Consumer-grade UAV imagery facilitates semantic segmentation of species-rich savanna tree layers

Abstract

Conventional forest inventories are labour-intensive. This limits the spatial extent and temporal frequency at which woody vegetation is usually monitored. Remote sensing provides cost-effective solutions that enable extensive spatial coverage and high sampling frequency. Recent studies indicate that convolutional neural networks (CNNs) can classify woody forests, plantations, and urban vegetation at the species level using consumer-grade unmanned aerial vehicle (UAV) imagery. However, whether such an approach is feasible in species-rich savanna ecosystems remains unclear. Here, we tested whether small data sets of high-resolution RGB orthomosaics suffice to train U-Net, FC-DenseNet, and DeepLabv3 + in semantic segmentation of savanna tree species. We trained these models on an 18-ha training area and explored whether models could be transferred across space and time. These models could recognise trees in adjacent (mean F1-Score = 0.68) and distant areas (mean F1-Score = 0.61) alike. Over time, a change in plant morphology resulted in a decrease of model accuracy. Our results show that CNN-based tree mapping using consumer-grade UAV imagery is possible in savanna ecosystems. Still, larger and more heterogeneous data sets can further improve model robustness to capture variation in plant morphology across time and space.

CEDAnet: Individual Tree Segmentation in Dense Orchard via Context Enhancement and Density Prior

“…In recent years, Unmanned Aerial Vehicles (UAVs) have provided the possibility for precise ITS with the ultra-high spatial resolution images [7], [8]. Satellite-based RS has made some remarkable progress in large-scale ITS, especially for sparse forests in Africa and regular economic forests [9]. However, faced with low height and dense distribution of artificial fruit trees in orchards, satellite-based RS with relatively coarse resolution makes it difficult to characterize the detailed morphology.…”