INT527 - Integration of GIS & Remote Sensing Data Analysis in Natural Resource Applications
This introductory level course will explore techniques and procedures required for spatially-explicit data analysis in forest resources, wildlife, and natural resources applications, specifically using ArcGIS 10.7 and ArcGIS Pro 2.4 Desktop software. The first one-third of the course explores vector- and raster- based GIS analysis in the context of wetland, wildlife habitat, and environmental assessment. The second third of the course will explore remote sensing (RS) fundamentals, image interpretation, land cover mapping, forest monitoring (change detection), ecosystem analysis and integration of raster and vector data. There will be two guest lectures demonstrating application of RS/GIS in natural resources management. Students will answer question sets and write brief technical summaries or reports on selected lab exercises during the first 2/3rds of the course. The final third of the course will be devoted to research and applications employing image processing and spatial data analysis in natural resources and environmental assessments for a final project. Student projects will involve developing a research question answerable using GIS and RS tools, writing a brief research proposal, collecting (web-available or your own data) and analyzing spatial data, preparing a final project report in the form of a poster, and presenting a summary of the project
to the class in a poster “conference” during the final lab session. The course has no prerequisites, although a cursory knowledge of GIS concepts is helpful but not required.
Course Offered: Annually (Spring Term).
Lecture Period: Mondays & Wednesdays: 9:00 AM – 10:00 AM (254 Nutting Hall)
Lab Period: Wednesdays 1:00 PM – 4:00 PM (254 Nutting Hall)
Instructors: Cynthia Loftin & Daniel Hayes
Office Hours: Open or by appointment (Nutting Hall, Room 236 – Cyndy Loftin or Room 233 – Dan Hayes)
Spring Term 2020 Syllabus (PDF)
Course Schedule with links to course material
Documents and data sharing via the Course Folder on the UMaine Box App
Remote Sensing Tutorial (Natural Resources Canada)