 Welcome to the NPTEL course on remote sensing and GIS for rural development. This is week 10 lecture 3. In this week, we have been looking at NDVI in particular and using multi-source, multi-theme data. Of the indicators that can be used for rural development in agriculture, NDVI has been widely used because it gives you the healthiness of the plant and also can be used as a proxy for area acre coverage for vegetation. And these can be trickled down to other stakeholders that we discussed in the previous lectures like government agencies, insurance agencies, farmers for storage, business professionals for buying and creating the demand for the product. All are linked to this healthy growing of agricultural crops. These have to be monitored regularly and since observation data can be expensive, satellite based data can be used. And in the previous exercise, we saw the 15 day window from Bovan, ISRO's website. So, now if a particular pixel is moving from light green to dark green, that means the plant is growing healthy. Similarly, if every 15 days I am saying, if in a time frame it converts from green to brown, red, then it means that the plant has attained the peak growth and now it's dying down or ready to be harvested. If you see wheat, rice, etc. when it is growing, it is very green and beautiful. But when you're going to harvest it, it turns the right color. And the right color has a different signature on satellites and remote sensing data. And that is where we have found that this NDVI as an indicator can help immensely on understanding the plant health, plant growth. Then based on that, what is the resources that are needed, both financially and natural resources or subsidies, infertillizers, water, pumping, etc. And we have seen one resource of it, which is the Bovan ISRO's database. Now, today, we will go to the other resources as promised. So, in the previous lecture, we looked at Bovan. And if time permits, we can also look at how a pixel converts from one to the other in a particular time frame. But I would like to show the Google Earth Engine, which is really, really impressive. Okay, so let me share my Google screen. So, the link is given. But as usual, I will share an empty window and start from scratch on how to search for it. So, this is the link which I've shared already. But suppose the link is not working, let's see. So, Google Earth Engine, okay, you can type NDVI directly, or you can actually just type data and then just click on it. So, you have the data sets, data catalog, so go to here, the first one will come up. And under that, there's multiple tabs, okay. So, you can say buy satellites. Landsat has been used for NDVI, Modus has been used for NDVI, Sentinel has been used for NDVI. But we will just look at tabs. So, if you just click tags and type vegetation, then you can see multiple vegetation. But before that, I want to explain what these tags are. The tags are like Twitter and Instagram that you put tags of a picture. So, when a dataset is created for quick access and search, these are like a keyword. So, the tags are given so that people can just type and then find. So, for example, for agriculture, there is one, if you come down this, arrange an alphabetical order. So, if you come down for crops, hydrology, you have hydrology, and then you have Landsat for the satellite itself, or Landfire as a product. Okay. So, Modus as a satellite and so we're coming at N now. So, N has NLSD, NIR, nitrogen, etc. So, we don't have it as NDVI, but we do have agriculture. So, let's quickly look at what do we have in agriculture. We also have deforestation, which is also very important. So, let's say agriculture and how many datasets are there. You have the UNFAO drained soils, the soils that are drained, organic soil emissions, decadal evaporation, net productivity, all these are related to agricultural productivity, etc. And then the National Agricultural Imaginary Program, and there is something for just the Chinese region. So, you have African regions also, very, very focusedly done in Australia. These could be their own satellites that they map only their regions. And we can also go by tags. So, you can click browse by tags. And if it's very specific, you can also click India, there's no match, not updated yet. ISRO is also no match yet, they don't have it as a tag. And as we saw NDVI, we have 11 datasets. And these are the 11 datasets. So, 11 datasets are available. And this is, so if you look at the movement, what was the resolution? It was 1 kilometer, 15 days. Here it is 16 days, almost 15 days, and then 500 meters. So, higher spatial resolution from this particular payload, VIIIRS, we'll be seeing this in the, when we download the NASA data base from the Earth Explorer. But for now, we can see that what are the different NDVI's that are there, land surface phenology, aster global emissivity, NDVI can be created, gyms NDVI, etc, etc. So, we have a global 16 day, one kilometer coverage, which is as as similar as our, the one we have from Bhuvan. And this is very, very recent also. So, the one in Bhuvan, we had stopped it. So, I'm doing this on the same day. Why? Because I want to show what is the difference between the datasets, the recordings I'm saying I'm doing it on the same day, so that you can witness that which dataset is best for your analysis. Suppose you have a region and you're working for an Indian region, and you need to see that if the data is available from a particular date. So, in the Bhuvan region, it was 2011. 2011 to 2021. So, I said around 11 years, whereas here it is 2012 till date. So, this is just so now today is around the March. So, yeah, so the last date, we have the 16th date, which is February end. So, this is very, very recent data. And it takes a year for this to appear in Bhuvan. You go to Bhuvan, you can get good data until 2021, but then you'll have to wait for a year to get 2022. So, and 2023. So, only 2024 you get 2022. So, there's approximately two years of, one year, one and a half years of delay. Whereas here you have it all. And it's not only one particular area. It is for the globe. And the resolution is much, much better than Bhuvan. It's 500 meters, because the average land holding size is one hectare. So, one hectare is pretty small. So, just let's say one hectare, two meters square. So, we get around 10,000 square meters. And this pixel is 500 times 500 meters. So, this pixel is around 250 meters. So, we have 250,000 meter square, this one. But think about one kilometer, that's double of this. So, somewhere these are still not as small as we wanted. But for free source, we can do it. And if we know that in a particular belt, we have almost the same things across 500 by 500 meters. This is very good. So, I'll show you how to use it one by one. So, we looked into this VIRS Vegetation Index 16-day. And it is a composite of indexes, not only NDVI. That is what Google Earth gives, engine gives. So, the Google Earth Engine collects the data sets that are available and couples it for the payload. So, this is the payload or the satellite. And we have these other bands. So, these are not the bands like, so this is the bands which are there, MIR, SWIR, etc. etc. And then they use this for creating these indicators. So, indicators will not have a wavelength, you can see. Whereas, these have a wavelength. And there are rescales. So, if you see that, they are rescaled to particular values which are double checked by them. So, normalized vegetation index is what we want. This is the enhanced vegetation index. Let's not get into that. Because every time they create new indexes, indices, but we will keep the NDVI, which is well known. And then we have the qualities, the band reflectance, green, blue, red. So, as we said, the red, near infrared. So, NIR minus red by divided by NIR plus red is NDVI, which has been done already by the Google Earth Engine. So, if you open the code, it lasts for which email you would like to use. So, you have multiple emails in Gmail. So, you can take which Gmail you would like to use. And then you can look at where and how you would like to put the dates. So, for this is for the globe, and it can be changed as per me. As I said, this learning into the coding world does take time, but we will not get into that for now in the basic remote sensing class. When we go to advanced level, yes, we will do some coding on this. I can share some codes that we have written through my students, one of my students, Shivana has written good codes. I'll show you how the results. This is mostly to showcase the remote sensing tools and how they are powerful compared to the other tools so that you can widely use them for NDVI analysis. So, here's the data set. The data set is taken as VIRS. The versions are given here. And then what it means is January 2018 data set. So, all these are mean they have taken. This is where you can pick and choose. So, here you could see that the mean January EVI is being taken and given here. So, we have 218 into 218 in 01. And this is the EVI, not the NDVI. But if you run it, we can see how the code runs. So, basically it just quickly takes up the data set and then plots the registration index. Very beautiful and very quick. And the resolution is very, very good. Okay. So, there it is. It is actually plotted in our own Indian region, which is good. But if you can zoom out, you can actually see India being the layers. So, the layers here get populated. So, this is a vegetation fraction for January. You can see January 1 to January 18. The composite vegetation has been taken. Here the legends are not clear. So, you'll have to download it, put it in GIS and do it. But what this does for you is it gets the data for you quickly. And all you have to do is download the data and use it on your QGIS platform as a raster and then do the other calculations. So, as I promised, I will show the other NDVI that I have. So, before that, let's close this so that I can show you the other tab that we opened. Okay. So, these are the things in the catalog. So, the link I've given on the presentation is the link for the datasets, the data catalogs. You can see here the data catalogs. And if you click that, you will get into this website, which is this earth data catalogs. Okay. How do you go there? You go to data catalog. It will come here. And then here you can say home or view all datasets. The home comes like this for earth engine. And you can see view all datasets, climate weather, climate, agriculture, et cetera, et cetera. High imagery you can take. You can say just view all datasets or browse by tags. The browse by tags we already have done. So, let's not get into that part now. We will only look into view all datasets. It does take some time depending on the internet. So, here we are. So, here we can just type NDVI. And there you are. We don't have type enter. You can type enter. But then you can also see all the NDVI data here. Here you will see one, two, three, four, four, eight, 12. In the other one, we saw 11. But here somewhere else, it searches for the word NDVI, not the tag. So, here somewhere it could have said that this data can be used for NDVI. So, then you can use it for NDVI and enter it. So, as we can look at this one, for the global, it is from 2002 till date. So, this is much, much older than the boon data which is available. Again, the boon data was from 2011. This is 20 years or more than 20 years of data. 21 years accounting, I would say, or like 227. So, yeah, approximately 20 years plus data you have. And you could see that what are the bands they have. They have NDVI with a range of minus 2000 to 10,000. Again, this range, you have to normalize it back to minus one to one, which a lot of papers will give you the details. And then, as I said, you can also have these values which are getting populated. Cloud cover, don't use this data. Good data, use with conferences, zero. Okay, summary of class statements. And then you have the NDVI that has been calculated. And then some more data about what are the visual, the data wavelengths, etc., are given here. So, here it is. The scale is also 500 meters approximately and rescanned back. So, here they give you 1000 meters, 1 kilometer. So, 1 kilometer is the same as the boon data. So, what is different here? The difference here is that it is having better spatial resolution in terms of time series. 1000 meters is the same, but you have here from 2002, whereas the boon was from 2011. So, if you open the code editor, let's see if they have NDVI. Otherwise, we'll have to plot NDVI. Okay, so we have a minimum max. And then what data is it taking NDVI? Okay, so it is selecting NDVI. So, select NDVI is the band. So, if I select EVI, I have to type EVI here. So, it's basically the var data set is a variable data set. We are defining the data set as the image. And within the image, there are multiple bands, which are given here and the band name. So, this is not a band name. Only these two are the band names. So, you can type whatever band you want, either EVI or NDVI. We have typed NDVI. And it will do the mean. The mean from 2018 to 2008, 11 to 51. So, it is just year, month, and date. So, you have the month, which is four or five months of data, four months of data. And then you have the palette, which is the coloring. Okay, again, I won't get into the details of coatings. And it goes to the map center. Lack lungs have been given. And the map add layer is NDVI. So, if I just quickly run it, you will see that the entire globe NDVI is being created. And there you are within a couple of seconds, based on my internet speed. There's no computing here. It just goes through the supercomputer and Google engine and then they give you the NDVI. So, here, if you see, this is the month of one to five. Maybe it's an average they have done, but let's do the, so I usually see Maharashtra main month is pretty, pretty in the summer. But now I'm going to run the post monsoon season or during the monsoon and the post monsoon season. Let's say September. So let's say nine to 11. Okay. And then I'm just going to run. So now I'm changing the date and then running it. So you could see how, see the indeed the initial version had all brown, but now all is green. So how quickly we could do this without monitoring is the beauty. So what we see here is a full layer of NDVI during the post monsoon season and beautifully it covers the entire globe and how we can extract it for the Indian region is by using a mask, which has already been taught in this lecture series. There is a lot of Google earth engine tutorials done by Google and other resources. Please go through it. It is very, very useful for understanding the practical use of this data. And also you would also get a lot of benefits of using the supercomputer, the computing facilities that can be accessed online. So here I'm also going to show you some other features. So in the task you could see like what has been done and it's been updating. You can open your task manager, all these you can learn from different resources. But as I promised this, I'm going to show you what advanced how just a small snippet of advanced computing in Google earth engine. So here I'm taking the Sentinel data set. Okay. I'm taking one village, which is Lonnie village. I have the boundary of Lonnie village in my assets. If you come down, you see I have Lonnie village. I have imported the Lonnie village into this Google earth engine in my account. That's why you have to go and choose your Google account. I have that as a table. So Lonnie village variable is being done. I've taken the Sentinel data set. I'm just going to click on the data set to show what is Sentinel. So what is the Sentinel to data set? It has a lot of bands. Let us open this data set in the whole full form. Yeah. So what are the bands available? We have aerosols, blue, green, red, red edge, red edge and NIR. So now what is the equation in this particular Sentinel? What would be the equation for NDVI? It is B8 minus red. Red is B4. So B8 minus B4 divided by B8 plus four. So and the resolution is 10 meters. Just look at how good the resolution is. 10 meters. And then this is also 10 meters. So this is the highest free open source image available for us to do these calculations. That is why we use these calculations. But this is the raw. They have not done NDVI. We will do NDVI based on this. So because here you could see that there is all these multi bands are available. And then the image properties, what they are, and then terms of use. It's open source so you can use it. So let's just see the Copernicus. This is the link to the data set. I'm just copying this going back to my Google Earth Engine data home. And you can click on view all the data sets or you can search it here. Just click OK. And then it comes here harmonize Sentinel multi spectral image. So let's click it. This is already imported here in my other resource. So I'm just going to close this because I've already, these are my scripts. We have written codes. As I said, one of my students has written these codes. And then we are here. So the same thing is populated here. Or all you could see is the image. Just check this name. If it is the same as to SR harmonized. We have the image collection from S2 multi spectral level two, right? Yes. Okay, so that is the image collection we have. And then you can also search for the band. So the bands, the names of the bands are here. B8 is 10 meters. And then B4, I said is also 10 meters. These are given in this properties. Image properties also the same description. It is the same that we used in the previous image, right? So it says that this is the code that we can download and use. Copernicus S2 SR harmonized is the image classification. And it is given here as 2A. Just 23 bands are there, right? And you could also bring it down into this element. So this is kind of advanced. But let me show you what this can do. So this can actually, this code can actually take Lonie village and only make Lonie village, which is in Maharashtra region, populate with the NDVI data. I don't want the entire globe, which will take some time of my internet. So I don't want that. So the processing is not done on your computer. But then when it comes to your computer, your internet and computing speed is needed to put it onto the screen. So we don't want to delay that. So I'm just using the village, which I want to use. And then, so this is the ID here, Copernicus SR2 harmonized, which is the same Copernicus SR2 SR harmonized, okay? And then the version is there, number of bands are there, which is fine. I'm just going to click this up. So then the feature feature one element is just created for our geometry. The map center is going to go to Lonie village. And then the filters only for that and the date service. So the dates are 11 to 1231. So one whole year, I'm going to take the data every 16 days, right? So that is what this data sets tells. Every 16 days, the data has been collected. So it's going to be 17 to 2023. So this is not as new as the Bovans OCM data. However, this is very, very high resolution. That is one kilometer. This is 10 meters. So I would recommend using this one, at least for now, 10 meters with some post processing, but at least good for 30 meters resolution, 90 meters resolution, right? So you could see here that this data set has been done. It is also bi-weekly as done here. So we have the files start and end date, which is given here. So as the start and end date is done, the data has been filtered. That is what this course says. And then it says normalized difference is B8 and B4. So you can come here and see which is the bands. B8 is NIR. And B4 is the red, which is also given in this link. If you click this link, the bands are here. So it's B8. B8 is NIR minus B4, which is red. Okay. So this is the NDVI formula, which we discussed earlier. And then it gives the dates, filter bound system. So it's just going to say NDVI is equal to map NDVI, print NDVI, etc., etc. So for this particular area, it is going to chart. So why this code was written is not to visualize, but to create a chart for a particular area. So let's do the updated time frame for this 2022 to 2022 December. I haven't run it because this is the first time I'm changing the date. I'm running it. Okay. So let's get here. So first, what it does, it goes to Loni village. And then you can see here, it is populating the village. And all the images are being, so always you have to keep making sure that the code is running as per the particular area of interest. So here also, I have given the location as the tables that you have seen here. And you can see also. So basically Google Engine is there. I would refrain from teaching the editing codes and stuff right now. But at least you know where the data has been stored and can be used for your purposes. So in the next class, I will teach about Earth Explorer and Sentinel Carbon. Thank you.