Use NumPy’s built-in functions to make sorting simple

Photo by Alex Block on Unsplash


Art and data all-in-one

All images by the author


This could be the most-used remote sensing index

NDVI calculated from a Landsat 8 image. All photos by Author.


Whether photography, topography, or something else it can probably be analyzed with a sliding window

Photo by bin foch on Unsplash


Scale-up your geoprocessing workflows with Python

Photo by author.


How to summarize raster data for polygon zones

Photo by Clay Banks on Unsplash
  1. Rasterize polygon features
  2. Mask input data to polygon extent
  3. Calculate zonal statistics for the polygon extent

1. Load raster data and vector polygons

Start by importing the necessary Python modules.


All you need to be successful is time

Photo by Miguel Henriques on Unsplash

A powerful data format once you break the learning curve

Create a NetCDF Dataset

Import the netCDF4 and numpy modules. Then define a file name with the .nc or .nc4


Access a slightly confusing, yet powerful, data format

Photo by Waldemar Brandt on Unsplash

Installation

NetCDF files can be read with a few different Python…


You don’t always need training data . . .

Konrad Hafen

Husband, father, outdoorsman, scientist. https://opensourceoptions.com. https://publiclandsjournal.com

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store