No description available.

No description available.

The Scottish Office Department of Agriculture and Fisheries Department has been concerned to know the area of bracken (Pteridium aquilinum ) in Scotland and how rapidly it is spreading. It offers a potential danger to animal health and a devaluation of land for agricultural practice. Bracken is present in patches which are often difficult to delimit because of variable density and appearance throughout the year and is not consistently identifiable at a single minimum spatial resolution. Bracken provided a candidate vegetation type for prototyping a knowledge-based system (KBS) which synthesized environmental data expressed spatially, with knowledge on vegetation dynamics and vegetation succession in particular. Temporal models of vegetation types, which have different degrees of reliability with analysis of static spatial data were incorporated. The knowledge-based system permits the user access to knowledge about botany, ecology, mapping, image processing and spatial data analysis for an informed and informative answer to their query. Bracken was mapped at three experimental sites using land survey, photogrammetric techniques and satellite image processing to quantify the extent of bracken and bracken change. An existing data model was enhanced by building-in the hierarchy of survey observations and associated error bands. The model was then implemented within an expert system shell with KBS routines written in POP-11 and Fortran. The coupling of the GIS and KBS routines was of the form of: 1. organisation of the transfer of descriptive values as parameters between the data-models of the KBS and the GIS routines and 2. read/write of the spatial data into the data-models for analyses by KBS routines.

No description available.

Satellite images have been used as a complementary information for geological studies. In order to realise the maximum potential of satillite imagery, then improvements are needed, both in the visual presentation of such images, and in their automatic classification , in order to reveal the rock differences. Methods of processing imagery, were evaluated (band ratio, principal components, decorrelated stretch and maximum likelihood) and new (canonical regression, hue-saturation-intensity HSI transform, with modified manipulation, and watershed) were evaluated with respect to their ability to reveal rock differences. It was found that the HSI method gave the best results, both for visual presentation and automatic classifcation. This method has the ability to enhance both spectral and spatial information simultaneously without any data loss which is not the case in the other image enhancement methods (band ratio, principal components or decorrelation stretch). For automatic classification, the 'hue' images produced by the HSI transformation typically gave accurate (91%) classification of all the major rock types. Further, it was shown that the watershed method of classification was superior error rate = 9% to the maximum likelihood method (error rate 14%) for the same inputs. The new method of canonical regression was evaluated and although it was not very successful, the results were encouraging and it was concluded that this method may enable the estimation of the chemical composition of exposed rocks directly from satellite imagery.

The proliferation of nuclear weapons is a great threat to world peace and stability. The question of strengthening the nonproliferation regime has been open for a long period of time. In 1997 the International Atomic Energy Agency (IAEA) Board of Governors (BOG) adopted the Additional Safeguards Protocol. The purpose of the protocol is to enhance the IAEA’s ability to detect undeclared production of fissile materials in member states. However, the IAEA does not always have sufficient human and financial resources to accomplish this task. Developed here is a concept for making use of human and technical resources available in academia that could be used to enhance the IAEA’s mission. The objective of this research was to study the feasibility of an academic community using commercially or publicly available sources of information and products for the purpose of detecting covert facilities and activities intended for the unlawful acquisition of fissile materials or production of nuclear weapons. In this study, the availability and use of commercial satellite imagery systems, commercial computer codes for satellite imagery analysis, Comprehensive Test Ban Treaty (CTBT)verification International Monitoring System (IMS), publicly available information sources such as watchdog groups and press reports, and Customs Services information were explored. A system for integrating these data sources to form conclusions was also developed. The results proved that publicly and commercially available sources of information and data analysis can be a powerful tool in tracking violations in the international nuclear nonproliferation regime and a framework for implementing these tools in academic community was developed. As a result of this study a formation of an International Nonproliferation Monitoring Academic Community (INMAC) is proposed. This would be an independent organization consisting of academics (faculty, staff and students) from both nuclear weapon states (NWS) and non-nuclear weapon states (NNWS). This community analyzes all types of unclassified publicly and commercially available information to aid in detection of violations of the non-proliferation regime. INMAC shares all of this information with the IAEA and the public. Since INMAC is composed solely by members of the academic community, this organization would not demonstrate any biases in its investigations or reporting.

The on-going calibration and validation of visible satellite imagery remains a core activity of the scientific community in pursuit of high quality data characterising the oceanic chlorophyll field, providing input to models assessing primary productivity and the potential role of the oceans in climatic regulation. This work serves to examine the operational characteristics of semi-analytical algorithms that are designed to derive key optical properties from space born observations of ocean colour. The collection of water samples contemporaneously with precision profiled radiometry conforming to similar spectral bands to those of the NASA Sea Viewing Wide Field of view Sensor (SeaWiFS) was planned and executed, with appropriate field sampling techniques developed in accordance with the SeaWiFS Ocean Optics protocols (Mueller and Austin, 1995). Data were collected during extensive fieldwork sampling at a near coastal survey site and during two deep Atlantic research programmes. Historical and recently developed algorithms designed to retrieve the diffuse attenuation coefficient at 490nm and chlorophyll-a pigment concentrations from upwelling radiances were applied to the optical data, to compare the mathematically retrieved in-water properties with the values measured in-situ. The radiometric data were then used to generate general and local algorithm modifications to assess possible differences in the mathematically retrieved values. Statistical analyses of the errors in mathematical retrieval of in-water properties identified stmctured variability resulting from the empirical approach to algorithm generation, supporting the point of view that locally constrained algorithms provide a method of achieving significantly improved results. The problems associated with the derivation of semi-analytical algorithms are then discussed and errors analysed. The new algorithms generated here are found to compare well with their source data and with work by other investigators. Systematic variability was found within the data sets and the affect this has on the determinations is discussed. It is suggested that data users be afforded details of the equations employed in the production of readily available remote sensing products, placing them in a position where they are better able to assess the data in the context of their work.

archives@tulane.edu / 1 / Sabrina Martinez

No description available.

Natural disasters can happen anywhere causing damage to land and infrastructure. When these disasters occur in remote areas without much human traffic, it may take a long time for someone to notice that an event has occurred and to respond to it. Response time and damages could be reduced if the area could be remotely monitored. Many satellites pass over the Earth everyday collecting valuable imagery data that is free to access. However, this data can be difficult to process and use in practical applications such as monitoring an area for changes. Existing programs that use satellite imagery to monitor areas for changes can cost a significant amount of money making it inaccessible to most people. In this paper, a software program is introduced to automatically retrieve, process, and analyze free satellite imagery data and notify the user of significant changes in their area of interest (AOI). First, a software program was developed to automatically download a package of satellite imagery data from Planet Labs that met certain requirements for AOI, date, and cloud cover. A second software program was developed to download this data from the Google Cloud Storage (GCS) space and compare a current image to the composite of previous images in order to detect a change. This program then creates a figure to display the current image, the previous image, the difference area, and a summary table of the difference metrics. This figure is saved and emailed to the user if the differences are greater than the set threshold. This program is also capable of running automatically in the background of a computer every time it is logged in. The success of the program in correctly identifying areas of change was tested in three locations using historical satellite image data. The software was successful in identifying areas of change and delivering this information to the user in an easy to understand summary figure. Overall, the software was able to utilize free satellite imagery to detect changes in disaster areas and deliver a summary report to a user to take action showing that this software could be used in the future as an easy way to monitor disaster areas. / Master of Science / Natural disasters have been increasing in severity in recent years causing damage to land and infrastructure. Response time to these events may be negatively impacted in areas that are difficult to access, but if these areas could be remotely monitored the impact of the events could be reduced. Satellites pass over the Earth each day collecting valuable imagery data; however, this information can be difficult to interpret and use in a practical applications. In this paper a software program is introduced to automatically collect and analyze satellite imagery data over a specified area. This analysis compares the current image to previous images to detect changes in the area. Once the comparison is complete a summary report is generated and delivered to the user. This program can also be set to run automatically in the background of the user's computer. The software was successful in identifying areas of change and delivering the information to the user showing that this software could be used in the future as an easy way to monitor disaster areas.