Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring
The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the re...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-04-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/7/1207 |
| _version_ | 1797571146881695744 |
|---|---|
| author | Jian Zhang Chufeng Wang Chenghai Yang Tianjin Xie Zhao Jiang Tao Hu Zhibang Luo Guangsheng Zhou Jing Xie |
| author_facet | Jian Zhang Chufeng Wang Chenghai Yang Tianjin Xie Zhao Jiang Tao Hu Zhibang Luo Guangsheng Zhou Jing Xie |
| author_sort | Jian Zhang |
| collection | DOAJ |
| description | The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R<sup>2</sup> = 0.812) and LAI (R<sup>2</sup> = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R<sup>2</sup> = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images. |
| first_indexed | 2024-03-10T20:36:08Z |
| format | Article |
| id | doaj.art-b63d81e135ae4d1eb6ced2840b81af47 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:36:08Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b63d81e135ae4d1eb6ced2840b81af472023-11-19T21:03:20ZengMDPI AGRemote Sensing2072-42922020-04-01127120710.3390/rs12071207Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth MonitoringJian Zhang0Chufeng Wang1Chenghai Yang2Tianjin Xie3Zhao Jiang4Tao Hu5Zhibang Luo6Guangsheng Zhou7Jing Xie8Macro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, ChinaMacro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, ChinaAerial Application Technology Research Unit, USDA-Agricultural Research Service, College Station, TX 77845, USAMacro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, ChinaMacro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, ChinaMacro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, ChinaMacro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan 430070, ChinaCollege of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Science, Huazhong Agricultural University, Wuhan 430070, ChinaThe spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R<sup>2</sup> = 0.812) and LAI (R<sup>2</sup> = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R<sup>2</sup> = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images.https://www.mdpi.com/2072-4292/12/7/1207multispectral cameraground sampling distance (GSD)unmanned aerial vehicle (UAV) remote sensinggrowth monitoringplant height (PH) |
| spellingShingle | Jian Zhang Chufeng Wang Chenghai Yang Tianjin Xie Zhao Jiang Tao Hu Zhibang Luo Guangsheng Zhou Jing Xie Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring Remote Sensing multispectral camera ground sampling distance (GSD) unmanned aerial vehicle (UAV) remote sensing growth monitoring plant height (PH) |
| title | Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring |
| title_full | Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring |
| title_fullStr | Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring |
| title_full_unstemmed | Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring |
| title_short | Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring |
| title_sort | assessing the effect of real spatial resolution of in situ uav multispectral images on seedling rapeseed growth monitoring |
| topic | multispectral camera ground sampling distance (GSD) unmanned aerial vehicle (UAV) remote sensing growth monitoring plant height (PH) |
| url | https://www.mdpi.com/2072-4292/12/7/1207 |
| work_keys_str_mv | AT jianzhang assessingtheeffectofrealspatialresolutionofinsituuavmultispectralimagesonseedlingrapeseedgrowthmonitoring AT chufengwang assessingtheeffectofrealspatialresolutionofinsituuavmultispectralimagesonseedlingrapeseedgrowthmonitoring AT chenghaiyang assessingtheeffectofrealspatialresolutionofinsituuavmultispectralimagesonseedlingrapeseedgrowthmonitoring AT tianjinxie assessingtheeffectofrealspatialresolutionofinsituuavmultispectralimagesonseedlingrapeseedgrowthmonitoring AT zhaojiang assessingtheeffectofrealspatialresolutionofinsituuavmultispectralimagesonseedlingrapeseedgrowthmonitoring AT taohu assessingtheeffectofrealspatialresolutionofinsituuavmultispectralimagesonseedlingrapeseedgrowthmonitoring AT zhibangluo assessingtheeffectofrealspatialresolutionofinsituuavmultispectralimagesonseedlingrapeseedgrowthmonitoring AT guangshengzhou assessingtheeffectofrealspatialresolutionofinsituuavmultispectralimagesonseedlingrapeseedgrowthmonitoring AT jingxie assessingtheeffectofrealspatialresolutionofinsituuavmultispectralimagesonseedlingrapeseedgrowthmonitoring |