In addition to the web-based IDE Google Earth Engine also provides a Python API that can be used on your local machine.
Export images, image collection, map tiles from Google Earth Engine collection directly to your computer.
The Normalized Difference Vegetation Index is a simple indicator of photosynthetically active biomass or, in layman’s terms, a calculation of vegetation health.
Google Earth Engine is a geospatial processing service with the motto "To organize the world's information and make it universally accessible and useful."
This tutorial will go over how to set up the API on your local machine as well as some basic Python scripts utilizing the Google Earth Engine Python API
The surface of the Earth is continuously changing at many levels; local, regional, national, and global scales. Changes in land use and land cover are pervasive, rapid, and can have significant impacts on people, the economy, and the environment.
The surface of the Earth is continuously changing at many levels; local, regional, national, and global scales. Changes in land use and land cover are pervasive, rapid, and can have significant impacts on people, the economy, and the environment.
There are several ways to use GEE and each one has its advantages and disadvantages. In this example, GEE is used for web mapping with python Django.
Normalized Difference Vegetation Index (NDVI) quantifies vegetation by measuring the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs).
The Python API provides a programmatic and flexible interface to Earth Engine for automating batch processing tasks, and leveraging the power of the command line.
FREE SATELLITE IMAGERY SOURCE LINK
The surface of the Earth is continuously changing at many levels; local, regional, national, and global scales. Changes in land use and land cover are pervasive, rapid, and can have significant impacts on people, the economy, and the environment.
Folium makes it easy to visualize data that’s been manipulated in Python on an interactive leaflet map.
While using remote sensing data several pre-processing steps can be applied that aim at removing artifacts from the images that are not related to the actual reflectance of the land cover such as sensor effects, atmospheric, and illumination conditions.
Supervised classification is the technique most often used for the quantitative analysis of remote sensing image data.