  Deep-learning-based image classification and object detection has been
applied successfully to tree monitoring. However, studies of tree crowns and
fallen trees, especially on flood inundated areas, remain largely unexplored.
Detection of degraded tree trunks on natural environments such as water,
mudflats, and natural vegetated areas is challenging due to the mixed colour
image backgrounds. In this paper, Unmanned Aerial Vehicles (UAVs), or drones,
with embedded RGB cameras were used to capture the fallen Acacia Xanthophloea
trees from six designated plots around Lake Nakuru, Kenya. Motivated by the
need to detect fallen trees around the lake, two well-established deep neural
networks, i.e. Faster Region-based Convolution Neural Network (Faster R-CNN)
and Retina-Net were used for fallen tree detection. A total of 7,590
annotations of three classes on 256 x 256 image patches were used for this
study. Experimental results show the relevance of deep learning in this
context, with Retina-Net model achieving 38.9% precision and 57.9% recall.
