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A comprehensive overview of remote sensing techniques and their applications in forestry and natural environments, empowering students with practical skills to analyse both active and passive remote sensing data for effective forest mapping, monitoring, and management.
This course provides a comprehensive exploration of advanced remote sensing datasets, techniques and their applications in forestry and natural environments. Students will gain in-depth knowledge and practical skills to analyse and interpret cutting-edge remote sensing data for mapping, monitoring, and managing forests and natural resources effectively. The course covers topics such as individual-tree segmentation from LiDAR data, forest health assessment from hyperspectral imaging, change detection in vegetation cover using time-series multispectral imagery, UAV photogrammetry for 3D forest modelling, as well as using advanced machine learning and deep learning models in analysing remote sensing data. The course will also equip students with hands-on data processing and analysis using open-source tools such as R packages.
Upon successful completion of this course, students will be able to:Demonstrate a comprehensive understanding of advanced remote sensing principles and their applications in forestry and natural resource management.Apply advanced remote sensing datasets to map, monitor, and manage forests and natural resources, including LiDAR, hyperspectral imaging, multispectral imaging and UAV photogrammetry. Evaluate and integrate various remote sensing sensors and analysis approaches, and select appropriate tools to address complex real-world challenges in forestry and natural resource management.Demonstrate proficiency in hands-on data processing and analysis using open-source tools such as R packages, to acquire, process, analyse, visualise, and interpret remote sensing datasets.Apply advanced artificial intelligence (AI) techniques, particularly machine learning and deep learning models, to analyse remote sensing data. Examine AI’s capabilities in identifying and interpreting patterns and trends for improved forest attribute estimation and mapping.
FORE342 or subject to approval from the Head of Department.
Students must attend one activity from each section.
Vega Xu
Domestic fee $1,268.00
International fee $6,238.00
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see School of Forestry .