Re: [GRASS-dev] OSGeo-SoC 2016 application

[Luca Delucchi]

On 16 March 2016 at 04:49, Yang, Bo (yangb2)  wrote:
> Hi Luca,
>

Hi Bo Yang,

> Thank you for the reply and info. It is great if you could co-mentor this 
> project. I would be more interest in implementing my spatio-temporal fusion 
> algorithm as an open source plug-in. Actually, I already have the preliminary 
> python code for the raster fusion, including modules of spatio-temporal 
> semi-variogram calculating, exponential/Gaussian fitting, spatio-temporal 
> fusion, uncertainty estimation and etc. Currently it runs well for fusing 
> MODIS and Landsat data. But it is just a preliminary program, I think a lot 
> more works need to be done to make it professional and could incorporated 
> into GRASS framework.

Could we see and test the code? I'm interesting to test it with MODIS data...

> I've read the link you sent to me. I think it is a good point to add it to 
> r.series.interp, which I noticed author is Sören Gebbert. Should I ask Sören 
> if he is interested to be a potential mentor? Furthermore, could you advise 
> what should I do now to get start for the programming environmental as well 
> as prepare the proposal?
>

Yes, I think you should ask Soeren, he knows a lot about this topic.
You have to compile GRASS [0], do you have a Windows or Unix OS?
After this you can start to read more about GRASS programming [1]

> Best,
> Bo Yang
>

[0] https://grasswiki.osgeo.org/wiki/Compile_and_Install
[1] https://grass.osgeo.org/programming7/

-- 
ciao
Luca

http://gis.cri.fmach.it/delucchi/
www.lucadelu.org
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Re: [GRASS-dev] OSGeo-SoC 2016 application

[Yang, Bo (yangb2)]

Dear Soeren and Luca,

First, let me introduce myself again to Soeren. My name is Bo Yang, a Ph. D. 
student in the Department of Geography, University of Cincinnati, OH, USA.  I 
have a bachelor degree in Mathematics and MS in Computer Science. I am really 
interested in OSGeo-SoC2016. It would be a great opportunity if I can make 
contributions as well as learn to become an open-source developer.

Currently I have an idea based on my MA thesis project: Spatio-temporal fusion 
of multi-scale data with in a cokriging framework.
This project extends traditional cokriging method for blending spatial data 
sets with different temporal sampling frequency and spatial resolution 
(density). It can be used for both raster data and vector data, effectively 
fill in data gaps due to severe weather condition, instrument malfunction, or 
other reasons, filtering out data noise, and generate reliable results at both 
high spatial resolution and high temporal frequency with associated uncertainty 
estimates.

Soeren, I noticed you are the author of r.series.interp. I discussed a little 
with Luca, I agree this project is highly related to the package. So I am 
writing to ask if you are interest in mentoring this project. Currently I have 
the preliminary python code for the raster fusion attached(ImageFusion_SoC.py). 
It was written during my master degree, so it is sort of rough and haven't been 
re-constructed to OOP yet. But it runs well for fusion MODIS and Landsat data. 

I attached an fusion example for NDVI[0] images.  The program is able to blend 
Landsat TM/ETM+ NDVI image (30m) with MODIS NDVI image (250m)[1]. The NDVI can 
be calculated from the combination of the red band (Band 3 of Landsat TM or 
ETM+ multispectral imagery, or Band 1 of MODIS multispectral imagery) and near 
infrared band (Band 4 of Landsat TM or ETM+ multispectral imagery, or Band 2 of 
MODIS multispectral imagery). MODIS data has been resampled to 270m to 
co-registered with Landsat pixels. 

I selected a relatively cloud free period (07/19/2002-07/29/2002) to 
demonstrate the fusion process, the study region is Lake Tahoe region, NV, USA. 
Both Landsat and MODIS NDVI images need to be converted to ASCII file, source 
data can be found here[2]. Text files start with "A" are daily MODIS NDVI 
images and "lt5ndvi_0716" is the Landsat TM data. The goal of this example is 
to fuse daily MODIS NDVI images with a Landsat NDVI images (30m) to generate 
images at 30 m spatial resolution for everyday, using spatio-temporal cokriging 
method. Namely, I intend to use a single high resolutions Landsat NDVI images 
to sharpen daily time series MODIS images. Also the program is able to fill in 
the missing value. I artificially generated a missing data region in each input 
MODIS image and we can see the result fill in the missing data region very 
well. One good application of this algorithm is to fill in the gaps in the 
Landsat ETM+ images after 2002 due to the sensor's malfunction. 

The fusion module is attached, it need an input exponential/Gaussian model 
parameter which was calculated via semi-variogram fitting module. I did export 
the parameters in the attached text file for this case so the fusion module can 
be run independently. To run the program quickly, just put attached text file 
and source data[2] in the working folder and apply it to line22 of the fusion 
module. Of course other MODIS data can be used for this program if converted to 
ASCII files. There are two fusion methods, first one (line 330: 
fusion_with_covariable) is used for the MODIS data, which can sharpening and 
fill-in the missing data values. Second one is cokriging which incorporated the 
fine Landsat image as co-variable, it can achieve much better sharpening result 
as well as fill-in missing data values. Both method generated the gap filled 
result at 30m spatial resolution.

Please let me know if you have and comments or suggestions. Luca, thank you for 
sending me the compile method and programming manual. I normally used windows 
OS, and Eclipse + Pydev as primary IDE. I am going to look into the manual and 
GRASS codes. Any more advice would be greatly appreciated.

Best regards,
Bo Yang



[0] https://en.wikipedia.org/wiki/Normalized_Difference_Vegetation_Index
[1] https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod09gq
[2] 
https://drive.google.com/folderview?id=0B25sQdmthpGJS0JOdEh5cDd4S1k=sharing

-Original Message-
From: Luca Delucchi [mailto:lucadel...@gmail.com] 
Sent: Wednesday, March 16, 2016 11:18 AM
To: Yang, Bo (yangb2) <yan...@mail.uc.edu>; Sören Gebbert 
<soerengebb...@googlemail.com>
Cc: grass-dev@lists.osgeo.org
Subject: Re: [GRASS-dev] OSGeo-SoC 2016 application

On 16 March 2016 at 04:49, Yang, Bo (yangb2) <yan...@mail.uc.edu> wrote:
> Hi Luca,
>

Hi Bo Yang,

> Thank you for the reply and info. It is great if you could co-mentor this 
> project. I would be more 

Re: [GRASS-dev] OSGeo-SoC 2016 application

[Yang, Bo (yangb2)]

Hi Luca,

Thank you for the reply and info. It is great if you could co-mentor this 
project. I would be more interest in implementing my spatio-temporal fusion 
algorithm as an open source plug-in. Actually, I already have the preliminary 
python code for the raster fusion, including modules of spatio-temporal 
semi-variogram calculating, exponential/Gaussian fitting, spatio-temporal 
fusion, uncertainty estimation and etc. Currently it runs well for fusing MODIS 
and Landsat data. But it is just a preliminary program, I think a lot more 
works need to be done to make it professional and could incorporated into GRASS 
framework. 
I've read the link you sent to me. I think it is a good point to add it to 
r.series.interp, which I noticed author is Sören Gebbert. Should I ask Sören if 
he is interested to be a potential mentor? Furthermore, could you advise what 
should I do now to get start for the programming environmental as well as 
prepare the proposal? 

Best,
Bo Yang


-Original Message-
From: Luca Delucchi [mailto:lucadel...@gmail.com] 
Sent: Tuesday, March 15, 2016 5:28 AM
To: Yang, Bo (yangb2) <yan...@mail.uc.edu>
Cc: grass-dev@lists.osgeo.org
Subject: Re: [GRASS-dev] OSGeo-SoC 2016 application

On 15 March 2016 at 03:54, Yang, Bo (yangb2) <yan...@mail.uc.edu> wrote:
> Hello,
>

Hi Bo Yang,

>
> In addition, I have an idea based on my MA thesis project:
>
> Spatio-temporal fusion of multi-scale data with in a cokriging framework.
>
> This project extends traditional cokriging method for blending spatial 
> data sets with different temporal sampling frequency and spatial 
> resolution (density). It can be used for both raster data and vector 
> data, effectively fill in data gaps due to severe weather condition, 
> instrument malfunction, or other reasons, filtering out data noise, 
> and generate reliable results at both high spatial resolution and high 
> temporal frequency with associated uncertainty estimates.
>
> It would be greatly appreciated if someone interested in my idea and 
> could guide me how to get start and prepare the proposal. Thanks for 
> your time and I am looking forward to hearing from you.
>

This is really interesting, In GRASS GIS Temporal Framework [0][1] already 
exists a module for raster gapfilling [2], it uses the module r.series.interp 
[3]. The idea could be to add a new method in r.series.interp and a new vector 
module something like v.series.interp and after you could add the new 
functionality to GRASS GIS Temporal Framework.

I could co-mentor this proposal, maybe Soeren could be a good mentor for this 
proposal

> Best regards,
>
> Bo Yang
>

[0] https://grass.osgeo.org/grass71/manuals/temporalintro.html
[1] https://grasswiki.osgeo.org/wiki/Temporal_data_processing
[2] https://grass.osgeo.org/grass71/manuals/t.rast.gapfill.html
[3] https://grass.osgeo.org/grass71/manuals/r.series.interp.html

--
ciao
Luca

http://gis.cri.fmach.it/delucchi/
www.lucadelu.org
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Re: [GRASS-dev] OSGeo-SoC 2016 application

[Yang, Bo (yangb2)]

Dear Moritz,

Thank you for the reply. Yes, I agree that I should focus on one subject and 
get start preparing the proposal based on it. After a quick research for both 
subjects. I would like to choose pursuing my latter idea about spatio-temporal 
image fusion algorithm. For one thing, It is my novel algorithm and I have the 
confidence and motivation to make it available for using. For another, I code 
in python more skillful than in C/C++ language. I will contact Luca Delucchi 
for the possibility of developing that project. 
Thank you again for guiding me earlier. Any more advice would be greatly 
appreciated. 

Best regards,
Bo Yang

-Original Message-
From: Moritz Lennert [mailto:mlenn...@club.worldonline.be] 
Sent: Tuesday, March 15, 2016 8:10 AM
To: Yang, Bo (yangb2) <yan...@mail.uc.edu>; grass-dev@lists.osgeo.org
Subject: Re: [GRASS-dev] OSGeo-SoC 2016 application

Dear Bo,

On 15/03/16 03:54, Yang, Bo (yangb2) wrote:
> Hello,
>
> My name is Bo Yang, a Ph. D. student in the Department of Geography, 
> University of Cincinnati.  I have a bachelor degree in Mathematics and 
> MS in Computer Science. I am proficient in Python, C/C++, R and have 
> utilized QGIS and GRASS a lot in my study and research project. I am 
> really interested in OSGeo-SoC2016. It would be a great opportunity if 
> I can make contributions as well as learn to become an open-source developer.
>
> For years I have worked with raster processing algorithms and 
> computation efficiency, for example, coding (python) to process large 
> volume of remote sensing data, therefore I am interested in the 
> existing
> idea:
>
> GRASS: Additional segmentation algorithms for i.segment.

In our previous exchange via private mail, I already sent you the necessary 
pointers for this subject. Markus Metz and I are willing to mentor a student on 
this. I think it would be a marvelous addition to GRASS' image treatment 
toolbox.

Don't hesitate to post on this list if you have any other questions concerning 
this subject.

I think you will have to chose quite quickly which one of your two subjects you 
wish to pursue, as the deadline for applications will arrive fast, and it is 
probably better to concentrate on one subject and do that well. There have been 
cases, however, of students proposing more than one subject.

Best wishes,
Moritz
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Re: [GRASS-dev] OSGeo-SoC 2016 application

[Moritz Lennert]

Dear Bo,

On 15/03/16 03:54, Yang, Bo (yangb2) wrote:

Hello,

My name is Bo Yang, a Ph. D. student in the Department of Geography,
University of Cincinnati.  I have a bachelor degree in Mathematics and
MS in Computer Science. I am proficient in Python, C/C++, R and have
utilized QGIS and GRASS a lot in my study and research project. I am
really interested in OSGeo-SoC2016. It would be a great opportunity if I
can make contributions as well as learn to become an open-source developer.

For years I have worked with raster processing algorithms and
computation efficiency, for example, coding (python) to process large
volume of remote sensing data, therefore I am interested in the existing
idea:

GRASS: Additional segmentation algorithms for i.segment.


In our previous exchange via private mail, I already sent you the 
necessary pointers for this subject. Markus Metz and I are willing to 
mentor a student on this. I think it would be a marvelous addition to 
GRASS' image treatment toolbox.


Don't hesitate to post on this list if you have any other questions 
concerning this subject.


I think you will have to chose quite quickly which one of your two 
subjects you wish to pursue, as the deadline for applications will 
arrive fast, and it is probably better to concentrate on one subject and 
do that well. There have been cases, however, of students proposing more 
than one subject.


Best wishes,
Moritz
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Re: [GRASS-dev] OSGeo-SoC 2016 application

[Luca Delucchi]

On 15 March 2016 at 03:54, Yang, Bo (yangb2)  wrote:
> Hello,
>

Hi Bo Yang,

>
> In addition, I have an idea based on my MA thesis project:
>
> Spatio-temporal fusion of multi-scale data with in a cokriging framework.
>
> This project extends traditional cokriging method for blending spatial data
> sets with different temporal sampling frequency and spatial resolution
> (density). It can be used for both raster data and vector data, effectively
> fill in data gaps due to severe weather condition, instrument malfunction,
> or other reasons, filtering out data noise, and generate reliable results at
> both high spatial resolution and high temporal frequency with associated
> uncertainty estimates.
>
> It would be greatly appreciated if someone interested in my idea and could
> guide me how to get start and prepare the proposal. Thanks for your time and
> I am looking forward to hearing from you.
>

This is really interesting, In GRASS GIS Temporal Framework [0][1]
already exists a module for raster gapfilling [2], it uses the module
r.series.interp [3]. The idea could be to add a new method in
r.series.interp and a new vector module something like v.series.interp
and after you could add the new functionality to GRASS GIS Temporal
Framework.

I could co-mentor this proposal, maybe Soeren could be a good mentor
for this proposal

> Best regards,
>
> Bo Yang
>

[0] https://grass.osgeo.org/grass71/manuals/temporalintro.html
[1] https://grasswiki.osgeo.org/wiki/Temporal_data_processing
[2] https://grass.osgeo.org/grass71/manuals/t.rast.gapfill.html
[3] https://grass.osgeo.org/grass71/manuals/r.series.interp.html

-- 
ciao
Luca

http://gis.cri.fmach.it/delucchi/
www.lucadelu.org
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[GRASS-dev] OSGeo-SoC 2016 application

[Yang, Bo (yangb2)]

Hello,
My name is Bo Yang, a Ph. D. student in the Department of Geography, University 
of Cincinnati.  I have a bachelor degree in Mathematics and MS in Computer 
Science. I am proficient in Python, C/C++, R and have utilized QGIS and GRASS a 
lot in my study and research project. I am really interested in OSGeo-SoC2016. 
It would be a great opportunity if I can make contributions as well as learn to 
become an open-source developer.
For years I have worked with raster processing algorithms and computation 
efficiency, for example, coding (python) to process large volume of remote 
sensing data, therefore I am interested in the existing idea:
GRASS: Additional segmentation algorithms for i.segment.
In addition, I have an idea based on my MA thesis project:
Spatio-temporal fusion of multi-scale data with in a cokriging framework.
This project extends traditional cokriging method for blending spatial data 
sets with different temporal sampling frequency and spatial resolution 
(density). It can be used for both raster data and vector data, effectively 
fill in data gaps due to severe weather condition, instrument malfunction, or 
other reasons, filtering out data noise, and generate reliable results at both 
high spatial resolution and high temporal frequency with associated uncertainty 
estimates.
It would be greatly appreciated if someone interested in my idea and could 
guide me how to get start and prepare the proposal. Thanks for your time and I 
am looking forward to hearing from you.
Best regards,
Bo Yang
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