Hello Amila
Le 18/04/13 20:47, AMILA RANATUNGA a écrit :
Thank you very much for the replies so far here and in SIS dev list as
well. They were really helpful. We are currently writing a research
paper regarding what kind of model that should be used by when
building a geoscience gateway. We intend to discuss issues that a
geoscientist face during his/her research and features that should be
inserted to such a gateway to overcome them. Then we can consider them
during our main project as well. It is appreciated if you can point
any research papers or resources where we can see this domain from an
eye of a geoscientist will be really useful. Even a good case study
will be really helpful.
There is a chapter in my Ph.D thesis that I wrote 10 years ago [1], but
it is in French... However there is some points:
Before to start coding open source software, I had a formation on ERDAS
Imagine. At that time, the raster data could be either measurement (e.g.
altitude in metres), or categories (land, forest, lake, etc.). However
for my work in oceanography I needed a mix of both in the same raster:
Sea Surface Temperature (SST) measurement, together with some NaN
(Not-a-Number) values indicating that the pixel was a cloud, or a land,
etc. The software 10 years ago was not allowing that.
My study was correlating data from remote sensing image, with fisheries
data. From OGC perspective, this is equivalent to getting WCS and
WebSensor to work together. Raster and sensor are two very different
kind of data, and doing some work of the kind "I want all temperature
data at the location and time of each sensor data, and also all
temperature data 10 days before the time of each sensor data" was needed.
On the remote images side, my study was using 4 different kind of data:
Sea Surface Temperature (SST), chlorophyll-a concentration, Sea Level
Anomaly (SLA) and Ekman pumping. Each kind of data have very different
characteristics in term spatial and temporal coverage, resolution and
format. Handling such heterogeneous source of data was a challenge.
Indeed, in my review of previous work, I saw many study correlating fish
populations with temperature, or correlating fish population with
chlorophyll, but I found no study correlating fish population to many
parameters taken together (e.g. some condition of temperature in same
time than some concentration of chlorophyll-a). Doing such combined
study has been a big development effort. However it was 10 years ago,
I'm sure the situation is different now.
For each time and location of a sensor data, I needed to interpolate the
temperature, chlorophyll-a, etc. measurement from the raster data, at
the sensor time, 5 days before, 10 days before, etc., compute on the fly
some derivative quantities like gradient of temperature (i.e. apply the
Sobel operator on rasters of SST data) again 0 day, 5 days, 10 days,
etc. before, handle the case of missing (NaN) values (e.g. if got a NaN
when interpolating a value using the bi-cubic interpolation, try again
with the bi-linear interpolation since it uses less data and thus reduce
the risk of getting NaN). So having a software doing the work
automatically was crucial.
An other way to explain the above paragraph would be to said that for
each sensor, we create many (potentially hundred) "virtual sensors"
derived from remote sensing data. For example if you had a sensor
measuring temperature inside your car, it is like attaching "virtual
sensors" to the real sensor, where the virtual sensors behave like any
real sensor but using the data from remote sensing images. Of course we
have to take in account that the car is moving, to pixel requested on
the remote sensing images is always changing.
The amount of data produced in the above step was huge. Some statistical
tools was needed for evaluating the coefficient of correlation between
the above "virtual sensors" and the real ones, so we can trim the
"virtual sensors" that do not seem relevant to our study. Again, because
of the amount of data, automation is key.
Not sure if it is of any help...
Martin
[1]
http://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers09-08/010035115.pdf
