The 4-course, 12-credit Remote Sensing and Geospatial Data Analytics Online Graduate Certificate is composed of two core courses and four electives, from which students choose two.
Program of Study:
| Course Number | Spring Semester |
Fall Semester |
| NRE 5525 (Core) | X | |
| NRE 5535 (Core) | X | |
| NRE 5215 (Elective) | X | X |
| NRE 5545 (Elective) | X | |
| NRE 5560 (Elective) | X | |
| NRE 5615 (Elective) | X | |
| NRE 5585 (Elective) | X |
Core Courses:
NRE 5525: Remote Sensing of the Environment (3 credits)
This intro-level course provides a jumpstart into remote sensing science and technology. Students will learn about various remote sensing sensors and their uses in environmental applications. It will help them gain a basic set of skills on remote sensing data analysis using software packages and a knowledge base for effective use of remote sensing in real-world problem solving.
NRE 5535: Remote Sensing Image Processing (3 credits)
This course will progress students’ knowledge on various kinds of remote sensing image processing techniques, primarily using optical satellite data (e.g., Landsat). The course covers a variety of related topics that include the physical processes involved in remote sensing and various image processing methods. Students will gain hands-on experience in using remote sensing image processing software.
Elective Courses:
NRE 5215: Intro to Geospatial Analysis with Remote Sensing (3 credits)
This course will help students building their knowledge and skills on basic geospatial data analysis and visualization. This course is geared towards beginners who want to develop their geospatial skills.
NRE 5545: Quantitative Remote Sensing Methods (3 credits)
This course will take students to the next level of remote sensing methods. It will cover advanced quantitative theories and methods to process satellite imagery using computer programming. If a student wants to delve deeper into advanced remote sensing data analytics, this would be the way to go.
NRE 5560: High Res Remote Sensing: Applications of UAS & LiDAR (3 credits)
This is ideal for gaining knowledge and hands-on experience in emerging remote sensing technologies. Students will gain knowledge on drone operations, data collections, and processing. The course will cover the fundamentals of LiDAR and analysis and visualization of 3D point clouds in various applications.
NRE 5615: EnviroAI: Artificial Intelligence Applications in Environmental Management (3 credits)
Introduces students to the application of artificial intelligence (AI) and machine learning (ML) techniques from the rapidly growing field of AI to address environmental challenges. Through case studies on topics such as earth observation, forest ecosystems, water resources, wildlife and fisheries, and climate resilience, students will gain experience applying AI techniques to real-world environmental data. The course is designed to balance breadth and practice - providing an overview of applications across environmental domains while also offering manageable hands-on experiences that build practical skills without requiring in-depth technical expertise. Students will work with open-source datasets and AI tools, while critically examining the promises and limits of AI in advancing sustainability. Emphasizes both technical literacy and policy relevance, and prepares students to evaluate, design, and responsibly apply AI in environmental management contexts.
Tailor electives to your interests and job requirements. Students can choose two of four different electives depending on their needs and interests. Our electives are designed to help you take full advantage of your new knowledge in your current job.