Automated mapping of climatic variables using spatio-temporal geostatistical methods

Javno dostupni meteorološki podaci, kako sa stanica tako i iz daljinske detekcije, korišćeni su za prostorno vremensku interpolaciju temperature vazduha iznad površine Zemlje. Zastupljenost i pogodnost javno dostupnih podataka je ocenjena, kroz tri aspekta kontrole kvaliteta: (a) zastupljenost u geografskom i prostornom domenu, (b) zastupljenost u karaktestičnom prostoru (feature space; bazirano na MaxEnt metodi), kao i (c) pogodnost korišćenja podataka za prostorno-vremensku predikciju (na osnovu kros-validacije prostorno-vremnskog regresionog kriginga). Rezultati pokazuju da je kombinovani set podataka (GSOD i ECA&D) značajno klasteriran i u geografskom i u karakterističnom prostoru. Uprkos klasteriranju, preliminarni rezultati globalne interpolacije primenom prostorno-vremenskog regresionog kriginga koristeći merenja sa stanica i snimke daljinske detekcije su pokazali da se tako mogu dobiti precizne globalne karte dnevne temperature. Oko 9000 stanica kombinovanog seta podataka (GSOD i ECA&D) je korišćeno za prostorno-vremensko geostatističko modeliranje i predikciju dnevnih temperatura u rezoluciji 1 km, iznad površine Zemlje. Za predikciju srednjih, minimalnih i maksimalnih temperatura korišćen je regresioni kriging uz pomoćne prediktore: MODIS LST 8-dnevni snimci, topografski lejeri (DEM i TWI) i geometrijski temperaturni trend. Model i predikcija se odnose na 2011 godinu, ali ista metodologija bi se mogla primeniti od 2001 godine do danas (od kada su dostupni MODIS snimci). Rezultati pokazuju da je prosečna tačnost predikcije za srednju, minimalnu i maksimalnu temperaturu vazduha oko ±2°C za oblasti gusto pokrivene stanicama i između ±2°C i ±4°C za oblasti koje su slabo pokrivene stanicama. Najniža tačnost predikcije je dobijena u planinskim predelima i na Antartiku, oko 6°C. R softverski paket, meteo, je razvijen kao resenje za automatsko kartiranje. Razvijen je i paket plotGoogleMaps za automatsku vizuelizaciju na Web-u, koristeći Google Maps API.Publicly available global meteorological data sets, from ground stations and remote sensing, are used for spatio-temporal interpolation of air temperature data for global land areas. Publicly available data sets were assessed for representation and usability for global spatio-temporal analysis. Three aspects of data quality were considered: (a) representation in the geographical and temporal domains, (b) representation in the feature space (based on the MaxEnt method), and (c) usability i.e. fitness of use for spatio-temporal interpolation (based on cross-validation of spatio-temporal regression-kriging models). The results show that clustering of meteorological stations in the combined data set (GSOD and ECA&D) is significant in both geographical and feature space. Despite the geographical and feature space clustering, preliminary tested global spatio-temporal model using station observations and remote sensing images, shows this method can be used for accurate mapping of daily temperature. Around 9000 stations from merged GSOD and ECA&D daily meteorological data sets were used to build spatio-temporal geostatistical models and predict daily air temperature at ground resolution of 1 km for the global land mass. Predictions were made for the mean, maximum and minimum temperature using spatio-temporal regression-kriging with a time series of MODIS 8 day images, topographic layers (DEM and TWI) and a geometrical temperature trend as covariates. The model and predictions were built for the year 2011 only, but the same methodology can be extended for the whole range of the MODIS LST images (2001–today). The results show that the average accuracy for predicting mean, maximum and minimum daily temperatures is RMSE = ± 2°C for areas densely covered with stations, and between ± 2°C and ± 4°C for areas with lower station density. The lowest prediction accuracy was observed in highlands (> 1000 m) and in Antarctica with a RMSE around 6°C. Automated mapping framework is developed and implemented as R package meteo. Likewise, package plotGoogleMaps for automated visualisation on the Web, base on Google Maps API is developed

Beyond Diophantine Wannier diagrams: Gap labelling for Bloch-Landau Hamiltonians

It is well known that, given a $2d$ purely magnetic Landau Hamiltonian with a constant magnetic field $b$ which generates a magnetic flux $\varphi$ per unit area, then any spectral island $\sigma_b$ consisting of $M$ infinitely degenerate Landau levels carries an integrated density of states $\mathcal{I}_b=M \varphi$. Wannier later discovered a similar Diophantine relation expressing the integrated density of states of a gapped group of bands of the Hofstadter Hamiltonian as a linear function of the magnetic field flux with integer slope. We extend this result to a gap labelling theorem for any $2d$ Bloch-Landau operator $H_b$ which also has a bounded $\mathbb{Z}^2$-periodic electric potential. Assume that $H_b$ has a spectral island $\sigma_b$ which remains isolated from the rest of the spectrum as long as $\varphi$ lies in a compact interval $[\varphi_1,\varphi_2]$. Then $\mathcal{I}_b=c_0+c_1\varphi$ on such intervals, where the constant $c_0\in \mathbb{Q}$ while $c_1\in \mathbb{Z}$. The integer $c_1$ is the Chern character of the spectral projection onto the spectral island $\sigma_b$. This result also implies that the Fermi projection on $\sigma_b$, albeit continuous in $b$ in the strong topology, is nowhere continuous in the norm topology if either $c_1\ne0$ or $c_1=0$ and $\varphi$ is rational. Our proofs, otherwise elementary, do not use non-commutative geometry but are based on gauge covariant magnetic perturbation theory which we briefly review for the sake of the reader. Moreover, our method allows us to extend the analysis to certain non-covariant systems having slowly varying magnetic fields.Comment: 17 pages, no figure

Kvantitativno i kvalitativno određivanje enrofloksacina u tkivima riba

Presence of enrofloxacin residues in fish liver, kidney and muscle tissue was investigated after per os application of the drug. For the purpose of determination of enrofloxacin, the following analytical methods were used: microbiological method - plate pH 8 with Escherichia coli ATCC 11303 and HPLC method with fluorescence detection. After a 5-day oral treatment of carps, enrofloxacin residues in tissues were determined up to the 10th day after the end of the drug application. Enrofloxacin content determined by the HPLC method was lower than MRL; drug residues were determined in liver on the 6th day after treatment, in kidney on the 7th day and in muscle on the 9th day after treatment. The results of enrofloxacin residues determination by screening method on the medium with E. Coli ATCC 11303, pH 8 show that this procedure can be used for qualitative determination of enrofloxacin. The screening method allows determination of enrofloxacin in fish tissues below the MRL. Cyprofloxacin was not detected in fish liver, kidney and muscle tissue.Prisustvo rezidua enrofloksacina u jetri, bubregu i mesu riba ispitano je posle njegovog peroralnog aplikovanja. Za ispitivanje rezidua su korišćene: mikrobiološ ka metoda - ploča pH 8 sa Escherichia coli ATTC 11303 i HPLC metoda sa flurescentnom detekcijom. Posle petodnevne oralne terapije šarana rezidue enrofloksacina u tkivima riba su dokazane i devetog dana po prestanku terapije. Sadržaj enrofloksacina dokazan HPLC postupkom, niži od MRL vrednosti, u jetri je dokazan šestog dana po prestanku terapije, u bubregu sedmog dana a u mišićnom tkivu devetog dana po prestanku terapije. Rezultati utvrđivanja rezidua enrofloksacina skrining postupkom na podlozi pH 8 E.coli ATCC 11303 pokazuju da se ovaj postupak može koristiti za kvantitativno dokazivanje enrofloksacina. Skrining postupkom u tkivima riba mogu da se dokažu količine enrofloksacina ispod MRL vrednosti. Ciprofloksacin nije utvrđen u jetri, bubrezima i mesu riba

3D urban solar potential maps - case study of the i-SCOPE project

Solar maps as web cartographic products that provide information on solar potential of surfaces on the Earth have been exploited in decision making, awareness raising, and promoting the use of solar energy. Web based solar maps of cities have become popular services as the use of solar energy is especially attractive in urban environments. The article discusses the concept and aspects of urban solar potential maps on the example of the i-Scope project as a case study. The i-Scope roof solar potential service built on 3-D urban information models was piloted in eight European cities. To obtain precise data on solar irradiation, a good quality digital surface model is required. A cost efficient innovative method for generation of digital surface model from stereophotogrammetry for urban areas where no advanced source data (e. g. LiDAR) exist is developed. The method works for flat, shed and gable roofs and provides sufficient accuracy of digital surface model

Automated mapping of climatic variables using spatio-temporal geostatistical methods

Javno dostupni meteorološki podaci, kako sa stanica tako i iz daljinske detekcije, korišćeni su za prostorno vremensku interpolaciju temperature vazduha iznad površine Zemlje. Zastupljenost i pogodnost javno dostupnih podataka je ocenjena, kroz tri aspekta kontrole kvaliteta: (a) zastupljenost u geografskom i prostornom domenu, (b) zastupljenost u karaktestičnom prostoru (feature space; bazirano na MaxEnt metodi), kao i (c) pogodnost korišćenja podataka za prostorno-vremensku predikciju (na osnovu kros-validacije prostorno-vremnskog regresionog kriginga). Rezultati pokazuju da je kombinovani set podataka (GSOD i ECA&D) značajno klasteriran i u geografskom i u karakterističnom prostoru. Uprkos klasteriranju, preliminarni rezultati globalne interpolacije primenom prostorno-vremenskog regresionog kriginga koristeći merenja sa stanica i snimke daljinske detekcije su pokazali da se tako mogu dobiti precizne globalne karte dnevne temperature. Oko 9000 stanica kombinovanog seta podataka (GSOD i ECA&D) je korišćeno za prostorno-vremensko geostatističko modeliranje i predikciju dnevnih temperatura u rezoluciji 1 km, iznad površine Zemlje. Za predikciju srednjih, minimalnih i maksimalnih temperatura korišćen je regresioni kriging uz pomoćne prediktore: MODIS LST 8-dnevni snimci, topografski lejeri (DEM i TWI) i geometrijski temperaturni trend. Model i predikcija se odnose na 2011 godinu, ali ista metodologija bi se mogla primeniti od 2001 godine do danas (od kada su dostupni MODIS snimci). Rezultati pokazuju da je prosečna tačnost predikcije za srednju, minimalnu i maksimalnu temperaturu vazduha oko ±2°C za oblasti gusto pokrivene stanicama i između ±2°C i ±4°C za oblasti koje su slabo pokrivene stanicama. Najniža tačnost predikcije je dobijena u planinskim predelima i na Antartiku, oko 6°C. R softverski paket, meteo, je razvijen kao resenje za automatsko kartiranje. Razvijen je i paket plotGoogleMaps za automatsku vizuelizaciju na Web-u, koristeći Google Maps API.Publicly available global meteorological data sets, from ground stations and remote sensing, are used for spatio-temporal interpolation of air temperature data for global land areas. Publicly available data sets were assessed for representation and usability for global spatio-temporal analysis. Three aspects of data quality were considered: (a) representation in the geographical and temporal domains, (b) representation in the feature space (based on the MaxEnt method), and (c) usability i.e. fitness of use for spatio-temporal interpolation (based on cross-validation of spatio-temporal regression-kriging models). The results show that clustering of meteorological stations in the combined data set (GSOD and ECA&D) is significant in both geographical and feature space. Despite the geographical and feature space clustering, preliminary tested global spatio-temporal model using station observations and remote sensing images, shows this method can be used for accurate mapping of daily temperature. Around 9000 stations from merged GSOD and ECA&D daily meteorological data sets were used to build spatio-temporal geostatistical models and predict daily air temperature at ground resolution of 1 km for the global land mass. Predictions were made for the mean, maximum and minimum temperature using spatio-temporal regression-kriging with a time series of MODIS 8 day images, topographic layers (DEM and TWI) and a geometrical temperature trend as covariates. The model and predictions were built for the year 2011 only, but the same methodology can be extended for the whole range of the MODIS LST images (2001–today). The results show that the average accuracy for predicting mean, maximum and minimum daily temperatures is RMSE = ± 2°C for areas densely covered with stations, and between ± 2°C and ± 4°C for areas with lower station density. The lowest prediction accuracy was observed in highlands (> 1000 m) and in Antarctica with a RMSE around 6°C. Automated mapping framework is developed and implemented as R package meteo. Likewise, package plotGoogleMaps for automated visualisation on the Web, base on Google Maps API is developed

Three-dimensional urban solar potential maps: Case study of the i-Scope Project

Solar maps as web cartographic products that provide information on solar potential of surfaces on the Earth have been exploited in decision making, awareness raising, and promoting the use of solar energy. Web based solar maps of cities have become popular services as the use of solar energy is especially attractive in urban environments. The article discusses the concept and aspects of urban solar potential maps on the example of the i-Scope project as a case study. The i-Scope roof solar potential service built on 3-D urban information models was piloted in eight European cities. To obtain precise data on solar irradiation, a good quality digital surface model is required. A cost efficient innovative method for generation of digital surface model from stereophotogrammetry for urban areas where no advanced source data (e. g. LiDAR) exist is developed. The method works for flat, shed and gable roofs and provides sufficient accuracy of digital surface model

SoilGrids250m: Global gridded soil information based on machine learning

This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total)