GIS Image Analysis in ArcGIS Pro
Master remote sensing and image analysis techniques using ArcGIS Pro for environmental monitoring and natural resource management.
Master remote sensing and image analysis techniques using ArcGIS Pro for environmental monitoring and natural resource management.
This comprehensive course teaches the fundamentals of remote sensing and image analysis using ArcGIS Pro software. Students learn to analyze various types of remotely sensed data, including high-resolution aerial photos and satellite imagery from Landsat and Terra MODIS. The course covers essential concepts in image processing, spectral analysis, and classification techniques, with practical applications in environmental monitoring, vegetation health assessment, and land cover mapping. Through hands-on exercises, participants learn to solve real-world problems in natural resource management, including tracking changes in water bodies, assessing vegetation health, and monitoring wildfire impacts.
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What you'll learn
Access and analyze satellite imagery and remote sensing data
Perform advanced image analysis using ArcGIS Pro tools
Create accurate land cover maps using classification techniques
Assess environmental changes using spectral indices
Produce professional quality visualizations and reports
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Hands-on exercises, Quizzes
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 4 modules in this course
This course provides comprehensive training in image analysis and remote sensing applications using ArcGIS Pro. The curriculum covers fundamental concepts of remote sensing, electromagnetic radiation, and image processing techniques. Students learn to work with various image types, perform spectral analysis, and create land cover maps using both supervised and unsupervised classification methods. The course emphasizes practical applications in environmental monitoring and natural resource management, including vegetation health assessment, wildfire impact analysis, and water body change detection.
Introduction to Remote Sensing and Image Analysis
Module 1
Introduction to ArcGIS Pro, Spectral Indices and Image Interpretation
Module 2
Image Classification
Module 3
Image Analysis Applications
Module 4
Fee Structure
Instructors
4 Courses
Distinguished Arctic Scientist and Remote Sensing Expert
Santosh K. Panda serves as Associate Professor of GIS and Remote Sensing at the University of Alaska Fairbanks, where he holds joint appointments with the Geophysical Institute and the Institute of Agriculture, Natural Resources and Extension. His research focuses on understanding Arctic landscape dynamics, particularly permafrost response to climate change and its effects on Arctic systems. After completing his PhD in Geology from UAF, he has pioneered high-resolution permafrost modeling across Alaska's national parks and developed innovative approaches to mapping boreal forest wildfire patterns using hyperspectral remote sensing. His work includes significant community engagement, notably building capacity for permafrost monitoring in native villages of Telida and Nikolai, and mapping Colville River bathymetry to support safe river navigation. As principal investigator for the Alaska NSF EPSCoR Fire and Ice project, he leads research on improving boreal wildfire fuel mapping. His publications have garnered over 795 citations, with significant contributions to permafrost dynamics modeling, remote sensing applications, and Arctic landscape change analysis. Beyond research, he maintains an active teaching role in remote sensing and GIS at both undergraduate and graduate levels while advancing the application of spatial technologies to understand Arctic environmental changes.
4 Courses
Distinguished Remote Sensing Expert and Boreal Forest Researcher
Dr. Anushree Badola serves as Postdoctoral Researcher at the Institute of Agriculture, Natural Resources and Extension, University of Alaska Fairbanks, where she specializes in hyperspectral remote sensing and machine learning applications for forestry and agriculture. Her groundbreaking research includes developing novel methods to simulate AVIRIS-NG hyperspectral imagery from Sentinel-2 data for improved vegetation and wildfire fuel mapping in boreal Alaska. After earning her Master's in Geoinformatics from the University of Twente and IIRS, ISRO, Dehradun, she completed her PhD at UAF's Geophysical Institute. Her work with the Alaska NSF EPSCoR Fire and Ice project has significantly advanced boreal forest monitoring techniques, achieving improved classification accuracies from 77.8% with Sentinel-2 to 94.4% with AVIRIS-NG data. Her research portfolio includes innovative approaches to tree species mapping in tropical forests, sub-pixel needleleaf cover fraction estimation in boreal forests, and development of spectral reconstruction methods for vegetation mapping. As a skilled programmer in Python and R with expertise in various GIS tools including QGIS, ArcGIS, ENVI, and Erdas Imagine, she continues to advance the field of remote sensing while focusing on improving wildfire fuel mapping technologies for better forest management.
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