Abstract
Detecting vegetation changes finds its application in several important areas, including city planning and urban science, climate change and ecological research. Several sensors and approaches can be used to measure the 3D geometry of vegetation, including aerial, mobile and terrestrial laser scanning, and photogrammetry. Other historical data sources, such as 2D shapefiles might also be available, however, the use of multi-source data presents challenges for vegetation change detection. This study presents a voxel-based approach to vegetation change detection from multi-source datasets, including laser scanning and 2D shape files from different years. A novel Octree data structure is utilised in this work that supports different operations for efficient vegetation change. We demonstrate the strengths of the approach with a case study to discuss the challenges and the future directions.
| Original language | English |
|---|---|
| Pages (from-to) | 23-30 |
| Number of pages | 8 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 48 |
| Issue number | G-2025 |
| DOIs | |
| Publication status | Published - 28 Jul 2025 |
| Event | 2025 International Society for Photogrammetry and Remote Sensing (ISPRS) Geospatial Week, GSW 2025 - Dubai, United Arab Emirates Duration: 6 Apr 2025 → 11 Apr 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 13 Climate Action
Keywords
- Aerial LiDAR
- Canopy footprint
- Mobile Laser Scanning
- Urban Environment
- Vegetation Change Detection
- Voxels
ASJC Scopus subject areas
- Information Systems
- Geography, Planning and Development
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