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.

Plant phenotyping relevance

UAV画像とCNNによる樹木の空間的な認識・セグメンテーション手法を開発・評価し、植物形態の時空間変動に対する頑健性も検証しているため、手法が中心である。

Code and data availability

The paper's authors explicitly state that all code used for model training and statistical analyses is publicly available on GitHub at the authors' repository (LELENet), which matches an allowed URL. This is the paper-specific analysis code for the CNN semantic segmentation of savanna tree species. No separate public影像