An automated GIS-based technique for evaluation of indirect growing season estimations
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00:00
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Transcript: English(auto-generated)
00:07
So I think we can start. Last people coming in, but OK. So Ivan, you have the floor. Thank you. Good day, dear colleagues. My name is Ivan Ricken, and I would like to introduce you as a part of my scientific work, an automated gaze-based
00:24
technique for evaluation of inter-growing season estimations. So the main idea of the work is to create an automated technique that allows to evaluate the accuracy
00:41
of indirect NDWI-based estimations using open source of growing seasons. So today, we can hear a lot of climate change and how can we automatically can monitor it
01:06
and how can we find some universal indicator which can automatically can be calculated.
01:20
So this universal index is called NDWI, which is calculated by the Gauss formula using near infrared and shortwave infrared. And after using this formula, we can
01:45
find some annual graphic in which we can estimate
02:07
with temperature data the same annual. As tested region was the Republic of Komi,
02:21
it is in the northern part of Russia. This territory is interested with its coverage of mountains, Ural Mountains in the western part in the western part of this republic.
02:40
There are a lot of dense river networks and it has a lot of presence of wetlands. This region is very difficult for optimization detection of some vegetation periods. So it's the best, I think, the best region
03:03
for teaching some methods. This territory is over 416,000 square kilometers. It's a very huge territory, but it
03:22
has a space meteorological station network. You can see here it's only 10 meteorological stations that have daily temperature data and only precipitation.
03:41
As the USSR climate reference book tells, it might be one meteorological station over 100 square kilometers coverage.
04:03
But if we have 4,000, it's only 400 square kilometers per one station covers. It isn't very good.
04:21
For this work, I choose the Google Earth Engine as it has a great database of satellite images and it is open source and has a free connection. In this IDE, I have created some calculations
04:50
where I have calculated the NDWI using second and fifth bands, infrared and shortwave infrared.
05:02
And then I have created a cloud mask and land mask because we don't use in our calculations clouds. And we don't use water because they
05:22
have a lot of high NDWI indexes and we refuse them. In this slide, you can see how this masking work.
05:41
In the left corner, you can see the Republic of Komi with true color where you can see clouds and water objects. And then after filtering this data, you can see only land without any clouds and water.
06:04
How can we calculate these masks? As we use MODIS because we need daily data of NDWI to validate with the daily temperature.
06:26
We have used here quality band. And after reading the symbols values of these rasters,
06:40
we have changed it to binaries. And then we have collected two masks, the cloud mask and the water mask. You can see these binaries like in the serial number of in the part right corner.
07:04
After preparing cloud masks, we have a database for each year which collects 365 elements per year, which includes only
07:26
NDWI data for each meteorological station, which we have only 10. And the radius of this data is averaged by the 10 kilometers
07:41
as the USSR climate reference book suggests. Because it is a great coverage for data that can validate to the temperature from this meteorological station.
08:04
At the end of this research, we have created some graphics of the NDWI and daily temperature and shows that days of beginning
08:25
and the end of growing season are similar, plus minus a few days. At this graphic, we can see that on 20 May of 2010,
08:46
there is a beginning of the vegetation period by the temperature and on 23rd is by NDWI.
09:02
And as we know, the beginning and the end of this growing season, we can calculate the beginning of the summer and the end of the summer.
09:22
It will be calculated like according to the reference book, the five degrees when you rise over by temperature, it's beginning summer, or 10 degrees
09:42
is beginning the summer. And by the NDWI, when the graphic is going up over the 0 degree data, it's beginning the summer. So if we paint the parallel, we will
10:07
see that over 5th or 10th June, it will begin the summer. So what we have got, the beginning of the summer season
10:29
is by temperature graphic, when the temperature goes up from 5 degrees. And if we are looking at the NDWI graphic,
10:45
we can see that this index has the minimum data. Has the minimum data. And the end of growing season will have the minimum index
11:07
too. The beginning of the summer and the end of the summer will calculate by the NDWI graph through the 0.
11:23
And maybe that's all that we have found it. Thank you. Yes. Another question. Please raise the second mic.
11:49
Just a comment about the calculation. You used radiance data to calculate, but you should use reflectance data to minimize the bare soil effect of the reflectance.
12:05
So not the ends, but reflectance. We use here the reflectance data from MOD09GA daily. And after calculation, we have some data.
12:24
OK, because it was written radiance in the presentation. OK, thanks. Any other questions? Thank you. So thank you, Ivan.