Skip to main content
U.S. flag

An official website of the United States government

The Land Change Monitoring, Assessment, and Projection (LCMAP) team is developing a series of tutorials showing users how to interact with LCMAP data in automated, Python-based Jupyter Notebook workflows.

LCMAP tutorials are available for download from the LCMAP Gitlab Repository. Be sure to sign up for the LCMAP listserv to be notified once a new chapter in the tutorial series is released.

Getting Started with LCMAP Data in Python Tutorial Series:

Chapter 1: Accessing LCMAP Data via the USGS EarthExplorer Machine-to-Machine API

This tutorial demonstrates how to perform spatial and temporal queries for LCMAP data by submitting requests to the USGS EarthExplorer (EE) Machine-to-Machine (M2M) API. The tutorial then shows how to download and unzip LCMAP tile bundles that intersect a given spatiotemporal query.

Visit the code repository for instructions on how to set up and execute the tutorial, or download the Jupyter Notebook (.html) output directly.

Chapter 2: Mosaicking, Clipping, Reprojecting, and Exporting LCMAP Science Products in Python

This tutorial demonstrates how to process LCMAP tile bundles retrieved from USGS EarthExplorer. Processing steps shown below include opening multiple tiles of LCMAP science products, mosaicking them into a single image, clipping to the bounds of a region of interest (ROI), reprojecting the clipped data to a new projection, and exporting the results as cloud optimized GeoTIFFs (COG).

Visit the code repository for instructions on how to set up and execute the tutorial, or download the Jupyter Notebook (.html) output directly.

Chapter 3: Quality Filtering and Masking LCMAP Science Products in Python

This tutorial demonstrates how to quality filter or mask LCMAP science products. Processing steps shown below include interpreting LCMAP quality data, defining a list of values to be masked, excluding or masking pixels that fall under the given criteria, visualizing the masked science products, and exporting the results as cloud optimized GeoTIFFs (COG).

Visit the code repository for instructions on how to set up and execute the tutorial, or download the Jupyter Notebook (.html) output directly.

Experiencing issues? Contact the USGS EROS Customer Services.