Faculty of AgriSciences

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https://scholar.sun.ac.za/handle/10019.1/9

The Faculty of AgriSciences at Stellenbosch University (SU) is held in high esteem at national and international levels for the quality of its training and research and also as consultant in the agricultural and forestry industry.

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Browsing Faculty of AgriSciences by browse.metadata.advisor "Ackermann, Simon Alexander"

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    The use of UAV derived photogrammetry data as a tool for tree measurement in industrial plantation forestry with the focus on different Local maxima and Machine learning detection methods.
    (2023-03) Zandberg, Jacobus Hugo Visser; Talbot, Bruce; Ackermann, Simon Alexander; Stellenbosch University. Faculty of AgriSciences. Dept. of Forest and Wood Science.
    ENGLISH ABSTRACT: With the growing interest in precision forestry, the use of low-cost multi-rotor UAVs in combination with photogrammetric software as a remote sensing tool in forestry has increased. Conventional forest inventory measurement is laborious and time-consuming which results in high operational costs, thus driving interest in alternative ways of obtaining accurate forest information. This study aimed to test the efficacy of multi-rotor UAVs structure from motion point clouds to be used as reliable source of data in forest mensuration practice, particularly in forest inventories with the focus on stocking and tree height. The study included three independent study sites planted with three different species at different planting densities which were observed at three different operational time frames. These three timeframes were before first thinning, after first thinning and before second thinning creating datasets with different conditions in terms of spacing and ground visibility. During this study three different DTM creation and normalization methods were considered, two structure from motion interpolated DTMs namely: UTG – which only consisted of compartment roads with interpolated surface between roads; and TG – which consisted of after first thinning visible ground points and compartment roads interpolated. The third DTM creation method was a DTM obtained from LiDAR data. The study also included three different tree detection methods the first two methods being local maxima based namely: CHM raster-based method where detections were done on the canopy height model and PTC Point-based method where detection were performed on the point cloud itself. The third tree detection method used was the machine learning approach where a custom object detector was trained to detect tree crowns from the created orthomosaic. The findings confirm that UAV Structure from Motion data is a valuable source of forest information. Both local maxima detection algorithms proved to be moderately reliable methods to detect trees and extract accurate information. However, optimal performance was found at lower tree density and increased levels of ground visibility. The Structure from Motion DTM created from images taken after first thinning, could produce accurate results comparable to LiDAR-derived DTM methods and manual infield statistics. Accurate stocking and tree height statistics were obtained, with mean average error ranging from -12.3% to 1% for stocking and 0.95% to 1.25% for mean tree height and 3.2% to 3.3% for dominant height. Therefore, it was shown that SfM derived DTMs obtained from after first thinning data capture flights, are capable of effectively normalising point clouds and providing accurate inventory statistics. Lastly, a few recommendations and findings from this study are mainly with regards to the capture of manual infield data; special care should be taken to minimise any measurement biases when the DBH height pairs are selected. It was also found that even though the machine learning detection method in this study did not produce reliable results, it could prove to be useful in detection of trees at lower planting densities and that it poses great potential as species classification tool as well.