Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery
Tu, Yu Hsuan
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Earth System Observation and Modelling
Environmental Science and Engineering Program
Hydrology, Agriculture and Land Observation Group, Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.
Water Desalination and Reuse Research Center (WDRC)
Online Publication Date2020-05-20
Print Publication Date2020-07
Embargo End Date2022-04-24
Permanent link to this recordhttp://hdl.handle.net/10754/662900
MetadataShow full item record
AbstractAustralia is one of the world’s largest producers of macadamia nuts. As macadamia trees can take up to 15 years to mature and produce maximum yield, it is important to optimize tree condition. Field based assessment of macadamia tree condition is time-consuming and often inconsistent. Using remotely sensed imagery may allow for faster, more extensive, and more consistent assessment of macadamia tree condition. To identify individual macadamia tree crowns, high spatial resolution imagery is required. Hence, the objective of this work was to develop and test an approach to map the condition of individual macadamia tree crowns using both multispectral Unmanned Aerial Vehicle (UAV) and WorldView-3 imagery for different macadamia varieties and three different sites located near Bundaberg, Australia. A random forest classifier, based on all available spectral bands and selected vegetation indices was used to predict five condition categories, ranging from excellent (category 1) to poor (category 5). Various combinations of the developed models were tested between the three sites and over time. The results showed that the multi-spectral WorldView-3 imagery produced the lowest out of bag (OOB) classification errors in most cases. However, for both the UAV and the WorldView-3 imagery, more than 98.5% of predicted macadamia condition categories were either correctly mapped or offset by a single category out of the five condition categories (excellent, good, moderate, fair and poor) for trees of the same variety and at one point in time. Multi-temporally, the WorldView-3 imagery performed better than the UAV data for predicting the condition of the same macadamia tree variety. Applying a model from one site to another site with the same macadamia tree variety produced OOB classification between 31.20 and 42.74%, but with > 98.63% of trees predicted within a single condition category. Importantly, models trained based on one type of macadamia tree variety could not be successfully applied to a site with another variety. The developed classification models may be used as a decision and management support tool for the macadamia industry to inform management practices and improve on-demand irrigation, fertilization, and pest inspection at the individual tree level.
CitationJohansen, K., Duan, Q., Tu, Y.-H., Searle, C., Wu, D., Phinn, S., … McCabe, M. F. (2020). Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 165, 28–40. doi:10.1016/j.isprsjprs.2020.04.017
SponsorsThis research was funded by the Department of Agriculture and Water Resources, Australian Government as part of its Rural R&D for Profit Program and Horticulture Innovation Australia Ltd., grant number ST15002 “Multi-Scale Monitoring Tools for Managing Australian Tree Crops – Industry Meets Innovation”. We would like to acknowledge the support of the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), under grant number CE140100049. We also acknowledge the farmers at the Welcome Creek and 1019 Moore Park Road macadamia sites in Bundaberg for allowing us access to collect field and UAV data. Prof Matthew F. McCabe and Dr Kasper Johansen were supported by KAUST.