Prediction of leaf area index using thermal infrared data acquired by UAS over a mixed temperate forest
The leaf area index (LAI) is a crucial biophysical variable for remote sensing vegetation studies. LAI estimation through remote sensing data has mostly been investigated using visible and near-infrared (0.4–1.3 μm, VNIR) and Shortwave Infrared (1.4–3 μm, SWIR) data. However, Thermal Infrared (3–14 ...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843222002370 |
| _version_ | 1828156835941580800 |
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| author | Philip Stobbelaar Elnaz Neinavaz Panagiotis Nyktas |
| author_facet | Philip Stobbelaar Elnaz Neinavaz Panagiotis Nyktas |
| author_sort | Philip Stobbelaar |
| collection | DOAJ |
| description | The leaf area index (LAI) is a crucial biophysical variable for remote sensing vegetation studies. LAI estimation through remote sensing data has mostly been investigated using visible and near-infrared (0.4–1.3 μm, VNIR) and Shortwave Infrared (1.4–3 μm, SWIR) data. However, Thermal Infrared (3–14 μm, TIR) data for LAI retrieval has rarely been explored. This study aims to predict LAI by integrating VNIR and TIR data from Unmanned Aerial Systems (UAS) in a mixed temperate forest, the Haagse Bos, Enschede, the Netherlands. The VNIR and TIR images were acquired in September 2020, in conjunction with fieldwork to collect LAI in situ data for 30 plots. TIR images were acquired at two heights (i.e., 85 m and 120 m above ground) to investigate the effect of flight height on the LAI prediction accuracy by means of UAS data. Land Surface Temperature (LST) and Land Surface Emissivity (LSE) were computed and extracted from the collected images. LAI was estimated using seven vegetation indices and Partial Least Squares Regression (PLSR). LAI prediction accuracy using VNIR reflectance spectra was compared to the accuracy achieved by integrating VNIR data with LST or LSE applying vegetation indices as well as PLSR. Among the applied vegetation indices, the Reduced Simple Ratio (RSR) gained the highest prediction accuracy using VNIR data (R2 = 0.5815, RMSE = 0.6972); the prediction accuracy was not improved when LST was integrated with VNIR data but increased when LSE was included (RSR: R2 = 0.7458, RMSE = 0.5081). However, when LST from 85 m altitude and VNIR data was applied as an input using PLSR (R2 = 0.5565, RMSECV = 0.7998), the LAI prediction accuracy was slightly increased compared to when only VNIR data was used (R2 = 0.4452, RMSECV = 0.8668). Integrating VNIR data with LSE significantly improved the LAI retrieval accuracy (R2 = 0.7907, RMSECV = 0.8351). These findings corroborate prior research indicating that combining LSE with VNIR data can increase the prediction accuracy of LAI. However, LST was found to be ineffective for this purpose. The relationship between LAI and LSE should be the subject of more investigation through various approaches to bridge the existing scientific gap. |
| first_indexed | 2024-04-11T23:18:00Z |
| format | Article |
| id | doaj.art-cf5af2e5d33a42c982b5c4b7c830df55 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-11T23:18:00Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-cf5af2e5d33a42c982b5c4b7c830df552022-12-22T03:57:32ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322022-11-01114103049Prediction of leaf area index using thermal infrared data acquired by UAS over a mixed temperate forestPhilip Stobbelaar0Elnaz Neinavaz1Panagiotis Nyktas2Corresponding author.; Department of Natural Resources Science, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, 7500AE Enschede, the NetherlandsDepartment of Natural Resources Science, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, 7500AE Enschede, the NetherlandsDepartment of Natural Resources Science, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, 7500AE Enschede, the NetherlandsThe leaf area index (LAI) is a crucial biophysical variable for remote sensing vegetation studies. LAI estimation through remote sensing data has mostly been investigated using visible and near-infrared (0.4–1.3 μm, VNIR) and Shortwave Infrared (1.4–3 μm, SWIR) data. However, Thermal Infrared (3–14 μm, TIR) data for LAI retrieval has rarely been explored. This study aims to predict LAI by integrating VNIR and TIR data from Unmanned Aerial Systems (UAS) in a mixed temperate forest, the Haagse Bos, Enschede, the Netherlands. The VNIR and TIR images were acquired in September 2020, in conjunction with fieldwork to collect LAI in situ data for 30 plots. TIR images were acquired at two heights (i.e., 85 m and 120 m above ground) to investigate the effect of flight height on the LAI prediction accuracy by means of UAS data. Land Surface Temperature (LST) and Land Surface Emissivity (LSE) were computed and extracted from the collected images. LAI was estimated using seven vegetation indices and Partial Least Squares Regression (PLSR). LAI prediction accuracy using VNIR reflectance spectra was compared to the accuracy achieved by integrating VNIR data with LST or LSE applying vegetation indices as well as PLSR. Among the applied vegetation indices, the Reduced Simple Ratio (RSR) gained the highest prediction accuracy using VNIR data (R2 = 0.5815, RMSE = 0.6972); the prediction accuracy was not improved when LST was integrated with VNIR data but increased when LSE was included (RSR: R2 = 0.7458, RMSE = 0.5081). However, when LST from 85 m altitude and VNIR data was applied as an input using PLSR (R2 = 0.5565, RMSECV = 0.7998), the LAI prediction accuracy was slightly increased compared to when only VNIR data was used (R2 = 0.4452, RMSECV = 0.8668). Integrating VNIR data with LSE significantly improved the LAI retrieval accuracy (R2 = 0.7907, RMSECV = 0.8351). These findings corroborate prior research indicating that combining LSE with VNIR data can increase the prediction accuracy of LAI. However, LST was found to be ineffective for this purpose. The relationship between LAI and LSE should be the subject of more investigation through various approaches to bridge the existing scientific gap.http://www.sciencedirect.com/science/article/pii/S1569843222002370Leaf area indexThermal infraredLand surface temperatureLand surface emissivityUnmanned aerial systemUnmanned aerial vehicle |
| spellingShingle | Philip Stobbelaar Elnaz Neinavaz Panagiotis Nyktas Prediction of leaf area index using thermal infrared data acquired by UAS over a mixed temperate forest International Journal of Applied Earth Observations and Geoinformation Leaf area index Thermal infrared Land surface temperature Land surface emissivity Unmanned aerial system Unmanned aerial vehicle |
| title | Prediction of leaf area index using thermal infrared data acquired by UAS over a mixed temperate forest |
| title_full | Prediction of leaf area index using thermal infrared data acquired by UAS over a mixed temperate forest |
| title_fullStr | Prediction of leaf area index using thermal infrared data acquired by UAS over a mixed temperate forest |
| title_full_unstemmed | Prediction of leaf area index using thermal infrared data acquired by UAS over a mixed temperate forest |
| title_short | Prediction of leaf area index using thermal infrared data acquired by UAS over a mixed temperate forest |
| title_sort | prediction of leaf area index using thermal infrared data acquired by uas over a mixed temperate forest |
| topic | Leaf area index Thermal infrared Land surface temperature Land surface emissivity Unmanned aerial system Unmanned aerial vehicle |
| url | http://www.sciencedirect.com/science/article/pii/S1569843222002370 |
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