Development Of An Integrated ANN-GIS Framework For Inland Excess Water Monitoring

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

Application Of Self-Organizing Neural Networks For The Delineation of Excess Water Areas

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

Satellite Based Analysis of Surface Urban Heat Island Intensity

The most obvious characteristics of urban climate are higher air and surface temperatures compared to rural areas and large spatial variation of meteorological parameters within the city. This research examines the long term and seasonal development of urban surface temperature using satellite data during a period of 30 years and within a year. The medium resolution Landsat data were (pre)processed using open source tools. Besides the analysis of the long term and seasonal changes in land surface temperature within a city, also its relationship with changes in the vegetation cover was investigated. Different urban districts and local climate zones showed varying strength of correlation. The temperature difference between urban surfaces and surroundings is defined as surface urban heat island (SUHI). Its development shows remarkable seasonal and spatial anomalies. The satellite images can be applied to visualize and analyze the SUHI, although they were not collected at midday and early afternoon, when the phenomenon is normally at its maximum. The applied methodology is based on free data and software and requires minimal user interaction. Using the results new urban developments (new built up and green areas) can be planned, that help mitigate the negative effects of urban climate

Land Cover Change Investigation in the Southern Syrian Coastal Basins During the Past 30-Years Using Landsat Remote Sensing Data

Land cover change and deforestation are important global ecosystem hazards. As for Syria, the current conflict and the subsequent absence of the forest preservation are main reasons for land cover change. This study aims to investigate the temporal and spatial aspects and trends of the land cover alterations in the southern Syrian coastal basins. In this study, land cover maps were made from surface reflectance images of Landsat-5(TM), Landsat-7(ETM+) and Landsat-8(OLI) during May (period of maximum vegetation cover) in 1987, 2002 and 2017. The images were classified into four different thematic classes using the maximum likelihood supervised classification method. The classification results were validated using 160 validation points in 2017, where overall accuracy was 83.75%. Spatial analysis was applied to investigate the land cover change during the period of 30 years for each basin and the whole study area. The results show 262.40 km2 reduction of forest and natural vegetation area during (1987-2017) period, and 72.5% of this reduction occurred during (2002-2017) period due to over-cutting of forest trees as a source of heating by local people, especially during the conflict period. This reduction was particularly high in the Alabrash and Hseen basins with 76.13 and 79.49 km2 respectively, and was accompanied by major increase of agriculture lands area which is attributed to dam construction in these basins which allowed people to cultivate rural lands for subsistence or to enhance their economic situation. The results of this study must draw the relevant authorities’ attention to preserve the remaining forest area

Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

Classification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image

Connection between road density and landscape fragmentation in Hungary using kernel density based on gis methods

Humans have recently caused significant landscape fragmentation by developing transportation infrastructure. We used Kernel density estimation (KDE) to analyze the road density distribution in Hungary, and then we assessed landscape fragmentation after imposing the road density onto the land-use map of Hungary, using Mean Patch Area, Patch Density, and Number of Patches as three important landscape metrics. Our analysis shows that roads, as expected, are mainly located in artificial lands (58.15%) and farmland (28.16%) landscapes. PD and NP increased by 69.59% and 69.51%, respectively, at the landscape scale, while AREA MN decreased by 41%. It has been proved by Spearman's rank correlation coefficient analysis which showed that the road density showed a positive correlation with PD and NP and a negative correlation with AREA_MN. This means that the higher the road density, the higher the PD and NP values, and the smaller the patch area. Furthermore, landscape fragmentation is positively related to road density, and as the road system became denser, the landscape became more fragmented. Understanding the effects of road networks on various land uses can aid in the development of sustainable road systems in Hungary