Effectiveness of tree count estimation in Sugi and Hinoki plantations using aerial orthophotos and deep learning

Abstract

Yuki Hirose and Naoto Matsumura: Effectiveness of tree count estimation in Sugi and Hinoki plantations using aerial orthophotos and deep learning. Jpn. J. For. Plann.　In this study, we focused on high-resolution aerial orthophoto data, which are being increasingly acquired by local governments, in order to explore efficient methods for obtaining forest resource information in medium- to large-scale regions, particularly with the aim of updating nationwide laser survey data. Using the aerial orthophotos acquired during laser surveys, combined with a deep learning model (EfficientNet) that has demonstrated excellent performance in the field of remote sensing, we estimated the number of standing trees in Sugi (Cryptomeria japonica) and Hinoki (Chamaecyparis obtusa) plantations (15×15m plots). The results showed an overall accuracy of approximately 80%. Notably, the Sugi plantations, due to their distinct crown shapes, maintained high estimation accuracy even in densely populated areas, showing superiority over the hinoki plantations. Additionally, it is expected that applying the proposed method to high-resolution satellite images and past aerial photographs will enable the acquisition of a wider range of spatiotemporal forest resource data.