Abstract
Abstract. The objective of this study was to explore the utilization of deep learning networks in individual tree crown (ITC) delineation, a very important step in individual tree analysis. Even though many traditional machine learning methods have been developed for ITC delineation, the accuracy remains low, especially for dense forests where branches, crowns, and clusters of trees usually have similar characteristics and boundaries of tree crowns are not distinct. Advance in deep learning provides a good opportunity to improve ITC delineation. In this study, U-net, Residual U-net, and attention U-net were implemented for the first time in ITC delineation. In order to ensure that the boundaries of tree crowns were classified correctly, a weight map was generated to give more weights to boundary pixels between two close crowns in the loss function. These three networks were trained and tested using optical imagery obtained over a study site within the Great Lakes-St. Lawrence forest region, Ontario Canada. Based on two test sites dominated by open mixed forest and closed deciduous forests, respectively, the overall accuracies were 0.94 and 0.90, respectively for U-net, 0.89 and 0.62 for Residual U-net, and 0.96 and 0.83 for attention U-net.
Highlights
Information on individual trees is required in a variety of forestrelated activities, such as silviculture treatments, selective cuts, and biodiversity assessments
Both visual observations and quantitative analysis revealed that the U-net implemented could delineate various-sized individual tree crowns in mixed wood and deciduous forests with accuracy comparable to manual interpretation
Further visual examination showed that most of the omitted crowns were low and small; most of the merged crowns belong to tree clusters containing no distinguishable between-crown valleys; and as for the split crowns, their subcrowns were falsely taken as individual tree crowns
Summary
Information on individual trees is required in a variety of forestrelated activities, such as silviculture treatments, selective cuts, and biodiversity assessments. Advances in high spatial resolution remote sensing technologies make individual tree-based analysis feasible. Individual tree crown (ITC) serves as a basic unit for many useful activities such as species identification, gap analysis, and volume or biomass estimation. ITC delineation has attracted the attention and research activities of remote sensing communities, which has driven the development of various methods of ITC delineation from remote sensing data (Ke et al, 2011). It remains challenging to delineate tree crowns with complicated structures found in natural and mixed wood forests. Over-segmentation may occur due to that the branches and sub-crowns of a deciduous tree may resemble small trees; and the fact that deciduous tree crowns are often touching or close to each other, making between-crown valleys so invisible that a tree clump (a group of trees growing closed together) can be falsely detected as one crown, leading to under-segmentation
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