 Welcome to the NPTEL course on remote sensing and GIS for rural development. This is week 11, lecture 5. In this week, we have been looking at synergized mapping using crowd sourcing data, along with remote sensing and GIS platforms to check the data and update the data, more on rural infrastructure mapping, etc. we have seen. We have also noted that using buffer as a tool helps a lot in making sure that we have an area of coverage and creating more infrastructure that is needed for hamlets and villages. As promised, we will be looking at in this week. We have already seen OSM use in this week with remote sensing and GIS of the attributes. We will be looking at crops and water bodies in this today's lecture. Crops is very important because as we have seen in LULC mapping, we need to go there and find data for ground routing. However, if we do not have that data, then you use unsupervised classification. So to use supervised classification, this OSM tool can come very handy and you can also go and put in your data and request. So let us get into today's lecture on mapping of crops and water bodies. But also I would like to add a school layer if possible. And here we are. I will be sharing my screen. So let me first share the school that I've added. We noted that this is my profile. I have this profile. And I have added the school at a government high secondary school. You'd have noticed that in Google Earth, that school was not captured problemately and OSM does not have it. So initially this layer was not there and I've added that layer. So I've added all these layers here. And now with due course of time, in OSM it will start reflecting as a school. So these parcels have been identified and anyone can start using the OSM-3F cost and actually add, you can see here just a minute ago I've added the school. So here is where you could add the school, export the data if you need and then also look at other regions that you would like to use. So if you look here, I've added this plot and it does go through a checking and then it gets updated. So anyone who wants to use it, you can actually go through multiple layers and then say you want to edit, add a layer and then you can start mapping it. So for example, if you just start clicking, clicking, you can add a layer. So here you can see this is the layer that I've added in OSM for that race school. You can edit this if needed for a different attribute. I've added this and you can edit. Only features I created, I can edit, not the other features. So for example, this has changed name or some of the data of it has changed. We can go here definitely and edit it. So I can edit it from the type that we have. So you can click it and here you have the type. I can add the address, the grades. Grades is the class numbers, 0 to 6. Initially, as I said, when my father was studying, it was 0 to 9. And then now it has become full 0 to 12. 0 means 1, 1 to 12. So you can find that 1 to 10 was also there and then it gets startedly updated. There's no 0 because in the case of kindergarten, it's not available in villages. You can see it's a similar village. A lot of land is in agriculture and I'd be happy to showcase that all of this land is in agriculture. Very, very small village and then this part of the village has increased. So like this, I would like you to map your own villages and just think about how much data can be put on an open source if a lot of people participate. The mapathon idea is also part like this. We invite you to participate. So I've added this. So now it is reflected in the OSM database. And I am now going to go to our slides again. Okay. And we are going to open our QGIS screen so that we will be now doing as the slide indicates, we'll be doing crops and water bodies. Let me share the first screen. So before that, I would like to show some exercises that I have run out of curiosity. I ran a Maharashtra state health care system and OSM and look at the coverage. Look at how many points are there, beautifully covering all the places, all the locations. You can see that more hospitals are in the Mumbai region, the urban regions of Pune and other things are there. There's a big gap. So this is the gap we need to address. Either it's a data gap or there is no health facilities. So think about having people travel so far. Let's say how far it is. Let's say if, for example, if there is no hospitals in this region, then people would have to travel, let's say kilometers and then here. So 50 kilometers in between, there's no hospitals. So that's a lot. Think about villages where snake bites is still common. Insect bites, lead, fever, all these are very, very common. So in this whole parcel, you do not have any hospitals, which is a big concern. Either, as I said, either there is no data that the hospital is there or not on OSM. So it has to be populated or it is not there. So we can quickly check. So what I'm going to do is I'm going to open this layer. And first let me see what district that is. So let me add the district layer. So when we add the district layer, we will be finding more of the districts that are in this picture. But as I said, let me first export this into our Google Earth. Okay, I'm going to bring my Google Earth Pro here. So Google Earth Pro is now here and first we have to zoom out to Maharashtra before we input the data inside. So this is the Maharashtra region and I'm going to go near and see which region. Ahmed Nagar is really not having much hospitals as per this location. But if you would like to see that more of. So this region would be very, very underrepresented with the number of hospitals. I'm going to remove the places. I'm going to remove the roads. So this region, Janna and Sillal region have very, very less hospitals, which we will be seeing pretty soon when we download this. So I'm just going to keep it zoomed at this angle. So because when I'm going to open my Maharashtra health care shapefile, what happens is it last there's so many points we cannot do it. It has more than 2,500 features. It cannot be populated. Do you want to import sample, which means only 2,500 of the first 2,000 final features will be taken and then subsequent. So there's more than 2,500 it has to stop. Okay, or you can restrict to my view. So as I said, I don't care about Mumbai side hospital locations because those will be more accurately located. All the Uber, Ola, everything is being mapping that location. Zomato, Swiggy, Uber, Ola, everything has because there needs transportation. But if you go to villages, there's no transportation. There is no Ola car there, right? So what we're going to see is we're going to first say restrict view. If I say important, it will take a lot of time. I'm just going to restrict to this view the features. It says 2,000 features were important. Okay. Do you want the style of the features to be ingested? I'll say no. I just want the buttons, whatever default size is okay. And as I said, we are going to do a reset date and this location. So this location has very, very limited number of hospitals. Okay. So from Bath to Parthur, there's very, very less number of hospitals. And if you zoom in, you will definitely see either locations of houses. All these are agricultural land, right? You can see a lot of agricultural land. And you can see there is a dairy and stores. There's a small village kind of a thing. There's no hospital. There's no hospital located as per the OSM data set. But for sure, there's no hospital here in terms of number of houses and stuff. But if you can search for it and map it, then you are creating better access to rural health and stuff. So it starts like this. If there's no mapping of hospitals, if there's no proper mapping done, then how do you know how much people are being catered to this hospital? And especially during COVID, how many vaccines to be transported? If we don't know the location, we don't know the distance. We don't know how many people are around it. For example, there is a hospital here or a primary health care center, PHC, we call them, and 30, 40 houses that depend on it. So this is what is needed. The houses look big. So maybe it is a progressive area, but still it needs a health care center somewhere nearby. So these are smaller. These are, again, kind of bigger villages. But if you go here, you'll have very, very small houses, a number of houses. And there's a hotel. I think it's a small restaurant, not a place to stay. And the distance is also long. So this is where we could get some help from the, let me just go to double click. So we can see all the points here. And only part of the points have been added because it is actually taking the view. It's restricting to the view. And then you can go to the properties to understand where the location is. As I said, if you can move now. So for example, the data is here, but it is accidentally placed there like in the schools and other database. You can move it. And then this can be used as an updated shapefile in your GIS for making connections, for making raster out of it, interpolations, and also most importantly, for making access maps, vulnerability maps, risk maps, which are very, very important for the rural entities. You will not see the name injected because we said we don't want the style to be injected. But I'll do that also for this location just as a case study. So when you say escaped, it just goes out. It doesn't bother putting down the equation. I'm just going to remove this for now. Delete contents. And then let's zoom in and then re-add the layer, but only the particular layer we want to re-add. And do you want to reload the file and lose any elements you've made? Yes, I will say. I want only the restricted view. And I want the, I'd say, OK, 1000. Yes, I want the ingested styles so that these fields can come. You can say OK. And then you want to store it. I don't want to store it. I don't want to say what we're going to say. OK. So now we have all these files. Again, the same files are there. You can change the colors, styles, everything here, some part of it, not all. And then sometimes the names also don't get populated if it is not there. So now if you click the properties, all the names have been ingested. And as I said, it is given the name Jainan district. And you can see that it is in the Jainan district. So let's see how the hospital is located. This also looks like a village, a small village with very, very small coverage. And then let's see what it is. It is a health center and health sub-centers as a sub-center deviating one, which is good. So we now can map it. So just to showcase that you also have roads and labels in Google Earth Pro, right? But these are more like roads based on the color of the image. So automatically you could see that they are extracting because I am saying this is, there is no name. There's no name for the road and the road cuts through. So but the road goes here. So all these are very important to give access. But more importantly, the hospital is missing. So there is a positive in using OSM plus Google Earth Pro remote sensing data plus your GIS data. So for example, this is the hospital, but it is placed here. All you have to do is go to properties. It just clicks. The name is already there in the properties. You just move it here and say, okay. And now if I click again, you will see all the data. But now in a moved building. Okay. Because I have moved it from here to there. So you can just move it again by saying properties and taking it back to this place. Normally if you go to health centers, you'll see a tree around it. People for people to sit and rest. And that is part and parcel of the networks. Okay. So we do have a good understanding now of how to use it in a big state, zoom into the location you want. Only that location take out because we do too much in Google Earth. Also it will suffocate and drag a lot of your memory and internet speed. So make sure you do that. Google also needs internet. So just make sure you just use what just truncate your work to what you want. Okay. So good. We have stopped here with the health care centers and stuff. Now we'll go to crops and agriculture fields. In again, we'll do Tamil Nadu or Maharashtra. Maharashtra is too big. So let us go down back to another area. Okay. So Punjab has been using a lot of Haryana. Punjab has been using a lot of groundwater. And we need, we can actually see what crops they're growing by extracting that. So let's do that. We will have this one selected. Okay. As usual, it does let you select one path. Let's see how to go to properties and then open attribute table. You can select one. And select all. Okay. Now it will allow you to select. Yeah. For some reason it does that. It's fine. So let's say this, this part is where the groundwater is typically extracted. We'll do, yeah, we'll do this one. And then I'm going to extract now on the India full states, the OSM for crops. So I'm going to first put inject my layer. So I'm going to say layer India full states and only the selected layer. So only this layer will come up because I've selected it. And then now I will say agriculture. I like to actually use this data rather than the preset. So let me go back here and say, I agree. And you will see in the agriculture key, how many are there, there's designated, non-designated, et cetera, et cetera. We'll just use everything. Okay. I'm going to say yes, no designated, permissive, private, official. We'll just say yes. So that will add it. And then we can say crops. This is the beauty now. We can actually map the different types of crops. So if you want to take out the agricultural land or just put poultry, chicken feed, chicken farms, et cetera, you can do. But I'm just going to say in there we have wheat. And yes, we have to map the wheat data. And then let's see how much of these are wheat. So see how interesting it could be just to make questions out of this. We can have advance. We can have nodes, polylines. We can have polygons also. Polygons is good. Run query. No, some object is selected. So, okay. Just say nodes and points, polygons. So always nodes has to be selected. We'll just run this now. Successful query, but no result. So for some reason, the data has not been mapped. And these are the regions where there's tons of data that are needed to be mapped. So if that is the case, if it doesn't go by the area, I'll show you the other way around in and around Punjab. Okay. We do know Punjab has a lot of agriculture and crops. And then we can see it's now done the same query again. If we are lucky, we'll find a lot of crop area. Okay. So it says again, which means that not much data has been given for that region, which is fine. We can use, let me double check this one. And what we'll do now is we will clear this aspect. And then we will now go to Tamil Nadu to take crop data. So let us first zoom into this layer. And then we can take Tamil Nadu as a selected area. And then we'll go to quick OSM. We would like to see what data they have. And then I'll also, I've done already some crops for Maharashtra. I'll show you how different they are. So this is where we could use this data for cross-checking the data for Maharashtra and Tamil Nadu for ground roofing, et cetera. So we can say agriculture, you have crops and agriculture as precepts. You can say crops, there's no precept for crops. So the best way is to put it here as crop data. And then in this, you can say that what do you want in terms of Tamil Nadu? I know they grow rice a lot. So there is no paddy. There is rice here. And let me add it and say, or you cannot have paddy and both paddy and rice and banana because it grows in different different seasons. So we can say canvas, layer extent, or we can say Tamil Nadu. I'm just going to say Tamil Nadu here just to see if we can have a different in aspects. I'm just going to run everything so that if we have polygons also we will get it. So the query is running now for rice and bananas. We know that, especially in some regions, there's a lot of rice growing there and bananas, especially the chips that they make grow there. A lot of export is being done to other places. And you can see that mostly it is a polygon that has been exported. Beautifully the data has come up as polygons. And we will just minimize this and close this and then go back here, remove the selections. Then we just see zoom to layer. And you can see that small, small particles are there but we will not see it until and unless we zoom into this part in terms of properties. It is very small. So what we do is OSM type is way as a path. So sometimes we do have, we can zoom into a particular region and then see if, for example, this region may or may not have crops into the dataset. But first let's export this as a safe feature as PN crops. And then say, okay, save to the file. And then we go here, say open PN crops. And say we don't want the style for now. Okay. So for some, okay. So you could see that you could select a particular crop out. And then I keep it in crops and bananas we have sent. The only issue here is are the boundaries the same? We can establish that by first double clicking here. And then we'll save borders on the bottom. And yeah, so it is, you cannot see that small in GIS. That is why we exported as a shape file and then created a link. So let me see, for example, they have a particular crop. And it says rice. We can see this. And then I'm going to open the properties, make it opaque, style color, go to opacity is 100%. Just going to add 50% so that we can see the land. It still is not visible. Style color is 50%. Area color can be 50% also. Yeah. So now you could see that they claim, they claim that all of this is rice, but technically it is coconut and some other things are there. So here's where the data issues happens. But in some regions, like a big belt of crops, like in Maharashtra, the sugarcane, et cetera, we are able to see a good link between the data sets. So first let's look at this and maybe it was when the data was taken, it was a rice. We don't know. So that is what we're going to see here in 2006. Yes. So all of this has been rice. So this is a very, very important finding we have here. I'll tell you why. So rice requires a lot of human time in terms of management, water irrigation, and harvesting. There's a lot of labor that is needed. Not always you can have a tractor that can come in because look at the small roads. It is very, very hard to bring a tractor into these fields for changing the landscape and stuff. So what normally happens is slowly, slowly like look here within four years. So I'm looking at 2006 where everything is the same land. There is no bifurcations. But then after that, what happens is there is bifurcations in the land and you have some growing coconuts and other things. So coconut does not require that much management like rice. There's no every year tilling. There's no every year fertilizer application and intense labor for sowing and stuff. Only one time you would sow for that. So look at this. They have divided the land now and slowly some part has been done for horticulture. These are like fruits or vegetables or flowers. And then you have coconut and then you have other other aspects. So everything is beautifully done here. And then this, this is how the power of remote sensing and satellites is with ground sourced data. You can actually map and take the data out for your particular area of interest. So in QGIS, as I said, you could not see it very, very small because the size is very small. But if you go to the attribute table, you did notice that, you know, it is, it is small, but the area is there. So for example here, the Jerusalem form is going to click it. And then we can, when you click it on to this map and then say zoom to layer, zoom to selection. There you can see the, see the parcel. So the parcel was not visible in the huge frame when Tamil Nadu, the entire Tamil Nadu was shown. You see, it's very, very small, very, very small. But still there's a lot of attributes like this. So the best way to see maybe the coloring also can help. And then you can see all of them to be zoomed out. Right. So here is a big farm here. There's a small farm there. And around Prichi, my area where a lot of rice is grown. We know that there is a lot of rice grown. Okay. So I'm just going to close this part, go back to Google Earth and then show that this is a quick way of assessing. So as I said, if you go back to this village at right on the banks of the Kaveri water allocations, you will see, so these are, these are, this is the waterway. As I said, a lot of agriculture that happens around it, right? All these are water, agriculture and very, very fertile land, rice, plantations, bananas, et cetera, very, very highly grown. However, when, when there is less water and because this is basically the Kaveri water release also is, is being shared. And if this water doesn't come, all these lands are dry. So there's groundwater being explored. And that is one of the reasons why these regions have migrated from rice to coconuts and plantations which are short grown and they don't have to put as much as effort and time as rice. Okay. So for some reason, we do not have much data here. And so, which is the need of the hour they have to have data. Let's look at another area. So entire terminal has been taken. I didn't take just to cheat. Another area for rice is here. Right in the city area, it looks like again, we can have this properties. We can see style and colors and everything is individual. So we cannot, we have to go every style and then look at it. So we have clicked on this one. Right. So this is going to have properties. I think there's one more we'll do for time. We'll just make it 50%. And then there you can see right in a village area and a seed area, you can have a lot of agricultural stress still happening. You'll do the same exercise for Bangalore. You will get a lot more data that is very, very accurate. Why I chose Tamil Nadu is because of my field work experience. I for sure know that some areas I can relate from my field experience and remote sensing and where some data, if it makes sense or not. So this is being claimed as rice, which may and may not be true. But then as I said, it depends on where, which season, which timeframe they're looking at. This is also rice. It says again, initially maybe it was rice. And this does look like a large piece of land where there has been rice cultivation because of the sizes and let's say 30% or 9%. Yes. So this is there. I can remove this part and then put a time frame on it to show you that it has always been under agriculture. This can be really, really a big rice field. One thing you can definitely check is if you can check an agricultural university, you'll see a lot of these croppings and patterns that they use. Okay. So this is about your crops. I would also like to showcase the Marashra one that I've truncated. It's a big data set. So I'll just truncate a part of it and then show for the class, which, which crops that we could use. But again, as I said, it does, it does rely more on the use of the data in terms of if you want to do cropping for sugarcane, et cetera. So since we know that sugarcane is a very important subject, let us quickly do one for sugarcane in the Maharashtra region. Because that is where a lot of water has been consumed. So we will go back here and go to the India full states zoom the layer. And this is where we want. So we'll try to see if it selects. It does select this time. Sometimes your QGIS does have some issues. And you can see here your crops and crops. And I know for sure that the plus banana not and or bananas and I'm just going to say their extent Marashra or India full states only the select features. And then I'm just going to close this part and then keep all of them on all of them on just for sake of continuity. Because the updated version sometimes does not run for specific calls. It does require it always. If you do more ways and stuff, what happens is it does neglect some data. So initially when we ran, it did not run the whole thing. But now it has run it. And you see that crops sugarcane and rice, which we ran are now sugarcane and bananas. If I can. So it says crop sugarcane crop bananas. In Tamil Nadu's crop rice sugarcane and bananas. So now I am going to export this as a safe feature as and then let us put it in the image. Sugarcane plus banana. This is a very interesting find. Why? Because I'll show you that normally you do not have again the step is first do it in QGIS then go to open file. And then open the shape file in. They want the same files. We need the data. You can say, okay, don't need to save it. Only when you like it, you can save it. You don't need to save all the data sets. And apparently it is very, very less compared to the number of data sets that are there. Okay. Double click. Okay. Very, very less number of monitoring. Only two for a state that runs on sugarcane. So which is interesting, but it's good. At least we have a style and color. Let's say we have 50%. And this is. Properties. You can see all the properties. Yeah, this is sugarcane. It says, so if you go close. Okay. This is a sugarcane plot. And in time frame, we can also see that yes, it is a lot of sugarcane. Some windows have been created by a lot of sugarcane. So now this, this plot, this plot, if we use in the university classification, we have data from OSM that this is a sugarcane. So I'm going to show you how this could be useful. So if I, if I'm going to take this out, and I know that this is sugarcane from a ground proof entity, then all the, all the pixels and all the colors that I take from here will reflect sugarcane in my supervised classification. So we just supervised classification. We're going to say all these green color and the growing grid. I'm going to show you how the leaves change color during the sugarcane period. So you can see here, slowly the big, big leaves are, are coming up in terms of the sugarcane growth. Yeah. See the rows are being planted and now the sugarcane comes up and then within two months and another six months, the crops are growing along the side. Also they will grow the same things. Okay. So almost every region will have the same cultivation. Maybe one or two farms will grow slower or later, depending on the budgets they have, but approximately all of them grow the same thing. Yes. So this is how you could take a data set for sugarcane and other things. And then let's see this one. I'm just going to go zoom in. This is also saying sugarcane, there's no bananas. So there is a conversion of bananas of the, of the area to bananas. And you can see here, okay. All this is sugarcane as per the data set. And I'm going to zoom in and zoom out to see how they grow. And now I have a spectral signature from a particular area, which says it is growing sugarcane. So all these is sugarcane aspects. So with this, I'm going to stop here for the crop part. We'll quickly do again, we'll just clear the selections and then see if Permanado can be selected again, or we can say water bodies, right? So we'll do the water bodies in the vector with OSM. Yep. And then say water. Waterway is there. We don't want that. We can say agriculture and then water. So to find this use, you can have different ways. There's no water, amenity. You can say amenity. And then water, what a basket disposal transfer, water point watering is there. And then government. There's no water for government. Let's say water. We have facilities, animal watering, drinking water, water park, man made water towers, water tap, and then utilities, shops, water. Let's say just water. There's a lot of waters. So there is or, or, or, or. So now I just click water as a preset. Do I need a river stream title, a waterfall dam? Let's do a dam in a particular waterfall. I don't need spring. I don't need, it depends on where you are. If you're in the Himalayan region, yes, there is a lot of need for that part. Fish pass, no breakwater, pressurized pipeline water basin reservoir. Reservoir is good. We can keep that covered water park, and river land, wetland. All these are not important for us for now. Okay, let's keep these two. And then let's do it for a particular state. Pune, Pune is known for a lot of waterways. And I'm just going to look. It depends on if the data is there. I know for sure Pune has a lot of dams, but we need to see if the data is there. So it has mapped, it has mapped as waterways and reservoirs in Pune. What I'm going to do is, again, we'll export this as a feature. Go here, put it in my folder as Pune, water, say okay, go to Google Earth, open Pune waterways, open. You want to apply the same style, you say yes. All the ways are there. Okay, fine. When it starts, it is empty. So just see that it goes to the space, but it's empty. So don't get scared that why it's not showing up. Just click this button and that's it. So you'll have all the waterways. In Pune water, there's three bodies only that has been mapped, very, very less that has been mapped, which is a concern also, one, two, and three. So all within the same region. Maybe they didn't mark Pune into it, which is fine. So you can see here that I'm just going to remove this and say that this is the water body, but for some reason they could not map it in terms of that. So there's a small reservoir type of thing on the side. That is what has been mapped, which is good. And the main water is there, and this is also been mapped. So you can see here how a water body can also be mapped and that spectral signature can be used later for making maps on water bodies and access to water bodies. So this has been also made. And you also have this one, which says it's a water body. We can quickly see if it is a water body. Okay, remove the time frame. It is not a water body, but it has been accidentally marked. Maybe they converted the land from a water body to a thing. Let us see in 1985, we don't have data, much data. 2004, no, it's always been a land. So maybe a land associated with that has been created, which is fine. So here also rivers and streams are there, but I don't expect rivers and streams to be mapped that easily because you need to go through this and do a path. It's not like a road. A road you can go through, drive through, cycle, and then take a point and then put it in OSM. How do you do that for a river? It is not going to be easy. So it's good that we have this exercise of picking and choosing data. Sometimes if your query doesn't work, do not get disheartened. Just make sure that you try different combinations that we did. Initially, when we also did, I also wanted to show that it is getting stuck so that how do you come out of it and still get data for a particular region is by using different query systems. And Punjab and Haryana, the crop mapping was not there, but definitely you can use other query sessions for it. Same thing, sugarcane builds in Maharashtra we can do. But more importantly, leaving the agriculture because we have NDVI, a lot of remote sensing satellite data that we could use. More than that, we were very successful in targeting rural infrastructures such as roads, schools, healthcare. Now just think other things that can be done for rural things like ration shops and how they call it may be different because they will have a different naming scheme. But just you can update your data on it and then say if the ration system is there. So there is different rationing systems here, but it solves shops, homes, and different aspects. But you can just definitely say government. It all depends on how they store it. Resistor, they will store it as ration, but it's not there. So just keep on searching. Food is there. What is there? It's not food. Yeah, food is there. And then ration shop is not there. I don't know what in food. What are the subclasses will be there? There's only less subclasses. Okay. So food could be a restaurant, food could be a ration shop, pre-processed food, et cetera. So this I think I've given some good indications of how to use this for mapping your infrastructures when there is no data, creating your own database for rural development, which is very, very important. Now I will conclude week 11 and look forward to week 12, which is the last week of this course. As you are, I'm also excited to see how your exam goes. And then in the week 12, we'll show a lot of applications, which will make you think on using these remote sensing data for greater lengths. This I would like to conclude here. And thank you.