Satellite SAR Remote Sensing of Great Lakes Ice Cover,Part 2. Ice Classification and Mapping

During the 1997 winter season, shipborne polarimetric backscatter measurements of Great Lakes (freshwater) ice types using the Jet Propulsion Laboratory C-band scatterometer, together with surface-based ice physical characterization measurements and environmental parameters, were acquired concurrently with Earth Resource Satellite 2 (ERS-2) and RADARSAT Synthetic Aperture Radar (SAR) data. This polarimetric data set, composed of over 20 variations of different ice types measured at incident angles from 0° to 60° for all polarizations, was processed to radar cross-section to establish a library of signatures (look-up table) for different ice types. The library is used in the computer classification of calibrated satellite SAR data. Computer analysis of ERS-2 and RADARSAT ScanSAR images of Great Lakes ice cover using a supervised classification technique indicates that different ice types in the ice cover can be identified and mapped, and that wind speed and direction can have an influence on the classification of water as ice based on single frequency, single polarization data. Once satellite SAR data are classified into ice types, the ice map provides important and necessary input for environmental protection and management, ice control and ice breaking operations, and ice forecasting and modeling efforts.     

Kelvin Probe measurements of polymer coated gold substrates: Mechanism studies

The origin of work-function changes induced in polymer/metal structures (polyacrylic acid (PAA), ammonium and sodium polyacrylate) upon ammonia exposure is studied. To understand and explain the mechanism systematic measurements of uncoated and polymer coated metals are performed in Kelvin Probe (KP) setups. Additional data have been acquired through gravimetric and electrochemical experiments (cyclic voltammetry (CV)). The results obtained confirm that the field effect sensitive processes are taking place at the metal/polymer interface and allow their modelling. The intrinsic sensitivity of metallic samples upon ammonia exposure is influenced by the presence of coating polymers, by their ability to sorb water and to keep the metal surface wet or dry.     

闽江口湿地遥感影像最佳景观观察尺度的选择

应用遥感和地理信息系统技术,对景观最佳观察尺度选择的方法进行了探讨;构建了“定量化景观指数粒度敏感区间,定性化尺度大小,定性与定量相结合决定最佳观察尺度值“的最佳景观观察尺度选择模式,并以ASTER遥感影像为数据源,应用该模式对闽江口湿地景观最佳观察尺度进行了研究,指出闽江口湿地的景观观察尺度应以50m和100m为主,最大尺度不应超过200m.     

遥测遥控技术及其在钢铁工业中的应用

对我国工业遥测、遥控技术及其在钢铁工业中的应用现状进行了述评。二遥技术作为信息技术的一个分支,在信息时代必将有一个大的发展,本文针对我国的现状指出了其发展方向。     

从TM影像中提取城镇建筑覆盖区专题信息的改进方法

选用TM影像作为城镇建筑覆盖区专题信息提取的数据源，在Bayes的最大似然法半自动提取城镇专题信息和建筑指数法自动提取城镇专题信息分类方法的基础上，通过二值逻辑演算，综合两种方法的分类结果，去掉不属于城镇的信息，得到真正的城镇建筑覆盖区专题信息。实验结果表明，改进后的分类方法，兼有自动与半自动的优点，其分类精度大大提高。特别是对于短期变化(如防洪区)较大、面积较大而复杂的研究区来说，这一方法的应用弥补了单一分类方法的不足。     

Optimal classification methods for mapping agricultural tillage practices

The classification of agricultural tillage systems has proven challenging in the past using traditional classification methods due to the similarity of spectral reflectance signatures of soils and senescent crop residues. In this study, five classification methods were examined to determine the most suitable classification algorithm for the identification of no-till (NT) and traditional tillage (TT) cropping methods: minimum distance (MD), Mahalanobis distance, Maximum Likelihood (ML), spectral angle mapping (SAM), and the cosine of the angle concept (CAC). A Landsat ETM+ image acquired over southern Michigan and northern Indiana was used to test these classification methods. Each classification method was validated with 293 ground truth sampling locations collected commensurate with the satellite overpass. Classification accuracy was then assessed using error matrix analysis, Kappa statistics, and tests for statistical significance. The results indicate that of the classification routines examined, the two spectral angle methods were superior to the others. The cosine of the angle concept algorithm outperformed all the other classification routines for tillage practice identification and mapping, yielding an overall accuracy of 97.2% (Kappa=0.959).     

A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data

Wheat is one of the key cereal crops grown worldwide, providing the primary caloric and nutritional source for millions of people around the world. In order to ensure food security and sound, actionable mitigation strategies and policies for management of food shortages, timely and accurate estimates of global crop production are essential. This study combines a new BRDF-corrected, daily surface reflectance dataset developed from NASA's Moderate resolution Imaging Spectro-radiometer (MODIS) with detailed official crop statistics to develop an empirical, generalized approach to forecast wheat yields. The first step of this study was to develop and evaluate a regression-based model for forecasting winter wheat production in Kansas. This regression-based model was then directly applied to forecast winter wheat production in Ukraine. The forecasts of production in Kansas closely matched the USDA/NASS reported numbers with a 7% error. The same regression model forecast winter wheat production in Ukraine within 10% of the official reported production numbers six weeks prior to harvest. Using new data from MODIS, this method is simple, has limited data requirements, and can provide an indication of winter wheat production shortfalls and surplus prior to harvest in regions where minimal ground data is available.     

Modeling of a scanning laser range sensor for robotic applications

The geometric and noise modeling of a scanning range sensor is described. The parameters of the model are determined from data collected during the calibration of the real sensor. The models are shown to predict range scan results which accurately match those of the real sensor.     

Comparative study of vision tracking methods for tracking of construction site resources

Vision tracking has significant potential for tracking resources on large scale, congested construction sites, where a small number of cameras strategically placed around the site could replace hundreds of tracking tags. The correlation of vision tracking 2D positions from multiple views can provide the 3D position. However, there are many 2D vision trackers available in the literature, and little information is available on which one is most effective for construction applications. In this paper, a comparative study of various vision tracker categories is carried out, to identify which one is most effective in tracking construction resources. Testing parameters for evaluating categories of trackers are identified, and benefits and limitations of each category are presented. The most promising trackers are tested using a database of construction operations videos. The results indicate the effectiveness of each tracker in relation to each parameter of the test, and the most suitable tracker needed to research effective 3D vision trackers of construction resources.