![]() Lc = ee.ImageCollection('MODIS/006/MCD12Q1') Now, to import the LC, LST and ELV collections, we can copy and paste the Earth Engine Snippets: # Import the MODIS land cover collection. The dataset descriptions provide us with all the information we need to import and manipulate these datasets: the availability, the provider, the Earth Engine Snippet, and the available bands associated with images in the collection. In the following sections, we work with the MODIS land cover (LC), the MODIS land surface temperature (LST) and with the USGS ground elevation (ELV), which are ee.ImageCollections. If you want to know more about different data models, you may want to visit the Earth Engine User Guide. For example, the Global Administrative Unit Layers giving administrative boundaries is a ee.FeatureCollection and the MODIS Land Surface Temperature dataset is an ee.ImageCollection. Collections which are groups of features or images.For example, the ground elevation given by the USGS here is an ee.Image. Images which are like features, but may include several bands.For example, a watershed with some properties such as name and area, is an ee.Feature. Features which are geometric objects with a list of properties.In the Earth Engine Data Catalog, datasets can be of different types: The output will contain instructions on how to grant this notebook access to Earth Engine using your account. Therefore, read the description carefully and make sure you know what kind of dataset you are selecting! Run me firstįirst of all, run the following cell to initialize the API. satellite image, interpolated station data, or model output) vary from one dataset to another. Of course the resolution, frequency, spatial and temporal extent, as well as data source (e.g. GRIDMET temperature, precipitation, and evapotranspiration, for example.OpenLandMap datasets with soil properties at a resolution of 250 m (e.g.SRTM global elevation with a resolution of 30 m,.Well, inside the Earth Engine Catalog we find: temperature, precipitation, evapotranspiration). clay, sand, silt content) and some meteorological observations (e.g. Let's say that we need to know the elevation of a region, some soil properties (e.g. Have you ever thought that getting a meteorological dataset could be as easy as finding the nearest pizzeria? To convince you, visit the Earth Engine Data Catalog and explore datasets using the search bar or browsing by tag. Exploration of the Earth Engine Data Catalog In this last part, we’ll see how to include some GEE datasets as tile layers of a folium map. Secondly, we will detail procedures for static mapping and exporting results as a GeoTIFF.įinally, the folium library will be introduced to make interactive maps. An application of this procedure will be done to extract land surface temperature in an urban and a rural area near the city of Lyon, France to illustrate the heat island effect. After some setup and some exploration of the Earth Engine Data Catalog, we’ll see how to handle geospatial datasets with pandas and make some plots with matplotlib.įirst, we’ll see how to get the timeseries of a variable for a region of interest. In this tutorial, an introduction to the Google Earth Engine Python API is presented. How can we manipulate these petabytes of data?. ![]()
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