Introduction to Geographic Information Systems and Remote Sensing

Course Number:

AG 205

Transcript Title:

Introduction to Geographic Information Systems and Remote Sensing

Created:

Nov 03, 2026

Updated:

Nov 03, 2026

Total Credits:

4

Lecture Hours:

22

Lecture / Lab Hours:

44

Lab Hours:

0

Satisfies Cultural Literacy requirement:

No

Satisfies General Education requirement:

No

Grading Options

A-F, P/NP, Audit

Default Grading Options

A-F

Repeats available for credit:

0

Suggested Outcome Assessment Strategies

The determination of assessment strategies is generally left to the discretion of the instructor. Here are some strategies that you might consider when designing your course: writings (journals, self-reflections, pre-writing exercises, essays), quizzes, tests, midterm and final exams, group projects, presentations (in person, videos, etc.), self-assessments, experimentations, lab reports, peer critiques, responses (to texts, podcasts, videos, films, etc.), student generated questions, Escape Room, interviews, and/or portfolios.

Required: A capstone project in the form of a comprehensive farm management plan, assessed uniformly across all sections.

Course Activities and Design

The determination of teaching strategies used in the delivery of outcomes is generally left to the discretion of the instructor. Here are some strategies that you might consider when designing your course: lecture, small group/forum discussion, flipped classroom, dyads, oral presentation, role play, simulation scenarios, group projects, service learning projects, hands-on lab, peer review/workshops, cooperative learning (jigsaw, fishbowl), inquiry based instruction, differentiated instruction (learning centers), graphic organizers, etc.

Required activity: Capstone project.

Course Content

Outcome #1: Gather agricultural remote sensing data and apply it to Global Information Systems (GIS).

• Principles of GIS and remote sensing

  • Historical development and practical applications

    • Ground based robotic sensing

    • Integration of GIS within Ground Based vehicles

  • Overview of the electromagnetic spectrum

• Key technologies and methodologies in spatial analysis

  • Spatial Database for agriculture monitoring

  • Artificial Intelligence & Machine Learning in Spatial Analysis

  • Spatial Interpolation for modeling and monitoring

  • 3D spatial analysis for geological survey.

• Labs:

  • Introduction to GIS software

  • Basic remote sensing tools

Outcome #2: Apply GIS tools and spatial mapping techniques to analyze environmental and agricultural variability.

• Spatial data models and visualization techniques

  • Introduction to data analysis and spatial data

  • Collection, processing and evaluation of data for meaningful insights

• GIS software applications in environmental monitoring

  • ArcGIS, QGIS, Google Earth Engine, ERDAS Image, TerrSet, SAGA GISs

  • Monitoring Temperature, atmospheric and changes in ecosystems.

• Labs:

  • Spatial data analysis and interpretation

  • Visualization of spatial datasets

Outcome #3: Utilize remote sensing sensors to monitor environmental changes, crop health, and land use.

• Types of remote sensors and platforms

  • Multispectral, hyperspectral, and thermal imaging

• Applications in agriculture and environmental management

  • GIS Soil Mapping, Parcel mapping and zoning

  • Optimize crop rotation strategies

• Labs:

  • Data acquisition using remote sensing tools

  • Practical analysis exercises

Outcome #4: Process and evaluate remote sensing imagery and GIS data to enable the development of actionable plans.

• Variable Rate Application

• Early Detection of crop diseases and pests.

• Yield Forecasting

• Soil Moisture Mapping

• Drought Monitoring

Outcome #5: Develop a comprehensive GIS and remote sensing agricultural plan.

• Clearly articulating the issue

• Measurable goals and objectives for GIS and remote sensing

• Select appropriate GIS datasets

• Data cleaning techniques (georeferencing, projection correction, cloud masking and noise reductions.

• GIS spatial analysis techniques including buffer analysis, overlay analysis and interpolation methods.

• Interpret maps and spatial outputs using classification validation and ground truthing techniques (field data collection to verify results)

• Translate GIS findings into recommendations by using proposed policy changes, mitigation strategies and monitoring plans.

  • Continuous monitoring and data updates.

• Automation techniques such as Google Earth, Periodic aerial surveys and AI models.

Suggested Texts and Materials

• Campbell, J. B., & Wynne, R. H. Introduction to Remote Sensing, Sixth Edition. Guilford Press. (Chapters: 1, 2, 4, 5, 6, and 17)

• Longley, P. A., et al. Geographic Information Science and Systems, Fourth Edition. Wiley. (Chapters: 1, 2, 3, 4, and 6)

Equipment: Access to GIS software and remote sensing tools.