Mapping pasture dieback impact and recovery using an aerial imagery time series: a central Queensland case study

Phillip B. McKenna; Natasha Ufer; Vanessa Glenn; Neil Dale; Tayla Carins; Trung h. Nguyen; Melody B. Thomson; Anthony J. Young; Stuart Buck; Paul Jones; Peter D. Erskine

doi:10.1071/CP23340

RESEARCH ARTICLE (Open Access)

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Mapping pasture dieback impact and recovery using an aerial imagery time series: a central Queensland case study

Phillip B. McKenna

^A ^* , Natasha Ufer ^A , Vanessa Glenn ^A , Neil Dale ^B , Tayla Carins ^B , Trung h. Nguyen ^C , Melody B. Thomson ^D , Anthony J. Young ^D , Stuart Buck ^E , Paul Jones ^F and Peter D. Erskine ^A

+ Author Affiliations

- Author Affiliations

^A Centre for Mined Land Rehabilitation, Sustainable Minerals Institute, The University of Queensland, Brisbane, Qld 4072, Australia.

^B Ensham Resources, Emerald, Qld 4720, Australia.

^C Centre for Agriculture and the Bioeconomy, Queensland University of Technology, 2 George Street, Brisbane, Qld 4000, Australia.

^D School of Agriculture and Food Sustainability, The University of Queensland, Gatton, Qld 4343, Australia.

^E Department of Agriculture and Fisheries, Rockhampton, Qld 4701, Australia.

^F Department of Agriculture and Fisheries, Emerald, Qld 4720, Australia.

^* Correspondence to: p.mckenna@cmlr.uq.edu.au

Handling Editor: Davide Cammarano

Crop & Pasture Science 75, CP23340 https://doi.org/10.1071/CP23340

Submitted: 7 December 2023 Accepted: 15 August 2024 Published: 12 September 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Context

Pasture dieback has emerged as a significant threat to the health and productivity of sown pastures in eastern Queensland and northern New South Wales, Australia.

Aims

We aimed to address knowledge gaps on spatial spread patterns, recovery trajectories and floristic changes using remote sensing and ground surveys.

Methods

We used a time series of high-resolution (12–25 cm) aerial imagery to quantify and compare pasture dieback spread over 7 years in three land-use areas: ungrazed pasture, grazed pasture and rehabilitation following mining. The green leaf index was applied using supervised random forest algorithms to classify areas affected between 2015 and 2021. Flora surveys were conducted to compare impacted and unimpacted areas for the three land uses and validate classifications.

Key results

The first emergence of pasture dieback was in ungrazed pasture, and these areas recorded the highest rate of dieback spread at 1.88 ha month⁻¹, compared with 0.54 and 0.19 ha month⁻¹ in rehabilitated and grazed pastures respectively. Field validation showed that dieback-impacted pastures shifted from buffel grass (Cenchrus ciliaris L.), to forb-dominated communities with significantly different species mix, biomass and cover conditions. An analysis of local climate data showed that winter night-time temperatures and rainfall were notably higher than long-term means in the year preceding the first detection of pasture dieback.

Conclusions

High resolution aerial imagery and ground surveys can be used to monitor pasture health by employing vegetation indices and random forest classifiers.

Implications

Ungrazed pastures and roadside areas should be managed to protect the region from further outbreaks.

Keywords: machine learning, pasture health, random forest, remote sensing, resilience.

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