Automatic tree detection in varying urban environments using airborne LiDAR data

Abstract. One of the essential factors in analyzing urban environments is the presence of trees. Thus, the development of automatic or semi-automatic tree detection strategies is important for monitoring and providing data for municipal authorities’ planning efforts. In this context, we propose an automatic method for detecting trees using LiDAR data collected by airborne platforms. The proposed strategy uses the omnivariance as a key attribute, which is estimated locally from eigenvalues. Additionally, it utilizes an adaptive process to determine the optimal radius, followed by successive filtering based on the majority filter and mathematical morphology operators. The effectiveness of the proposed approach was evaluated on six study areas from two distinct datasets (Presidente Prudente/Brazil and Palmerston/New Zealand). In general, the results indicate a completeness rate around 99% and a correctness rate around 91%, resulting in an average Fscore of 95%. These findings suggest that the proposed approach has potential to detect trees in urban regions using airborne LiDAR data. Compared to related works, the proposed strategy tends to have a better result in terms of completeness.

Light detection and ranging (LIDAR) is a remote sensing technique that uses light, often using pulses from a laser to measure the distance to a target. Both terrestrial- and airborne-based LIDAR techniques have been frequently used to map landslides. Airborne LIDAR has the advantage of identifying large scarps of landslides covered by tree canopies and is widely applied in identifying historical and current active landslides hidden in forested areas. However, because landslides naturally have relatively small vertical surface deformation in the foot area, it is practically difficult to identify the margins of landslide foot area with the limited spatial resolution (few decimeters) of airborne LIDAR. Alternatively, ground-based LIDAR can achieve resolution of several centimeters and also has the advantages of being portable, repeatable, and less costly. Thus, ground-based LIDAR can be used to identify small deformations in landslide foot areas by differencing repeated terrestrial laser scanning surveys. This study demonstrates a method of identifying the superficial boundaries as well as the bottom boundary (sliding plane) of an active landslide in National Rainforest Park, Puerto Rico, USA, using the combination of ground-based and airborne LIDAR data. The method of combining terrestrial and airborne LIDAR data can be used to study landslides in other regions. This study also indicates that intensity and density of laser point clouds are remarkably useful in identifying superficial boundaries of landslides.

Data registration is a prerequisite in the integration of multi-platform LiDAR for various applications. A new approach is proposed for automatic registration of airborne LiDAR data and terrestrial LiDAR data, by synthetically using building boundaries and corners. Building boundaries and corners are firstly extracted from terrestrial and airborne LiDAR data with existing methods; a strategy using corner matching with boundary constraints is then proposed, which is the key idea of this approach. The building corners extracted from airborne LiDAR data and the building corners extracted from terrestrial LiDAR data are reliably matched during an iterative processing in a fully automatic way by taking the distances between airborne boundaries and terrestrial boundaries as constraints. The proposed approach provides both high reliability and high geometric accuracy in the final registration of airborne and terrestrial LiDAR data.

The leaf area density (LAD) plays an important role in describing the vertical canopy structure. Light Detection and Ranging (LiDAR) is an active remote-sensing technology that has already been applied to canopy measurements. In this paper, the vertical profiles of the LAD of three different size plots $(15 \mathrm{x} 15 \mathrm{m}, 10 \mathrm{x} 10\mathrm{m}$ , and $5 \mathrm{x} 5 \mathrm{m})$ of forest canopy were estimated and compared based on a voxel-based model using airborne LiDAR data and terrestrial LiDAR data, respectively. The LAD profiles retrieved from airborne LiDAR data were different from those obtained by terrestrial LiDAR data. The height of the maximum LAD estimated from the airborne LiDAR data was significantly higher than that estimated from the terrestrial LiDAR data. In addition, the middle and lower parts of the forest canopy LAD were underestimated by airborne LiDAR while the upper part of the forest canopy LAD were underestimated by terrestrial LiDAR compared with the actual canopy vertical structure.

A new hierarchical method for the automatic registration of airborne and vehicle light detection and ranging (LiDAR) data is proposed, using three-dimensional (3D) road networks and 3D building contours. Firstly, 3D road networks are extracted from airborne LiDAR data and then registered with vehicle trajectory lines. During the registration of airborne road networks and vehicle trajectory lines, a network matching rate is introduced for the determination of reliable transformation matrix. Then, the RIMM (reversed iterative mathematic morphological) method and a height value accumulation method are employed to extract 3D building contours from airborne and vehicle LiDAR data, respectively. The Rodriguez matrix and collinearity equation are used for the determination of conjugate building contours. Based on this, a rule is defined to determine reliable conjugate contours, which are finally used for the fine registration of airborne and vehicle LiDAR data. The experiments show that the coarse registration method with 3D road networks can contribute to a reliable initial registration result, and the fine registration using 3D building contours obtains a final registration result with high reliability and geometric accuracy.

Snow is among the most significant natural disturbance agents in Finland. In silviculture, maps of snow disturbance are needed to recognize severely disturbed forests where the risk of subsequential disturbances, such as insect outbreaks, is high. We investigated the potential of unitemporal airborne lidar (light detection and ranging) data and aerial images to detect snow disturbance at the tree level. We used 81 healthy and 128 snow-disturbed field plots established in a 63 800 ha study area in Eastern Finland. A subset of trees (n = 675) was accurately positioned in the field plots. We carried out individual tree detection (ITD) using airborne lidar data (5 p/m2), and a random forest classifier was used to classify healthy and broken trees. Tree features were extracted from a terrain elevation model, lidar data, and aerial imagery. We compared canopy height model–based (ITDCHM) and point cloud–based (ITDPC) ITD approaches. We explored random forest variable importance scores and evaluated the classification performance by an F1-score and its components (precision and recall). Performance was also evaluated at the plot level to investigate errors associated with the predicted number of broken trees. We achieved F1-scores of 0.66 and 0.85 for the tree- and plot-level classifications, respectively. The variable importance scores showed that elevation above sea level was the most important predictor variable followed by ITD-based features characterizing the neighborhood of trees. The ITDCHM slightly outperformed the ITDPC at the tree level, while they both underestimated the number of broken trees at the plot level. The proposed approach can be carried out alongside lidar-assisted operational forest management inventories provided that a set of positioned broken and healthy trees are available for model training. Since airborne lidar data often have a temporal resolution of several years for the same areas, future research should consider the utilization of other remotely sensed data sources to improve the temporal resolution.

This study aimed to develop simultaneous models with universal applicability for the estimation of the main factors of forest stands based on airborne LiDAR data and to provide a reference for standardizing the approach and evaluation indices of main forest factor modeling. Using airborne LiDAR and field survey data from 190 sample plots in spruce (Picea spp.), fir (Abies spp.), and spruce–fir mixed forests in Northeast China, the simultaneous models for estimating the main factors of forest stands were developed. To develop the models, the relationships between mean tree height, stand basal area, stand volume, and the main metrics of the LiDAR data and the correlations between eight quantitative factors of forest stands were considered, and the error-in-variable simultaneous equations approach was employed to fit the models. The results showed that the mean prediction errors (MPEs) of eight forest stand factors estimated by the simultaneous models were mostly within 5%, and only the MPE of the number of trees per hectare exceeded 5%. The mean percentage standard errors (MPSEs) of the estimates, including the mean diameter at the breast height (DBH), mean tree height, and mean dominant tree height, were within 15%; the MPSEs of the estimates of the stand basal area, volume, biomass, and carbon stock per hectare were within 25%; and only the MPSE of the estimated number of trees per hectare exceeded 30%. The coefficients of determination (R2) of the core prediction models for the volume, biomass, and carbon storage were all greater than 0.7. It can be concluded that estimating the main factors of forest stands based on the combination of LiDAR and field survey data is technically feasible, and the simultaneous models developed in this study for the estimation of the eight main stand factors of spruce–fir forests can meet the precision requirements of forest resource inventory, except for the number of trees, indicating that the models can be applied in practice.

After the operation of GPS/IMU direct geo-referencing, segmentation, filtering, classification of scattered point data and aerial triangulation on airborne LiDAR(Light Detection and Ranging) data, the accurate and high-resolution DEM of the study area in the west part of Zengcheng city, Guangdong, China was constructed. In addition, unmanned aerial vehicle (UAV) images were used for ground objects identification. Landslides occur frequently in summer in the city because of heavy rainfall. The LiDAR data (point cloud) and the mosaic images were then combined to produce the suitability distribution maps by considering Several factors, such as slope gradient, slope aspect, on-the-spot investigation data etc The maps can then be used to analyze the potential risk of landslides and assess the risk level around some buildings. The experiment results show that the method based on LiDAR data and UAV images can rapidly and accurately survey the terrain of the study area and also provides useful information for architectural design.

The fraction of photosynthetically active radiation (FPAR) is a key parameter in controlling mass and energy exchanges between vegetation and atmosphere. LiDAR data-derived canopy vertical structural information and hyperspectral image-derived vegetation spectral information can be considered as complementary for vegetation FPAR estimation. To the best of our knowledge, few studies have estimated vegetation FPAR by both LiDAR and hyperspectral data based on physical models. This letter aims to explore the ability of combining airborne LiDAR and hyperspectral data to retrieve maize FPAR based on the energy budget balance principle. First, canopy gap probability and openness were estimated from airborne LiDAR data. Next, canopy reflectance and soil background reflectance were retrieved from hyperspectral image. Then, we estimated maize FPAR based on the energy budget balance principle. Finally, model validity was assessed by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> data and results showed the physical FPAR estimation model estimated maize FPAR accurately. These results indicated that the physical method proposed in this letter was efficient and reliable to estimate maize FPAR, and FPAR retrieval can benefit from the complementary nature of LiDAR-captured canopy structural information and hyperspectral-detected vegetation spectral characteristics.

DEMs (digital elevation models) are very important in many fields, such as in Geomatics and in water conservation of mountainous areas. Geomorphic feature lines are necessary data for the topography interpolation and computation from DEMs. Instead of the parameter space, we propose a novel automatic extraction of Geomorphic feature lines in the feature space from discrete airborne LiDAR (Light detection and ranging) data by TVM (tensor voting method) developed originally for image data in this article. A tensor field for discrete airborne LiDAR points is first established and then utilizing the TVM, a new geometric feature metric of data, the line feature strength, was captured. A practical line growing method based on the local maximum line feature strength is proposed in the article. Compared with the conventional line growing that is based on a certain threshold, our line growing method is quite effective, for the extraction of primary and minor ridge and valley lines in mountainous areas, particularly. The method presented in this paper is fast and automated and can furnish operators with a wealth of detailed information about minor line features. This will enable the extraction of ridge and valley lines tailored to specific requirements. There is no doubt that the method developed here can be generalized to a large amount of LiDAR data.

Abstract. TanDEM-X mission has been acquiring InSAR data to produce high resolution global DEM with greater vertical accuracy since 2010. In this study, TanDEM-X CoSSC data were processed to produce DEMs at 6 m spatial resolution for two test areas of India. The generated DEMs were compared with DEMs available from airborne LiDAR, photogrammetry, SRTM and ICESat elevation point data. The first test site is in Bihar state of India with almost flat terrain and sparse vegetation cover and the second test site is around Godavari river in Andhra Pradesh (A.P.) state of India with flat to moderate hilly terrain. The quality of the DEMs in these two test sites has been specified in terms of most widely used accuracy measures viz. mean, standard deviation, skew and RMSE. The TanDEM-X DEM over Bihar test area gives 5.0 m RMSE by taking airborne LiDAR data as reference. With ICESat elevation data available at 9000 point locations, RMSE of 5.9 m is obtained. Similarly, TanDEM-X DEM for Godavari area was compared with high resolution aerial photogrammetric DEM and SRTM DEM and found RMSE of 5.3 m and 7.5 m respectively. When compared with ICESat elevation data at several point location and also the same point locations of photogrammetric DEM and SRTM, the RMS errors are 4.1 m, 3.5 m and 4.3 m respectively. DEMs were also compared for open-pit coal mining area where elevation changes from -147 m to 189 m. X- and Y-profiles of all DEMs were also compared to see their trend and differences.

Forest above-ground biomass (AGB) is an important indicator for understanding the global carbon cycle. It is hard to obtain a geographically and statistically representative AGB dataset, which is limited by unpredictable environmental conditions and high economical cost. A spatially explicit AGB reference map was produced by airborne LiDAR data and calibrated by field measurements. Three different sampling strategies were designed to sample the reference AGB, PALSAR backscatter, and texture variables. Two parametric and four nonparametric models were established and validated based on the sampled dataset. Results showed that random stratified sampling that used LiDAR-evaluated forest age as stratification knowledge performed the best in the AGB sampling. The addition of backscatter texture variables improved the parametric model performance by an R2 increase of 21% and a root-mean-square error (RMSE) decrease of 10 Mg ha−1. One of the four nonparametric models, namely, the random forest regression model, obtained comparable performance (R2=0.78, RMSE=14.95 Mg ha−1) to the parametric model. Higher estimation errors occurred in the forest stands with lower canopy cover or higher AGB levels. In conclusion, incorporating airborne LiDAR and PALSAR data was proven to be efficient in upscaling the AGB estimation to regional scale, which provides some guidance for future forest management over cold and arid areas.

Forest carbon stock is an important indicator reflecting a forest ecosystem’s structures and functions. Its spatial distribution is valuable for managing natural resources, protecting ecosystems and biodiversity, and further promoting sustainability, but accurately mapping the forest carbon stock distribution in a large area is a challenging task. This study selected Changting County, Fujian Province, as a case study to explore a method to map the forest carbon stock distribution using the integration of airborne Lidar, Sentinel-2, and ancillary data in 2022. The Bayesian hierarchical modeling approach was used to estimate the local forest carbon stock based on airborne Lidar data and field measurements, and then the random forest approach was used to develop a regional forest carbon stock estimation model based on the Sentinel-2 and ancillary data. The results indicated that the Lidar-based carbon stock distribution effectively provided sample plots with good spatial representativeness for modeling regional carbon stock with a coefficient of determination (R2) of 0.7 and root mean square error (RMSE) of 12.94 t/ha. The average carbon stocks were 48.55 t/ha, 55.51 t/ha, and 57.04 t/ha for Masson pine, Chinese fir, and broadleaf forests, respectively. The carbon stock in non-conservation regions was 15.2–16.1 t/ha higher than that in conservation regions. This study provides a promising method through the use of airborne Lidar data as a linkage between sample plots and Sentinel-2 data to map the regional carbon stock distribution in those subtropical regions where serious soil erosion has led to a relatively sparse forest canopy density. The results are valuable for local government to make scientific decisions for promoting ecosystem restoration due to water and soil erosion.

Regional-scale application of lidar: Variation in forest canopy structure across the southeastern US

This paper presents a new real-time method for the DSM (Digital Surface Model) generation from the airborne LiDAR (Light Detection And Ranging) data. In this method, the positions and elevation values of the laser footprints in each obtained scanning line are firstly transformed into a regular discrete array by the operations of linearization and gridding. After that, mathematical morphology is implemented to the array to obtain the digital models of the ground and objects on ground surface. In the airborne LiDAR scanning process, while the scanning lines are continually obtained, the digital models in the scanning lines are real-timely generated and displayed. Finally, when the scanning process is completed, the DSM of the whole acquired point cloud is attained by combining the digital models in all scanning lines. The proposed method is validated by using real airborne LiDAR data.