Methodological ambiguity and inconsistency constrain unmanned aerial vehicles as a silver bullet for monitoring ecological restoration
The last decade has seen an exponential increase in the application of unmanned aerial vehicles (UAVs) to ecological monitoring research, though with little standardisation or comparability in methodological approaches and research aims. We reviewed the international peer-reviewed literature in orde...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Language: | English |
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MDPI
2019
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/IC150100041 http://hdl.handle.net/20.500.11937/84620 |
| _version_ | 1848764665640779776 |
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| author | Buters, Todd Bateman, Bill Robinson, Todd Belton, David Dixon, Kingsley Cross, Adam |
| author_facet | Buters, Todd Bateman, Bill Robinson, Todd Belton, David Dixon, Kingsley Cross, Adam |
| author_sort | Buters, Todd |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The last decade has seen an exponential increase in the application of unmanned aerial vehicles (UAVs) to ecological monitoring research, though with little standardisation or comparability in methodological approaches and research aims. We reviewed the international peer-reviewed literature in order to explore the potential limitations on the feasibility of UAV-use in the monitoring of ecological restoration, and examined how they might be mitigated to maximise the quality, reliability and comparability of UAV-generated data. We found little evidence of translational research applying UAV-based approaches to ecological restoration, with less than 7% of 2133 published UAV monitoring studies centred around ecological restoration. Of the 48 studies, > 65% had been published in the three years preceding this study. Where studies utilised UAVs for rehabilitation or restoration applications, there was a strong propensity for single-sensor monitoring using commercially available RPAs fitted with the modest-resolution RGB sensors available. There was a strong positive correlation between the use of complex and expensive sensors (e.g., LiDAR, thermal cameras, hyperspectral sensors) and the complexity of chosen image classification techniques (e.g., machine learning), suggesting that cost remains a primary constraint to the wide application of multiple or complex sensors in UAV-based research. We propose that if UAV-acquired data are to represent the future of ecological monitoring, research requires a) consistency in the proven application of different platforms and sensors to the monitoring of target landforms, organisms and ecosystems, underpinned by clearly articulated monitoring goals and outcomes; b) optimization of data analysis techniques and the manner in which data are reported, undertaken in cross-disciplinary partnership with fields such as bioinformatics and machine learning; and c) the development of sound, reasonable and multi-laterally homogenous regulatory and policy framework supporting the application of UAVs to the large-scale and potentially trans-disciplinary ecological applications of the future. |
| first_indexed | 2025-11-14T11:22:58Z |
| format | Journal Article |
| id | curtin-20.500.11937-84620 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:22:58Z |
| publishDate | 2019 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-846202021-08-04T06:43:11Z Methodological ambiguity and inconsistency constrain unmanned aerial vehicles as a silver bullet for monitoring ecological restoration Buters, Todd Bateman, Bill Robinson, Todd Belton, David Dixon, Kingsley Cross, Adam Science & Technology Life Sciences & Biomedicine Physical Sciences Technology Environmental Sciences Geosciences, Multidisciplinary Remote Sensing Imaging Science & Photographic Technology Environmental Sciences & Ecology Geology ecological restoration drone UAS rehabilitation revegetation HIGH-RESOLUTION HYPERSPECTRAL IMAGERY INFRARED IMAGERY LOW-ALTITUDE UAV SYSTEM DRONES CLASSIFICATION VEGETATION AIRCRAFT The last decade has seen an exponential increase in the application of unmanned aerial vehicles (UAVs) to ecological monitoring research, though with little standardisation or comparability in methodological approaches and research aims. We reviewed the international peer-reviewed literature in order to explore the potential limitations on the feasibility of UAV-use in the monitoring of ecological restoration, and examined how they might be mitigated to maximise the quality, reliability and comparability of UAV-generated data. We found little evidence of translational research applying UAV-based approaches to ecological restoration, with less than 7% of 2133 published UAV monitoring studies centred around ecological restoration. Of the 48 studies, > 65% had been published in the three years preceding this study. Where studies utilised UAVs for rehabilitation or restoration applications, there was a strong propensity for single-sensor monitoring using commercially available RPAs fitted with the modest-resolution RGB sensors available. There was a strong positive correlation between the use of complex and expensive sensors (e.g., LiDAR, thermal cameras, hyperspectral sensors) and the complexity of chosen image classification techniques (e.g., machine learning), suggesting that cost remains a primary constraint to the wide application of multiple or complex sensors in UAV-based research. We propose that if UAV-acquired data are to represent the future of ecological monitoring, research requires a) consistency in the proven application of different platforms and sensors to the monitoring of target landforms, organisms and ecosystems, underpinned by clearly articulated monitoring goals and outcomes; b) optimization of data analysis techniques and the manner in which data are reported, undertaken in cross-disciplinary partnership with fields such as bioinformatics and machine learning; and c) the development of sound, reasonable and multi-laterally homogenous regulatory and policy framework supporting the application of UAVs to the large-scale and potentially trans-disciplinary ecological applications of the future. 2019 Journal Article http://hdl.handle.net/20.500.11937/84620 10.3390/rs11101180 English http://purl.org/au-research/grants/arc/IC150100041 http://creativecommons.org/licenses/by/4.0/ MDPI fulltext |
| spellingShingle | Science & Technology Life Sciences & Biomedicine Physical Sciences Technology Environmental Sciences Geosciences, Multidisciplinary Remote Sensing Imaging Science & Photographic Technology Environmental Sciences & Ecology Geology ecological restoration drone UAS rehabilitation revegetation HIGH-RESOLUTION HYPERSPECTRAL IMAGERY INFRARED IMAGERY LOW-ALTITUDE UAV SYSTEM DRONES CLASSIFICATION VEGETATION AIRCRAFT Buters, Todd Bateman, Bill Robinson, Todd Belton, David Dixon, Kingsley Cross, Adam Methodological ambiguity and inconsistency constrain unmanned aerial vehicles as a silver bullet for monitoring ecological restoration |
| title | Methodological ambiguity and inconsistency constrain unmanned aerial vehicles as a silver bullet for monitoring ecological restoration |
| title_full | Methodological ambiguity and inconsistency constrain unmanned aerial vehicles as a silver bullet for monitoring ecological restoration |
| title_fullStr | Methodological ambiguity and inconsistency constrain unmanned aerial vehicles as a silver bullet for monitoring ecological restoration |
| title_full_unstemmed | Methodological ambiguity and inconsistency constrain unmanned aerial vehicles as a silver bullet for monitoring ecological restoration |
| title_short | Methodological ambiguity and inconsistency constrain unmanned aerial vehicles as a silver bullet for monitoring ecological restoration |
| title_sort | methodological ambiguity and inconsistency constrain unmanned aerial vehicles as a silver bullet for monitoring ecological restoration |
| topic | Science & Technology Life Sciences & Biomedicine Physical Sciences Technology Environmental Sciences Geosciences, Multidisciplinary Remote Sensing Imaging Science & Photographic Technology Environmental Sciences & Ecology Geology ecological restoration drone UAS rehabilitation revegetation HIGH-RESOLUTION HYPERSPECTRAL IMAGERY INFRARED IMAGERY LOW-ALTITUDE UAV SYSTEM DRONES CLASSIFICATION VEGETATION AIRCRAFT |
| url | http://purl.org/au-research/grants/arc/IC150100041 http://hdl.handle.net/20.500.11937/84620 |