Study of radiometric variations in Unmanned Aerial Vehicle remote sensing imagery for vegetation mapping

Abstract

Unmanned Aerial Vehicles (UAVs) provide a flexible method for acquiring high-resolution imagery with relative simple operation and cost-effectiveness. This technology emerged 30 years ago and it is widely used by commercial, scientific, and military communities due to its versatility. However, new technology brings new challenges. One of them is the radiometric accuracy of the UAV imagery. Radiometric accuracy is especially important when working with different illumination conditions, dates or sensors.
The present study focuses on reducing radiometric errors of UAV images for vegetation mapping. The fieldwork took place from September 2016 to May 2017 in an agricultural area and a mire. The effect in incident light variations was studied flying in different dates and at different times of the day. Sun elevation angle and presence of clouds gave significant variations in UAV imagery. The study of the sun elevation angle showed that suitable hours for UAV surveys at higher latitudes surveys is within 2 hours of solar noon, since the amount of shadows is low because the sun elevation angle is between 20° and 40°.
The difference in the type of radiation affected the homogeneity of the UAV imagery and the radiometric correction, making the correction of UAV imagery from days with clear sky more difficult, when the direct radiation is predominant. The BRDF effects were less pronounced under overcast conditions, when the predominant incident radiation is diffuse.
Nine correction methods were tested, and their effect on different vegetation indices was compared, showing that the irradiance correction method prior to an empirical line calibration provide less errors than other methods. However, the errors are still high when compared with ground spectral samples (lowest RMSE 38% under overcast conditions for the NDVI).
A simple workflow was developed for vegetation mapping purposes for the Micasense Rededge camera. We suggest to use the automatic dark current-corrected and automatic reduced vignetting effect images, plus irradiance compensation and the use of empirical line calibration to obtain reflectance values in single images before generating the orthomosaic. The radiometric correction process should be done for each spectral band, having a new calibration equation per mission due to the change in sky conditions.
Unfortunately, this workflow will not provide accurate results in the calculation of vegetation indices that assess small variations like the case of chlorophyll indices or vegetation indices that combine several bands. Further research is needed to improve the accuracy of the correction.

Popular Abstract

Remote sensing is a technique used to obtain information about an object from a distance. In the case of vegetation mapping, the objects from which the information is obtained are plants. The devices used for remote sensing of vegetation can be sensors located on platforms such us satellites, airplanes or Unmanned Aerial Vehicles (UAV). The term UAV is applied to any aerial platform that is capable to fly without a person on board, and they are popularly referred to as drones.
The advantages of UAVs is that they provide a flexible method for acquiring very detailed images, they are relatively simple to operate, and they are cost-effective. This technology emerged 30 years ago and it is widely used by commercial, scientific, and military communities due to its versatility. However, new technology brings new challenges. One of them is the radiometric accuracy of the UAV images. Radiometry is the relationship between the value of sun’s energy reflected from the target, and the values that a camera captured when recording the same target. In order to obtain more homogeneous and accurate UAV images for vegetation mapping, radiometric accuracy is especially important when working with different illumination conditions, different dates or different cameras. For this reason, radiometric correction is a very important step when processing UAV imagery.
The effect of light variations during the flights was studied in different dates and at different times of the day. The position of the sun in the sky and presence of clouds gave significant variations in UAV imagery. The analysis of the sun’s position showed that suitable time for UAV flights in northern latitudes is within 2 hours of solar noon, when there are less shadows in the images. The presence of clouds in the sky reduces the amount of direct sunlight. Because of this, the UAV images will be more homogeneous. Performing radiometric corrections on images taken on days when the sky was clear is more difficult due to higher reflectance in objects when the camera is in direct alignment between the sun and the object.
We tested nine radiometric correction methods, and compared their effect on different vegetation indices. The correction method that gave least amount of error was the irradiance correction method prior to an empirical line calibration. However, the errors are still high when compared with values measured with a field spectrometer (lowest RMSE 38% under overcast conditions for the NDVI).
A simple workflow was developed for vegetation mapping purposes for the Micasense Rededge camera. We suggest to use the automatic dark current-corrected and automatic reduced vignetting effect images, plus irradiance compensation and the use of empirical line calibration to obtain reflectance values in single images before generating a mosaic. We also suggest to perform the radiometric correction process for each spectral band, and to have a new calibration equation for each mission due to the change in sky conditions. Unfortunately, this workflow will not provide good results in the calculation of vegetation indices that assess small variations like the case of chlorophyll indices or vegetation indices that combine several bands. Further research is needed to improve the accuracy of the correction.

• author: Tagle Casapia, Maria Ximena
• supervisor: Lars Eklundh
• organization: Dept of Physical Geography and Ecosystem Science
• course: NGEM01 20171
• year: 2017
• type: H2 - Master's Degree (Two Years)
• subject: Earth and Environmental Sciences
• keywords: vegetation mapping, UAV, radiometric correction, vegetation indices, GEM
• publication/series: Lund University GEM thesis series
• report number: 23
• funder: Erasmus Mundus Programme
• language: English
• id: 8918735
• date added to LUP: 2017-06-27 11:47:47
• date last changed: 2017-06-27 11:47:47