Kelcey, J, Blaxland, D, Smith, B & Gove, A 2019, 'The analysis and validation of landform stability using unmanned aerial vehicles', in AB Fourie & M Tibbett (eds), Proceedings of the 13th International Conference on Mine Closure
, Australian Centre for Geomechanics, Perth, pp. 1127-1138, https://doi.org/10.36487/ACG_rep/1915_90_Kelcey
Mine closure relies on proof of best practice in both design and performance of rehabilitation. Field techniques have been the traditional approach for producing detailed supporting empirical evidence for mine closure. Although field sampling provides a detailed snapshot of key performance criteria within small areas, these areas themselves may not be representative of the overall performance of rehabilitation. Additionally, their limited scale may miss broader spatial characteristics that could further strengthen arguments for relinquishment. Remote sensing is a complimentary approach to field sampling that can produce an entire census of a rehabilitation site at a reduced scale. However, uncertainty still surrounds the adoption of a remote sensing approach, and whether such techniques can capture key performance indicators accurately and consistently.
This paper provides both a demonstration of capacity, and quantification of accuracy, of remotely sensed data analytics for the production of empirical evidence to support mine closure management. Using rehabilitated landforms in the Western Australian Goldfields as case studies, remote sensing was adopted in two supporting roles: the validation of landform construction, and the ongoing monitoring of landform performance. The geometry of constructed landform surfaces was measured through photogrammetric techniques and assessed against design specifications. Ongoing monitoring assessed both vegetative colonisation and relative stability of established landform surfaces. Coupled together, the broader scale impact of non-compliant areas upon local rehabilitation performance was explored and discussed.
Underpinning these data analytics is the accuracy of remotely sensed data. The quantification of uncertainty within the data was derived through a comparison against precision field measurements. Quantification of this uncertainty allowed the establishment of confidence intervals on derived measurements. Furthermore, the impact of changing environmental complexity upon analysis performance was quantified. This allowed for the modelling of compensation factors that dynamically counterbalance the increased uncertainty of complex environments. The result of the study demonstrates the capacity for a remote sensing approach to empirically support mine closure and relinquishment.
Keywords: rehabilitation, mine closure, unmanned aerial vehicles, remote sensing, gully, erosion, criteria
Deng, L, Mao, Z, Li, X, Hu, Z, Duan, F & Yan, Y 2018, ‘UAV-based multispectral remote sensing for precision agriculture:
a comparison between different cameras’, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 146, pp. 124–136.
Ganci, G, Cappello, A, Bilotta, G, Herault, A, Zago, V & Del Negro, C 2018, ‘Mapping volcanic deposits of the 2011–2015 Etna eruptive events using satellite remote sensing’, Frontiers in Earth Science, vol. 6, p. 83.
Guo, Q, Su, Y, Hu, T, Zhao, X, Wu, F, Li, Y & Zheng, Y 2017, ‘An integrated UAV-borne lidar system for 3D habitat mapping in three forest ecosystems across China’, International Journal of Remote Sensing, vol. 38, no. 8–10, pp. 2954–2972.
Housman, I, Chastain, R & Finco, M 2018, ‘An evaluation of forest health insect and disease survey data and satellite-based remote sensing forest change detection methods: case studies in the United States, Remote Sensing, vol. 10, no. 8, p. 1184.
Lucieer, A, Turner, D, King, DH & Robinson, SA 2014, ‘Using an unmanned aerial vehicle (UAV) to capture micro-topography of Antarctic moss beds, International Journal of Applied Earth Observation and Geoinformation, vol. 27, pp. 53–62.
Feng, Q, Liu, J & Gong, J 2015, ‘UAV remote sensing for urban vegetation mapping using random forest and texture analysis’, Remote Sensing, vol. 7, no. 1, pp. 1074–1094.
Ma, J, Sun, W, Yang, G & Zhang, D 2018, ‘Hydrological analysis using satellite remote sensing big data and CREST model’, IEEE Access, vol. 6, pp. 9006–9016.
Zhou, D, Xiao, J, Bonafoni, S, Berger, C, Deilami, K, Zhou, Y & Sobrino, J 2019, ‘Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives’, Remote Sensing, vol. 11, no. 1, p. 48.