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USING UAV-BASED 3D IMAGES OF INDIVIDUAL TREE SPECIES IN DISTANCE EDUCATION IN FORESTRY
The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLVI-4/W5-2021:533-537, 2021.
Article in English | ProQuest Central | ID: covidwho-1598843
ABSTRACT
Distance education has been offered for years, but the integration of technological developments and opportunities into education has recently increased its popularity and event it became an indispensable method during the Covid-19 pandemic period. In distance education, accessing all class materials such as lecture presentations, class notes, reading materials, videos, live chats or class hours, and archive records allow students (participants) to learn without being in the same environments with teachers or learners. Technology has made vast contributions to the field of education. For instance, 3D as a teaching tool for the class attracts studentsattention, makes the learning process more enjoyable, and increases participation. In particular, for the disciplines, such as forestry, earth, and environmental sciences, which require laboratory exercises, field observation, field trips, and in-situ measurements, 3D modeling has provided many benefits in distance education. It enables 3D demonstration of the individual tree species to develop a virtual field laboratory. This study focused on the data sources and techniques to generate a 3D model of the individual tree species that forestry students used for distance education. The capabilities of the method in the generation of 3D models were evaluated by using UAV-based SfM photogrammetry. The results indicated that implementing 3D images of individual tree species can be a promising method that may increase the interest, interaction and satisfaction of the students in distance education in forestry.
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Full text: Available Collection: Databases of international organizations Database: ProQuest Central Language: English Journal: The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: ProQuest Central Language: English Journal: The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences Year: 2021 Document Type: Article