无定向点的碎部测量方法

提出一种在测站没有定向点的情况下,利用全站仪进行碎部测量的作业方法,阐述该方法的作业过程、精度情况和注意事项,为解决全野外数字测图过程中经常遇到的由于控制点无通视方向而影响碎部测量作业的问题提供了有效途径。     

DLG产品版权标记技术研究综述

数字线划地图(DLG)产品因其生产成本高昂、精度高,与经济发展和国防安全大局息息相关,其版权保护问题意义重大。以数字水印为代表的版权标记技术是DLG产品目前主要的版权保护技术。基于进一步研究与应用需要,从版权标记理论基础、标记生成、标记嵌入、标记检测以及实时操作监控等五个方面,综述当前应用版权标记技术对DLG数据产品进行版权保护的现状及发展趋势。     

Light Requirements for Growth and Survival of Eelgrass (<Emphasis Type="Italic">Zostera marina</Emphasis> L.) in Pacific Northwest (USA) Estuaries

We developed light requirements for eelgrass in the Pacific Northwest, USA, to evaluate the effects of short- and long-term reductions in irradiance reaching eelgrass, especially related to turbidity and overwater structures. Photosynthesis-irradiance experiments and depth distribution field studies indicated that eelgrass productivity was maximum at a photosynthetic photon flux density (PPFD) of about 350–550 μmol quanta m⁻² s⁻¹. Winter plants had approximately threefold greater net apparent primary productivity rate at the same irradiance as summer plants. Growth studies using artificial shading as well as field monitoring of light and eelgrass growth indicated that long-term survival required at least 3 mol quanta m⁻² day⁻¹ on average during spring and summer (i.e., May-September), and that growth was saturated above about 7 mol quanta m⁻² day⁻¹. We conclude that non-light-limited growth of eelgrass in the Pacific Northwest requires an average of at least 7 mol quanta m⁻² day⁻¹ during spring and summer and that long-term survival requires a minimum average of 3 mol quanta m⁻² day⁻¹.     

Automated Kerogen Classification in Microscope Images of Dispersed Kerogen Preparation

We develop the classification part of a system that analyses transmitted light microscope images of dispersed kerogen preparation. The system automatically extracts kerogen pieces from the image and labels each piece as either inertinite or vitrinite. The image pre-processing analysis consists of background removal, identification of kerogen material, object segmentation, object extraction (individual images of pieces of kerogen) and feature calculation for each object. An expert palynologist was asked to label the objects into categories inertinite and vitrinite, which provided the ground truth for the classification experiment. Ten state-of-the-art classifiers and classifier ensembles were compared: Naïve Bayes, decision tree, nearest neighbour, the logistic classifier, multilayered perceptron (MLP), support vector machines (SVM), AdaBoost, Bagging, LogitBoost and Random Forest. The logistic classifier was singled out as the most accurate classifier, with an accuracy greater than 90. Using a 10 times 10-fold cross-validation provided within the Weka software, we found that the logistic classifier was significantly better than five classifiers (p<0.05) and indistinguishable from the other four classifiers. The initial set of 32 features was subsequently reduced to 6 features without compromising the classification accuracy. A further evaluation of the system alerted us to the possible sensitivity of the classification to the ground truth that might vary from one human expert to another. The analysis also revealed that the logistic classifier made most of the correct classifications with a high certainty.