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Using unmanned aerial vehicle multispectral data for monitoring the outcomes of ecological restoration in mining areas

Bibliographic Data

ID21648624
AuthorsZanxu Chen (0000-0003-3781-5580, School of Public Policy and Management China University of Mining and Technology Xuzhou China), Huping Hou (0000-0002-6815-6461, School of Public Policy and Management China University of Mining and Technology Xuzhou China), Shaoliang Zhang (0000-0002-2063-8218, Ministry of Education Engineering Research Center for Mine Ecological Restoration Xuzhou China, corresponding author), Tristan Campbell (0000-0002-3796-9582, ARC Centre for Mine Site Restoration, School of Molecular and Life Sciences Curtin University Perth Western Australia Australia), Yongjun Yang (0000-0002-0131-439X, Ministry of Education Engineering Research Center for Mine Ecological Restoration Xuzhou China), Mu Tu (Institute of Territorial and Spatial Planning of Inner Mongolia Hohhot China), Yang Yuan (0000-0003-4193-9439, Institute of Territorial and Spatial Planning of Inner Mongolia Hohhot China), Kingsley W Dixon (0000-0001-5989-2929, ARC Centre for Mine Site Restoration, School of Molecular and Life Sciences Curtin University Perth Western Australia Australia)
Year2024
Volume35
Issue4
Pages1599-1613
Publication date2024-02-28
Open AccessYes
TypeARTICLE
VenueLand Degradation and Development (JOURNAL)
Journal identifiersISSN: 1085-3278 • E-ISSN: 1099-145X
PublisherWiley (PUBLISHER • GB)
DOI10.1002/ldr.5010
OpenAlexW4390124606
LanguageEN
References cited87

The effective and efficient monitoring of revegetation outcomes is a key component of ecosystem restoration. Monitoring often involves labor‐intensive manual methods, which are 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 semiautomate 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 (normalized difference vegetation index [NDVI], difference vegetation index [DVI], visible‐band difference vegetation index, soil‐adjusted vegetation index, modified soil‐adjusted vegetation index, and excess green–excess red) were used to assess vegetation diversity obtained from on‐ground survey plot data (the Margalef, Pielou, Simpson, and Shannon–Wiener indices). Our results showed that SVM outperformed other algorithms in species identification accuracy (overall accuracy of 84%). Significant relationships were observed between vegetation indices and diversity indices, with the DVI performing significantly better than many more commonly used indices such as the 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

Cartography · Diversity index · Enhanced vegetation index · Geography · Leaf area index · Multispectral image · Normalized Difference Vegetation Index · Remote sensing · Revegetation · Vegetation Index · Environmental Science · Land Use and Ecosystem Services · Remote Sensing in Agriculture · Wildlife-Road Interactions and Conservation · Ecology

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