Cotton Leaf Area Index Estimation Using Unmanned Aerial Vehicle Multi-Spectral Images
| Autor: | Pengfei Chen |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Irrigation
Index (economics) 010504 meteorology & atmospheric sciences Mean squared error 0211 other engineering and technologies Multi spectral 02 engineering and technology 01 natural sciences Calibration Leaf area index Vegetation Index 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing Mathematics |
| Zdroj: | IGARSS |
| Popis: | The objective of this study is to estimating cotton leaf area index (LAI) using multi-spectral images acquired by Unmanned Aerial Vehicle (UAV). For this purpose, cotton nitrogen and irrigation experiment was conducted with four N treatments and four irrigation treatments in the suburbs of Shihezi city, Xinjiang province. A five bands multi-spectral senor was carried on a four rotors UAV and used to acquire images. Meantime, field campaigns were conducted to measure LAI in each plot. Based on above data, commonly used spectral indices were selected and used to design LAI estimation model. During this process, three quarters of samples were used to make model, and the reminders were used to validation. The results showed images acquired by UAV can be used to monitor cotton LAI. Among the spectral indices, Ratio Vegetation Index (RVI) is the best index for LAI estimation, with R2 value of 0.70 and RMSE value of 0.59 during calibration, and R2 value of 0.65 and RMSE value of 0.62 during validation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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