Mangroves are vital ecosystems that provide a variety of ecological services, including carbon sequestration, storm protection, and habitat for wildlife. Monitoring mangrove forests is important for understanding their ecological dynamics and the potential impacts of climate change. One way to monitor mangrove forests is by measuring their volume using remote sensing techniques.
The objective of this project is to develop a methodology for accurately mapping the volume of mangrove forests using low orbit satellite imagery. The project will involve identifying suitable low orbit satellite data sources and developing a machine learning model that can accurately estimate the volume of mangrove trees.
The project will focus on the mangrove forests located in the Sarasota Bay region of Florida. The project will involve the following tasks:
- Identifying suitable low orbit satellite data sources for the study area.
- Developing a machine learning model that can accurately estimate the volume of mangrove trees using the satellite data.
- Testing the accuracy of the model against ground-based measurements of mangrove volume.
- Developing a user-friendly interface to visualize and analyze the results of the model.
The project will be completed within 12 months. The following is a breakdown of the project timeline:
- Month 1-2: Data acquisition and preprocessing.
- Month 3-6: Development of the machine learning model.
- Month 7-9: Testing and validation of the model.
- Month 10-12: Development of the user interface and final report.