 Hello everyone, welcome to remote sensing and GIS for rural development. This is the NPTEL course focusing on rural development using remote sensing and GIS as two. We are now at week five lecture two. In week five lecture, we have been looking at specific data types in GIS of which vectors were discussed in week four and week five we are looking at rasters. There are only two types of data vectors and raster and we are looking at each one specifically for each week and in the last lecture which is week five lecture one, we define what raster data is supposed to have, the specifics and properties of raster data and we also looked at the formats in which raster data is stored. We also looked at the difference between vectors and rasters. In today's lecture, we will look into more specifics about using raster for rural development. What we see here is some examples are going to be very specific for rural development whereas some have to be tailor made into your objective. As I said, satellites are collecting some physical properties as data and it is converted to remote sensing image or information after some post processing. This post process data which is still raw data for you because you are going to take the data and process it in GIS has to be worked upon in different objectives so that we know what objective we are going to have and what data we are going to use. So let us look at some raster data for rural development. I am going to revisit the slide which I used in the introduction lectures however with more specifics. This is the luminescence image taken for the Indian region by a NASA satellite in 2012 and 2016. So how can we use this for assessing rural development? We have defined what is rural development earlier and one key aspect is connectivity and rural electrification. So this image while it does show that the urban centers are expanding you can see the bigger dots in both the images are expanding the bigger dogs Delhi, Chennai, Mumbai all these major metropolitan cities are expanding it is called spidering effect. However we also see some positive rural development because in the central part of India and around the major cities you see some small lights also popping up and you see also a road kind of a network connectivity which is the highways over the 4 years between 2012 to 2016 there has been tremendous growth of highways thanks to the different schemes of the government that we looked upon and also rural electrification has been happening and at large scale. So these also can lead to access to electricity and power which in turn can be converted to a livelihood option, shifts that can be operated shift as in morning shift and night shift for running cottage industries and most importantly storage and processing hardware can run on power. So these can be installed in villages now because there is access to rural electrification programs which is evident from this image. So this is one that we would revisit the other is the image taken by a satellite of a river this is before flooding that I explained earlier but let us look at it per se for rural development. Once we have a water network we can also assess where the fertile land is again agriculture is the key livelihood option for rural regions and for agriculture water supply is key not everywhere we have irrigation through groundwater because it is expensive however we do have surface water irrigation. Surface water irrigation can be through rivers and channels mostly gravity fed. So now if we know where the substreams go through the land near the substreams can be managed to support agriculture. This is what this image can tell but as I discussed earlier in the introductory slides this also helps in assessing the damage after a natural calamity. These natural calamities are known to impact mostly the rural regions because they are more vulnerable and for example let us look at the image after the flooding has happened. You could see that the banks of the river has swelled, gohathi is here where you could see lot of swelling happening we call it swelling or the water parameters increased. Now the lands which were under the swelling are going to be flooded and or sedimentation would have been spilt so the sediments soil would have been spilt across. So these are not fertile soil some of them and can impede agricultural development. So for rural development one key aspect is resilience climate change resilience which means how does the system bounce back from a natural calamity. How fast or how what is the investment needed these kind of things. So on that note this image plays vital role in assessing the rural infrastructures land and housing locations where the floods could have damaged thereby letting officials to support these regions through a satellite data which is a raster all these are raster because as I said satellite data is mostly raster we are looking at some raster that can be applied. So now if the blue color is only going to be taken out as water and then the rest green is going to be converted as land so the raster is going to have only two values suppose blue for water green for land. In this first image you have blue at a certain location the raster is going to only have blue here whereas in the second location some land parcels have been converted to water. So these are where initially land now it is water. So you could see that these raster cells which have been converted to water cells are the inundated cells water inundation which is affecting rural development. As I said earlier also access to water is key and initially groundwater was a point data it was at a point how much ground data is available and that is catered to the local land around the well. It was good assessments when groundwater was less used however India becoming the leader in using groundwater over extraction is reported in many regions across India and this could mean because of climate change because of overuse of pumping etc etc however it is important to monitor groundwater levels and with the current point source. So what is the point source this is a vector data whereas here you have raster data the point source will only give the groundwater level in a particular location. So we have location specific groundwater data however in a raster image so this is a very specific satellite called grace in this image you could see that the coverage is continuous. So not giving groundwater at a point but across Gujarat and that helps because we understand where the regional pumping is happening not a point pumping. The point pumping can have impact only in the location whereas a regional pumping can have impact across the region you could see that here there is more pumping happening during in the south raster region central regions but after water has been supplied there is a lot of recharge groundwater recharge happens and so there is groundwater recharge in these regions. The other thing that we would like to discuss is the inundation of the land. So land inundation happens because climate change creates a water level increase a sea level rise happens and that can impact the low line areas. Most of the low line areas are rural regions except if it is cities like Chennai Mumbai etc. Most of the other regions are still under rural entities. Why? Because these support predominant agriculture plus fisheries occupation and there is a lot of people who are dependent on these for their livelihoods. So now if you look at this the level increase can only be looked upon as a climate change impact. How do you create early warning systems by using these kind of satellite images and you can see that it is a pure raster and a raster which had land in some spaces now has water. So this is where pixels that had land has been converted into water because of sea level rise and sea level inundating the land. So both you saw the water in the river coming out because of floods and the sea level rise increasing because of snow melt and more water joining the rivers the sea level rise increases mostly global warming has been linked to the sea level rise. We are not going to talk about the processes that happen but because of the process the impact that is affecting the rural regions can be studied on a raster scale. Can this be studied using vectors? It is possible but not at this high coverage. You may have a point here I am going to put some points. A point data which is vector data a point here, point here, let me make it big. So let us say you have a pointer sensor here, sensor here to collect point data of the water level rising. How does it show the entire coverage? It cannot. So you need a surface to show the entire coverage and that is what this satellite image is giving us. All these are remote sensing images and are rasters. In some cases the rasters can be converted to points and the points can be smoothed to a final resolution of rasters along with observation data. But again this is kind of a data less intensive course. So we will not be getting into high processing of GIS and remote sensing but introducing methods and tools that can be used for rural development. One of the most important application of rasters for rural development is mapping. Mapping the land cover, acreage. So there are multiple terms that can be used. Let us stick with the agricultural and agricultural allied livelihoods for example grazing of animals, dairy, all these require land. Land as in not just a plain barren land but a land that needs cultivation, water and after that there is a harvest and then post-production. Cows need grazing, cows need food for dairy industry to flourish. If you try to bring the food from other resources it is going to be expensive. So there is a grazing land and then you have goats. These are predominant livelihood options in the rural regions which are allied to the farming. Farming is key but then these are like poultry is also there, chickens, ducks etc but mostly it is agriculture followed by dairy and goats and stuff. In agriculture you have different types of agriculture, potty culture, cash crops, food crops etc. Knowing the spread, knowing the acreage of this helps in understanding the natural resource requirement for optimum yield. Let us say you have one hectare land which is growing sugarcane and another one hectare land which is growing cotton. You are not going to apply the same resources to the land which means same water, fertilizer, pesticides. These differ because of the land use cover. So this land use cover is very important for attaining sustainable rural development and other sustainable development goats you have food security, poverty reduction all are linked to the optimum use of the land. So here in this map what you can see is which is produced using the European Space Agency's satellite data, globe cover version 2, 300 meters per pixel. You could see that the land has been coloured based on the type. Here we are, we look at multiple colouring that has been used this, I am just zooming in, I am just zooming in this region for you to see. You could see that a lot of cultivated and managed areas, rain pad areas are an orange in colour and the dark greens are more the forest. So now if you have a time lapse image which is an image taken in 2004 for example and a current image that is taken in 2022 or 2023, if you can assess the difference. The difference in forest cover, the difference in the agricultural land cover all these would provide the assessment of resources that are needed for rural development. Let us take some more focused angle on this. We have the decadal land use land cover map for India, so decadal means in 10 years over 10 years. So you can see that a land use land cover map of India for 2005 versus 2015 which I am going to pull up. But before that, this is a raster, see how the data is continuous. I have clipped the boundaries out so that you can only see the data for India. Because the discussion is going to be on India, we are not going to look at the other regions of the world. So we are going to look at here for example, you could see here, you are looking at the colours which are red for built up area and then fallow land etc. So this is very important to understand where we are having built up area and where the land of rural land has been compromised or changed into multiple different land use types. So let us look at what the land has been used for. What the land has been used for is, it has been used for rice production, other cereals, pulses and oil seeds. So this is a spread of land use land cover across India. The top catchment parts do not have much agriculture because of the Himalayan region. But if you look at here, let us say for example, Pakistan, it was growing rice but not high. So where was the rice growing regions? It was here. So just looking at this rice image, you could see that most of the rice is grown in the Chattisgarh Odisha belt and the southern regions a little bit. But cereals are grown mostly in Karnataka, Andhra, Telangana and pulses in Tamil Nadu, Madhya Pradesh and Gujarat whereas oil seeds are in Rajasthan. Wheat is still there but we are not having it in this paper so all these are done. So these are extracted first as a generic crop land. So if you could see, crop land is given in brown color in the left image and then the crop land is divided into multiple crop types. I gave the example of a person has two hectares, one is sugarcane and one is cotton. So if you look at the top image, the initial image of raster, you will combine both the one hectares as crop land because it is used for agriculture. However in the second phase which is the second level of raster estimation, raster classification, the crop land area is converted or classified into rice, pulses, oil seeds, horticulture, whatever it is for that location. So you could see here initially this image just shows it as crop land but what crop? So that can be answered by here which is pulses and rice. These are taken by high resolution satellites which can capture the difference between the green color of a sugarcane and the green color of a soybean. From the top it might look all green but the satellite can differentiate between many multiple green levels. And based on the green level, sugarcane has a signature green whereas soybean has a or cotton has a different color of green. So both the greens are not the same. So once you know the signature of sugarcane and the signature of cotton, you could easily divide it and make these kind of maps. Which says that the land, yes it is crop land in Maharashtra, eastern side of Maharashtra. But we also see the type of crop land wherein you could see, let us take Madhya Pradesh. In Madhya Pradesh you have some crop land here but most of the crop land which has been aligned is having pulses. Same for Tamil Nadu you have lot of crop land which part of it is oil seeds, part of it is rice and most of it is pulses. So in this image you could see that you have multiple land use land cover done by ISRO. So knowing the importance of this land use land cover for Indian rural development ISRO produces these land use land cover maps at different temporal scales. So they aggregate the data and then they make 2004-2005 example till date. So they are publishing it. This book is 2008 book so that is why you see the image of 2008. However you could see clearly how the, at least here because all the other regions are overlapping, the image is overlapping. Whereas here you could see 2004-2007, how the green color in Assam region, the northeast region has changed. Is it good or not, will that kind of assessment, will you have to go to the ground and assess the benefits and impact for rural regions. But mostly it is how the land has been converted to industry or over production which leads the land as barren. So if you too much exploit the land for water and nutrients, it becomes barren thereby not supporting plant life for a couple of years. And that is where rural development requires resilience mapping and land use land cover mapping. How is this done? They first collect and data which is of two types. The first data is your remote sensing data which is your satellite raster data. The raster data is classified into different colors and each color is given a particular land use land cover type LULC which stands for land use land cover. And the remote sensing data is both imagery or satellite data. And then some surveys are done where people go down and look at each pixel in a region and then report the pixel's color to the satellite data. So now you have a same location through the satellite you assess the green color but you do not know what that green color refers to. So you send people to collect one or two points in the pixel to show what color that is. For example, the green color could be sugarcane or soybean from looking on the top view you cannot differentiate it. But if a person goes down to the survey and then assess it we can be taken up. So that is called ground proofing plots through plot scale surveys. Then you have sensors data which can give you data on yield, agricultural sensors yield or agricultural crop production data. These can also reflect the land use land cover change. We can only know that one hectare there's rates. One hectare can produce a ton of a particular crop. You won't expect 50 tons, 100 tons suddenly. So that is where the sensors data can play a vital role. And then proxy data, other data that you can use to map land use land cover. The key here is to classify your satellite data, the image, the rasters into multiple colors. And the multiple colors should be given names as in labeling of what that color means. So identification of key classifications is very important. Now let's look at in other regions and other scales how raster image, a satellite image can help. One is the cropping calendar. And in this you can clearly see that an image, an infrared image that has been taken by a satellite. Some pixels are red whereas some pixels are brown in color. The red indicates a healthy growing green crop. So if you look at the trees, the trees are also red. So this is an infrared camera in the satellite which is picking the green color a lot. And you could see that green, because trees are always green, it's not red in color. So green has been picked up which means the winter month wheat crop because this was taken October or early November that period. So in between maybe early November this image was taken. And you could see that the winter wheat, the winter sowing of wheat is happening in the same image. And some land is ready to be harvested because the green color is gone, it is turning brown. The plant when it starts, it is green in color slowly converts to dark green and the green color comes down thereby converting to orange, brown and then the plant is wilted or the plant dies or harvested. So this is where the coloring of the plant through a satellite imagery can give you the cropping calendar. Each pixel takes a color and that color can be represent when the crop was stoned, was sold and harvested. Also it can be used to assess what kind of technology has been used. Let's say for example this, this image shows clean cropping boundaries and also the harvest has been very clean. So if it is done manually, you will see some one or two not correctly harvested or it will not be smoothly harvested because people cut on the top, cut on the bottom. So the height of the stump which is remaining back in the land will have different colors. Whereas this is very smooth, this smooth color comes because it is almost unique and that unique comes because of a machine like this harvester which actually cuts through the crop land at a particular height. So you have a clean image and this image reflects the technology used. So now farmers can know that what technology is used in the neighboring farms, the neighboring rural areas and those technologies could be brought in for their use also. Instead of them going and looking at it, these images can help and also help the rural authorities. This is corn crop. So it can help the rural authorities to put budgets for farmers because these technologies actually help. And you can also see a river which is going across a water drainage that brings water. So to conclude, there are a lot of satellite data which are raster images and each data differs in spatial and temporal resolution. Spatial resolution is the pixel size whereas temporal resolution is how often does the satellite come to the same location to collect the data for the pixel. In this graph, you would see that the temporal resolution increases from top to bottom because 100 years is low resolution, 0.001 years is high resolution. The same thing, you have 100 kilometers as a high space, high spatial coverage but low resolution because at 100 kilometer pixel you cannot find what crop it is. You cannot find your road. It just takes the dominant color. So if that 100 kilometer pixel, most of it is let's say a forest, you will lose the housing, you will lose the agricultural land and only forest color will be there. So 100 kilometers is coarse and 0.1 is fine or hyperfine resolution. So now what you could see here is the linear point of collision is right there, the 45 degree angle and this is where improving spatial and temporal resolution happens, optimal range we can say because at this point it's very expensive. If you look at which satellites are giving these kind of data, these are not free data. You have to pay a lot of money together. These are private data collection agencies like for example, airborne camera through your drones and small aeroplanes whereas Quickbird is a commercial satellite data agency where it collects data at very, very high resolution and sells it for optimal views. So now where does rural development come? Fortunately the rural development mostly comes in this part. Emergency response is there during floods and droughts. The geology and topography is also a one time investment but mostly the land cover, agriculture, water quality all come at a not too fine resolution or too coarse resolution but somewhere in the middle. And those middle ones are dominated by satellites which are open source. So you could see that the bands are given and mostly these are also temporal resolution is 18 days, 8 days, 16 days which are okay for agricultural development work. You cannot have 100 years data, it is of no use or a one year data. One year data how do you accumulate? So if you remember the groundwater map I showed it was monthly and now this image in the center says bimonthly every 15 days, every 16 days if you collect data that is good for rural development. We will revisit this slide more after we discuss the satellites and the processes involved in assessing the data. With this I conclude today's lecture. Thank you.