Multi-class Land Cover Map Accuracy Assessment, Area Estimation, and Uncertainty Quantification

This repository provides a tool for assessing the accuracy of multi-class land cover maps, estimating area, and quantifying uncertainty. It includes a graphical user interface (GUI) that simplifies input of data and displays results, with options to save the results for further analysis.

Table of Contents

• Features
• Requirements
• Installation
• Usage
• Methodology
• Output Explanation
• Contact

Features

• GUI for Input: Easily enter pixel size, error matrix values, and mapped pixel counts using an intuitive interface.
• Interactive Tooltips: Hover over fields for guidance on required input.
• Accuracy Metrics: Calculates user’s accuracy, producer’s accuracy, and overall accuracy for each class.
• Area Estimation: Provides error-adjusted area estimates per class.
• Uncertainty Quantification: Computes standard errors and 95% confidence intervals for each metric.
• CSV Export: Option to save the results to a CSV file.

Requirements

• Python: Version 3.7 or higher
• Libraries: Install necessary Python libraries by running: bash pip install numpy pandas scipy tkinter > Note: tkinter comes pre-installed with most Python distributions.

Installation

Option 1: Clone the Repository

Download the repository using Git:

bash git clone https://github.com/ccgeoinformatics/MultiLC_AreaEstimation_UQ cd MultiLC_AreaEstimation_UQ

Option 2: Download as a ZIP File

1. Go to the repository on GitHub.
2. Click on the "Code" button.
3. Select "Download ZIP" and extract the downloaded file.

Usage

1. Run the script in your Python environment: bash python multilc_accuracy_areaEstimation_uq_interactive.py
2. The GUI will open, prompting you to:
   • Enter the number of land cover classes and configure the matrix.
   • Input the pixel size.
   • Fill out the error matrix with counts for each class, and provide the total mapped pixels for each class.
3. Click Run Analysis to calculate metrics.
4. Save the results as a CSV file if desired.

Methodology

The methodology follows these references: 1. Olofsson et al. (2013): Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sensing of Environment, 129, 122-131. https://doi.org/10.1016/j.rse.2012.10.031 2. Olofsson et al. (2014): Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42-57. https://doi.org/10.1016/j.rse.2014.02.015

Users of the scripts are advised to read the above references for the theoretical concepts behind the methods, including the assumptions (for example, the error matrix shall be generated from a stratified random sampling design).

Output Explanation

The following explanations are from Olofsson et al. (2013): - User's Accuracy: "the proportion of the area mapped as a particular category that is actually that category “on the ground” where the reference classification is the best assessment of ground condition." - Producer's Accuracy: "the proportion of the area that is a particular category on the ground that is also mapped as that category." - Overall Accuracy: the proportion of the area mapped correctly. It provides the user of the map with the probability that a randomly selected location on the map is correctly classified." - Error-Adjusted Area: The area estimate per class, adjusted based on accuracy metrics. - Standard Errors and Confidence Intervals: Provides uncertainty metrics for accuracy metrics and area estimates.

Each metric includes: - Standard Error (SE): The variability or uncertainty in the metric. - 95% Confidence Interval (CI): The range within which the true value is likely to fall, with 95% confidence.

Contact

For any questions or issues, please reach out to:

Jojene R. Santillan
Institute of Photogrammetry and GeoInformation (IPI), Leibniz University Hannover, Germany
& Caraga Center for Geo-Informatics & Department of Geodetic Engineering, College of Engineering and Geosciences, Caraga State University, Butuan City, Philippines
santillan@ipi.uni-hannover.de, jrsantillan@carsu.edu.ph