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Interface Design for Haul Truck Proximity Advisory Systems

Open Cut » Health and Safety

Published: February 19Project Number: C27005

Get ReportAuthor: Robin Burgess-Limerick, Danellie Lynas, Guy Wallis | The University of Queensland

The aim of this project was to develop methods and tools to assist the coal industry to better monitor the consequences of longwall mine subsidence (LWMS) on surface environments.  The project focus was to improve monitoring quality and potentially reduce costs through the incorporation of high-resolution remotely sensed data into LWMS monitoring programmes.  Further, the project aimed to develop case studies that documented the consequence of LWMS on agricultural production. The project was conducted at three mine sites (Kestrel, Bowen Basin; Beltana, Hunter Valley and Dendrobium, Illawarra) across native vegetation and agricultural landscapes. The agricultural section of this project was completed in early 2009 and the methods and results were published by ACARP as an interim report annexed to this report.  The details of the agricultural component of this project are presented as an outline only in this final project report.  The purpose of the final report is to provide detail on the study that focussed on the native vegetated environments.


The project sought to develop and verify techniques to incorporate high-resolution remotely sensed data into routine surface environment monitoring.  Two key datasets were used: high-resolution multi-spectral satellite imagery (Quickbird, Ikonos and Spot); and airborne laser scanning (ALS, also called Lidar).  The multi-spectral imagery was used to develop indicators of plant health and density through the application of vegetation indices that incorporate near infrared and red light.  ALS data were used to examine information of plant structure and foliar density.  Selected field sampling using the relevant industry standard was also implemented, often using remotely sensed data to direct the sampling effort.


The agricultural section of the project adapted and applied methods used commonly in precision agriculture so that the methods were already used and accepted by the relevant agricultural industry.  Results for the agricultural component showed no statistically significant effect that could be related to LWMS for the 2 year project duration.


The native vegetation section of the project adapted, developed and verified remote sensing and GIS techniques to create tools capable of monitoring fine scale impacts of LWMS on native vegetation.  A precision technique for mapping upland swamps was developed using ALS data, allowing swamp boundaries to be located to within half an average tree canopy (ca. 5 m).  Further, the within boundary dynamics of plant health and density were monitored using Quickbird satellite imagery over a two year period.  In woodland areas an approach of using topographic and solar radiation indices was developed to provide a statistically robust sampling design across an entire mining lease into off-lease areas.  ALS data were then used to develop parameters relating to woodland health.  In particular the derivation of projected foliar cover, a key ecological parameter, from ALS data was found to be highly repeatable between two discrete ALS captures.  The resulting projected foliar cover data proved to be much more reliable than expert field assessment from a group of field surveyors.


Remotely sensed data were found to be an invaluable tool to improve the quality and repeatability of any programme to monitor the consequences of LWMS on surface environments.  Incorporation of these datasets into the monitoring programme: improves monitoring repeatability and statistical design; potentially decreases any field based assessment requirements; improves OH&S aspects of field survey; and provides a synoptic, holistic dataset that can sample across an entire mining lease and off-site regions.

An e-newsletter has also been published for this project, highlighting its significance for the industry.


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