Bestand wählen
Merken

Satellite Snow Cover Products Evaluation and Validation Platform Developed Entirely With Floss Software

Zitierlink des Filmsegments
Embed Code

Automatisierte Medienanalyse

Beta
Erkannte Entitäten
Sprachtranskript
my name is a signatures from from may now among working at the remote sensing and GIS laboratory inside of Romanian Metrological Administration I would like to present you a few of the things we did we play it pose plate recently on and managing the Snow related to data mostly satellite to do like products say as you may
all know snow is said to be important parameter of the importance of the source of all this in my country is a number of things depends on the amount of snow we we had so it's very important what a hydropower is very important and I want to know we have like floods or avalanches or if you able to go skiing during the winter and and and so on and there it's that's why it's so it's necessary to have a good time estimation of what what what does amount of snow What is the extent of the snow to occur at any moment and of course we have a network of flow of notorious stations with the our spots we could not cover all the areas and especially in the mountains the uh they stand the devil there's no it's it's it's it's varies a lot saying this points remote sensing data could offer you a some extra information to be able to to map the the snow so deferral FOAF of a few projects we here and we created a database so for different and products created by some other projects and but it just created by dealers were our contour for our 0 interest integrated some tools to be able to analyze inspected the this kind of multidimensional the of circuit to move data so is only open source from this and this alone standards from all GC kind of kind of same things I was shown so only by year by under there so I was
emotionally we did this kind of work in there is some research projects so the main product written this is this snowball who started of last year and at this time so it's like it will work is almost 1 year but they also rely on the some datasets and some knowledge we we obtained from from some some other projects and the most important is this prior and project how
mention later so if we go to the data as mentioned we have Michael who will flow of of data it that that is 1st snow so we have this online . Tories with open access that could do to get to snowing 80 parameters like snow wanted to snow so we stand so what equivalent or snow temperature and and and so on with very different resolutions knows the system and the resources costs of course but the number of this kind of report it was online each different and with different specifications so what we did was still to to get all these data in our own home imaginaries format and we have this kind of data for the last 10 15 years with a different regional so dissolution in temporal are in the spatial resolution I mention
something other crime and that was a F P 7 project at the end of this year and the aim of the project or a part of this project and the aim was to create a downstream service in Europe accompanied with downstream surface to provide the products related to snow and ice of Copernicus is the European program for the for space uh has a number of core services which share like land marine atmosphere and so on and how this concept of Dostum services for more refined needs where you could have like this kind of on the same principle you could have this kind of services that all 4 of data that are more specialized or more refined and in many ways so if you
want some good thought I'd died data related to snow from Europe you could choose the crowd and your portal is all built on open source technology as well
OK but we don't have only this kind of data we also have the data from the from the benefits of stations from all kind of measurements in field on the also interpolated data and outputs for numerical models all kind of outputs like snow depth mother precipitation and temperature and and and so on the so
hum as I mentioned we we have this service in the in our in our institution and the created some moms some links to the existing services so of to be able to connect to that service and so integrate data from that that service blowing in our service or dollar the subset and and so on in case of the crime and so it is very easy because they they they have expose OGC services so you don't really have to get that date on your machine what depends but for some others who have just to to find some easy scripts to get they got active TB FTP and so on and then once in form it could be accessed from from our from from our service then you could draw on all kind of service processing but we we define for for the count this kind of data and you could also be integrated other metrological para meters that are published flowers the i our as
a mentioned is open for totally open-source stack for this um so we get beat up in this kind of this kind sources may come all the survey data are easy to be done some of our reference data on and on and on the left part we have is the users that that actually are my colleagues initiated 1 have extended users yet because it I was not the case and they could use to 3 types of clients web client not so much work kinds of now just a client and the the command line client and this propose stage of the project most of the the the you could do lot for the common line to that revolve so for this kind of client you could do it if you look in the in in and into that of a database and you could get back a number of problems like a map the chart some real are statistics animations Viet apart from the from the server we have which would take time for the in plane flies impose pose gyrus we have some kind of GIS servers like you you will use the grass demand to explore our pupil data and you your server and your cash for all the reference data we have and for the web processing services we use pi WPS and then we have a catalog of products and that catalog this is based on on on June at work and as I mentioned we have a number of is not that long climates we have something that at this stage of the product that some some do demos build with the the affair for web applications so not only command line but some very early uh web web clients that are based on OpenLayers AGS QX would step this it's a whole mess all 4 of the and here this we use the TGS workshop things and to acquire and so on and of course we have some other core softer defined on every every several saying so to summarize using the clients we all recall colleagues we could get the results as map charts mostly charts statistics or or any nations
I as I mentioned the function idea that most stand on duos wrapped right now it's very consistent and that command-line thing but otherwise we did not say have a real client for for everything we have there but as an idea we
could you of course interactive visualization of the debate that you right I mentioned we could In integrate this which are the to become metrological data sets we could do all kind of queries we could do navigate temporary navigate for the datasets and extract data are on on on this criteria was to Donald state out into some kind of processing with with this with this data so good to slice sees you could resample would create profiles we have after algebra more you we do synthesis so we need talk about products from satellite data which are derived from from optical data out its you always get this type of problem and the usually the daily data on so we store data data mostly so most of all the problem we have is is based on the data but we consume that it's not really on work like that that especially during the winter will not find sigh days all these just this he was as the extent of the of the of the snow so then you have to do to create this kind of synthesis that doesn't pay for my colleagues from from from some time so now we have this kind of model you could just select your interval or it could cyclic you want to do 10 years of the 10 synthesis and just from a simple command and you get this kind in to have the fires and we use it and in in some other analysis and some other so that was a 1 of the most important things we did and also kind of statistics so as a mention of some
small web interfaces is good to allow you just to to point to the capital and the display some of the some of it being temper slices also not not very much you could integrate all kind of problems
as a measure for the entire catalog of the of the institution because while directly the data here 1 and as
a mentioned the dual of a number of processing things for the web interface is most of them is available only for online the complex 1 but you could do so this is from the web interface you could do profiles you could do for of at an example so for the snow water equivalent problem problems we are using at the European level on the left and a could do profile on 1 coverage or you could to get a pixel and go from 10 years of data and that the sum of this in the chart and see what what is what what is happening with that part that Pixar would digitize stream of an area and you could get statistics for for 14 for all the pixels inside the the area
the say as I met my conclusions and future work so now we have more of an almost 50 thousand horses core in this database we have very valuable on campus of temporal and spatial resolution so it's is not difficult to work with data which is roughly at 1 kilometres but go up to 60 located in the cell size that are poison treaty pretty bloom satisfied with what the system to do right now where we try to move with many of us as many of the the processing them operation from the command-line tool to some web interfaces so get all of the stand-alone the was on the interface migrate from the NGS clients we have announced some of the more friendly where we have a little more cop using bootstrap and but this is not so easy the but some more processing modules and the possibility to train the models in 1 single operations select a number of processing steps and and be able to run in energy so you I I I
think that it's thank you if you have questions and if there the I type of I defend semantics in remote-sensing image you know there are many of our wasn't techniques to find some values like classification Nixon of over as presented as a nation we are not doing deep the processing of the as in that we saw that the the table of the auditory so for instance the crime product where was very was involved so we use this service European Service was producing this kind of snow we stand snow water equivalent or snow temperature of maps and there's quite a complicated the infrastructure behind and then tested and validated our for the for the Europe so to find this kind of snow pixels and so it's not like it's so easy in the 1st time I'm doing but what they presented here we're not doing this time just get the product already processed by others either other services so the spatial resolution of your data is very the goal is low cost so have you ever compared with high high resolution and yes and there was while doing day ADD validation and evaluation was done at the high resolution data the problem is you don't have daily highs emission data to produce no maps you could get multi-state identity which is 250 by 250 of the the the maximum resolution of the this has which would get daily data we high-resolution images that even expensive or you could not get this kind of temporal resolution because the snow is quite dynamic in up to not been the Monitoring Service all of monthly the images some can my opinion have other thing you you you knew of very high rate of Monday but not monthly or something like that you also is quite expensive to to have like for entire Romania to have like sports data for 2 . 5 meters allusion it's expensive and it's not easy to to create a mosaic at the in in you not making it 1 day he's it's difficult to the satellite not think all the images in 1 day I learned from is fine because you have a linear in yellow is yeah but you could have clouds in the day in the that's not the way even if you're not gets the lowest no map from from from from velocity will only for lucky because otherwise it's it would be value that they when have the data needs you have good which Woodchester have it in 10 days or in 7 days to have a full full color image of the of the consequences in our case different but I problems of course in the Forest maybe analysis know what's knows this fall from the trees and so you see it's sort of problems with this kind of and you questions in we have some time you promised soon as set of items were the same and only the high the whole which grows with them let's see if our and the 1st question astute man I you movement probe uh showing enhancement maps on results or something and not Hugh and now we now is intended but is not only a problem it would be open because I think it's still is open even if we don't advertising is on a public service right now but still we its yes or Romanian language over it's it's the it's the other group and collaborating with the Norwegian metrological institutes momentum at political Watson someone lonely Norway we call 0 yes we but much of them met all of but it introns sort of behind the Norwegian Computing Centre located and hosts are missing no maps from our preparations you're addition America and so I know this this uh um this institute in the way which is working with a mobile ad for and snow cover in
Norway get this project a snowball is cooperation between Romanian Norway so probably the assignments it's the other 2 delegates specialize in 11 of it's 2 to extract snow from the remote sensing data yeah but thanks the the same would he
Leistungsbewertung
Metrologie
Systemverwaltung
Systemplattform
Biprodukt
Überlagerung <Mathematik>
Biprodukt
Computeranimation
Satellitensystem
Offene Menge
Punkt
Momentenproblem
Snake <Bildverarbeitung>
Information
Computeranimation
Standardabweichung
Information Retrieval
DoS-Attacke
Parametersystem
Perspektive
Datennetz
Prozess <Informatik>
Datenhaltung
Güte der Anpassung
Prognostik
Biprodukt
Arbeitsplatzcomputer
Extreme programming
Näherungsverfahren
Datenverwaltung
Framework <Informatik>
Logiksynthese
Projektive Ebene
Information
Standardabweichung
Schnittstelle
Server
Stochastischer Prozess
Zahlenbereich
Instant Messaging
Dienst <Informatik>
Open Source
Datennetz
Hasard <Digitaltechnik>
Arbeitsplatzcomputer
Biprodukt
Maßerweiterung
Strom <Mathematik>
Ereignishorizont
Schätzwert
Open Source
Datenmodell
Bildauflösung
Systemplattform
Datenfluss
Satellitensystem
Flächeninhalt
Digitaltechnik
Parametersystem
Räumliche Anordnung
Manufacturing Execution System
Programm
Regulärer Graph
Zahlenbereich
Äquivalenzklasse
Raum-Zeit
Computeranimation
Web Services
Flächentheorie
Torus
Theoretische Physik
MIDI <Musikelektronik>
Biprodukt
Bildauflösung
Web Services
Umwandlungsenthalpie
Parametersystem
Approximationstheorie
Eichtheorie
Imaginäre Zahl
Physikalisches System
Biprodukt
Auflösungsvermögen
Satellitensystem
Offene Menge
Mereologie
Server
Dateiformat
Speicherabzug
Projektive Ebene
Verkehrsinformation
Pixel
Manufacturing Execution System
Open Source
Güte der Anpassung
Datenmodell
Arbeitsplatzcomputer
Computeranimation
Datenfeld
Typentheorie
Diskrete Simulation
Datennetz
Parametersystem
Arbeitsplatzcomputer
Messprozess
Interpolation
Einflussgröße
Funktion <Mathematik>
Ebene
Information Retrieval
Resultante
Prozess <Physik>
Filetransferprotokoll
Browser
Web-Applikation
Zahlenbereich
Online-Katalog
Stammdaten
Zählen
Sondierung
Computeranimation
Virtuelle Maschine
Bildschirmmaske
Benutzerbeteiligung
Client
Web Services
Datentyp
Meter
Metrologie
Skript <Programm>
Gerade
Web Services
Statistik
Architektur <Informatik>
Open Source
Datenhaltung
Gebäude <Mathematik>
Systemplattform
Binder <Informatik>
Biprodukt
Satellitensystem
Näherungsverfahren
Mereologie
Server
GRASS <Programm>
Speicherabzug
Projektive Ebene
Modelltheorie
Retrievalsprache
Satellitensystem
Prozess <Physik>
Desintegration <Mathematik>
Interaktives Fernsehen
Computeranimation
Computeralgebra
Client
Informationsmodellierung
Interaktives Fernsehen
Reelle Zahl
Datentyp
Metrologie
Visualisierung
Maßerweiterung
Benutzerprofil
Analysis
Lineares Funktional
Statistik
Prozess <Informatik>
Logiksynthese
Abfrage
Biprodukt
Benutzerprofil
Rahmenproblem
Menge
Computeralgebra
Visualisierung
Aggregatzustand
Schnittstelle
Benutzeroberfläche
Datensichtgerät
Program Slicing
Online-Katalog
Einflussgröße
Computeranimation
Schnittstelle
Gewichtete Summe
Pixel
Benutzeroberfläche
Wasserdampftafel
Zahlenbereich
Äquivalenzklasse
Computeranimation
Übergang
Streaming <Kommunikationstechnik>
Flächeninhalt
Mereologie
Benutzerprofil
Information Retrieval
Resultante
Satellitensystem
Impuls
Prozess <Physik>
Extrempunkt
Desintegration <Mathematik>
Gruppenkeim
Computeranimation
Formale Semantik
Netzwerktopologie
Client
Web Services
Nichtunterscheidbarkeit
Metrologie
Meter
Kette <Mathematik>
Metropolitan area network
Bildauflösung
Nichtlinearer Operator
Addition
Datenhaltung
Mobiles Internet
Temporale Logik
Applet
Personalcomputer
Bitrate
Biprodukt
Menge
Server
Tabelle <Informatik>
Instantiierung
Geschwindigkeit
Schnittstelle
Subtraktion
Stochastischer Prozess
Wasserdampftafel
Zellularer Automat
Zahlenbereich
Äquivalenzklasse
Physikalisches System
Benutzerbeteiligung
Informationsmodellierung
Modul <Datentyp>
Migration <Informatik>
Datentyp
Skript <Programm>
Gruppoid
Operations Research
Bildgebendes Verfahren
Analysis
Leistungsbewertung
Wald <Graphentheorie>
Pixel
Validität
Bildauflösung
Systemplattform
Physikalisches System
Migration <Informatik>
Modul
Auflösungsvermögen
Quick-Sort
Einfache Genauigkeit
Mapping <Computergraphik>
Energiedichte
Speicherabzug
Kantenfärbung
Visualisierung
Streuungsdiagramm
Perspektive
Datenverwaltung
Framework <Informatik>
Parametersystem
Hasard <Digitaltechnik>
ATM
Systemplattform
Vorlesung/Konferenz
Räumliche Anordnung
Projektive Ebene
Datenfusion
Computeranimation

Metadaten

Formale Metadaten

Titel Satellite Snow Cover Products Evaluation and Validation Platform Developed Entirely With Floss Software
Serientitel FOSS4G Seoul 2015
Autor Craciunescu, Vasile
Lizenz CC-Namensnennung - keine kommerzielle Nutzung - Weitergabe unter gleichen Bedingungen 3.0 Deutschland:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen und nicht-kommerziellen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen und das Werk bzw. diesen Inhalt auch in veränderter Form nur unter den Bedingungen dieser Lizenz weitergeben.
DOI 10.5446/32082
Herausgeber FOSS4G, Open Source Geospatial Foundation (OSGeo)
Erscheinungsjahr 2015
Sprache Englisch
Produzent FOSS4G KOREA
Produktionsjahr 2015
Produktionsort Seoul, South Korea

Inhaltliche Metadaten

Fachgebiet Informatik
Abstract The monitoring of snow cover extent is important for the management of natural resource, extreme events prediction such as snowmelt floods, avalanches etc. The current status is that the network of weather stations is too sparse in regions with seasonal snow cover to provide reliable snow monitoring and impact applications. Remote sensing can regularly provide maps of snow cover extent, under limitations imposed by satellite cycles or cloud cover. A number of daily or synthesis snow cover extent products, covering Romania, with different resolutions and specifications, are available for free (e.g. GLOBSNOW, CryoLand, H-SAF, IMS). These products were homogenized and included, along with reference and in-situ data, into an application that make possible for user to inspect, process, analyze and validate the information, using a web based interface. The platform, created by National Meteorological Administration of Romania offers services based on Open Geospatial Consortium standards for data retrieval (WMS, WCS, WFS) and server-side processing (WPS, WCPS). The services were built upon open source solutions such as GeoServer, OpenLayers, GeoExt, PostgreSQL, GDAL, rasdaman. The application is composed of several software modules/services. The modules are split into two categories: server-side modules/services and client side modules - responsible for interaction with the user. A typical usage scenario assumes the following steps: 1. The user is operating the client functionality to select a temporal and spatial slice from a product cube (e.g. 5 months archive of daily CryoLand FSC data); 2. The users select a statistic method to be applied; 3. The request is sent to the server side processing applications wrapped as WPS or WCPS calls; 4. The process will trim/slice the coverage cube, perform the statistic operation for the pixels within the ROI for each day in the selected time interval; 5. The results are sent back encoded in a standard file format; 6. The web client display the results in a relevant form.

Ähnliche Filme

Loading...
Feedback