P. McKenna

A multi-disciplinary approach to understanding the impacts of mines on traditional uses of water in Northern Mongolia

Mongolia is an example of a nation where the rapidity of mining development is outpacing capacity to manage the potential land and water resources impacts. Further, Mongolia has a particular social and economic reliance on traditional uses of land and water, principally livestock herding. While some mining operations are setting high standards in protecting the natural resources surrounding the mine site, others have less incentive and capacity to do so and therefore are having adverse effects on surrounding communities. The paper describes a case study of the Sharyn Gol Soum in northern Mongolia where a range of mining types, from artisanal, small-scale mining to a large coal mine, operate alongside traditional herding lifestyles. A multi-disciplinary approach is taken to observe and attribute causes to the water resources impacts in the area. Surveys of the herding household community, land use mapping, and monitoring the spatial variations in water quality indicate deterioration of water resources. Collectively, the different sources of evidence suggest that the deterioration is mainly due to small-scale gold mining. The evidence included the perception of 78% of the interviewed herders that water quality had changed due to mining; a change in the footprint of small-scale gold mining from 2.8 to 15.2km(2) during the period 1999 to 2015; and pH and sulphate values in 2015 consistently outside the ranges observed at a baseline site in the same region. It is concluded that the lack of baseline data and effective governance mechanisms are fundamental challenges that need to be addressed if Mongolias transition to a mining economy is to be managed alongside sustainability of herder lifestyles.

Structural development of vegetation on rehabilitated North Stradbroke Island: Above/belowground feedback may facilitate alternative ecological outcomes

IntroductionThis study depicts broad-scale revegetation patterns following sand mining on North Stradbroke Island, south-eastern Queensland, Australia.MethodsBased on an ecological timeline spanning 4–20 years post-rehabilitation, the structure of these ecosystems (n = 146) was assessed by distinguishing between periods of ‘older’ (pre-1995) and ‘younger’ (post-1995) rehabilitation practices.ResultsThe general rehabilitation outlook appeared promising, whereby an adequate forest composition and suitable levels of native biodiversity (consisting of mixed-eucalypt communities) were achieved across the majority of rehabilitated sites over a relatively short time. Still, older sites (n = 36) appeared to deviate relative to natural analogues as indicated by their lack of under-storey heath and simplified canopy composition now characterised by mono-dominant black sheoak (Allocasuarina littoralis) reaching up to 60% of the total tree density. These changes coincided with lower soil fertility parameters (e.g., total carbon, total nitrogen, and nutrient holding capacity) leading us to believe that altered growth conditions associated with the initial mining disturbance could have facilitated an opportunistic colonisation by this species. Once established, it is suspected that the black sheoak’s above/belowground ecological behaviour (i.e., relating to its leaf-litter allelopathy and potential for soil-nitrogen fixation) further exacerbated its mono-dominant distribution by inhibiting the development of other native species.ConclusionsAlthough rehabilitation techniques on-site have undergone refinements to improve site management, our findings support that putative changes in edaphic conditions in combination with the competitive characteristics of some plant species can facilitate conditions leading to alternative ecological outcomes among rehabilitated ecosystems. Based on these outcomes, future studies would benefit from in depth spatio-temporal analyses to verify these mechanisms at finer investigative scales.

Measuring fire severity using UAV imagery in semi-arid central Queensland, Australia

ABSTRACT Remote-sensing methods for fire severity mapping have traditionally relied on multispectral imagery captured by satellite platforms carrying passive sensors such as Landsat Thematic Mapper /Enhanced Thematic Mapper Plus or Moderate Resolution Imaging Spectroradiometer. This article describes the analysis of high spatial resolution Unmanned Aerial Vehicle (UAV) imagery to assess fire severity on a 117 ha experimental fire conducted on coal mine rehabilitation in an open woodland environment in semi-arid Central Queensland, Australia. Three band indices, Excess Green Index, Excess Green Index Ratio, and Modified Excess Green Index, were used to derive differenced (d) fire severity maps from UAV data. Fire severity data sets derived from aerial photograph interpretation were used to assess the utility of employing UAV technology to determine fire severity impacts. The dEGI was able to separate high severity, low severity, and unburnt areas with an overall classification accuracy of 58% and Kappa statistic of 0.37; outperforming the dEGIR (overall accuracy 55%, Kappa 0.31) and the dMEGI (overall accuracy 38%, Kappa 0.06). Classification accuracy increased for all indices when canopy shadows were masked, with dEGI improving to an overall accuracy of 68% and 0.48 Kappa. The McNemar’s test indicated that there was no significant difference between the classification accuracies for dEGI and dEGIR (p < 0.05). The test also demonstrated that dMEGI was significantly lower in accuracy compared to dEGI and dEGIR (p < 0.05). We quantified the proportion of burnt area within each severity class and calculated that 32% of the site was burnt at high severity, 34% was burnt at low severity, and 34% of the block was unburnt due to the patchy nature of the fire. We discuss the UAV-specific errors associated with fire severity mapping, and the potential for UAVs to assist land managers to assess the extent and severity of fire and subsequent recovery of burnt ecosystems at local scales (104m2–1 km2).