Radiometric correction of multispectral images collected by a UAV for phenology studies

Sammanfattning: Vegetation monitoring over time is important in a changing world due to climate change. Remote sensing, especially with the use of unmanned aerial vehicles (UAV), can be utilized to monitor vegetation at flexible scales at an accurate degree. However, the relatively new remote sensing platforms that are UAVs imply the requirement of understanding how to best monitor vegetation in an accurate way with the system in mind. This study aims to test a method for the radiometric calibration of images captured by a Parrot Sequoia multispectral camera to derive reflectance images. The radiometric correction method was tested and evaluated against Spectralon reflectance plates and in-situ normalized difference vegetation index (NDVI) during a field campaign. The technical properties of the camera were tested during different experiments to determine what factors propagate to the product reflectance images. The results show that the radiometric correction method could produce accurate estimates of Spectralon reflectance plates. However, not all Spectralon reflectance plates can be accurately estimated. The calculated NDVI from the UAV in the field after a radiometric calibration was far closer to the NDVI derived from the handheld spectrometer. The technical properties and thus limitations of the camera can be rectified by to a certain degree by radiometric calibration and pre-processing method used. However, more accurate reflectance estimates require a rigorous pre-processing of the data used to derive the radiometric calibration.