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Using Unmanned Aerial Vehicle multispectral data for monitoring outcomes of ecological restoration in mining areas
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  • Zanxu Chen,
  • Huping Hou,
  • SHAOLIANG ZHANG,
  • Tristan Campbell,
  • Yongjun Yang,
  • Mu Tu,
  • Yang Yuan,
  • Kingsley Dixon
Zanxu Chen
China University of Mining and Technology School of Public Policy and Management
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Huping Hou
China University of Mining and Technology School of Public Policy and Management
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SHAOLIANG ZHANG
China University of Mining and Technology

Corresponding Author:[email protected]

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Tristan Campbell
Curtin University School of Molecular and Life Sciences
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Yongjun Yang
China University of Mining and Technology
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Mu Tu
Institute of Territorial and Spatial Planning of Inner Mongolia
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Yang Yuan
Institute of Territorial and Spatial Planning of Inner Mongolia
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Kingsley Dixon
Curtin University School of Molecular and Life Sciences
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Abstract

The effective and efficient monitoring of revegetation outcomes is a key component of ecosystem restoration. Monitoring often involves labour intensive manual methods which can be difficult to deploy when sites are inaccessible or involve large areas of revegetation. This study aimed to identify plant species and quantify α-diversity index on a sub-meter scale at Manlailiang Mine Site in Northwestern China using unmanned aerial vehicles (UAVs) as a means to semi-automate large-scale vegetation monitoring. UAVs equipped with multispectral sensors were combined with three industry-standard supervised classification algorithms (support vector machine (SVM), maximum likelihood, and artificial neural network) to classify plant species. Spectral vegetation indices (NDVI, DVI, VDVI, SAVI, MSAVI, EXG - EXR) were used to assess vegetation diversity obtained from on-ground survey plot data (Margalef, Pielou, Simpson, Shannon indices). Our results showed that SVM outperformed other algorithms in species identification accuracy (overall accuracy 84%). Significant relationships were observed between vegetation indices and diversity indices, with DVI performing significantly better than many more commonly used indices such as NDVI. The findings highlight the potential of combining UAV multispectral data, spectral vegetation indices and ground surveys for effective and efficient fine-scale monitoring of vegetation diversity in the ecological restoration of mining areas. This has significant practical benefits for improving adaptive management of restoration through improved monitoring tools.
20 Jul 2023Submitted to Land Degradation & Development
20 Jul 2023Submission Checks Completed
20 Jul 2023Assigned to Editor
20 Jul 2023Review(s) Completed, Editorial Evaluation Pending
23 Jul 2023Reviewer(s) Assigned
17 Sep 2023Editorial Decision: Revise Major
15 Nov 2023Reviewer(s) Assigned