Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R-CNN

Abstract

Tropical forests are a major component of the global carbon cycle and home to two-thirds of terrestrial species. Upper-canopy trees store the majority of forest carbon and can be particularly vulnerable to drought events and storms. Monitoring their growth and mortality is essential to understanding forest resilience to climate change, but large trees are underrepresented in traditional field surveys, so estimates are poorly constrained. Aerial photographs provide spectral and textural information to discriminate between tree crowns in diverse, complex tropical canopies, potentially opening the door to landscape monitoring of large trees. Here we describe a new deep convolutional neural network machine learning method, Detectree2, which builds on the Mask R-CNN computer vision framework to recognise the irregular edges of individual tree crowns from airborne RGB imagery. We trained and evaluated this model with 3,600 manually delineated tree crowns at three sites in Malaysian Borneo and one site in French Guiana. As an example application of this tool, we combined the delineations with repeat lidar surveys of the four sites to estimate the growth and mortality of upper-canopy trees. Detectree2 delineated 65,000 upper-canopy trees across 14 km^2 of aerial images. The skill of the automatic method in delineating the upper-canopy tree crowns was good (F1 = 0.71). As predicted from previous field studies, we found that growth rate declined with tree height and tall trees had higher mortality rates than intermediate-size trees. In addition, tall trees in French Guiana had higher growth and mortality rates than those in Borneo. Our approach demonstrates that machine learning methods can automatically segment trees in abundant, cheap RGB imagery. This tool has many potential applications in forest ecology and conservation, from estimating carbon stocks to monitoring forest restoration. We demonstrate its use in tracking the growth and mortality rates of upper-canopy trees at scales much larger than those achievable with field data.

Comparing Deep Learning and MCWST Approaches for Individual Tree Crown Segmentation

“…Conversely, Mask R-CNN has the ability to recognize objects at a pixel level and provide a comprehensive outline of the object. Therefore, some researchers have explored ITC segmentation based on Mask R-CNN with remote sensing data, such as UAV images (G. Braga et al, 2020;Hao et al, 2021;Ball et al, 2023). Hao et al (2021) annotate 1605 trees in a 4 ha plot, with 16% of the data reserved for testing and the remaining data used for training and validation.…”

Integrating Crowd-sourced Annotations of Tree Crowns using Markov Random Field and Multispectral Information

“…Tree arrangement: Trees are for the most part in the ground without a specific distribution pattern. Especially in dense forests, it is extremely challenging to distinguish a single tree with the naked eye (Freudenberg et al, 2022;Ball et al, 2023). 3.…”

Diameter, height and species of 42 million trees in three European landscapes generated from field data and airborne laser scanning data

“…as in Weinstein et al 2021 1 , Ball et al . 2022 2 , or spaceborne imagery, as in Tucker, Brandt, Hiernaux, et al 2023 3 .…”