Jan Smid

Lifetime responsivity behavior of the Landsat-5 thematic mapper

The responsivities of the Landsat-5 Thematic Mapper (TM) reflective bands are characterized over the lifetime of the instrument using its internal calibration system. This system illuminates only the focal planes and aft optics of the TM so that it does not capture changes in the telescope. The observed changes are quantified and categorized as to whether they are likely to be true instrument responsivity changes or changes in the internal calibrator system itself. Changes observed that are likely to be true instrument changes are: (1) 7 percent, 5 percent, 8 percent and 7 percent exponential-like decreases in responsivity with decay half lives of 250, 180, 60 and 110 days in bands 1 to 4, respectively, during the initial on-orbit period and (2) an oscillation in response of about 5 percent peak-to-peak in bands 5 and 7. The first effect is believed to originate in the TM spectral bandpass filters and the second effect is believed to be due to an icing build up in the cold focal plane window. Two rapid apparent responsivity changes, one a decrease and one an increase, which are peculiar to particular internal calibrator lamps are believed to be due to changes in the lamp assemblies themselves as is a gradual increase in all detectors responsivities with time. An annual oscillation of up to 2 percent peak-to-peak in all bands is likely the product of both a temperature sensitivity of the IC and the TM primary focal plane.

Concept and sensor network approach to computing: the lexicon acquisition component

In this paper, we describe an on-going project called Concept and Sensor Networks (CSN). The development of this project has been described and discussed in past PSMP workshops [1]. The purpose of the project is to develop a framework for entities that can process sensor information into concepts. One of the features of this proposed network is the ability for the entities to use language communication to exchange concepts. These entities can potentially represent concepts, knowledge and information using different kinds of semantics. To further this project, we will to implement the proposed framework using physical and virtual sensors. In this paper, we overview the key components of the project, primarily focusing on lexical acquisition and the corresponding algorithm.

Ontology-based multiagent approach to data fusion

In this paper, we describe an ontology based multi-agent approach to data fusion from heterogeneous data sources. We assume that the data can come from various sensors or databases. In our approach, each data source is handled by one agent. The agent is able to deliver the data and the description of the data. The data description is provided in the form of specialized ontology. This description is the basis for fusion and integration of data from different sources or agents. A specialized agent fuses the data by evaluating these descriptions under the given query. Since data source agents themselves maintain the description of the data, the whole system of data sources is extensible. This means that a new data source agent may enter the system of data sources at any time. After proper registration with the fusion agent, a new agent can contribute to the overall process. The fusion agent is requested to provide data from different sources with type and location specification. The possible process outcomes are fused data, transformation rules or a failure message. After finding fusion rules, the agent is able to provide data continuously. In our examples of ontologies, we describe primarily numerical data sources and their relations, however symbolic data fusion is also considered.

Modeling and change-point detection for satellite instrumentation calibration coefficient time series: an example based on Landsat-5 thematic mapper data

The purpose of this study is to propose and study algorithms for off-line trend monitoring and change-point detection for calibration coefficient data streams. This type of algorithm is suitable for monitoring different sensors e.g. AVHRR, ETM+ and MODIS. Based on these algorithms we can produce flags for he instruments indicating normal and abnormal behavior. These algorithms can also help to discover trends and features in the calibration data. Some of these algorithms will be used by the Landsat-7 ETM+ Image Assessment System and the EOS AM1 MODIS calibration system for modeling the gain behavior of the instruments over time. Mathematically we used quadratic splines and statistical concepts for building incremental models.

Interaction and communication of agents in networks and language complexity estimates

Knowledge acquisition and sharing are arguably the most critical activities of communicating agents. We report about our on-going project featuring knowledge acquisition and sharing among communicating agents embedded in a network [7,8]. The applications we target range from hardware robots to virtual entities such as internet agents. Agent experiments can be simulated using a convenient simulation language. We analyzed the complexity of communicating agent simulations using Java and Easel [2]. Scenarios we have studied (see also our previous work [6]) are listed below. The communication among agents can range from declarative queries to sub-natural language queries. A set of agents monitoring an object are asked to build activity profiles based on exchanging elementary observations. A set of car drivers form a line, where every car is following its predecessor. An unsafe distance can create a strong wave in the line. Individual agents are asked to incorporate and apply directions how to avoid the wave. A set of micro-air vehicles form a grid and are asked to propagate information and concepts to a central server.

Calibration, regression models and the Web

The purpose of this paper is to provide a multifacetted approach to interactive analysis and modeling of time series, signals, and dynamical data sets. The models use statistical regression analysis and are built in an incremental way from a set of simple functional units. The analysis is supported by data visualization and on-line adaptive tutorials accessed on the World Wide Web. This work extends our results obtained for the Landsat-5 and Landsat-7 calibration data. The another objective is to educate the user about available mathematical models and also to allow the user to build those models interactively through applets prepared by the authors on a Web server. These models may be constructed for the users own data set. The approach is illustrated using the calibration data sets for the Landsat sensors, we also discuss agent communication framework of regression models and calibration data. However, the same integrated approach can be used for other data domains. This type of approach is consistent with other recent activities regarding semantic web.

Calibration data models for the Landsat 7 ETM+ internal calibrator

12 The Landsat-7 ETM+ incorporates three on-board calibration devices: (1) an internal calibrator, (2) a partial aperture solar calibrator and (3) a full aperture solar calibrator. The internal calibrator consists of two tungsten lamps and optical and mechanical components to transfer the light to the primary and cooled focal planes. In the present contribution we analyze the ETM+ internal calibrator signal in several spectral bands and model them functionally using several instrument-state quantities available in the Landsat-7 telemetry. Models were tested and compared in terms of their complexity, explanatory power and fit to the data. The intent of such models is to provide a way to correct for the internal calibrator instabilities and allow for its more precise usage in calibrating ETM+ data on-orbit. Some of the computer models we developed can be accessed interactively on the World Wide Web.

Algorithms and analysis tools for Landsat detector trending

The purpose of this study is to improve mathematical modeling of calibration curves produced by the Landsat calibrators. We explain one band and one lamp modeling and then one calibration band and multiple lamps averaging. The algorithm has three parts at the present, namely one dimensional modeling that includes a change-point removal and two or more signals averaging. A demo of the algorithm and the data is available from the Internet using any Web browser.

Algorithms and analysis tools for carbon content modeling in soil based on satellite data

Estimate of the organic carbon content in soil is critical for global change modeling activities. Therefore, the predictive model for estimating soil carbon would provide an important tool for the scientific community. We used remotely sensed TM imaginary data together with the soil profiles and moss layer carbon data for the Northern Study Area (NSA) of the BOREAS project. Different classification and functional models of the carbon dependency on remotely sensed data were developed. The complexity of the models was scrutinized. Based on these techniques, we have developed a set of analysis tools. These tools and an Internet based access to some of these tools will be presented.

Hubble Space Telescope battery voltage-current modeling

Is it possible to predict long-term trends of battery capacity based on a few orbits of spacecraft data? Our approach to answering this question has been to study the trends in Ni-H/sub 2/ battery properties as derived from in-orbit measurements. The behavior of batteries is driven by complex chemical and physical phenomena. The complexity of electrochemical processes prevents the use of fundamental equations of physics and chemistry in practical situations. Therefore, we have developed semi-empirical models with the ability to simulate the battery properties and to measure the degree of degradation without the need to explain electrochemical processes in battery cells. The limitation of these models is that their predictive capability depends on a set of known charge and discharge conditions. Mathematically, we have used the linear and nonlinear mappings and time-delayed nonlinear mappings between the integrated raw current and the voltage data with noise. We studied local models of the voltage and current relationship in terms of their predictive power and the sensitivity with respect to other measured data (e.g. temperature and cell pressure). We define a local model as one based on a few spacecraft orbits. We have studied the information content of other measurements such as temperature and pressure in functional local models based on the current, charge, voltage and their short term history.