UAV‐derived imagery for vegetation structure estimation in rangelands: validation and application
Abstract The purpose of this study is to develop an unmanned aerial vehicle (UAV)‐based remote sensing method that can estimate vegetation indicators in arid and semiarid rangelands. This method was used to quantify six rangeland indicators (canopy size, bare soil gap size, plant height, scaled heig...
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Wiley
2021
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oai:doaj.org-article:eceffad7a78842d9bdebac980a23efa22021-11-29T07:06:42ZUAV‐derived imagery for vegetation structure estimation in rangelands: validation and application2150-892510.1002/ecs2.3830https://doaj.org/article/eceffad7a78842d9bdebac980a23efa22021-11-01T00:00:00Zhttps://doi.org/10.1002/ecs2.3830https://doaj.org/toc/2150-8925Abstract The purpose of this study is to develop an unmanned aerial vehicle (UAV)‐based remote sensing method that can estimate vegetation indicators in arid and semiarid rangelands. This method was used to quantify six rangeland indicators (canopy size, bare soil gap size, plant height, scaled height, vegetation cover, and bare soil cover) in a semiarid grass–shrub ecosystem. The drone‐based estimates were validated with field measurements by using the standard transect methods (gap intercept, drop disk, and line‐point intercept methods) in the spring and summer of 2017. The drone‐based estimates showed strong agreements with in situ measurements in cases where deciduous vegetation (mesquite) had leaves with R2 for bare soil gap size and vegetation height of 0.97 and 0.89 in the summer, respectively. The RMSE of bare soil gap size and vegetation height are 0.2 m and 6.72 cm in the summer, respectively. Based on these results, we found that drone‐based remote sensing proved to be an efficient and highly accurate method that serves as a complement to field measurements for rangeland indicator estimation. We discussed the possible applications of drone‐based products on arid and semiarid rangelands: the spatially explicit input of an ecological model, to detect and characterize non‐stationarity, and to detect landscape anisotropy.Junzhe ZhangGregory S. OkinBo ZhouJason W. KarlWileyarticlerangeland indicatorsremote sensingSpecial Feature: Dynamic Desertsstructure from motionunmanned aerial vehicle (UAV)EcologyQH540-549.5ENEcosphere, Vol 12, Iss 11, Pp n/a-n/a (2021) |
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DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
rangeland indicators remote sensing Special Feature: Dynamic Deserts structure from motion unmanned aerial vehicle (UAV) Ecology QH540-549.5 |
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rangeland indicators remote sensing Special Feature: Dynamic Deserts structure from motion unmanned aerial vehicle (UAV) Ecology QH540-549.5 Junzhe Zhang Gregory S. Okin Bo Zhou Jason W. Karl UAV‐derived imagery for vegetation structure estimation in rangelands: validation and application |
| description |
Abstract The purpose of this study is to develop an unmanned aerial vehicle (UAV)‐based remote sensing method that can estimate vegetation indicators in arid and semiarid rangelands. This method was used to quantify six rangeland indicators (canopy size, bare soil gap size, plant height, scaled height, vegetation cover, and bare soil cover) in a semiarid grass–shrub ecosystem. The drone‐based estimates were validated with field measurements by using the standard transect methods (gap intercept, drop disk, and line‐point intercept methods) in the spring and summer of 2017. The drone‐based estimates showed strong agreements with in situ measurements in cases where deciduous vegetation (mesquite) had leaves with R2 for bare soil gap size and vegetation height of 0.97 and 0.89 in the summer, respectively. The RMSE of bare soil gap size and vegetation height are 0.2 m and 6.72 cm in the summer, respectively. Based on these results, we found that drone‐based remote sensing proved to be an efficient and highly accurate method that serves as a complement to field measurements for rangeland indicator estimation. We discussed the possible applications of drone‐based products on arid and semiarid rangelands: the spatially explicit input of an ecological model, to detect and characterize non‐stationarity, and to detect landscape anisotropy. |
| format |
article |
| author |
Junzhe Zhang Gregory S. Okin Bo Zhou Jason W. Karl |
| author_facet |
Junzhe Zhang Gregory S. Okin Bo Zhou Jason W. Karl |
| author_sort |
Junzhe Zhang |
| title |
UAV‐derived imagery for vegetation structure estimation in rangelands: validation and application |
| title_short |
UAV‐derived imagery for vegetation structure estimation in rangelands: validation and application |
| title_full |
UAV‐derived imagery for vegetation structure estimation in rangelands: validation and application |
| title_fullStr |
UAV‐derived imagery for vegetation structure estimation in rangelands: validation and application |
| title_full_unstemmed |
UAV‐derived imagery for vegetation structure estimation in rangelands: validation and application |
| title_sort |
uav‐derived imagery for vegetation structure estimation in rangelands: validation and application |
| publisher |
Wiley |
| publishDate |
2021 |
| url |
https://doaj.org/article/eceffad7a78842d9bdebac980a23efa2 |
| work_keys_str_mv |
AT junzhezhang uavderivedimageryforvegetationstructureestimationinrangelandsvalidationandapplication AT gregorysokin uavderivedimageryforvegetationstructureestimationinrangelandsvalidationandapplication AT bozhou uavderivedimageryforvegetationstructureestimationinrangelandsvalidationandapplication AT jasonwkarl uavderivedimageryforvegetationstructureestimationinrangelandsvalidationandapplication |
| _version_ |
1718407559465926656 |