Contents lists available at ScienceDirect Int J Appl Earth Obs Geoinformation journal homepage: www.elsevier.com/locate/jag Apple orchard inventory with a LiDAR equipped unmanned aerial system Edyta Hadas ⁎ , Grzegorz Jozkow, Agata Walicka, Andrzej Borkowski Wroclaw University of Environmental and Life Sciences, Institute of Geodesy and Geoinformatics, Grunwaldzka 53, 50-375, Wroclaw, Poland ARTICLE INFO Keywords: UAV UAS LiDAR Orchards Trees Alpha-shape ABSTRACT Knowledge about the number of trees in an orchard and their geometric parameters is beneficial in precise farming and together with other information may be used to predict the yield. These parameters can be obtained based on time-consuming field measurements or more effectively, from very high resolution 3D data collected with Unmanned Aerial Vehicles (UAV). Numerous UAV experiments have been conducted in agricultural areas; however, most of studies are limited to the use of a passive optical sensor (camera). This study demonstrates an experiment on the novel remote sensing approach of determining selected geometric parameters of trees in an apple orchard, based on a high-density point cloud obtained from a Velodyne HDL-32E laser scanner mounted on a small UAV platform Leica Aibot X6 V2. Reference data of selected geometric parameters of trees was obtained from orthophotomap and with geodetic surveying methods. Original and robust methodology is proposed for the point cloud processing, which is the inventive combination of an alpha-shape algorithm, principal component analysis and detection of local minima on crown profiles. The developed approach allowed for the correct identification of 99% of the trees in the test orchard. The root mean square error of determined crown areas was equal to 0.98 m 2 . The accuracy of tree top identification, tree height and crown base height determination was equal to 0.38, 0.09 and 0.09 m, respectively. 1. Introduction Unmanned Aerial Vehicle (UAV) technology has improved over the last decade, and advances in technology have made the price of UAV platforms and sensors far more accessible for various businesses. UAV combined with special sensors creates a tailored Unmanned Aerial System (UAS). Applications of UAS are expanding in different domains. Precision farming, understood as a management that minimises pesti- cides, but increases yields and the quality of crop (Bongiovanni and Lowenberg-Deboer, 2004), is a relatively new area for UAS (Huang et al., 2013; Mulla, 2013). For agriculture, UAS can be a progressive tool, which rapidly provides detailed information on crop growth and development. Up-to-date and accurate determination of selected tree geometric parameters is beneficial for precision farming, and allows estimation of the biomass of fruit trees (Aguaron and Roberts, 2014). A single UAS flight may cover areas up to 1 ha with much higher spatial resolution (Lottes et al., 2017) at a lower cost than ground surveys or classical manned airborne surveys (Matese et al., 2015). There is a variety of practically deployed UAS applications for agriculture (Christiansen et al., 2017; Primicerio et al., 2012), and many recent developments have already reached the market, e.g., monitoring of wheat crops (Gómez-Candón et al., 2014), vineyards (Weiss et al., 2017) and orchards (Perry et al., 2016; Zarco-Tejada et al., 2014). UAS equipped with multispectral cameras containing visible and near-infrared bands are used for determination of vegetation indexes, e.g., normalised difference vegetation index (NDVI) (Duan et al., 2017), that are subsequently used to assess the health of crops (Garcia-Ruiz et al., 2013). UAV-based imaging, including multispectral and thermal sensors, is used for pine tree biomass estimation (Karpina et al., 2016), assessment of the variability in water status (Katsigiannis et al., 2016), detection of potassium deficiencies (Severtson et al., 2016), measure- ments of nitrogen variability (Hunt et al., 2018) and deriving man- agement decisions, such as fertiliser application (Link et al., 2013). Recently, the application of UAS Light Detection and Ranging (LiDAR) has been widely investigated (Jozkow et al., 2017; Pilarska et al., 2016). Laser beams, in contrary to optical sensors, penetrate vegetation and can reach the ground. Therefore, LiDAR sensors are used for deriving digital terrain models (DTM) (Esposito et al., 2014; Hsieh et al., 2016), landslide monitoring (Eker et al., 2018; Lindner et al., 2016) and forest inventory (Sankey et al., 2017; Wallace et al., 2012). In December 2018, a Global Ecosystem Dynamics Investigation (GEDI) was launched. GEDI’s primary goal is to retrieve forest structures using space-borne LiDAR, but the main limitation of this technique is the relatively large footprint of 25 m. Airborne LiDAR is still a dominant https://doi.org/10.1016/j.jag.2019.101911 Received 11 March 2019; Received in revised form 21 June 2019; Accepted 28 June 2019 ⁎ Corresponding author. E-mail address: edyta.hadas@upwr.edu.pl (E. Hadas). Int J Appl Earth Obs Geoinformation 82 (2019) 101911 0303-2434/ © 2019 Elsevier B.V. All rights reserved. T