Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/29464
Title: Macadamia Orchard Planting Year and Area Estimation at a National Scale
Contributor(s): Brinkhoff, James  (author)orcid ; Robson, Andrew J  (author)orcid 
Publication Date: 2020-07-13
Open Access: Yes
DOI: 10.3390/rs12142245
Handle Link: https://hdl.handle.net/1959.11/29464
Abstract: Accurate estimates of tree crop orchard age and historical crop area are important to develop yield prediction algorithms, and facilitate improving accuracy in ongoing crop forecasts. This is particularly relevant for the increasingly productive macadamia industry in Australia, where knowledge of tree age, as well as total planted area, are important predictors of productivity, and the area devoted to macadamia orchards is rapidly increasing. We developed a technique to aggregate more than 30 years of historical imagery, generate summary tables from the data, and search multiple combinations of parameters to find the most accurate planting year prediction algorithm. This made use of known planting dates of more than 90 macadamia blocks spread across multiple growing regions. The selected algorithm achieved a planting year mean absolute error of 1.7 years. The algorithm was then applied to all macadamia features in east Australia, as defined in an recent Australian tree crops map, to determine the area planted per year and the total cumulative area of macadamia orchards in Australia. The area estimates were refined by improving the resolution of the mapped macadamia features, by removing non-productive areas based on an optimal vegetation index threshold.
Publication Type: Journal Article
Source of Publication: Remote Sensing, 12(14), p. 1-13
Publisher: MDPI AG
Place of Publication: Switzerland
ISSN: 2072-4292
Fields of Research (FoR) 2008: 070104 Agricultural Spatial Analysis and Modelling
Fields of Research (FoR) 2020: 300206 Agricultural spatial analysis and modelling
Socio-Economic Objective (SEO) 2008: 820206 Macadamias
Socio-Economic Objective (SEO) 2020: 260507 Macadamias
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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