Todo Fir Crown Instance Segmentation in Dense Plantation Forest Using Polar-FFT and Treetop Queries

Abstract

Abstract. Monitoring individual trees from unmanned aerial vehicle (UAV)-derived orthomosaics and digital surface models (DSMs) is important for forest management, but instance segmentation remains difficult in dense planted forests in Japan, where Structure from Motion (SfM)-derived DSMs often exhibit blurred crown boundaries, noise, and substantial variation in quality. Existing approaches can benefit from treetop information, but they are sensitive to threshold selection and may introduce false positives when incorrect treetop candidates are provided. To address this problem, we propose a method based on the polar coordinate transform and the fast Fourier transform (PFFT) that represents the local DSM shape around treetop candidates as compact descriptors and integrates them into Mask2Former. The descriptors are used both to suppress low-reliability candidates and to provide spatially meaningful treetop queries to the decoder, thereby improving the separation of adjacent crowns. We evaluated the method on Abies sachalinensis (Todo fir) plantation data acquired at two sites in Hokkaido, Japan, using Mask R-CNN and Mask2Former as baselines. Compared with standard Mask2Former, the proposed method improved mAP50 from 90.14% to 90.87%, mAP75 from 52.18% to 55.47%, and the F1 score at a confidence threshold of 0.5 from 89.86% to 92.08%. It also reduced the number of false positives by 41% without increasing false negatives. Qualitative results showed fewer over-merged crowns and better crown-boundary delineation. These results indicate that treetop-centered local shape cues are effective for instance segmentation in densely planted forests, although further validation across additional species and regions is needed.

Plant phenotyping relevance

UAV由来データから個体樹冠を分離・ delineate する画像解析手法を開発し、複数ベースラインと定量比較しているため、植物形態計測法が中心である。

Code and data availability

The supplied article blocks describe UAV-derived orthomosaic/DSM data of Todo fir plantations in Hokkaido and a PFFT-based Mask2Former method, but contain no data availability, code deposit, or repository statements. The Hokkaido dataset and trained models are not stated as publicly available, and no authors' public or

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