Accurate tree segmentation is a key step in extracting individual tree metrics from forest laser scans, and is essential to understanding ecosystem functions in carbon cycling and beyond. Over the past decade, tree segmentation algorithms have advanced rapidly due to developments in AI. However, existing public 3D forest datasets are not large enough to build robust tree segmentation systems. Motivated by the success of synthetic data in other domains such as self-driving, we investigate whether similar approaches can help with tree segmentation. In place of expensive field data collection and annotation, we use synthetic data during pretraining, and then require only minimal, real forest plot annotation for fine-tuning. We have developed Cambridge Arboreal Modelling Panoptic 3D (CAMP3D), a new synthetic data generation pipeline to do this for forest vision tasks, integrating advances in game engines with physics-based LiDAR simulation. Using CAMP3D, we have produced a comprehensive, diverse, annotated 3D forest dataset on an unprecedented scale. Extensive experiments with a state-of-the-art tree segmentation algorithm and a popular real dataset show that our synthetic data can substantially reduce the need for labelled real data. After fine-tuning on just a single, real, forest plot of less than 0.1 hectare, the pretrained model achieves segmentations that are competitive with a model trained on the full scale real data. We have also identified critical factors for successful use of synthetic data: physics, diversity, and scale, paving the way for more robust 3D forest vision systems in the future. Our CAMP3D pipeline and the resulting dataset are available at https://github.com/yihshe/CAMP3D.git.

Ash dieback (Hymenoscyphus fraxineus) is an introduced fungal disease that is causing the widespread death of ash trees across Europe. Remote sensing hyperspectral images encode rich structure that has been exploited for the detection of dieback disease in ash trees using supervised machine learning techniques. However, to understand the state of forest health at landscape-scale, accurate unsupervised approaches are needed. This article investigates the use of the unsupervised Diffusion and VCA-Assisted Image Segmentation (D-VIS) clustering algorithm for the detection of ash dieback disease in a forest site near Cambridge, United Kingdom. The unsupervised clustering presented in this work has high overlap with the supervised classification of previous work on this scene (overall accuracy = 71%). Thus, unsupervised learning may be used for the remote detection of ash dieback disease without the need for expert labeling.

Developing a robust algorithm for automatic individual tree crown (ITC) detection from airborne laser scanning (ALS) data sets is important for tracking the responses of trees to anthropogenic change. Such approaches allow the size, growth, and mortality of individual trees to be measured, enabling forest carbon stocks and dynamics to be tracked and understood. Many algorithms exist for structurally simple forests, including coniferous forests and plantations. Finding a robust solution for structurally complex, species-rich tropical forests remains a challenge; existing segmentation algorithms often perform less well than simple area-based approaches when estimating plot-level biomass. Here, we describe a multiclass graph cut (MCGC) approach to tree crown delineation. This uses local 3D geometry and density information, alongside knowledge of crown allometries, to segment ITCs from airborne light detection and ranging point clouds. Our approach robustly identifies trees in the top and intermediate layers of the canopy, but cannot recognize small trees. From these 3D crowns, we are able to measure individual tree biomass. Comparing these estimates with those from permanent inventory plots, our algorithm can produce robust estimates of hectare-scale carbon density, demonstrating the power of ITC approaches in monitoring forests. The flexibility of our method to add additional dimensions of information, such as spectral reflectance, make this approach an obvious avenue for future development and extension to other sources of 3D data, such as structure from motion data sets.

In this paper, we present a graph-based semi-supervised framework for hyperspectral image classification. We first introduce a novel superpixel algorithm based on the spectral covariance matrix representation of pixels to provide a better representation of our data. We then construct a superpixel graph, based on carefully considered feature vectors, before performing classification. We demonstrate, through a set of experimental results using two benchmarking datasets, that our approach outperforms three state-of-the-art classification frameworks, especially when a extremely small amount of labelled data is used.

In this paper we present a study on the estimation of the aboveground biomass in tropical forests at single tree level using airborne laser scanning (ALS) data. Individual tree crowns (ITCs) are firstly detected using a method based on an adaptive window that change its size according to tree height. The diameter at breast height (DBH) and the aboveground biomass (AGB) of each ITC then are predicted using standard allometric models. Lastly, the AGB values are aggregated at plot level, and compared with field measured values. The results show that it is possible to accurately predict the aboveground biomass of tropical forests at single tree level using ALS data.

Individual tree species mapping is important to understand forest dynamics and species distribution patterns. Airborne LiDAR with hyperspectral imaging has been extensively used to extract biophysical traits of vegetation and detect species. However, its application for individual tree mapping is limited due to technical problems. To address the problems, this paper presents effective and efficient algorithms in terms of tackling co-alingment of LiDAR and hyperspectral datasets, classifying individual trees, thus detecting tree species and leaf chemistry from the tree mapping.

There is much current interest in using multisensor airborne remote sensing to monitor the structure and biodiversity of woodlands. This paper addresses the application of nonparametric (NP) image-registration techniques to precisely align images obtained from multisensor imaging, which is critical for the successful identification of individual trees using object recognition approaches. NP image registration, in particular, the technique of optimizing an objective function, containing similarity and regularization terms, provides a flexible approach for image registration. Here, we develop a NP registration approach, in which a normalized gradient field is used to quantify similarity, and curvature is used for regularization (NGF-Curv method). Using a survey of woodlands in southern Spain as an example, we show that NGF-Curv can be successful at fusing data sets when there is little prior knowledge about how the data sets are interrelated (i.e., in the absence of ground control points). The validity of NGF-Curv in airborne remote sensing is demonstrated by a series of experiments. We show that NGF-Curv is capable of aligning images precisely, making it a valuable component of algorithms designed to identify objects, such as trees, within multisensor data sets.