Google Earth Engine QGIS Tutorial: Google Earth Engine Plugin and Python

hi everyone welcome to this video in this video we'll learn about how you can access geospatial satellite data using ours Engine Python API and a qjs platform so to do that you need to have an ARS engine account to create an ARS engine account to go to ours engine Google calm and then once you're there if you'll sign up by clicking the sign up button and then once you click the sign up button this is well you get and then you'll sign up for ARS engine by providing your you know email and the reason that your you need access for Aires engine for educational purpose and then you'll be granted access once you're an arse engine user or you have an Origin account then what you'll need to do is to go to QGIS and install the QGIS ARS Engine plugin so this is a plug-in that will let you apply or use Google Earth engine in a QGIS environment so you open a QGIS environment and install the ARS engine plug-in and you'll be able to access our engine in a QGIS graphic user interface as well as you know code editor in Python okay so to access the QGIS Earth engine plug-in you go to this website okay or just to Google QGIS ours engine plug-in and then you'll get this end you'll download the you know QGIS the QGIS library so you go to the qjs as in your plug-in you know and then download that and install it in a QGIS environment so to do that you open a qg s environment and then go to plugins and then install you know Google Earth engine plug-in but for me I already have installed the googlers engine and I can see it here so I can you know run some Python code in a Python API and be able to access our engine okay so in your kids you need to install Google Earth engine plug-in if you have not already so so because I have already installed the Google Earth engine plug-in I'll go ahead and open my so the first thing you need to do is just click this Python I can to open a you know console a Python console or just code editor since I already click this it's already open the python console is open the next thing I need to do is just open my Python script so in this example what we'll be looking at is I'll show you how you can load a Landsat 8 satellite data and then filter it by date and also by area okay and then visualize it in a QGIS environment using a Python API owners engine and to look at some of the data that you have on ours engine you know this is the Mainers engine website so you have you click datasets here right and then there's a lot of data that our engine archives that's a big geospatial data you know archive so for example you find you know climate and weather data you know surface temperature you know you have atmospheric and weather data and you also have the major ARS observation Lanza and sin la satellite and also the modest instrument in some high-resolution data some other geophysical parameters such as terrain you know land cover and cropland and also you know nighttime light data okay so you can you're able to access all this data using a QGIS you know environment you know using a Python script or a Python API so let's get started with the code so the first thing I need to do is load import the ARS engine library so import EE will import urges engine and also I need to you know import the map you know library which is called ie plug-in okay that specifically will help us to map some of the you know map outputs on on a map canvas here in a QGIS environment so the next thing I would do is just import countries boundary database here on ours engine asset management which is a feature collection here countries and because this is a global data I'll select Zambia for example in the situation so what I'll do here is filter countries here and you know called country is equal to some beer so that it will select Zambia let me just drag this a little bit so that we have okay so now the fun part is that here so I'll declare a variable data set right and so ie image collection is how you call an image collection from ours engine as I've seen once I've shown you here so these are in image collections that have large data set covering global data and also a large volume of a time series so these are called this this data archive using an image collection so you need to know the ID for those specific image collections so once you have those you know image collection ID in this case Landsat 8 LC 0 8 and also collection 1 and top of atmospheric reflectance and what I'll do is because it's a global there in large time series I'll filter it by date in this case our only select you know data in 2017 in additional also focus only in Zambia so I'll declare dot filter bounds this will select only for the area that's overlapping in in Zambia okay so the next thing I'll do is because Lanza it has multiple bands I will select only the bands that I need to plot right so here data set here and I'll select from data sets here which is the Landsat image collection select only band four three and two right so in addition what I'll do is just create some sort of visualization parameter for my the map that I'll be displaying here so through color for each of these dots I'll declare a name this is just you can call it anything it doesn't matter then I'll just declare the minimum and the maximum values when I display that and in addition to that I'll declare map dot Center object in Zambia the am bi is the shape file here so what did what this does is that it will focus the map canvas only to the area that's located in Zambia and the other thing is just actually displaying the non sedated ear so map dot add layer and then through color for e 2 is the Landsat data that we have already selected here I'll call that here through color four three two and because it is an animal data as you can see here it's data that ranged every you know two weeks of lanza observation for the entire 2070 I just want to run a median static so that I'll have one single image for the entire year so that's why I'm declaring here dot median and I'll pull here the visualization parameter I declared here and named a true color that's the name of the the layer that'll be displaying here and the last thing is just I'll also need to display you know the Zambia shapefile so that you know have a context right so map a layer and I'll you know image paint and then called Zambia here the shapefile that I already created and it just name is Zambia you can give it a different name if you like so once I have completed my mice you know script and just click you know this run script button in an acute gi's python console and then it will so the script is running here so it will display you know The Lancet median image for the entire 2017 for Zambia so let me just click a zoom to layer and also I can do a little bit of a better job by zooming in and kind of alright it's taking a little bit of time to load because it's it's longshot obviously it's large data set so it takes a bit of time yes you can see it's busy here so now we can display lotsa data a true color composite here as you can see you know true color composite for the entire Zambia you can see the scene edge here you can clip it in future videos I'll show you how to do that but now you know this is how you can you know composite an image pulling formers engine using a Python API you know environment in the Earth engine you know library and use QGIS and the urgent plugin and then write some Python code and be able to visualize you know Python balls in a API as well as in a graphic user interface here

Channel: Spatial eLearning

Views: 3,104

Keywords: qgis, earth engine, google earth engine, earth engine plugin, qgis earth engine pluging, earth engine python API, Python API, google earth engine python API, GIS, Landsat, colab, earth engine python api, earth engine qgis, gee, google earth engine python, google earth engine python api, google earth engine python tutorials, google earth engine qgis plugin, google earth engine tutorials, python, python tutorials, remote sensing, spatial data analysis

Id: 9k1Xlp31soE

Length: 10min 36sec (636 seconds)

Published: Sat Jan 25 2020