For CNN (Convolutional Neural Network) training and calibration we started working in the historical park areas (Park Glienicke) in the South-West of Berlin. These forest stands are dominated by very dry soil conditions and the beech trees suffer here as much as in some regions of the Hainich National Park area. There is however more information available for these trees here in Berlin since this areas has been monitored for a while now and the regional governmental institutions also developed a tree cadastre with species information and health classification. The parks in the South-West are therefore a perfect test area for training the CNN for different damage categories and for species classification.
In summer 2020 weather conditions only allowed a few forenoon flights over the park region but the time windows were large enough to do a full mapping of the 125 ha area. With the P4M this took approx. 2 hours since 5 batteries are needed with 82/82 overlap setting.
For these flights a EDR-4 clearance was approved by BFA and we also informed the HMI nuclear reactor facility that we are doing drone flights. However some people must have observed the start/landing sequence and they aggressively asked me to stop spying the neighborhood. There is – as always – low understanding for the potential applications of drone/UAV data in general in the public. Perception is mainly focused on the surveillance aspect or military applications and not on the potential for environmental ecological monitoring and modeling applications. Some more in depth information quickly calmed down the situation and being open minded and showing details of flight plan, allowances, contracts and insurance documentation and applicational ideas in the context of the beech tree dying in this area made transparent that this was (also for the public) a useful activity and the person quickly changed its attitude.
Surface model and point cloud model processing was done in Agisoft Metashape – whereas the P4M multispectral data was processed with DJI Terra. Somehow Terra works better with the P4M data. It seems to utilize the sun illumination sensor data and the ortho mosaic was much better color balanced. However there is basically no documentation about how Terra processed the data. Reference reflectance measures are taken from concrete surfaces but we also measured a Micasense 50% reflectance panel. Processing of the reflectance panel data was not possible within the Agisoft workflow. Somehow the reflectance values are totally offset. We are working with Agisoft support on that right now – but so far it seems as if Agisoft does not use the Illumination information at all and the reflectance panel data might be misread.