Environmental Monitoring Supported by Aerial Photography: – a Case Study of the Burnt Down Bugac Juniper Forest, Hungary

Wildfire poses a serious risk in several regions of the world threatening urban, agricultural areas and natural ecosystems as well. Nature conservation has important role to be prepared for the management of postfire environmental degradation and restoration for protected areas preserving valuable ecosystems. The improving temporal and spatial resolution of remote sensing and GIS methods significantly contributes to map the changes for accelerating management steps of restoration. In this study a severe wildfire and its impacts were assessed in case of a protected area of the Kiskunság National Park in Hungary, which was partly burnt down in 2012. The aim of this research was to efficiently and accurately assess the damages and to plan and execute the restoration works using remote sensing tools. Aerial data collection was performed one month, and one year after the fire. In 2014 the regenerated vegetation was surveyed and mapped in the field. Using the aerial photographs and the field data, the degree and extent of the fire damages, the types and the state of the vegetation and the presence and proportion of the invasive species were determined. Semi-automatic methods were used for the classification of completely, partially damaged and undamaged areas. Based on the results, the reforestation of the burnt area is suggested to prevent the overspreading of white poplar against common junipers and to clean the area from the most frequent invasive species. To monitor the regeneration of the vegetation and the spreading of the invasive species, further aerial photography and field campaigns are planned

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

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

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

Spatiotemporal Assessment of Vegetation Indices and Land Cover for Erbil City and Its Surrounding Using Modis Imageries

The rate of global urbanization is exponentially increasing and reducing areas of natural vegetation. Remote sensing can determine spatiotemporal changes in vegetation and urban land cover. The aim of this work is to assess spatiotemporal variations of two vegetation indices (VI), the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), in addition land cover in and around Erbil city area between the years 2000 and 2015. MODIS satellite imagery and GIS techniques were used to determine the impact of urbanization on the surrounding quasi-natural vegetation cover. Annual mean vegetation indices were used to determine the presence of a spatiotemporal trend, including a visual interpretation of time-series MODIS VI imagery. Dynamics of vegetation gain or loss were also evaluated through the study of land cover type changes, to determine the impact of increasing urbanization on the surrounding areas of the city. Monthly rainfall, humidity and temperature changes over the 15-year-period were also considered to enhance the understanding of vegetation change dynamics. There was no evidence of correlation between any climate variable compared to the vegetation indices. Based on NDVI and EVI MODIS imagery the spatial distribution of urban areas in Erbil and the bare around it has expanded. Consequently, the vegetation area has been cleared and replaced over the past 15 years by urban growth

Small Format Aerial Photography: Remote Sensing Data Acquisition For Environmental Analysis

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

Extraction of digital surface models from corona satellite stereo images

Satellite images can be utilised for observing surf icial changes, especially efficient in the monitoring of larger areas. The comparative analysis of high resolution images from earlier periods with recent data can provide insight in the scale of changes in topography, and with meteorological, hydrological and other historic records, can lead to better understanding and more reliable modelling of the predominant processes causing mass movement. More accurate morphometric and visual analysis of the topographic changes is possible using digital surface model (DSM), which can be obtained from satellite stereo images. In this paper, the authors evaluated methods of creation digital surface models obtained from satellite images from the CORONA program in monitoring surfic ial massmovement processes in the Fruška Gora mountain area , in the southern part of the Vojvodina province in Serbia. This area is of particular interest because of its favourable geographic location, rich geo- and cultural heritage and increasing demand for exploitation, which results in greater impact of natural hazards. The CORONA images were chosen because of good availability of high resolution coverage for the whole area from the period of past four decades

Extraction of digital surface models from CORONA satellite stereo images

Satellite images can be utilised for observing surficial changes, especially efficient in the monitoring of larger areas. The comparative analysis of high resolution images from earlier periods with recent data can provide insight in the scale of changes in topography, and with meteorological, hydrological and other historic records, can lead to better understanding and more reliable modelling of the predominant processes causing mass movement. More accurate morphometric and visual analysis of the topographic changes is possible using digital surface model (DSM), which can be obtained from satellite stereo images. In this paper, the authors evaluated methods of creation digital surface models obtained from satellite images from the CORONA program in monitoring surficial mass movement processes in the Fruška Gora mountain area, in the southern part of the Vojvodina province in Serbia. This area is of particular interest because of its favourable geographic location, rich geo- and cultural heritage and increasing demand for exploitation, which results in greater impact of natural hazards. The CORONA images were chosen because of good availability of high resolution coverage for the whole area from the period of past four decades

Extraction of digital surface models from corona satellite stereo images

Satellite images can be utilised for observing surf icial changes, especially efficient in the monitoring of larger areas. The comparative analysis of high resolution images from earlier periods with recent data can provide insight in the scale of changes in topography, and with meteorological, hydrological and other historic records, can lead to better understanding and more reliable modelling of the predominant processes causing mass movement. More accurate morphometric and visual analysis of the topographic changes is possible using digital surface model (DSM), which can be obtained from satellite stereo images. In this paper, the authors evaluated methods of creation digital surface models obtained from satellite images from the CORONA program in monitoring surfic ial massmovement processes in the Fruška Gora mountain area , in the southern part of the Vojvodina province in Serbia. This area is of particular interest because of its favourable geographic location, rich geo- and cultural heritage and increasing demand for exploitation, which results in greater impact of natural hazards. The CORONA images were chosen because of good availability of high resolution coverage for the whole area from the period of past four decades