Chemical composition and source characterization of spring aerosol over Horqin sand land in northeastern China

IEECAS SKLLQG

Characterization and sources of regional-scale transported carbonaceous and dust aerosols from different pathways in coastal and sandy land areas of China

IEECAS SKLLQG

公用陆面模式（Community Land Model 3.0）简介

IEECAS SKLLQG

64通道一维位置灵敏电流分布探测器及其数据获取系统；A 64-Channel Position Sensitive Current Distribution Detector with Data Acquisition System

针对高流强粒子束与绝缘毛细管相互作用的特点,设计制作了一套64通道一维位置灵敏电流分布探测器及其配套的数据获取系统,该探测器可分辨最小直径为1mm的束斑,通过数据获取系统可实现可视化自动数据采集。用2nA和200—2000eV电子对探测器进行了定标,并用10μA和2000eV的电子束穿越锥形毛细管后的出射电子,对探测器及数据获取系统进行测试,获得了出射粒子的位置分布谱及能量信息。

Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.