The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3, 139–144, 2014
https://doi.org/10.5194/isprsarchives-XL-3-139-2014
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3, 139–144, 2014
https://doi.org/10.5194/isprsarchives-XL-3-139-2014

  11 Aug 2014

11 Aug 2014

Automatic Single Tree Detection in Plantations using UAV-based Photogrammetric Point clouds

T. Kattenborn1, M. Sperlich1, K. Bataua2, and B. Koch1 T. Kattenborn et al.
  • 1FeLis, Chair of Remote Sensing and Landscape Information Systems, University Freiburg, Tennenbacherstr. 4, 79106 Freiburg, Germany
  • 2SOPAC, Applied Geoscience and Technology Division of Secretariat of the Pacific Community – SPC, 241 Mead Road, Nabua Suva, Fiji Islands

Keywords: Single Tree Detection, UAV, Palm plantation, Structure From Motion, Point Clouds, Segmentation, Terrain Models

Abstract. For reasons of documentation, management and certification there is a high interest in efficient inventories of palm plantations on the single plant level. Recent developments in unmanned aerial vehicle (UAV) technology facilitate spatial and temporal flexible acquisition of high resolution 3D data. Common single tree detection approaches are based on Very High Resolution (VHR) satellite or Airborne Laser Scanning (ALS) data. However, VHR data is often limited to clouds and does commonly not allow for height measurements. VHR and in particualar ALS data are characterized by high relatively high acquisition costs. Sperlich et al. (2013) already demonstrated the high potential of UAV-based photogrammetric point clouds for single tree detection using pouring algorithms. This approach was adjusted and improved for an application on palm plantation. The 9.4ha test site on Tarawa, Kiribati, comprised densely scattered growing palms, as well as abundant undergrowth and trees. Using a standard consumer grade camera mounted on an octocopter two flight campaigns at 70m and 100m altitude were performed to evaluate the effect Ground Sampling Distance (GSD) and image overlap. To avoid comission errors and improve the terrain interpolation the point clouds were classified based on the geometric characteristics of the classes, i.e. (1) palm, (2) other vegetation (3) and ground. The mapping accuracy amounts for 86.1 % for the entire study area and 98.2 % for dense growing palm stands. We conclude that this flexible and automatic approach has high capabilities for operational use.