Geoinformation Issues Vol. 9, No 1, 2017
Editorial pages, contents, reviewers of journal "Geoinformation Issues"
editorial pages, contents, reviewers of journal to download
Monika Wilde-Piórko, Przemysław Dykowski, Marcin Polkowski, Tomasz Olszak, Marek Grad, Jan Kryński, Marcin Sękowski, Andrzej Krankowski, Marcin Rajner
Expanding seismic surface waves measurements towards low periods with gravity measurements
DOI: 10.34867/gi.2017.1
Abstract: Seismic events in gravity measurements are considered as disturbances and are usually removed from the records. However, the physical properties of tidal gravimetric instruments allow researchers to record seismic surface waves of very long periods. In the case of a superconducting gravimeter, periods of even up to 400 s can be determined. Simultaneous seismic and gravity records at the same locations allow the study of a wider response for incoming seismic waves by using two quite different instruments. For test purposes 4 seismometer-gravimeter pairs were temporarily deployed in Poland at three locations: Borowa Gora Geodetic-Geophysical Observatory (BG), Jozefoslaw Astro-Geodetic Observatory (JO), and Lamkowko Satellite Observatory (LA). During the test period from December 2016 to May 2017 several large teleseismic events were observed with well-formed surface waves. Group velocity dispersion curves for long surface waves, as well as periods of free oscillations are presented for selected events. The correlation of a broadband seismometer signal with different types of gravimetric sensors signals gives the opportunity to analyse gravimeter noise components, in the instrumental and micro-seismic domains.
Zbigniew Bochenek, Katarzyna Dąbrowska-Zielińska, Radosław Gurdak, Fabrizio Niro, Maciej Bartold, Patryk Grzybowski
Validation of the LAI biophysical product derived from Sentinel-2 and Proba-V images for winter wheat in western Poland
DOI: 10.34867/gi.2017.2
Abstract: The main objective of the work presented is to assess applicability of new-generation satellite data for deriving Leaf Area Index (LAI) information. Two types of data were used in the study: Sentinel-2 and Proba-V images. Various vegetation indices were derived from these data and correlated with ground measured LAI values. The results of the study revealed that there is a good relationship between satellite based indices – Normalized Difference Vegetation Index (NDVI), Normalized Difference Infrared Index (NDII), and Disease Water Stress Index (DSWI) – and ground based LAI, but the strength of this relationship depends on the phase of crop development. It was also found that the accuracy of LAI determination with the use of the vegetation index derived from Sentinel-2 and Proba-V data is similar when applying the image acquisition at the proper date – the heading stage for winter wheat.
Anna Mirończuk, Agata Hościło
Mapping tree cover with Sentinel-2 data using the Support Vector Machine (SVM)
DOI: 10.34867/gi.2017.3
Abstract: The knowledge on forest resources is important for sustainable forest management at local and national level. The aim of this paper is to examine the efficacy of the Support Vector Machine (SVM) approach for tree cover mapping based on Sentinel-2 images and to explore the potential of the Sentinel-2 data for the assessment of tree cover. Sentinel-2 is a constellation of two European satellites providing innovative wide-swath (up to 290 km), high-resolution and multispectral data (13 spectral bands at 10, 20 and 60 m spatial resolution). The study area is located in the Forest Promotion Complex, which is a part of the Knyszyn Forest Landscape Park in Poland. The SVM classification was performed on the single images (spring and summer season) and on multi-date Sentinel-2 images (images from two dates classified simultaneously). In addition, the use of high-resolution bands and a combination of the 10 m and 20 m spatial resolution data was examined. The overall accuracy for all performed classification was very high and reached the level of 96.7%–99.6%, which confirms that SVM classification can be successfully applied for tree cover mapping. The analysis showed that the Sentinel-2 images acquired in the middle of the vegetation season, when the leaves are fully developed are more suitable for tree cover mapping than the images acquired in spring.
Jan Musiał, Jędrzej Bojanowski
AVHRR LAC satellite cloud climatology over Central Europe derived by the Vectorized Earth Observation Retrieval (VEOR) method and PyLAC software
DOI: 10.34867/gi.2017.4
Abstract: The study presents the first edition of cloud coverage and cloud physical properties climate data records (CDRs) over Central Europe compiled from 1 × 1 km resolution AVHRR imagery. The CDRs cover a climatological period of 30 years from 1986 to 2016. The dataset was generated using a novel Vectorized Earth Observation Retrieval (VEOR) algorithm that is an extension of the fast look-up table approach implemented in the Probabilistic Cloud Mask (PCM) method. AVHRR local area coverage (LAC) L1b data were pre-processed to reflectances and brightness temperatures using the PyLAC software, which is a modification of the PyGAC package used to generate CM SAF CLARA-A2 dataset from AVHRR global area coverage (GAC) imagery. The main motivation for the study was the analysis of small scale changes in cloudiness and its physical properties induced by local factors that are not apparent at coarse GAC resolution. A secondary goal was to create a framework for VEOR training against MODIS imagery and MODIS-derived cloud products, and then applying it to data originating from other sensors such as AVHRR.
Martyna Gatkowska, Karolina Wróbel, Karol Paradowski, Michał Wyczałek
Development of a satellite data based model for homogeneous polygons delimitation, for operational use for the agricultural sector in Poland
DOI: 10.34867/gi.2017.5
Abstract: The paper presents a model for homogeneous land-soil units delimitation on the basis of three main components: NDSI index (Normalized Difference Soil Index) calculated on the basis of Sentinel-2 satellite data for bare soil, NDVI (Normalized Difference Vegetation Index) calculated also on the basis of Sentinel-2 satellite data for vegetation in the end of growing phase and the DTM (Digital Terrain Model) derived from the SRTM mission. The stages of development of the designed model are presented together with the assumptions and thesis applied. The applicability of the method used and further steps to be performed are explained. The comparison of the results of land-soil units delimitation derived on the basis of the proposed model with the results of in-field electromagnetic scanning measurements (EM-38) is demonstrated. The model is proved to be applicable for the demarcation of soil managements zones in the fields and the need for its further validation is stated.