Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite
Rangelands cover ~23 million hectares and support a $3.4 billion annual cattle industry in California. Large variations in forage production from year to year and across the landscape make grazing management difficult. We here developed optimized methods to map high-resolution forage production usin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-03-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/11/5/595 |
| _version_ | 1798026968435785728 |
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| author | Han Liu Randy A. Dahlgren Royce E. Larsen Scott M. Devine Leslie M. Roche Anthony T. O’ Geen Andy J.Y. Wong Sarah Covello Yufang Jin |
| author_facet | Han Liu Randy A. Dahlgren Royce E. Larsen Scott M. Devine Leslie M. Roche Anthony T. O’ Geen Andy J.Y. Wong Sarah Covello Yufang Jin |
| author_sort | Han Liu |
| collection | DOAJ |
| description | Rangelands cover ~23 million hectares and support a $3.4 billion annual cattle industry in California. Large variations in forage production from year to year and across the landscape make grazing management difficult. We here developed optimized methods to map high-resolution forage production using multispectral remote sensing imagery. We conducted monthly flights using a Small Unmanned Aerial System (sUAS) in 2017 and 2018 over a 10-ha deferred grazing rangeland. Daily maps of NDVI at 30-cm resolution were first derived by fusing monthly 30-cm sUAS imagery and more frequent 3-m PlanetScope satellite observations. We estimated aboveground net primary production as a product of absorbed photosynthetically active radiation (APAR) derived from NDVI and light use efficiency (LUE), optimized as a function of topography and climate stressors. The estimated forage production agreed well with field measurements having a R2 of 0.80 and RMSE of 542 kg/ha. Cumulative NDVI and APAR were less correlated with measured biomass ( R 2 = 0.68). Daily forage production maps captured similar seasonal and spatial patterns compared to field-based biomass measurements. Our study demonstrated the utility of aerial and satellite remote sensing technology in supporting adaptive rangeland management, especially during an era of climatic extremes, by providing spatially explicit and near-real-time forage production estimates. |
| first_indexed | 2024-04-11T18:44:00Z |
| format | Article |
| id | doaj.art-4019e0c4dbf54d618163a5cf617d2461 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T18:44:00Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4019e0c4dbf54d618163a5cf617d24612022-12-22T04:08:52ZengMDPI AGRemote Sensing2072-42922019-03-0111559510.3390/rs11050595rs11050595Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope SatelliteHan Liu0Randy A. Dahlgren1Royce E. Larsen2Scott M. Devine3Leslie M. Roche4Anthony T. O’ Geen5Andy J.Y. Wong6Sarah Covello7Yufang Jin8Department of Land, Air and Water Resources, University of California, Davis, CA 95616-8627, USADepartment of Land, Air and Water Resources, University of California, Davis, CA 95616-8627, USAUniversity of California Cooperative Extension, San Luis Obispo, CA 90001, USADepartment of Land, Air and Water Resources, University of California, Davis, CA 95616-8627, USADepartment of Plant Science, University of California, Davis, CA 95616-8627, USADepartment of Land, Air and Water Resources, University of California, Davis, CA 95616-8627, USADepartment of Land, Air and Water Resources, University of California, Davis, CA 95616-8627, USAUniversity of California Cooperative Extension, San Luis Obispo, CA 90001, USADepartment of Land, Air and Water Resources, University of California, Davis, CA 95616-8627, USARangelands cover ~23 million hectares and support a $3.4 billion annual cattle industry in California. Large variations in forage production from year to year and across the landscape make grazing management difficult. We here developed optimized methods to map high-resolution forage production using multispectral remote sensing imagery. We conducted monthly flights using a Small Unmanned Aerial System (sUAS) in 2017 and 2018 over a 10-ha deferred grazing rangeland. Daily maps of NDVI at 30-cm resolution were first derived by fusing monthly 30-cm sUAS imagery and more frequent 3-m PlanetScope satellite observations. We estimated aboveground net primary production as a product of absorbed photosynthetically active radiation (APAR) derived from NDVI and light use efficiency (LUE), optimized as a function of topography and climate stressors. The estimated forage production agreed well with field measurements having a R2 of 0.80 and RMSE of 542 kg/ha. Cumulative NDVI and APAR were less correlated with measured biomass ( R 2 = 0.68). Daily forage production maps captured similar seasonal and spatial patterns compared to field-based biomass measurements. Our study demonstrated the utility of aerial and satellite remote sensing technology in supporting adaptive rangeland management, especially during an era of climatic extremes, by providing spatially explicit and near-real-time forage production estimates.http://www.mdpi.com/2072-4292/11/5/595DroneMicaSense RedEdgeCommercial satelliteLight use efficiencyData fusionRangelandAboveground biomassEnvironmental stress |
| spellingShingle | Han Liu Randy A. Dahlgren Royce E. Larsen Scott M. Devine Leslie M. Roche Anthony T. O’ Geen Andy J.Y. Wong Sarah Covello Yufang Jin Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite Remote Sensing Drone MicaSense RedEdge Commercial satellite Light use efficiency Data fusion Rangeland Aboveground biomass Environmental stress |
| title | Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite |
| title_full | Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite |
| title_fullStr | Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite |
| title_full_unstemmed | Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite |
| title_short | Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite |
| title_sort | estimating rangeland forage production using remote sensing data from a small unmanned aerial system suas and planetscope satellite |
| topic | Drone MicaSense RedEdge Commercial satellite Light use efficiency Data fusion Rangeland Aboveground biomass Environmental stress |
| url | http://www.mdpi.com/2072-4292/11/5/595 |
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