Drone4Tree: A cloud-based geospatial platform for large-scale UAV data processing and tree canopy detection

Abstract

Forests cover approximately 31% of the global land area and are home to 80% of the Earth's terrestrial biodiversity. Humans depend on forests for countless ecosystem services, but these ecosystems are highly vulnerable to human-induced climate change. As our climate undergoes dynamic changes, it is imperative to implement automated monitoring systems to quantify canopy growth and assess changes occurring within forest structures, especially at the level of individual trees, to determine the response of forests to climate anomalies. In this context, tree canopy detection can be considered one of the most important applications using Unmanned Aerial Vehicles (UAVs) as it can be used to obtain information on numerous essential ecosystem variables (EEVs) such as gross primary productivity, leaf area index, etc. for individual trees or shed light on essential biodiversity variables (EBVs) such as ecosystem structure and function. However, due to the plethora of information available, users may find it challenging to apply UAVs and algorithms to their specific projects. Hence, an integrated, seamless platform that can process UAV-acquired images to generate ortho-mosaics, detect individual trees, and monitor specific traits (including ecosystem structure and function) is the need of the hour. In this study, a platform, Drone4Tree, has been developed using Streamlit and Flask to provide an end-to-end solution for generating orthomosaics and delineating individual tree crowns from UAV images. Users simply upload raw UAV survey data and receive the final results. The complete processing chain is carried out on our high-end servers, which is an advantage for users with limited computing resources. The developed web application uses open-source algorithms, models, and frameworks for easy implementation of components such as orthomosaic (structure from motion in OpenDroneMap), tree canopy detection (DeepForest and U-Net segmentation), and downloading of results. The platform offers two processing modes: standard and advanced. The standard mode comes with default parameters for orthomosaic generation and tree canopy detection, benefiting users with no experience in UAV image processing. The advanced mode allows users to customize the processes, such as the scale of the generated canopy boundary or patch size for large images. It also extends its functionality towards analysis-ready drone image time series (incl. a co-registration of orthomosaics to a reference image using the AROSICS method and reprojection using the geospatial data abstraction library (GDAL)). Finally, the processing outcomes can be easily downloaded using the generated links.  The web app was used to generate a time series of individual tree canopies, which provided a deeper understanding of changes in EEVs during a phenological cycle. The canopy boundaries can also be used to generate spectral libraries for tree species from high spatial resolution hyperspectral images, which has several applications in species detection and mapping. This platform can guide other users wishing to efficiently produce individual tree canopy boundaries for large areas without investing substantial time tailoring imagery acquisition and processing parameters. The resulting tree canopy boundaries can provide opportunities to characterize individual trees' species, size, condition, and location and are critical resources for advancing ecological theory and informing forest management.