This article aims to present the change detection methodology as experienced in the use of optical remote sensing imagery (Landsat) and its pitfalls when multitemporal analyses are performed with ...pixel-based (raster algebra) techniques. The existing methodologyrecommends fundamental data preparation (geometric, radiometric, topographic corrections) and offers numerous change detection techniques. Regardless of the carefully performed preparationscertain noise remains, which can drastically weight the imagery comparisons. This noise behaves as a detected change and could have such a false effect on the identified change pattern (i.e. false, non-intrinsic changes) that the quantitative evaluation might fail.Since this noise originates from the pre-processing algorithms as well as the natural and technological conditions during data acquisition it can not be completely removed by data corrections. A multiresolution change detection approach is therefore proposed. Taking into account the neighbourhood and change information from joining different spatial scales, the multi-resolution approach effectively reduces the amount of false changes. In the discussion the remote sensing imagery for surface change detection is evaluated.
The problems of transition to the new national coordinate system are discussed. The stress is laid upon the transformation of various spatial databases of the Surveying and Mapping Authority of the ...Republic of Slovenia. The ways of transformation between the old and new national coordinate systems and problems with the inhomogeneous accuracy of the old system are presented. The main goal of the paper is the presentation of transformation models with metre and decimetre levels of accuracy – the so called simple and complex models. These are the proposed transformation models that originate from the empirical knowledge on the Slovene national coordinate system. Analyses of consequences of various transformations for spatial data (e. g. areal distortions) have been carried out. The proposal of implementation of transition to the new coordinate system for spatial databases is presented. The course of events isdetermined for each of the spatial database. They are distinguished between two models according to the way of maintenance and the required accuracy of transformation. Some specific demands on theparticular databases were also discussed. Finally, the transformation of orthophoto – the first database that has already been transformed into the new coordinate system – is presented.
V 76 vzorcih mnogocvetne ljuljke, trpežne ljuljke, mačjega repa in črne detelje, košenih v različnih fazah morfološkega razvoja, smo določili vsebnost surove vlaknine (SV) po metodi Naumann in ...Bassler (1976) ter vsebnosti v nevtralnem detergentu netopne vlaknine (NDV), v kislem detergentu netopne vlaknine (KDV) in v kislem detergentu netopnega lignina (KDL) po metodi Goering in Van Soest (1970). Vsebnosti SV, NDV, KDV in KDL v teh vzorcih smo določili tudi s pomočjo filtrskih vrečk in jih označili kot SVFV, NDVFV, KDVFV in KDLFV. Za razliko od trav, kjer so bile razlike med vsebnostmi NDV in NDVFV, KDV in KDVFV ter KDL in KDLFV majhne (v povprečju največ 20 g kg–1 SS med vsebnostma KDV in KDVFV pri trpežni ljuljki), smo pri črni detelji ugotovili velike razlike med vsebnostmi NDV in NDVFV (v povprečju 46 g kg–1 SS), KDV in KDVFV (v povprečju 66 g kg–1 SS) ter KDL in KDLFV (v povprečju 35 g kg–1 SS). Med vsebnostmi SV in SVFV tako pri travah kot pri detelji tako velikih razlik ni bilo (v povprečju je bila največja razlika 17 g kg–1 SS pri črni detelji). Če smo vzorce črne detelje pred določanjem vsebnosti NDVFV, KDVFV in KDLFV sprali z acetonom (acNDVFV, acKDVFV in acKDLFV), smo ugotovili, da se razlike v vsebnosti med njimi in vsebnostmi NDV, KDV in KDL močno zmanjšajo (v povprečju na 15, 17 oz. 5 g kg–1 SS). Stopnjo povezanosti med vsebnostmi SV, NDV, KDV in KDL ter vsebnostmi vlaknine v vzorcih, ki smo jih določili s pomočjo filtrskih vrečk, smo ugotavljali s koeficientom determinacije (R2) in standardno napako ocene (SEE). Najboljše ocene vsebnosti SV (R2 = 0,89, SEE = 1,73), NDV (R2 = 0,98, SEE = 1,73), KDV (R2 = 0,90, SEE = 1,73) in KDL (R2 = 0,69, SEE = 1,73) smo dosegli, ko smo v enačbe kot posamezne odvisne spremenljivke vključili SVFV, NDVFV, KDVFV in KDLFV trav ter SVFV, acNDVFV, acKDVFV in acKDLFV detelj. Rezultati so pokazali, da je metoda s filtrskimi vrečkami je primerna za določanje vsebnosti SV, NDV in KDV, ne pa tudi za določanje vsebnosti KDL.
The Slovenian permanent GPS stations network named SIGNAL is presented. The discussion about the users, user-access statistics and operation cost items is included. Positive and negative sides of ...various future financing and tariff systems are analysed. The articleconcludes with the discussion on the factual usability of such positioning system in the light of Galileo and GLONASS development.
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