Cyanobacteria and cyanotoxins in Polish freshwater bodies.

In this work, the authors examined the presence of cyanobacteria and cyanotoxins in 21 samples collected from fresh water bodies located in 5 provinces in Poland: Lublin (2), Podlasie (1), Pomerania (6), Warmia-Masuria (1) and Wielkopolska (11). In addition, to determine the general pattern of geographical distribution, frequency of cyanobacteria occurrence, and cyanotoxins production, the published data from 238 fresh water bodies in Poland were reviewed. On the basis of these collected results, we concluded that Planktothrix, Aphanizomenon, Microcystis and Dolichospermum were dominant. The general pattern in geographical distribution of the identified cyanobacterial genera was typical of other eutrophic waters in Europe. The production of cyanotoxins was revealed in 18 (86%) of the 21 samples analyzed in the present work and in 74 (75%) of the 98 total water bodies for which the presence of toxins had been examined. Among the 24 detected microcystin variants, [Asp3]MC-RR was most common. These results can be verified when more data from the less explored water bodies in the southern and eastern parts of Poland are available.The authors would like to acknowledge the European Cooperation in Science and Technology, COST Action ES 1105 "CYANOCOST- Cyanobacterial blooms and toxins in water resources: Occurrence, impacts and management" for adding value to this study through networking and knowledge sharing with European experts and researchers in the field.42435837

Spectral library of herbaceous species of the University of Warsaw’s Botanic Garden

An objective of this paper is to form a spectral library of endmembers of the Polish Lowland vegetation species, which were collected in the Botanic Garden of the University of Warsaw, which is one of the oldest (it was founded in 1818) and the smallest (5 ha) botanic gardens in Poland. For the data acquisition ASD FieldSec3 JR, Chlorophyll Content Meter CCM-200 and a digital camera were used. Each spectral library set contains: 300 separate spectrometric measurements (100 dark current, 100 white reference and 100 ASD Plant Probe Leaf Clip); Chlorophyll Content Index and biometric information (e.g. LAI, fAPAR); 3 digital photos, time and localisation data. The spectral library contains 73 characteristics of the most important plant species (from the “red list” of protected plants and the most famous plants of the Polish Lowland Flora). Now all these data will be upgrading the Swiss SPECCHIO library as a local Polish input to the European spectral database

The influence of natural environment’s components on spatial diversity of thermal emissivity of the Gąsiennicowa Valley

Hyperspectral remote sensing is still being discovered as a tool about analytical possibilities for the research on areas about diversifi ed character, like mountain areas. This study investigated the relationship between spatial variability of surface temperature of the Gąsienicowa Valley (the Tatra Mountains) and chosen components of the natural environment, such as: near-surface lithology layers, soil surfaces, land cover types, altitudes, slopes and aspects. Image of the surface radiation temperature was processed basing on the Digital Airborne Imaging Spectrometer (DAIS 7915) data. Thematic layers were: acquired from the Tatra National Park GIS Office (geology, lithology and soil layers), generated from DTM (altitude, slopes and aspects) and created from the DAIS RGB compositions data (land cover). The analysis of relationship between components and surface temperatures were measured by the power connection index (Richling, 1983) and connection index (Zagajewski, 2003). It has been stated that the greatest power of connections occurred between the radiation temperature and the soil surface, however on the majority surface of the Gąsienicowa Valley temperature responses most strongly to the land cover type

Assessment of geometry of radiation source-plant-detector on value of the remote sensing indices

The aim of this study is an analysis of an influence of geometry electromagnetic radiation (lamp or sun) - research target (leaves) - detector. The electromagnetic radiation was emitted by the lamp ASD ProLamp, which was installed at 30°, 45°, 90°, 135°, 150° angles. Reference measurements was a system in which the lamp and detector were set vertically. During the laboratory measurements spectral properties of Rhoeo spathacea were acquired. Based on the measured spectral curves of vegetation remote sensing indices were calculated and statistical ANOVA tests were applied. The results confirmed the relationship between the geometry of the lamp - plant - detector. The higher the angle the incident radiation results were less diverse and close to optimum values were observed. Analysis of the indicators showed that the high variability characterized by the indicators measuring water, chlorophyll contents and overall vigor parameters of plants. While the tests can be used for measuring rates of nitrogen content, the absorption of carotenoids and photosynthetically active radiation

Application of Sentinel-2 and EnMAP new satellite data to the mapping of alpine vegetation of the Karkonosze Mountains

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 use of the artificial neural networks to update the CORINE Land Cover maps

Aktualne mapy pokrycia terenu są podstawą wielu dyscyplin nauki oraz mają szerokie zastosowanie aplikacyjne. Jednym z problemów aktualizacji map jest proces aktualizacji danych. Teledetekcja dostarcza codziennie nowych zobrazowań satelitarnych, które mogą zaspokoić potrzeby aktualizacji baz danych. W niniejszym artykule autorzy przedstawiają metodę klasyfikacji pokrycia terenu sztucznymi sieciami neuronowymi fuzzy ARTMAP zgodnie z założeniami i legendą Corine Land Cover na podstawie danych satelitarnych Landsat, które wykorzystywane są do opracowania map pokrycia terenu. W artykule użyto jako danych referencyjnych i weryfikacyjnych najnowszą mapę Corine Land Cover (CLC) 2012. Do przeprowadzenia klasyfikacji symulatorem wykorzystano trzy zdjęcia satelitarne Landsat TM (21.04.2011, 05.06.2010, 27.08.2011). Obszarem badań były okolice Warszawy. Wynikami pracy symulatora są mapy klasyfikacji pokrycia terenu oraz macierze błędów klasyfikacji. Uzyskane wyniki potwierdzają, że sztuczne sieci neuronowe mogą z powodzeniem być wykorzystywane do aktualizacji map pokrycia terenu.Modern land cover maps are the basis of many scientific disciplines and they are widely applied. One of the problems connected with the revision of maps is the data updating procedure. Remote Sensing daily provides us with the new satellite images, that can meet the needs of database updates. In this article the method of classification for land cover with the artificial, neural, fuzzy ARTMAP networks is presented by the authors in accordance with the objectives and legend of the CORINE Land Cover Map on the basis of the Landsat satellite data, which are used to elaborate the land cover maps. The latest CORINE Land Cover map 2012 polygons are used as the reference and verification data. Three satellite Landsat TM images of 21.04.2011, 05.06.2010, 27.08.2011 are processed by a fuzzy, artificial, neural network classificatory simulator. The area of research was Warsaw and its surrounding area. The results of this research are the classificatory land cover maps and error matrices. Acquired results confirm that the artificial neural networks can be successfully used for land cover updating