Optical reflectance across spatial scales—an intercomparison of transect-based hyperspectral, drone, and satellite reflectance data for dry season rangeland
Drone-based multispectral sensing is a valuable tool for dryland spatial ecology, yet there has been limited investigation of the reproducibility of measurements from drone-mounted multispectral camera array systems or the intercomparison between drone-derived measurements, field spectroscopy, and s...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Canadian Science Publishing
2023-01-01
|
| Series: | Drone Systems and Applications |
| Subjects: | |
| Online Access: | https://cdnsciencepub.com/doi/10.1139/dsa-2023-0003 |
| _version_ | 1797788863777734656 |
|---|---|
| author | Glenn Slade Dominic Fawcett Andrew M. Cunliffe Richard E. Brazier Kamal Nyaupane Marguerite Mauritz Sergio Vargas Karen Anderson |
| author_facet | Glenn Slade Dominic Fawcett Andrew M. Cunliffe Richard E. Brazier Kamal Nyaupane Marguerite Mauritz Sergio Vargas Karen Anderson |
| author_sort | Glenn Slade |
| collection | DOAJ |
| description | Drone-based multispectral sensing is a valuable tool for dryland spatial ecology, yet there has been limited investigation of the reproducibility of measurements from drone-mounted multispectral camera array systems or the intercomparison between drone-derived measurements, field spectroscopy, and satellite data. Using radiometrically calibrated data from two multispectral drone sensors (MicaSense RedEdge (MRE) and Parrot Sequoia (PS)) co-located with a transect of hyperspectral measurements (tramway) in the Chihuahuan desert (New Mexico, USA), we found a high degree of correspondence within individual drone data sets, but that reflectance measurements and vegetation indices varied between field, drone, and satellite sensors. In comparison to field spectra, MRE had a negative bias, while PS had a positive bias. In comparison to Sentinel-2, PS showed the best agreement, while MRE had a negative bias for all bands. A variogram analysis of NDVI showed that ecological pattern information was lost at grains coarser than 1.8 m, indicating that drone-based multispectral sensors provide information at an appropriate spatial grain to capture the heterogeneity and spectral variability of this dryland ecosystem in a dry season state. Investigators using similar workflows should understand the need to account for biases between sensors. Modelling spatial and spectral upscaling between drone and satellite data remains an important research priority. |
| first_indexed | 2024-03-13T01:42:37Z |
| format | Article |
| id | doaj.art-48a4e291b950429c8157db114535f840 |
| institution | Directory Open Access Journal |
| issn | 2564-4939 |
| language | English |
| last_indexed | 2024-03-13T01:42:37Z |
| publishDate | 2023-01-01 |
| publisher | Canadian Science Publishing |
| record_format | Article |
| series | Drone Systems and Applications |
| spelling | doaj.art-48a4e291b950429c8157db114535f8402023-07-03T12:02:08ZengCanadian Science PublishingDrone Systems and Applications2564-49392023-01-011112010.1139/dsa-2023-0003Optical reflectance across spatial scales—an intercomparison of transect-based hyperspectral, drone, and satellite reflectance data for dry season rangelandGlenn Slade0Dominic Fawcett1Andrew M. Cunliffe2Richard E. Brazier3Kamal Nyaupane4Marguerite Mauritz5Sergio Vargas6Karen Anderson7Department of Geography, Faculty of Environment, Science and Economy, University of Exeter, Exeter, UKDepartment of Geography, Faculty of Environment, Science and Economy, University of Exeter, Exeter, UKDepartment of Geography, Faculty of Environment, Science and Economy, University of Exeter, Exeter, UKDepartment of Geography, Faculty of Environment, Science and Economy, University of Exeter, Exeter, UKEnvironmental Science and Engineering Program, The University of Texas at El Paso, 500 W University Avenue, El Paso, TX 79968, USABiological Sciences, The University of Texas at El Paso, 500 W University Avenue, El Paso, TX 79968, USAEnvironmental Science and Engineering Program, The University of Texas at El Paso, 500 W University Avenue, El Paso, TX 79968, USAEnvironment and Sustainability Institute, University of Exeter, Penryn Campus, Exeter TR109FE, UKDrone-based multispectral sensing is a valuable tool for dryland spatial ecology, yet there has been limited investigation of the reproducibility of measurements from drone-mounted multispectral camera array systems or the intercomparison between drone-derived measurements, field spectroscopy, and satellite data. Using radiometrically calibrated data from two multispectral drone sensors (MicaSense RedEdge (MRE) and Parrot Sequoia (PS)) co-located with a transect of hyperspectral measurements (tramway) in the Chihuahuan desert (New Mexico, USA), we found a high degree of correspondence within individual drone data sets, but that reflectance measurements and vegetation indices varied between field, drone, and satellite sensors. In comparison to field spectra, MRE had a negative bias, while PS had a positive bias. In comparison to Sentinel-2, PS showed the best agreement, while MRE had a negative bias for all bands. A variogram analysis of NDVI showed that ecological pattern information was lost at grains coarser than 1.8 m, indicating that drone-based multispectral sensors provide information at an appropriate spatial grain to capture the heterogeneity and spectral variability of this dryland ecosystem in a dry season state. Investigators using similar workflows should understand the need to account for biases between sensors. Modelling spatial and spectral upscaling between drone and satellite data remains an important research priority.https://cdnsciencepub.com/doi/10.1139/dsa-2023-0003dronemultispectralParrot SequoiaMicaSense RedEdgeSentinel-2dryland |
| spellingShingle | Glenn Slade Dominic Fawcett Andrew M. Cunliffe Richard E. Brazier Kamal Nyaupane Marguerite Mauritz Sergio Vargas Karen Anderson Optical reflectance across spatial scales—an intercomparison of transect-based hyperspectral, drone, and satellite reflectance data for dry season rangeland Drone Systems and Applications drone multispectral Parrot Sequoia MicaSense RedEdge Sentinel-2 dryland |
| title | Optical reflectance across spatial scales—an intercomparison of transect-based hyperspectral, drone, and satellite reflectance data for dry season rangeland |
| title_full | Optical reflectance across spatial scales—an intercomparison of transect-based hyperspectral, drone, and satellite reflectance data for dry season rangeland |
| title_fullStr | Optical reflectance across spatial scales—an intercomparison of transect-based hyperspectral, drone, and satellite reflectance data for dry season rangeland |
| title_full_unstemmed | Optical reflectance across spatial scales—an intercomparison of transect-based hyperspectral, drone, and satellite reflectance data for dry season rangeland |
| title_short | Optical reflectance across spatial scales—an intercomparison of transect-based hyperspectral, drone, and satellite reflectance data for dry season rangeland |
| title_sort | optical reflectance across spatial scales an intercomparison of transect based hyperspectral drone and satellite reflectance data for dry season rangeland |
| topic | drone multispectral Parrot Sequoia MicaSense RedEdge Sentinel-2 dryland |
| url | https://cdnsciencepub.com/doi/10.1139/dsa-2023-0003 |
| work_keys_str_mv | AT glennslade opticalreflectanceacrossspatialscalesanintercomparisonoftransectbasedhyperspectraldroneandsatellitereflectancedatafordryseasonrangeland AT dominicfawcett opticalreflectanceacrossspatialscalesanintercomparisonoftransectbasedhyperspectraldroneandsatellitereflectancedatafordryseasonrangeland AT andrewmcunliffe opticalreflectanceacrossspatialscalesanintercomparisonoftransectbasedhyperspectraldroneandsatellitereflectancedatafordryseasonrangeland AT richardebrazier opticalreflectanceacrossspatialscalesanintercomparisonoftransectbasedhyperspectraldroneandsatellitereflectancedatafordryseasonrangeland AT kamalnyaupane opticalreflectanceacrossspatialscalesanintercomparisonoftransectbasedhyperspectraldroneandsatellitereflectancedatafordryseasonrangeland AT margueritemauritz opticalreflectanceacrossspatialscalesanintercomparisonoftransectbasedhyperspectraldroneandsatellitereflectancedatafordryseasonrangeland AT sergiovargas opticalreflectanceacrossspatialscalesanintercomparisonoftransectbasedhyperspectraldroneandsatellitereflectancedatafordryseasonrangeland AT karenanderson opticalreflectanceacrossspatialscalesanintercomparisonoftransectbasedhyperspectraldroneandsatellitereflectancedatafordryseasonrangeland |