Regional authorities require detailed and georeferenced information on the status of forests to ensure a sustainable forest management. One of the objectives in the FP7 project EUFODOS was the development of an operational service based on airborne laser scanning and satellite data in order to derive forest parameters relevant for the management of protective forests in the Alps. The estimated parameters are forest type, stem number, height of upper layer, mean height and timber volume. RapidEye imagery was used to derive coniferous and broadleaf forest classes using a logistic regression-based method. After the generation of a normalised Digital Surface Model and a forest mask, the forest area was segmented into homogeneous polygons, tree tops were detected, and various forest parameters are calculated. The accuracy of such an assessment was comparable with some previous studies, and the R-square between the estimated and measured values was 0.69 for tree top detection, 0.82 for upper height and 0.84 for mean height. For the calculation of timber volume, the R² for modelling is 0.82, for validation with an independent set of field plots, the R² is 0.71. The results have been successfully integrated into the regional forestry GIS and are used in forest management.
ENVISAT ASAR Images, Earth observation for ocean-atmosphere interactions science, Conference, 28-31.10.(2014), Frascati (Italy). 5. A. Krężel, K. Bradtke: Estimation of solar energy influx to the sea in the light of fast satellite technique development, in Rugescu Radu ed., Solar Power, Rijeka, InTech, (2012), ch. 10, pp. 171-192. 6. A. Krężel, Ł. Kozłowski, M. Paszkuta: A simple model of light transmission through the atmosphere over the Baltic Sea utilising satellitedata, Oceanologia, 50 (2), (2008), 125-146. 7. A. Mazur, A. Krężel: Object-based classification of
Introduction Flood hazard monitoring covers a wide range of activities: from forecasting rainfalls to monitoring flood infrastructures. The research of flood risk monitoring based on the optical satellitedata was conducted as part of the SAFEDAM project – “Advanced technologies in the prevention of flood hazard”. The project is financed by the National Centre for Research and Development in Defence and Security Programme. The aim of the SAFEDAM project is to develop an innovative system enabling the risk of river floods in Poland to be monitored using, inter
, Vienna. Jensen JR (2000): Remote sensing of the environment: an earth resource perspective. Prentice-Hall. Upper Saddle River, New Jersey. Justice CO, Townshend JRG, Holben BN, Tucker C J (1985): Analysis of the phenology of global vegetation using meteorological satellitedata. Int J Remote Sens 6: 1271-1318. Kepka P, Brom J, Procházka J, Vinciková H, Pecharová E (2010): Developing of GIS based decision-support tools for agricultural counter-measurements after radioactive accident. Bezpečnost jaderné energie 18: 172-173 (in Czech). Knorn J, Rabe A, Radeloff VC
Mediterranean regions have experienced significant soil degradation over the past decades. In this context, careful land observation using satellite data is crucial for understanding the long-term usage patterns of natural resources and facilitating their sustainable management to monitor and evaluate the potential degradation. Given the environmental and political interest on this problem, there is urgent need for a centralized repository and mechanism to share geospatial data, information and maps of land change. Geospatial data collecting is one of the most important task for many users because there are significant barriers in accessing and using data. This limit could be overcome by implementing a WebGIS through a combination of existing free and open source software for geographic information systems (FOSS4G).
In this paper we preliminary discuss methods for collecting raster data in a geodatabase by processing open multi-temporal and multi-scale satellite data aimed at retrieving indicators for land degradation phenomenon (i.e. land cover/land use analysis, vegetation indices, trend analysis, etc.). Then we describe a methodology for designing a WebGIS framework in order to disseminate information through maps for territory monitoring. Basic WebGIS functions were extended with the help of POSTGIS database and OpenLayers libraries. Geoserver was customized to set up and enhance the website functions developing various advanced queries using PostgreSQL and innovative tools to carry out efficiently multi-layer overlay analysis. The end-product is a simple system that provides the opportunity not only to consult interactively but also download processed remote sensing data.
Multi-Scale Effect on Landscape Pattern Analysis Using Satellite Data with a Range of Spatial Resolutions
In recent years, identifying the relationship between pattern and scale has emerged as a central issue in ecology and geography. Scale has been defined by grain or resolution but bias in results will occur if the scale is wrongly selected relevant to the landscape evaluation. In this research, satellite data of varying resolution, QuickBird (2.5m), ALOS/AVNIR-2 (10m), Terra/ASTER (15m) and Landsat/ETM+ (30m), were employed to analyze the scale effects of grain size. The research was implemented at Azeta, a typical rural landscape located in Sakura City, central Japan. Land-cover classifications were first implemented using the Maximum Likelihood Method on satellite data of varying resolution. Based on the results of these classifications, a number of landscape metrics imbedded in the FRAGSTATS were extracted for landscape pattern analysis. The results indicate that most landscape patterns show some degree of consistency and scaling relations such as power-law among the various satellite resolutions. The applicability of these various satellite data resolutions for landscape analysis in the target area was also evaluated.
The aim of the study is to find the relationship between the land surface temperature and air temperature and to determine the hot spots in the urban area of Bucharest, the capital of Romania. The analysis was based on images from both moderate-resolution imaging spectroradiometer (MODIS), located on both Terra and Aqua platforms, as well as on data recorded by the four automatic weather stations existing in the endowment of The National Air Quality Monitoring Network, from the summer of 2017. Correlation coefficients between land surface temperature and air temperature were higher at night (0.8-0.87) and slightly lower during the day (0.71-0.77). After the validation of satellite data with in-situ temperature measurements, the hot spots in the metropolitan area of Bucharest were identified using Getis-Ord spatial statistics analysis. It has been achieved that the “very hot” areas are grouped in the center of the city and along the main traffic streets and dense residential areas. During the day the "very hot spots” represent 33.2% of the city's surface, and during the night 31.6%. The area where the mentioned spots persist, falls into the "very hot spot" category both day and night, it represents 27.1% of the city’s surface and it is mainly represented by the city center.
://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts .> (accessed 25.9.2018). Fick, S., Hijmans, R. 2017. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology . DOI: 10.1002/joc.5086. Google, 2018. Google Map. < https://www.google.si/maps/ > (accessed 25.9.2018). Gouveia, C., Trigo, R. M., DaCamara, C. C. 2009. Drought and vegetation stress monitoring in Portugal using satellitedata. Natural Hazards and Earth System Sciences 9(1), 185–195. DOI: 10.5194/nhess-9-185-2009 Hasenauer, S., Mistelbauer, T., Kokalj, Ž., Grlj, A., Hochströger, S., Bucur, A., Bartošová
Effective assessment of environmental changes requires an update of vegetation maps as it is an indicator of both local and global development. It is therefore important to formulate methods which would ensure constant monitoring. It can be achieved with the use of satellite data which makes the analysis of hard-to-reach areas such as alpine ecosystems easier.
Every year, more new satellite data is available. Its spatial, spectral, time, and radiometric resolution is improving as well. Despite significant achievements in terms of the methodology of image classification, there is still the need to improve it. It results from the changing needs of spatial data users, availability of new kinds of satellite sensors, and development of classification algorithms. The article focuses on the application of Sentinel-2 and hyperspectral EnMAP images to the classification of alpine plants of the Karkonosze (Giant) Mountains according to the: Support Vector Machine (SVM), Random Forest (RF), and Maximum Likelihood (ML) algorithms. The effects of their work is a set of maps of alpine and subalpine vegetation as well as classification error matrices. The achieved results are satisfactory as the overall accuracy of classification with the SVM method has reached 82% for Sentinel-2 data and 83% for EnMAP data, which confirms the applicability of image data to the monitoring of alpine plants.
The process of formation and rotting of ice on lakes is an integral part of the hydrological cycle of many lakes. The conditions of the ice regime significantly influence the ecological system of lakes. The article includes calculation and analysis of errors in the determination of the spatial ice distribution (spatial resolution of 4–6 km) on Lake Onego, Lake Ladoga, Lake Segozero and Lake Vigozero within the period of 2006−2017 according to National Snow and Ice Data Center (NSIDC), National Oceanic and Atmospheric Administration National Environmental Satellite, Data, and Information Service (NOAA NESDIS) data with regard to reliable Moderate Resolution Imaging Spectroradiometer (MODIS) data (spatial resolution of 500 m). It was established that within the monitoring period, NSIDC data have the minimum mean values of errors in determining the spatial distribution of ice on lakes (3−10%) compared to NOAA NESDIS data (11−19%) and are also of more practical interest in estimating the ice coverage of lakes. The dependence of the mean value of errors that occur in the determination of the spatial distribution of ice (according to NSIDC, NOAA and NESDIS data) on the actual value of ice coverage (according to MODIS) was revealed. The results show that the NSIDC data allow estimating adequately the phases of the ice regime; however, the formation of a daily time series of ice coverage during freeze-up and break-up phases is possible only with a significant error (mean value of absolute deviations according to MODIS data is up to 35%).