Using Uncrewed Aerial Vehicles for Identifying the Extent of Invasive <i>Phragmites australis</i> in Treatment Areas Enrolled in an Adaptive Management Program
Higher spatial and temporal resolutions of remote sensing data are likely to be useful for ecological monitoring efforts. There are many different treatment approaches for the introduced European genotype of <i>Phragmites australis</i>, and adaptive management principles are being integr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/10/1895 |
| _version_ | 1797534353362780160 |
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| author | Colin Brooks Charlotte Weinstein Andrew Poley Amanda Grimm Nicholas Marion Laura Bourgeau-Chavez Dana Hansen Kurt Kowalski |
| author_facet | Colin Brooks Charlotte Weinstein Andrew Poley Amanda Grimm Nicholas Marion Laura Bourgeau-Chavez Dana Hansen Kurt Kowalski |
| author_sort | Colin Brooks |
| collection | DOAJ |
| description | Higher spatial and temporal resolutions of remote sensing data are likely to be useful for ecological monitoring efforts. There are many different treatment approaches for the introduced European genotype of <i>Phragmites australis</i>, and adaptive management principles are being integrated in at least some long-term monitoring efforts. In this paper, we investigated how natural color and a smaller set of near-infrared (NIR) images collected with low-cost uncrewed aerial vehicles (UAVs) could help quantify the aboveground effects of management efforts at 20 sites enrolled in the <i>Phragmites</i> Adaptive Management Framework (PAMF) spanning the coastal Laurentian Great Lakes region. We used object-based image analysis and field ground truth data to classify the <i>Phragmites</i> and other cover types present at each of the sites and calculate the percent cover of <i>Phragmites</i>, including whether it was alive or dead, in the UAV images. The mean overall accuracy for our analysis with natural color data was 91.7% using four standardized classes (Live <i>Phragmites</i>, Dead <i>Phragmites</i>, Other Vegetation, Other Non-vegetation). The Live <i>Phragmites</i> class had a mean user’s accuracy of 90.3% and a mean producer’s accuracy of 90.1%, and the Dead <i>Phragmites</i> class had a mean user’s accuracy of 76.5% and a mean producer’s accuracy of 85.2% (not all classes existed at all sites). These results show that UAV-based imaging and object-based classification can be a useful tool to measure the extent of dead and live <i>Phragmites</i> at a series of sites undergoing management. Overall, these results indicate that UAV sensing appears to be a useful tool for identifying the extent of <i>Phragmites</i> at management sites. |
| first_indexed | 2024-03-10T11:28:28Z |
| format | Article |
| id | doaj.art-1ab55ccaece944e5afbbc3c9eacdac81 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T11:28:28Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1ab55ccaece944e5afbbc3c9eacdac812023-11-21T19:25:32ZengMDPI AGRemote Sensing2072-42922021-05-011310189510.3390/rs13101895Using Uncrewed Aerial Vehicles for Identifying the Extent of Invasive <i>Phragmites australis</i> in Treatment Areas Enrolled in an Adaptive Management ProgramColin Brooks0Charlotte Weinstein1Andrew Poley2Amanda Grimm3Nicholas Marion4Laura Bourgeau-Chavez5Dana Hansen6Kurt Kowalski7Michigan Tech Research Institute, Michigan Technological University, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USAMichigan Tech Research Institute, Michigan Technological University, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USAMichigan Tech Research Institute, Michigan Technological University, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USAMichigan Tech Research Institute, Michigan Technological University, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USAMichigan Tech Research Institute, Michigan Technological University, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USAMichigan Tech Research Institute, Michigan Technological University, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USANational Park Service, Alaska Regional Office, 240 W. 5th Ave., Anchorage, AK 99501, USAU.S. Geological Survey, Great Lakes Science Center, 1451 Green Road, Ann Arbor, MI 48105-2807, USAHigher spatial and temporal resolutions of remote sensing data are likely to be useful for ecological monitoring efforts. There are many different treatment approaches for the introduced European genotype of <i>Phragmites australis</i>, and adaptive management principles are being integrated in at least some long-term monitoring efforts. In this paper, we investigated how natural color and a smaller set of near-infrared (NIR) images collected with low-cost uncrewed aerial vehicles (UAVs) could help quantify the aboveground effects of management efforts at 20 sites enrolled in the <i>Phragmites</i> Adaptive Management Framework (PAMF) spanning the coastal Laurentian Great Lakes region. We used object-based image analysis and field ground truth data to classify the <i>Phragmites</i> and other cover types present at each of the sites and calculate the percent cover of <i>Phragmites</i>, including whether it was alive or dead, in the UAV images. The mean overall accuracy for our analysis with natural color data was 91.7% using four standardized classes (Live <i>Phragmites</i>, Dead <i>Phragmites</i>, Other Vegetation, Other Non-vegetation). The Live <i>Phragmites</i> class had a mean user’s accuracy of 90.3% and a mean producer’s accuracy of 90.1%, and the Dead <i>Phragmites</i> class had a mean user’s accuracy of 76.5% and a mean producer’s accuracy of 85.2% (not all classes existed at all sites). These results show that UAV-based imaging and object-based classification can be a useful tool to measure the extent of dead and live <i>Phragmites</i> at a series of sites undergoing management. Overall, these results indicate that UAV sensing appears to be a useful tool for identifying the extent of <i>Phragmites</i> at management sites.https://www.mdpi.com/2072-4292/13/10/1895<i>Phragmites australis</i>uncreweddronemonitoringinvasiveadaptive management |
| spellingShingle | Colin Brooks Charlotte Weinstein Andrew Poley Amanda Grimm Nicholas Marion Laura Bourgeau-Chavez Dana Hansen Kurt Kowalski Using Uncrewed Aerial Vehicles for Identifying the Extent of Invasive <i>Phragmites australis</i> in Treatment Areas Enrolled in an Adaptive Management Program Remote Sensing <i>Phragmites australis</i> uncrewed drone monitoring invasive adaptive management |
| title | Using Uncrewed Aerial Vehicles for Identifying the Extent of Invasive <i>Phragmites australis</i> in Treatment Areas Enrolled in an Adaptive Management Program |
| title_full | Using Uncrewed Aerial Vehicles for Identifying the Extent of Invasive <i>Phragmites australis</i> in Treatment Areas Enrolled in an Adaptive Management Program |
| title_fullStr | Using Uncrewed Aerial Vehicles for Identifying the Extent of Invasive <i>Phragmites australis</i> in Treatment Areas Enrolled in an Adaptive Management Program |
| title_full_unstemmed | Using Uncrewed Aerial Vehicles for Identifying the Extent of Invasive <i>Phragmites australis</i> in Treatment Areas Enrolled in an Adaptive Management Program |
| title_short | Using Uncrewed Aerial Vehicles for Identifying the Extent of Invasive <i>Phragmites australis</i> in Treatment Areas Enrolled in an Adaptive Management Program |
| title_sort | using uncrewed aerial vehicles for identifying the extent of invasive i phragmites australis i in treatment areas enrolled in an adaptive management program |
| topic | <i>Phragmites australis</i> uncrewed drone monitoring invasive adaptive management |
| url | https://www.mdpi.com/2072-4292/13/10/1895 |
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