出发,在“指标-模拟-评估-指标修改-模拟-评估”模式的基础上,构建了由软件系统、硬件系统、技术标准和技术方法4部分组成的评估系统,建立了较为完整的卫星数据应用评价技术方法体系,为实现在卫星研制初期就真正开展天地一体的我国卫星研制和应用的发展路线,提供了新的思路和技术体系支撑。     

浅谈航测遥感网络生产中的数据安全与管理

针对航测遥感网络生产过程中的数据安全问题,本文不仅提出了常用的网络生产安全解决措施,而且指出了使用航测遥感生产管理信息系统进行管理应当注意的问题和数据备份的注意事项。     

全站仪三角高程测量的精度分析和计算自动化

全站仪三角高程测量具有效率高,实施灵活等优点,经研究并通过实践验证,在对观测结果进行相关改正的条件下,全站仪三角高程测量完全能达到三、四等水准测量的精度要求,同时可借助Excel所具备的强大数据处理能力,使观测数据的处理更为方便快捷。     

Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis

In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach.