Remote Sensing is the process of acquiring the information about the surface of earth with out being in contact with the object. Similarly GIS stands for Geographic Information System , which consists of hardware, software and user to make decisions on the basis of location based data. From the remote sensed that by the Landsat 8 imagery, we have used the NIR band of the landsat 8 imageries to calculate the Paddy Monitoring Index (PMI). This will show where the paddy are grown.
By this we can know how the trend of production in the last years and how the productions this year has been affected due to COVID - 19 . Due to COVID - 19 ,people in Nepalese will face the scarcity of seeds, fetilizers and manpower. This will lead to the decrease in the production of rice which may lead to the famine if the alternative food or source of food won't be managed. This will also calculated the statistical data like production (tons/ kg) in last year and this year. So that we can also conclude the amount of food we need to manage.
The COVID -19 spread will no doubt decrease the production of paddy in the Nepal. As the paddy transplantation will be difficult in June/ July due to scarcity of seeds, fertilizers and the manpower. So it is necessary to know the trend of the production of paddy in the recent years and this year in order to win the famine in Nepal.
We have here used the technologies like Remote Sensing and Geographical Information System (GIS) along with the programming concept to make our projects.
We have used the satellite imageries from the Landsat 8 satellite as the main source of data in our projects. The landsat 8 imagery are easily available free of cost. The satellite imageries are used to calculate the Paddy Monitoring Index (PMI) which show the production of the paddy.
The tools we used here are Google Earth Engine (GEE) , Django Framework, Leaflet Javascript Library. It is an platform which provides the millions of datasets of satellite imageries. We used the GEE Python API for the backend coding. We have used the Python API for GEE as a backend in the django framework. We have sent the tiles generated from the django to the HTML templates. Also the Leaflet js is used to display the maps, tiles from the Earth Engine and Geometry boundaries from the .geojson file.
The coding language we used here are the HTML, Javascript , Python. HTML is used to make the front end of the project. The main page of the project is divided into different division. Also the Javascript is used for making the leaflet maps and also making the front end more functional . The python programming is used to code the GEE Python API in the django framework .
The hardware used here are some personal laptops by the programmer. It doesn't require any specific hardware but require the laptops or personal computer to view the project.
The software here is a Visual Studio code editor and browser. Visual studio code editor is used to write code. Any sort of browser can be used to view the project.
We have been successful to make the complete front end for the project. Here we can see the province of Nepal by selecting from the drop down button. We can also upload our Region of Interest (ROI) by uploading geojson file.
We have also been able to generate the tile from the Earth Engine which show the production of Paddy in the selected ROI. The layers like "Paddy 2017 , Paddy 2018, Paddy 2019" can be turned on and off.
But we are not able to complete our project due to lack of time. The charts for showing the the production of paddy per year, loss or gain of paddy year wise are yet to be made. The statistical data like area of production and production amount are yet to be coded.
NASA , Google Earth Engine