LCMAP Pathfinder Workflow: Tracking Thematic Land Cover Change

Working with the Annual Land Cover Change Product to answer the question: what are the top four land cover change conversions between 1985 and 2020 in the U.S. State of West Virginia?

This workflow demonstrates an approach to tracking annual thematic land cover change for a Land Change Monitoring, Assessment, and Projection (LCMAP) time series. The tutorial shows how to (1) identify LCMAP ARD tiles that intersect an area of interest (AOI), (2) open and mosaic multiple tiles, (3) clip to the AOI, and (4) export as GeoTIFF. The tutorial then shows how to calculate statistics on the amount of change by year and by land cover conversion, and shows how to visualize and export those statistics.

Tracking Change with LCACHG

LCMAP's Annual Land Cover Change product (LCACHG) is used for tracking thematic land cover change. LCACHG is not a direct output of the LCMAP analytical process, but is a synthesis product of the Primary Land Cover (LCPRI) of any given year and the LCPRI from the previous year. LCACHG identifies both locations that are unchanged between the two years (single-digit values) and locations that have been observed to change from one thematic land cover to another (two-digit values). LCACHG uses the same 8 values representing land cover that are used in LCPRI.

Land Cover Class LC Value
Developed 1
Cropland 2
Grass/Shrub 3
Tree Cover 4
Water 5
Wetland 6
Ice/Snow 7
Barren 8

An unchanged location is identified with its single-digit LC value, remaining identical to LCPRI. A changed location is identified with a two-digit value representing both the previous and the new LC value with a concatenation of values. For example, a value of 21 in LCACHG for 1998 would represent a thematic change from Cropland (2) in 1997 to Developed (1) in 1998. Additional information is available in the LCMAP Science Product Guide.

Use Case Example:

This workflow demonstrates an approach to tracking annual thematic land cover change over the U.S. State of West Virginia for the years 1985-2020. The goal of this workflow is to identify and quantify the four most common land cover conversions during this time period, and visualize those conversions over time. Below are the results from the use case showing the top four overall land cover conversions, plotted as area converted per year for the State of West Virginia. Total annual change is also tracked in the figure below as a bar chart, and includes the total area changed for all land cover conversions.

Introduction Figure

Follow along in the sections below to re-create the figure from above, or generate a new figure over your desired AOI and time period of interest.

Data Used in the Example:

Topics Covered:

  1. Get Started
    1.1 Import Packages
  2. Search for LCMAP Data Intersecting Area Of Interest
    2.1 Open AOI and Landsat ARD Tilemap and Find Intersecting Tiles
    2.2 Visualize AOI and Intersecting LCMAP Tiles
  3. Explore and Visualize LCACHG Data
    3.1 Locate Intersecting Data in Local Directory
    3.2 Open and Mosaic LCMAP Tiled Files
    3.3 Clip to AOI, Visualize, and Export Processed Files
  4. Extract Thematic Change Statistics
    4.1 Generate Quantity of Changes and Convert to Area
  5. Automate Thematic Change Statistics for LCMAP Time Series
    5.1 Perform Steps 3-4 for Each Year
  6. Time Series Statistics and Visualization
    6.1 Set Up Annual Land Cover Change Dataframe
    6.2 Determine and Visualize Top Four Land Cover Changes
    6.3 Visualize Top Four Land Cover Changes by Year
    6.4 Export Results
  7. Define Function for Steps 1-6

Setup and Dependencies

It is recommended to use Conda, an environment manager to set up a compatible Python environment. Download Conda for your OS here: Follow the instructions below to successfully setup a Python environment on Linux, MacOS, or Windows.

This workflow has been tested using Python version 3.9 on Windows 10 Enterprise.

TIP: Having trouble activating your environment, or loading specific packages once you have activated your environment? Try the following:

Type: conda update conda or conda update --all

Still having trouble getting a compatible Python environment set up? Contact USGS EROS Customer Services

If you prefer to not install Conda, the same setup and dependencies can be achieved by using another package manager such as pip.

1. Get Started

1.1 Import Packages

Import the packages required to execute this tutorial.