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JoEG Volume 1 (2008) Issue 3-4
Development Of An Integrated ANN-GIS Framework For Inland Excess Water Monitoring
Abstract:Inland excess water on the Great Hungarian plain is an environmental and economic problem that has attracted a lot of scientific attention. Most studies have tried to identify the phenomena that cause inland excess water and combined them using regression functions or other linear statistical analysis. In this article, a different approach using a combination of artificial neural networks (ANN) and geographic information systems (GIS) is proposed. ANNs are particularly suitable for classifying large complex non-linear data sets, while GIS has very strong capabilities for geographic analysis. An integrated framework has been developed at our department that can be used to process inland excess water related data sets and use them for training and simulation with different types of ANNs. At the moment the framework is used with a very high resolution LIDAR digital elevation model, colour infrared digital aerial photographs and in-situ fieldwork measurements. The results of the simulations show that the framework is operational and capable of identifying inland excess water inundations.Manuscript:Evaluation Of Changes And Instability Of Water Content Using Remote Sensing Methods In A Nature Conservation Area
Abstract:The most significant landscape forming factors in the Great Hungarian Plain are humans and water. Before the regulation of the waterways one quarter of the present-day territory of Hungary belonged to the complex network of periodically or permanently inundated flood plains, marshes and swamps. Owing to human activities and the climatic changes observed in the last decades, processes that indicate landscape change have occurred in the Great Hungarian Plain (Rakonczai J. 2007). Loss of wetlands is a major process of landscape change. Evaluation of geographical changes caused primarily by water shortage is a difficult task as on the one hand only a limited data set is available and, on the other hand all the processes taking place in the area have to be known and understood in order to recognize the exact change. Habitats are extremely changeable and after the early summer floods, sometimes they entirely dry up to the end of the season. For detection and accurate evaluation of the long term changes lasting from the 19th century to present days the spatial and temporal development of instability has to be revealed. This has been determined on the basis of a series of high time resolution satellite images by digital image processing methods for the geographically very interesting period 1999-2003.Manuscript:Application Of Self-Organizing Neural Networks For The Delineation of Excess Water Areas
Abstract:In recent times Artificial Neural Networks (ANNs) are more and more widely applied. The ANN is an information processing system consisting of numerous simple processing units (neurons) that are arranged in layers and have weighted connections to each other. In the present study the possible application of an unsupervised neural network model, the self-organizing map (SOM), for the delineation of excess water areas have been examined. By means of the self-organizing map high-dimensional data of large databases could be mapped to a low-dimensional data space. Within a data set, it is able to develop homogeneous clusters, thus it can be effectively applied for the classification of multispectral satellite images. The classification was carried out for an area of 88 km2 to the south of Hódmezővásárhely situated in the south-eastern part of Hungary, which is frequently inundated by excess water. As input data, the intensity values of the pixels measured in six bands of a Landsat ETM image taken on 23rd April 2000 were used. To perform the classification, three different sized neural network models were created, which classified the pixels of the satellite image to 9, 12 and 16 clusters. By using the gained clusters three thematic maps were created, on which different types of excess water areas were delineated. During the validation of the results it was concluded that the applied neural network model is suitable for the delimitation of excess water areas and it could be an alternative to the traditional classification methods.Manuscript:Small Format Aerial Photography – Remote Sensing Data Acquisition For Environmental Analysis
Abstract:Since February 2008, an advanced system has been developed to acquire digital images in the visible to near infrared wavelengths. Using this system, it is possible to acquire data for a large variety of applications. The core of the system consists of a Duncantech MS3100 CIR (Color-InfraRed) multi-spectral camera. The main advantages of the system are its affordability and flexibility; within an hour the system can be deployed against very competitive costs. In several steps, using ArcGIS, Python and Avenue scripts, the raw data is semi-automatically processed into geo-referenced mosaics. This paper presents the parts of the system, the image processing workflow and several potential applications of the images.Manuscript:
