From LiDAR Waveforms to Hyper Point Clouds: A Novel Data Product to Characterize Vegetation Structure

Current research into the fusion of Hyperspectral Imagery (HI) and full waveform LiDAR (Light detection and ranging) has relied on first processing the full waveform LiDAR (FWL) data to a set of discrete returns before combining. However, more information about target properties can potentially be recovered if the raw waveform is preserved in the fusion with HI. This paper proposes a voxelization method to fuse raw FWL data with HI by dividing the waveform data into voxels, and then synthesizing all waveforms which intersect a voxel into one 3D superposition waveform. The efficacy of this method is evaluated by comparing the synthesized waveform with an actual nadir LiDAR waveform from the voxel of interest. Results show that this method of voxelizing and fusion of FWL data can preserve raw waveform characteristics while effectively representing the FWL data on a 3D raster basis that can be directly co-registered with the HI.

For the purpose of forest management, high resolution LIDAR and optical remote sensing imageries are used for treetop detection, tree crown delineation, and classification. The purpose of this study is to develop a self-adjusted dominant scales calculation method and a new crown horizontal cutting method of tree canopy height model (CHM) to detect and delineate tree crowns from LIDAR, under the hypothesis that a treetop is radiometric or altitudinal maximum and tree crowns consist of multi-scale branches. The major concept of the method is to develop an automatic selecting strategy of feature scale on CHM, and a multi-scale morphological reconstruction–open crown decomposition (MRCD) to get morphological multi-scale features of CHM by: cutting CHM from treetop to the ground; analysing and refining the dominant multiple scales with differential horizontal profiles to get treetops; segmenting LiDAR CHM using watershed a segmentation approach marked with MRCD treetops. This method has solved the problems of false detection of CHM side-surface extracted by the traditional morphological opening canopy segment (MOCS) method. The novel MRCD delineates more accurate and quantitative multi-scale features of CHM, and enables more accurate detection and segmentation of treetops and crown. Besides, the MRCD method can also be extended to high optical remote sensing tree crown extraction. In an experiment on aerial LiDAR CHM of a forest of multi-scale tree crowns, the proposed method yielded high-quality tree crown maps.

Extraction of information about individual trees using remotely sensed data is essential to supporting ecological and commercial applications in forest environments. Data acquired by consumer-grade cameras onboard unmanned aerial vehicles (UAV) offer an affordable option of high-spatial resolution imagery that can be used to extract forest structural information at a tree level. The aim of this work is to investigate the potential and accuracy of UAV time-series data to automatically detect and delineate tree crowns across an entire woodland. The workflow (presented in a step-by-step manner) involves the construction of a canopy height model (CHM) based on digital elevation models derived from the UAV photogrammetric point clouds. A watershed-based approach is modified to automatically detect and delineate the tree crowns, based on the CHM and the brightness information from the UAV orthomosaics. The accuracy of the proposed method was evaluated by comparing its results against manually delineated tree crowns. The results show an overall accuracy of 63%, where conifer species were more accurately delineated (up to 80%), while broadleaf species returned lower accuracies (<50 % ). Continued research is necessary to improve the confidence of automated individual tree crown detection and delineation, especially over complex forest structures.

The paper proposes a procedure based on Kohonen’s Self Organizing Maps (SOMs) to perform the unsupervised classification of raw full-waveform airborne LIDAR (Light Detection and Ranging) data, without the need of extracting features from them, that is without any preprocessing. The proposed algorithm allows the classification of points into three classes (“grass”, “trees” and “road”) in two subsequent stages. During the first one, all the raw data are given as input to a SOM and points belonging to the category “trees” are extracted on the basis of the number of peaks that characterize the waveforms. In the second stage, data not previously classified as “trees” are used to create a new SOM that, together with a hierarchical clustering algorithm, allows to distinguish between the classes “road” and “grass”. Experiments carried out show that raw full-waveform LIDAR data were classified with an overall accuracy of 93.9%, 92.5% and 92.9%, respectively.

We propose a context-based classification method for point clouds acquired by full waveform airborne laser scanners. As these devices provide a higher point density and additional information like echo width or type of return, an accurate distinction of several object classes is possible. However, especially in dense urban areas correct labelling is a challenging task. Therefore, we incorporate context knowledge by using Conditional Random Fields. Typical object structures are learned in a training step and improve the results of the point-based classification process. We validate our approach with two real-world datasets and by a comparison to Support Vector Machines and Markov Random Fields.

Road detection in forested area gained its popularity in recent years, which has an important significance in the field of forest management, fire prevention and so on. The full-waveform LiDAR has unique advantages to forest road extraction, it can record the whole returned waveform while obtaining the information of 3D coordinates of targets points, by processing waveform data, the points density can be increased and the waveform features reflecting the properties of targets can be obtained. This paper deals with road detection in forested area by using full-waveform LiDAR data. Firstly, the returned waveforms were processed, including waveform denoising, waveform decomposition and features extraction. The extracted features were distance, intensity, Full Width at Half Maximum (FVVHM) and back scattering cross section. In order to decrease the differences between the features of same land cover type and further improve the effectiveness of road detection, this paper has made comprehensive correction on the extracted features. Secondly, four pseudo color feature images illustrating the values of extracted features were generated. Then, a region growing algorithm of image segmentation was carried out to retrieve the full roads from the feature images. The algorithm started from a pixel seed which was manually selected. Finally, four different segmentations were merged to obtain the road detection results. It showed that the forest road detection based on full-waveform data could achieve very good effect.

Accurate crown detection and delineation of dominant and subdominant trees are crucial for accurate inventorying of forests at the individual tree level. The state-of-the-art tree detection and crown delineation methods have good performance mostly with dominant trees, whereas exhibits a reduced accuracy when dealing with subdominant trees. In this paper, we propose a novel approach to accurately detect and delineate both the dominant and subdominant tree crowns in conifer-dominated multistoried forests using small footprint high-density airborne Light Detection and Ranging data. Here, 3-D candidate cloud segments delineated using a canopy height model segmentation technique are projected onto a novel 3-D space where both the dominant and subdominant tree crowns can be accurately detected and delineated. Tree crowns are detected using 2-D features derived from the projected data. The delineation of the crown is performed at the voxel level with the help of both the 2-D features and 3-D texture information derived from the cloud segment. The texture information is modeled by using 3-D Gray Level Co-occurrence Matrix. The performance evaluation was done on a set of six circular plots for which reference data are available. The high detection and delineation accuracies obtained over the state of the art prove the performance of the proposed method.

Tropical forests are essential ecosystems recognized for their carbon sequestration and biodiversity benefits. As the world undergoes a simultaneous data revolution and climate crisis, accurate data on the world’s forests are increasingly important. Completely novel in approach, this study proposes a methodology encompassing two bespoke deep learning models: (1) a single encoder, double decoder (SEDD) model to generate a species segmentation map, regularized by a distance map in training, and (2) an XGBoost model that estimates the diameter at breast height (DBH) based on tree species and crown measurements. These models operate sequentially: RGB images from the ReforesTree dataset undergo preprocessing before species identification, followed by tree crown detection using a fine-tuned DeepForest model. Post-processing applies the XGBoost model and custom allometric equations alongside standard carbon accounting formulas to generate final sequestration estimates. Unlike previous approaches that treat individual tree identification as an isolated task, this study directly integrates species-level identification into carbon accounting. Moreover, unlike traditional carbon estimation methods that rely on regional estimations via satellite imagery, this study leverages high-resolution, drone-captured RGB imagery, offering improved accuracy without sacrificing accessibility for resource-constrained regions. The model correctly identifies 67% of trees in the dataset, with accuracy rising to 84% for the two most common species. In terms of carbon accounting, this study achieves a relative error of just 2% compared to ground-truth carbon sequestration potential across the test set.

Benefiting from current unmanned air vehicle (UAV) and remote sensing techniques, the present study aims to estimate tree count (TC), tree height (TH), and tree crown cover area (TCCA) in a young Calabrian pine stand via canopy height model (CHM). Overlay images obtained using Quadcopter were used to generate two spatial three-dimensional (3D) cloud points in two different qualities. Point clouds were processed using R program in order to produce tree data using CHM. The sensitivity of CHM-based tree data was revealed using 318 tree measurements in 32 different sampling units. Estimation and measurement values were classified based on their structure from motion (SfM) quality and cover classes, and the statistical relationships among them were analyzed. Without any classification, R2 was calculated for TC, THMean, and TCCATotal estimations and field measurements. R2 values were calculated as 0.865, 0.778, and 0.869, respectively, for SfMHighest CHM, while they were calculated as 0.863, 0.736, and 0.843, respectively, for SfMMedium CHM. In addition, sensitivity and performance ranking in different groups were determined based on root mean square error (RMSE) and mean absolute percentage error (MAPE) values. A significant difference was observed among groups in terms of quality and cover for TH, while no significant differences were observed for TCCA. Therefore, it is possible to estimate the properties of SfM CHM-based young coniferous stand. It was understood that tree density, crown shape, and branching influenced the accuracy of the present study. The developed UAV (Drone)-SfM is a promising technique for further small-scale forestry studies.

Detection and delineation of individual trees is important for the estimation of forest information using Light Detection and Ranging (LiDAR) point clouds. However, over-segmentation of canopy height models (CHMs) is frequently caused by subcrowns and branches, whereas under-segmentation is caused by adjacent crowns, especially in deciduous forests. To eliminate such problems, an approach for delineating individual trees based on curvature attributes derived from LiDAR data is proposed. To detect accurate treetops for reducing over-segmentation, the dominant sizes of tree crowns are first determined and used to fit the dome-shaped tree crowns in CHMs, and then the minimum curvature values of fitted CHMs at different scales are calculated for treetop detection. For delineation of individual trees, tree features such as height variation and curvature are chosen to form a feature vector as a pixel-level logistic regression classifier. Then an ensemble classifier combining multiple logistic regression classifiers is trained to analyze the similarity between neighboring points to reduce under-segmentation. Finally, segment maps at different scales are integrated to generate the final segment map. Our experimental results on 2 test areas demonstrate that the proposed method has promising applications for eliminating over-segmentation and under-segmentation in deciduous forests.

Accurate and efficient delineation of individual tree crowns is crucial for urban forest inventories and ecosystem service assessments but is often limited by the manual selection of parameters for segmentation algorithms. This study investigates the impact of parameter optimization on the performance of four common raster-based segmentation algorithms—Watershed, Marker-Controlled Watershed, Dalponte, and Silva—for individual tree crown detection. Utilizing UAV-derived Canopy Height Models from the Lobachevsky University campus, we employed Random Search and Differential Evolution methods to systematically optimize algorithm parameters. Our findings reveal that relying on default or field-data-derived parameters significantly constrains segmentation accuracy. Parameter optimization led to substantial performance improvements across all algorithms. Notably, after optimization, the final performance (F-score values) for all algorithms converged to within a narrow range of 0.3, demonstrating that optimized simpler algorithms can achieve comparable performance to more complex ones. This research underscores that the key to accurate tree crown detection lies not solely in the choice of the segmentation method but critically in its preliminary tuning and optimization. The proposed optimization approaches enhance the accuracy and objectivity of urban tree crown delineation, providing a robust framework for improving urban forest inventories and enabling more effective application of remote sensing techniques in assessing urban ecosystem services.

The current full-waveform data at a single wavelength can mainly retrieve the geometric attributes of targets along the light path by detecting waveform components, resulting in the lack of spectral or color attribute information. This kind of device relies on a digital camera for acquiring the color information, however, which is inevitably limited by the lighting conditions and geometric registration errors. With the development of multispectral light detection and ranging (LiDAR) or even hyperspectral LiDAR that often utilize a supercontinuum laser source covering the whole visible light band, including red, green and blue bands, the simultaneous acquisition of color and spatial information becomes possible and makes passive imaging data no longer necessary. In this study, we propose a color restoration method for a full-waveform multispectral LiDAR (FWMSL) system. Additionally, we develop a multispectral lognormal function to fit the tailing echoes measured by FWMSL further accurately. Experimental data from our FWMSL system are used to evaluate the performance of the proposed method. The relative standard deviation, correlation coefficient (R2) and color difference ( Δ E ) metrics suggest that the color restoration for the full-waveform multispectral data is feasible.

Waveform-based point cloud classification in land-cover identification

The invasive phytopathogen Phytophthora ramorum has caused extensive infection of larch forest across areas of the UK, particularly in Southwest England, South Wales and Southwest Scotland. At present, landscape level assessment of the disease in these areas is conducted manually by tree health surveyors during helicopter surveys. Airborne laser scanning (ALS), also known as LiDAR, has previously been applied to the segmentation of larch tree crowns infected by P. ramorum infection and the detection of insect pests in coniferous tree species. This study evaluates metrics from high-density discrete ALS point clouds (24points/m2) and canopy height models (CHMs) to identify individual trees infected with P. ramorum and to discriminate between four disease severity categories (NI: not infected, 1: light, 2: moderate, 3: heavy). The metrics derived from ALS point clouds include canopy cover, skewness, and bicentiles (B60, B70, B80 and B90) calculated using both a static (1m) and a variable (50% of tree height) cut-off height. Significant differences are found between all disease severity categories, except in the case of healthy individuals (NI) and those in the early stages of infection (category 1). In addition, fragmentation metrics are shown to identify the increased patchiness and intra-crown height irregularities of CHMs associated with individual trees subject to heavy infection (category 3) of P. ramorum. Classifications using a k-nearest neighbour (k-NN) classifier and ALS point cloud metrics to classify disease presence/absence and severity yielded overall accuracies of 72% and 65% respectively. The results indicate that ALS can be used to identify individual tree crowns subject to moderate and heavy P. ramorum infection in larch forests. This information demonstrates the potential applications of ALS for the development of a targeted phytosanitary approach for the management of P. ramorum.

Tropical forests concentrate the largest diversity of species on the planet and play a key role in maintaining environmental processes. Due to the importance of those forests, there is growing interest in mapping their components and getting information at an individual tree level to conduct reliable satellite-based forest inventory for biomass and species distribution qualification. Individual tree crown information could be manually gathered from high resolution satellite images; however, to achieve this task at large-scale, an algorithm to identify and delineate each tree crown individually, with high accuracy, is a prerequisite. In this study, we propose the application of a convolutional neural network—Mask R-CNN algorithm—to perform the tree crown detection and delineation. The algorithm uses very high-resolution satellite images from tropical forests. The results obtained are promising—the R e c a l l , P r e c i s i o n , and F 1 score values obtained were were 0.81 , 0.91 , and 0.86 , respectively. In the study site, the total of tree crowns delineated was 59,062 . These results suggest that this algorithm can be used to assist the planning and conduction of forest inventories. As the algorithm is based on a Deep Learning approach, it can be systematically trained and used for other regions.