 Welcome to remote sensing and GIS for rural development, NPTEL lecture. This is week 8, lecture 3. In this week, we have been looking at land use land cover, how it is related to rural development and what other data needs for doing a good land use land cover map. We have defined what is land use land cover, specifically what is land use and what is land cover. Then we merged it into one word as LULC and then we added a change word. So LULC is there and then LULC change between two time periods. We also noted that there is lot of data issues in mapping LULC and for that we are going to devote the next two lectures on data sources, data access and how you could bring data from multiple sources into resource mapping. So let us start with the first example. We have ISRO's resources for LULC and as you could see, there is a very detailed Booban portal looking at the land use land cover with multiple attributes, roads, houses, buildings, airports, infrastructure, etc. And there is also another portal within the Booban which showcases the agricultural rural aspect. As I said, we cannot neglect rural and urban as separate entities. The demand in urban is taken up by rural areas and when the urban system increases, the rural regions feel the pinch or they are impacted. For them to flourish and sustainably develop, the urban systems also have to be checked. So we will now look at multiple resources. As I said, there are multiple hierarchies in ISRO, that is ISRO, this is SPAC, state space agencies and then there is NRSC, National Remote Sensing Center and then there is subdivisions. Just for the state, there are multiple ISRO products. ISRO works with multiple software agencies to collect data and map it. And there is the ready-made Booban data which is sometimes made with ISRO data and other satellite data like NASA, MODIS, etc. and some products are given. So we are going to look at the access to these data, but first let us look at the tutorial on how to access it. For this, we will go to this website. So allow me to share the screen on the Booban Wiki page where how you can access thematic maps, how you can go into learning to use these different data sets will be showcased. So now I am opening the data set page. So the link I have clicked and this comes up. And what you could see is it is like a book with a user guide on thematic maps. So you can click on the thematic data URL which opens out a specific portal which will come after this, going through this tutorial. So it tells about the URL is this and you can have multiple mapping systems. It is provided in Booban, it can be used for linemans, flood hazard, flood annual erosion, etc. Our need is this part. You can see that land use land cover data is the first very, very dominant part in rural development, urban development, etc. And that is why I am using it here in this lecture series as one week. We may spill over in the next week where we may have a hands on tutorial on using downloaded data for land use land cover. You can open and access these layers using WMS service through various clients like Open Layers, QGIS, ArcGIS, WMS service, you can just Google it through YouTube and it will open up its own web page on how to use the WMS service. Again it is apart from the lecture content. We are not here to give one specific data set and how it interacts in GIS but that is what the WMS is. We will just focus on the data access, data download which we will do in the hands on session. We will use NASA's data because it is more appropriate and more relevant in terms of time and space and you will find that even the Booban data houses NASA data. It is not made with this source data. We will find it pretty soon. So these services are linked to this website. It is good to use the best service available. It is free open source. So what NASA's data does is it mixes with the flow data and comes as a bigger product. So it is always good to use the best service available. Yes, we are here to use more Indian products but we should also understand that we should use the best service available for the Indian public so that they manage the land and other resources well. So coming back, we will look into what the datas are there, there is land use man cover. If you click this, it is going to be the same thematic region, just two links given for the same part. So WMS service is there and then there is a step to step guide on how to download the data. If you just go to the Booban geographical indications, GI of India or just the Booban website, you can also click on thematic services. I have given the link but let us search it for ourselves so that in the new course of time if the link does not work, you can just go ahead. So just type Booban. The first thing which comes on Booban is this, the web page opens as entire data fee. So if you look at this Wikipedia page, this page is different than the current page. Why? Because the page has been updated. The schema is same but it has been updated with new, new buttons. So you can see all these different layers and if you come down, you can see that there is thematic layers. Here it is. So you click thematic layers and this web page comes up. So even if the link doesn't work, all the methodologies I have shown will bring you to the same web page where you have the thematic layers. I'm just going to close all the other layers. And just maybe open one layer just for our sake and let's see how it does. So we open the thematic layer and then you can search option to select the theme, what type of data you want and then you can do some statistics-based approach to see how the data differs for a particular region. Click on statistics option to see the land use land cover in each category. You can also click on analysis option for drawing an area of interest and then doing a quick analysis. All these data can be downloaded as an image or as a table CSV file with the results. So you don't have to do this in GIS. So whatever I'm going to show took nearly a master's thesis to do but now with just a click of a button you'll be doing this in the class. So I'll be showing all these resources on how to use it. Click on the metadata option to read the data about the data and then the web service URL so that you can call this into GIS. So by example QGIS, you can just click copy this, open in QGIS, it will quickly open. Click on the overlay option to put other data within the booban surface into your own surface and then print option to print the map which you see. So we're going to see these things and so before that there's a lot of other tags that you can see. Video tutorials, as I said, if you click on the video tutorials link you could see how to download the satellite data, how to add booban WMS layer in QGIS. You could see here that specifically QGIS is given because it is open source and everyone should have access. So what we use in this current lecture is also QGIS. So we are using what all industries use, we are using what all people use for the software QGIS open source. So you can go through this lecture which has been given in the ISRO platform as I said, some lectures are already linked to the lectures within the QGIS platform in ISRO. I think that you can use. So here you can see that web service is given. You can open it, it will directly open in your QGIS web service location. You generate it, you will go here and then you will put it into your QGIS software and then it says add WMS service. You put in the layers, it will just open out and come up in QGIS software. I don't want to redo this entire exercise but it works and you can see clearly that they are showing how the layer works and gets out of office. So here it's getting populated and you can visualize it. They have used here RGIS in this example, so this is a RGIS, QGIS is used much, much better because it is free and open source. So I will come back, even though it says add QGIS, you can see that it is RGIS the link they have given but it's fine. We will do a quick analysis using QGIS in this lecture series. So all the other tutorials and other things are there. You can see how you can register on this portal, you can see for example here. So you can see how you can register and what data is needed. It is secure as per their comments, you can have an account. Then there is free satellite data, as I said the DEM that we had in the lecture 7, we had around 1 arc second approximately equal to 32 meters per pixel. So that is within the equatorial region, we said 30 degrees, 30 meters, etc. So that is almost the same. And then we have Cotter DEM, Cotter satellite 1 DEM version 1.1 R1, which is also very good in Indian regions, then the version 2, version 3, and then there is IMS hyperspectral image, 17 bands for hyperspectral image, which is more on land-use land cover and other resources. Here we have the resource set, which is also giving you the land-use land cover. If you remember that in the previous lecture, we had this is being used for LUNC preparation maps by the ISRO agency and that is at 56 meters. So now here's a question, if you have an open source data, which is at 30 by 30 meter resolution, which is good enough for an Indian region, you will definitely use it. The ortho is also good, LIS 3, which is at 24 meters, but the data is only available until May 2019. And it comes twice, two times a year at 15 minutes. So you have all these data, which is freely given, last updated one year ago. So now let's come back to the thematic slides where we are going to cover what we are going to cover. So the tutorial has been explained, the slide has come up, the tutorial has been explained. We hope that the tutorial is helpful for you. Now we're going to look at what the thematic area is going to cover. I'm going to select UP as the region of the province. Why? Because UP has the highest number of villages. So this link will take us to the webpage for the thematic services, which is also the same, which was given in the wiki page. But what we will be doing is we will see, take case study as UP as an example. Why UP? Because UP has the highest number of rural population and villages. Again, this course is for rural development. So let's take a rural entity as our data set for the tutorial. Then we'll be doing these steps. We hope we could cover most of it in one lecture, but we can also trickle this to the next lecture, which is the fourth lecture of the eighth week. Because this is very important to understand each and every tab and how it works. So I'll patiently go through this with you. I'll explain in detail how this can be helpful. Downloading, you can just look at the YouTube tutorials from ISRO's webpage on downloading. But maneuvering and linking the rural development is the goal of this course. So as we suggested, let's go back to this slide. So the thematic area is going to be showcased. It's going to be shared. So close this and now the thematic area has come. You can see the boundaries are clear, entire India is there. And we can go zoom in to this particular level. So let's look into all these links. So you can print the map, add WMS layer, updates some things and then log in. I will not log in for now. If needed, we can log in when we download the data. So normally you can keep it here so that we can see the entire solution of India. I can double click to zoom in. We'll keep it like this so that we all can see what region that we're going to work on. So in the search box, the first is select team. If you click this, there's multiple thematic layers that have been made. Of this, yes, urban sprawl. What does urban sprawl means? How does the urban area increase? OK, let's just click one urban sprawl and then select some states. You know, all states are there. Let's say Maharashtra and then view. This kind of a land use land cover, but basically base layer is 2011-12. So this is 2011-12. You can see how Mumbai region is. The red color means the urban area. But if you overlay this with 2005-2006, you could see that I'm zooming out. So some layers are coming out as pale, which means there has been initially the area was here, but now it's expanded here in 2012. So now if you increase this and then move it across, you could see that here. Yeah, this example is good. You could see that initially in 2011-2012, this is the urban sprawl. And if you click this part, then you can see that the light red is 2005-2006. But now it has increased when urban area increases. As I said, it consumes rural or peri-rural, peri-urban regions. It consumes the resources that were promised for rural regions and there is an imbalance. So to document that, it is important to do land use land cover. So let's go back to our initial theme that we wanted to work on. So I'm just going to refresh it. We have the India slide again. You go here. So there is water bodies. Again, part of your land use land cover, flood annual, flood hazard. These are just basic flood analysis for a particular year. It's not full in term. Lineament is a geological fractures and lines where they are present in India. It basically maps the earthquake prone zones. Geomorphology is, as the name suggests, a lot of geology and the morphology of the rivers. Glacier lakes where water bodies, again water bodies, wasteland, where land has not been cultivated, a kind of barren land, urban land use, et cetera. Land degradation, and how far the land has been degraded. Let's look at 2015 just quickly. You could see only some states are there, but we can see Uttar Pradesh. And you can see here as the legend is here. So legend is just the color and what the color represents. You see that water erosion happens a lot. Water logging happens a lot because the Ganges flows through this region. Less glacial and some data is not complete. So you can see here there's data gaps and stuff. But the methodology, if you look at the technical document, which is the same that you can apply for using any other data. So we'll try to do a quick analysis of a NASA data for you so that you can download and do this exercise on your own. As I said, there is land degradation, but our course for this week is land use, land cover. So you could see that there is 50,000, 50,000. So one is to 50,000, that is how you should say. So one unit on the map is equal to 50,000 units on the ground. So one centimeter is equal to 50,000 centimeters, likewise. One feet is equal to 50,000 feet. So there is a very fine resolution is one is to 50,000. And then we have a 250K. So one is to 250K, which is not as fine resolution as the previous one. There is a 10K, which is the highest, but it is urban, not rural areas. So when you do land use land cover, it is entire. So they have done three years. We have 2005, 2006, 2011, 2012, 2015, 2016. So three years have been mapped. But still, as I said, 2016 is the latest data available. Today's date is 23 March, and there is a seven year graph. So how can we use a seven year data for a current scenario is not clear. But for sure, you can use the previous and then map the current to see how it works. We will be showcasing that part. How do you take a current data and then do a quick land use land cover? However, this requires a lot of manpower, time and cost. We will do the basic requirement, which is free and quick. It is not as good as these maps that have been populated. And that is why it takes time. So seven years is too long to take. Maybe a year time should have been taken to take the ground points across India because all the institutes can cover. There is a tremendous locations of the ISRO's database centers across India. They can pitch in the data and can work on this. OK, so let's go back to what we want to see. So I have looked into the authorization and you could see that the boundaries are very accurate in these maps because these are, as per the government rules and laws, we should also use these boundaries. So the first we're going to see is the land use land cover, let's say 2005, 2006. As I said, I'm going to check UP, so Uttar Pradesh and click view. So beautifully, the map comes up. You could see that minimize it a little bit so that you can see the entire UP and there are a lot of base layers. OK, you can zoom into one particular location. I've been to Lucknow. As I said, the last week I was there for a particular conference. So I'm just going to do that. You could see here that it doesn't populate the current legend. The legend is stuck, so you'll have to refresh it. So this happens. So don't get concerned. It's just the map did not update. So don't worry about it. Again, click UB, then view, beautiful. Now the legend has been updated. You can see that again because the cropland is what we want to see. And as I said, let's look at Lucknow. Lucknow is still a major hub, but still you can see a lot of again cropland is given as yellow and you could see the different classes. So now come back to the lesson that we have seen. The classification is this is the classification. The pixel has been classified each pixel inside each pixel each grid. What is the dominant land use land cover? If it is 50% urban, 50% rural, it's sticky. But never it comes like that. It will be not a straight 50%. It will be like 50.12% and 49.88% is your rural. So now we have a slight improvement or slight benefit for the urban. So the data goes for urban and the pixel is covered red. But if the agriculture is there, for example, 60% and 40% or even 51% and 49%, 51% rural, 49% urban, then it becomes yellow. So now you could see that a built up urban is red. Built up rural is darker red, which is on this part. So this part is darker red. You can see that that is a rural area but built up. And then I'm just a little bit outside Lucknow, but yeah, here's now all these areas. The red part is the city in 2005-2006. Built up mining is not there, agricultural land, crop land, plantations. All these are plantations and agricultural land. There's fellow land, forest deciduous. There's not much forest here, but there's a lot of pink color. And what does pink color give? It is barren land, wasteland, or scrub land, et cetera. It's type of barren lands. It is a lot of them are there. There are small wetlands here. So some part of the land has a lot of wetlands. And then water bodies, blue color for water bodies. So the basics have been met. And as I said, you can go as the district boundary or you can just say select and then remove. It gets removed. You can just go back up and down and then say, particularly view, now it views, it gets loading. You can see that this is the overall. And in the overall, you'll see forests on the top, Vihmalian regions. The ganges flowing, which is bringing water bodies. And in the lower part, you'll see a lot of barren wasteland, et cetera. Land has not been used well along the ganges, along the river channels. These are prime land for agriculture. You see a lot of agricultural activities. So on a whole, you do see that these lands have more agriculture. And it relates to the statistics I just shared as per census data. UP has the highest number of rural population and villages. So definitely there will be a lot of agricultural activities. And so we also have a lot of wasteland in barren land and then some forests in the bottom, forests on the top and a lot of urbanization these hotspots. So these are hotspots. Let's take some of the berylline can be taken. I've worked in berylline. So let's zoom in and you can see some city. And then most of it is the ganges region along the ganges. There has been a lot of waste shrublands, barren unculturable wasteland, et cetera, et cetera. The colors are not as perfect, but you see a lot of red dots. The red dots, the maroon kind of red is the built up rural areas, a lot of rural areas. And then this is the berylline city, very, very nice posh city, very small, but still not as big as Lucknow, but still is good. OK, so this is how you will access the land use land cover. You can look at the technical document as promised. Let us go and see what is the data source that has been used. You see that a projection of land use land cover, one is to 50,000 scale. Metadata is always important. It is your duty to read through these before using the data because anyone can question you what is the source of the data. You could say ISRO is the source, but still ISRO is what is the question. And this is the list three data, linear imaging, self-scanning sensor three. It has good high resolution compared to the previous ones. You can see that it has mapped all these different water bodies and land use land cover. OK, and they give you what does it mean? So as I said, in here, you say the two legends are here. And what do what constitutes when they say what is built up urban? You can come here and say what is built up urban is. It's a mix of public school, public, semi-public, communication centers, industries, recreational, ash, dump, reclaim land, which is a reclaim land is what the Bandrakurla complex is in Mumbai. It's a beautiful land, but it's already claimed from the ocean. They put in a lot of soil and gravel to claim the land. So that is all called as built up area. And then within the built up, there is rural, just rural area within the rural community region. If it is built up, it is called built up rural and then built up mining. So we don't have any mining, but we have three built ups. And that is what you see here, built up and then three descriptions. Then you have agriculture crop land. It constitutes curry, frubi, zai, two crop seasons or more than two crop plantation includes plantation, which is rubber, tea, et cetera, or even banana. And then you have agriculture, horticultural, et cetera. All these come under the plantation. Fallow is current and long fallow, current, shifting cultivation, all comes under agriculture. So you can see here there is agriculture crop, agriculture plantation, agriculture fallow. There is a comma. So there is a first description and then the second description and it is separated by a comma. And then there's grass, grazing, barren. I would recommend you to go through these. And as I said, it uses the FAO scenarios. And we have used the same description in our class. You would have seen that land cover is defined as observed physical features of the surface. I've added bio because there is a lot of bio also in it, which is forest cover, crop cover, all these things. And they're given detailed description of each bullet point, what it means, what is fallow, what is forest. So let's look at agriculture. For example, we have agriculture. These are the land primaries for farming, production of food, fiber, and other commercial horticulture crops in India. It constitutes of crop land, plantations and fallow, crop land areas appear bright red in the red spectrum. They are widely distributed in different terrains, prominently up here in the irrigated and non-irrigated areas. It includes curry, ravi, zai crops. Zai is mostly the winter crops, along with areas under double or triple crops. So this is the definition of agricultural crop land. So then you have salt-infected, barren land, et cetera. What is the year? When we downloaded, we didn't notice that it was 2005, 2006. So now it says resource and data from list three sensor of three sensors pertaining to 2005, 2006 I use. Three seasons are monsoon, ravi, and zai. Curry is the time. So August to October is your curry, the rainy season. Ravi is your December to March, which is mostly your winter crops and your three summer crops. Zai is your peak summer crops. In some regions, it is called the winter crop. Here, they use it as the summer crop. So as I said, you can use it for watershed management, agricultural productivity, and improvement, which is what the rural development scenario we are looking at. How do you increase agricultural productivity? How do you map regions where agricultural productivity is needed? And then your energy budgets, hydrological budgets, et cetera. There's some disclaimer, and then data, et cetera. Partner institutions, as I said, they work with other regions. So UP looks like UP Space Center was very, very helpful. And then so maybe UP is a good section to look at. And then the project directors, personnel, et cetera. Citations, if you use the data, you could cite this NRC. ISRO is also very good to cite. They have their own citations of what is land-island classification. They use the manners on FAO. So good thing that we are also using the FAO citations in our lecture series. So we are currently on track, and we use these technologies also. So with this, I think the metadata is clear. You can zoom out, zoom in using your mouse. And you can use different locations also. So let's look at 10 years difference. So I'm just going to view UP. And then let's save you. So just for 2015, 2016, you have a map and statistics. You can download both or read both. We use Birelli. So let's put the Birelli again. And Birelli comes up. And you can see the same legend is used. However, the data is different because we had two different years. You can move the screen up and down if you don't want to see this part. But then you can also use it. So the technical document for 2015, 2016 is they have used, let's see what data they have used. They have used ISRO's data cycle. 55 classes of land use land cover has been done. The years that have been used, whatever legends, et cetera. So we can also see that it has used the resource set. A project land use land cover, it is using multi-source resource set, linear self-scanning, this three, et cetera, et cetera. And they also use other ISRO satellite data products. And the same documentation has been given. Here they are given different updated data products. And the previous citations have been used. This is a 2019 citation. So it is approximately three years old. But the data was from 2015, 2016. So let's stop here. We have looked at, OK, let's look one more quickly. And select UP, view, technical document is the same. And then we have map. Map, you can download the map. You click the map quickly. The map comes up with all the land use land cover and data source, resource set. So this is the metadata of the data. They have taken three seasons, 11, 20, 12, so 2011 to 2012, was used to make this map. And they have published it in 2016, 2017. So you can zoom in and zoom out and then use this for your studies. You can georeference this map because you know where Birelli is and click a point in Birelli, the center of Birelli, take the GCPs, the ground control points, and then use it in your studying area. For some reason, statistics also can be looked at. So basically gives you the statistics of the percentage of land cover and land use, et cetera. How much land is in cropland. Total land area and square kilometers has been given in statistics. Comparisons is not done yet, but we can definitely do it in the next lecture series. So let's look at in detail the next ones. As I said, in 2005, 2006, if you go to Uttar Pradesh, you do not see the map and statistics. It is not readily available. You will have to download and put it in the map, but there are other reasons you have them. So with this, I stop here. I'll see you in the next lecture. Thank you.