Precise Quantification of Land Cover before and after Planned Disturbance Events with UAS-Derived Imagery
This paper introduces a detailed procedure to utilize the high temporal and spatial resolution capabilities of an unmanned aerial system (UAS) to document vegetation at regular intervals both before and after a planned disturbance, a key component in <i>natural disturbance-based management<...
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MDPI AG
2022-02-01
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Series: | Drones |
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Online Access: | https://www.mdpi.com/2504-446X/6/2/52 |
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author | Zachary Miller Joseph Hupy Sarah Hubbard Guofan Shao |
author_facet | Zachary Miller Joseph Hupy Sarah Hubbard Guofan Shao |
author_sort | Zachary Miller |
collection | DOAJ |
description | This paper introduces a detailed procedure to utilize the high temporal and spatial resolution capabilities of an unmanned aerial system (UAS) to document vegetation at regular intervals both before and after a planned disturbance, a key component in <i>natural disturbance-based management</i> (NDBM), which uses treatments such as harvest and prescribed burns toward the removal of vegetation fuel loads. We developed a protocol and applied it to timber harvest and prescribed burn events. Geographic image-based analysis (GEOBIA) was used for the classification of UAS orthomosaics. The land cover classes included (1) bare ground, (2) litter, (3) green vegetation, and (4) burned vegetation for the prairie burn site, and (1) mature canopy, (2) understory vegetation, and (3) bare ground for the timber harvest site. Sample datasets for both kinds of disturbances were used to train a support vector machine (SVM) classifier algorithm, which produced four land cover classifications for each site. Statistical analysis (a two-tailed <i>t</i>-test) indicated there was no significant difference in image classification efficacies between the two disturbance types. This research provides a framework to use UASs to assess land cover, which is valuable for supporting effective land management practices and ensuring the sustainability of land practices along with other planned disturbances, such as construction and mining. |
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id | doaj.art-ff11fc0b483a4745a4f703de0dac978a |
institution | Directory Open Access Journal |
issn | 2504-446X |
language | English |
last_indexed | 2024-03-09T22:09:01Z |
publishDate | 2022-02-01 |
publisher | MDPI AG |
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series | Drones |
spelling | doaj.art-ff11fc0b483a4745a4f703de0dac978a2023-11-23T19:36:05ZengMDPI AGDrones2504-446X2022-02-01625210.3390/drones6020052Precise Quantification of Land Cover before and after Planned Disturbance Events with UAS-Derived ImageryZachary Miller0Joseph Hupy1Sarah Hubbard2Guofan Shao3School of Transportation & Aviation Technology, Purdue University, 1401 Aviation Drive, West Lafayette, IN 47907, USASchool of Transportation & Aviation Technology, Purdue University, 1401 Aviation Drive, West Lafayette, IN 47907, USASchool of Transportation & Aviation Technology, Purdue University, 1401 Aviation Drive, West Lafayette, IN 47907, USADepartment of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907, USAThis paper introduces a detailed procedure to utilize the high temporal and spatial resolution capabilities of an unmanned aerial system (UAS) to document vegetation at regular intervals both before and after a planned disturbance, a key component in <i>natural disturbance-based management</i> (NDBM), which uses treatments such as harvest and prescribed burns toward the removal of vegetation fuel loads. We developed a protocol and applied it to timber harvest and prescribed burn events. Geographic image-based analysis (GEOBIA) was used for the classification of UAS orthomosaics. The land cover classes included (1) bare ground, (2) litter, (3) green vegetation, and (4) burned vegetation for the prairie burn site, and (1) mature canopy, (2) understory vegetation, and (3) bare ground for the timber harvest site. Sample datasets for both kinds of disturbances were used to train a support vector machine (SVM) classifier algorithm, which produced four land cover classifications for each site. Statistical analysis (a two-tailed <i>t</i>-test) indicated there was no significant difference in image classification efficacies between the two disturbance types. This research provides a framework to use UASs to assess land cover, which is valuable for supporting effective land management practices and ensuring the sustainability of land practices along with other planned disturbances, such as construction and mining.https://www.mdpi.com/2504-446X/6/2/52UASGEOBIANDBMburn managementdisturbance ecologywildfire |
spellingShingle | Zachary Miller Joseph Hupy Sarah Hubbard Guofan Shao Precise Quantification of Land Cover before and after Planned Disturbance Events with UAS-Derived Imagery Drones UAS GEOBIA NDBM burn management disturbance ecology wildfire |
title | Precise Quantification of Land Cover before and after Planned Disturbance Events with UAS-Derived Imagery |
title_full | Precise Quantification of Land Cover before and after Planned Disturbance Events with UAS-Derived Imagery |
title_fullStr | Precise Quantification of Land Cover before and after Planned Disturbance Events with UAS-Derived Imagery |
title_full_unstemmed | Precise Quantification of Land Cover before and after Planned Disturbance Events with UAS-Derived Imagery |
title_short | Precise Quantification of Land Cover before and after Planned Disturbance Events with UAS-Derived Imagery |
title_sort | precise quantification of land cover before and after planned disturbance events with uas derived imagery |
topic | UAS GEOBIA NDBM burn management disturbance ecology wildfire |
url | https://www.mdpi.com/2504-446X/6/2/52 |
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