Crop residue mapping using UAV and satellite images
Prerequisites: programming skills are required to successfully implement this MSc research topic
This research focuses on mapping crop residues in Ethiopia using UAV (Unmanned Aerial Vehicle) and satellite imagery. Crop residues are the remnants of crops left on fields after harvest. They play a critical role in soil health, nutrient cycling, and carbon sequestration. Accurate and scalable mapping of crop residues can provide insights into sustainable farming practices and support climate change mitigation strategies. This study aims to develop a robust methodology for detecting and quantifying crop residues by integrating UAV and satellite imagery with advanced image processing and machine learning techniques.
Objectives
- Develop a multi-scale workflow for crop residue detection by integrating UAV and satellite data to create a multi-scale mapping approach.
- Analyze spatial variability of crop residues across areas
The study will use UAV images and complementary satellite data (e.g., Sentinel-2, Landsat 8) to crop residues. Advanced machine learning algorithms will be implemented for this task. This research will give you the opportunity to interact with a team of international collaborators from Ethiopia and France.
Dong, Y., Xuan, F., Huang, X., Li, Z., Su, W., Huang, J., Li, X., Tao, W., Liu, H., Chen, J., 2024. A 30-m annual corn residue coverage dataset from 2013 to 2021 in Northeast China. Scientific Data 11, 216.