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

Author:

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

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.

Funder

Australian Coal Industry’s Research Program

Publisher

CSIRO Publishing

Reference61 articles.

1. Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data.;GIScience and Remote Sensing,2020

2. Agforce (2021) Pasture dieback survey 2019. Agforce, Brisbane, Qld.

3. Detection of white leaf disease in sugarcane crops using UAV-derived RGB imagery with existing deep learning models.;Remote Sensing,2022

4. Anderson AJ, Gorley R, Clarke K (2008) ‘PERMANOVA+ for PRIMER: guide to software and statistical methods.’ (PRIMER-E: Plymouth, UK)

5. Rubber leaf fall phenomenon linked to increased temperature.;Agriculture, Ecosystems & Environment,2023

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3