Airborne LiDAR Scanning Research Articles

Non-uniform dune development in the presence of standalone beach buildings

Shoreward sand transport and dune development are increasingly influenced by the urbanization of beach-dune systems in the Netherlands. Three topographic datasets, on various spatio-temporal scales, are used to study the effect of standalone buildings on long term local dune development. On the smallest scale, terrestrial laser scans are used to study the geomorphological effects of two sea containers on the beach. On the intermediate scale, the geomorphological effects of a beach pavilion on the local dune development are studied with a 2-year topographic dataset of (bi) monthly permanent laser scans. Finally, 15 yearly airborne lidar scans of the beach-dune system in Noordwijk are used to evaluate the effect of multiple beach pavilions on dune growth variations. The small-scale experiment shows that horseshoe-shaped deposition patterns developed on the leeside of the containers. These depositions follow daily wind changes and leave deposits corresponding to the residual wind direction over the whole measuring period. Similar patterns are found around the larger beach pavilion, but anthropogenic activities like bulldozing and beach shaping make the determination of the effect on dune development harder to discern. Evaluation of the longer-term dataset reveals large variations in dune height and volume around beach pavilions. Dune height/volume increases vary between 1 and 8 m in height and 0–200 m3 in volume. A variability analysis shows that the length scale of alongshore variability in dune height/volume of urbanized dunes can be 10 times smaller than for natural dunes. For about half the beach pavilions, variations in dune height and volume are significantly correlated to the location of beach pavilions but correlation to particular beach pavilion properties is yet inconclusive.

Unveiling the role of forests in landslide occurrence, recurrence and recovery

Abstract Rain‐triggered landslides cause significant socioeconomic loss and long‐term ecological impact, disrupting topsoil and seed banks and creating unfavourable conditions for vegetation establishment. Although hazard management effectively reduces landslide risk, most management plans primarily focus on the societal impacts (loss of property and human lives), while environmental impacts related to landslide occurrence and recurrence are often overlooked. The role of different vegetation types in mitigation strategies remains uncertain due to the time and spatial constraints of traditional field study methods. Therefore, the role of vegetation in landslide restoration should be reconsidered. In this study, we investigated post‐landslide vegetation recovery patterns, analysed the duration of vegetation recovery, identified factors influencing these patterns, examined the role of forests in occurrence and recurrence probability and explored the implications of these findings for each element within an integrated management framework for landslide restoration. We utilised long‐term landslide inventory data (1924–2018) covering the entire city of Hong Kong (~1100 km2) and combined it with structural data derived from airborne LiDAR scanning in 2020 to capture landslide recovery. Our findings revealed significant differences in recovery trajectories between the main bodies and foot areas of scars, with the former taking approximately 46 years to recover and the latter recovering in about 38 years. Scar age, average wind speed, elevation and proximity to the forest were identified as key factors influencing structural recovery on scars, while the proximity to forest also regulated recovery rate of scars. We observed decreased landslide occurrence in forested areas, with recurrent landslides primarily appearing on older, unrecovered scars on barren hillsides. Synthesis and applications: Our research highlights the dynamic patterns, recovery time and drivers of vegetation recovery on landslide scars; and the higher probability of landslide occurrence in non‐forest areas and increased recurrence on bare, low vegetated scars. This information provides valuable insights for informing restoration efforts and managing landslide‐prone areas. Such knowledge is crucial for developing effective mitigation strategies and enhancing the resilience of vulnerable ecosystems facing climate change and increased frequency of extreme weather events.

METAMORPHISM OF ALS POINT DATA FOR MULTITUDE APPLICATION

Abstract. Technologically assisted decision-making in urban planning and governance is significant to envisage Sustainable Development Goal (SDG) 11, of developing sustainable cities and communities. In the current millennium, planners and decision-makers require knowledge-rich virtual models for managing the man-made and natural resources in the city. To effectively utilize the technological advancement for sustainable urban development, there is a need for expeditious entail towards accurate urban resource mapping, development of flexible monitoring and information extraction framework, and enticing visualization. Airborne LiDAR Scanning (ALS) is capable of producing very precise 3D geometric data over expansive urban areas in a timely and economically efficient manner. However, it is challenging to derive viable outcomes from the unstructured and voluminous point cloud. This work proposes an intermediary metamorphosed point cloud storage framework to enhance the utility of point clouds for multiple pragmatic applications. The proposed methodological approach transforms the unstructured, massive point cloud into an ontologically stored urban object collection to utilize the large-scale urban point cloud in decision-making. Further, the paper demonstrates the direct applicability of the metamorphosed point cloud storage framework for two specific applications related to sustainable urban development. Experiments carried out using the proposed framework on DALES benchmark dataset show promising results.

A Kriging Method for the Estimation of ALS Point-Cloud Accuracy without Ground Truth

Airborne LiDAR scanning is a promising approach to providing high-resolution products that are appropriate for different applications, such as flood management. However, the vertical accuracy of airborne LiDAR point clouds is not constant and varies in space. Having a better knowledge of their accuracy will assist decision makers in more accurately estimating the damage caused by flood. Data producers often report the total estimation of errors by means of comparison with a ground truth. However, the reliability of such an approach depends on various factors including the sample size, accessibility to ground truth, distribution, and a large enough diversity of ground truth, which comes at a cost and is somewhat unfeasible in the larger scale. Therefore, the main objective of this article is to propose a method that could provide a local estimation of error without any third-party datasets. In this regard, we take advantage of geostatistical ordinary kriging as an alternative accuracy estimator. The challenge of considering constant variation across the space leads us to propose a non-stationary ordinary kriging model that results in the local estimation of elevation accuracy. The proposed method is compared with global ordinary kriging and a ground truth, and the results indicate that our method provides more reliable error values. These errors are lower in urban and semi-urban areas, especially in farmland and residential areas, but larger in forests, due to the lower density of points and the larger terrain variations.

Combining multi-temporal airborne LiDAR and Sentinel-2 multispectral data for assessment of disturbances and recovery of mangrove forests

Disturbances such as tropical cyclones and insect pests in mangroves can cause defoliation, tree mortality, and other changes in ecosystem processes. Understanding the resistance and resilience of mangroves to disturbance is critical to developing strategies for conservation. However, most studies apply multi-temporal optical data which have limited power to detect structural changes, especially for forests with complex architectures. We combined multispectral Sentinel-2 (S2) images and airborne LiDAR Scanning (ALS) datasets to assemble a comprehensive view of the effects of two disturbance events (a moth pest and a super-typhoon) on mangroves in Mai Po, Hong Kong. A series of normalized difference vegetation index (NDVI) estimates derived from S2 data indicated changes in greenness before and after the moth pest and typhoon events. An object-based stratification method was applied with ALS data to separate the overstory and understory to distinguish stratum changes. The results showed that moth larvae were more likely to encroach leafy mangroves of Avicennia marina. Double-layered and single-layered short mangroves have better resistance to typhoons than younger tall mangroves without understory beneath. NDVI recovered rapidly after three to six months post-disturbance but significant changes in canopy structures were found from the ALS data. Canopy gaps increased both in size and quantity in mature overstory areas, likely benefitting the growth of the understories beneath. Finally, the understory area grew resulting in a transition from single-layered to double-layered structures. The combination of multi-temporal LiDAR and multispectral data used here highlights the power of complementary remote sensing products in documenting mangrove ecosystem processes.

Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering.

Light detection and ranging (LiDAR) data can provide 3D structural information of objects and are ideal for extracting individual tree parameters, and individual tree segmentation (ITS) is a vital step for this purpose. Various ITS methods have been emerging from airborne LiDAR scanning (ALS) or unmanned aerial vehicle LiDAR scanning (ULS) data. Here, we propose a new individual tree segmentation method, which couples the classical and efficient watershed algorithm (WS) and the newly developed connection center evolution (CCE) clustering algorithm in pattern recognition. The CCE is first used in ITS and comprehensively optimized by considering tree structure and point cloud characteristics. Firstly, the amount of data is greatly reduced by mean shift voxelization. Then, the optimal clustering scale is automatically determined by the shapes in the projection of three different directions. We select five forest plots in Saihanba, China and 14 public plots in Alpine region, Europe with ULS or ALS point cloud densities from 11 to 3295 pts/m2. Eleven ITS methods were used for comparison. The accuracy of tree top detection and tree height extraction is estimated by five and two metrics, respectively. The results show that the matching rate (R match) of tree tops is up to 0.92, the coefficient of determination (R 2) of tree height estimation is up to .94, and the minimum root mean square error (RMSE) is 0.6 m. Our method outperforms the other methods especially in the broadleaf forests plot on slopes, where the five evaluation metrics for tree top detection outperformed the other algorithms by at least 11% on average. Our ITS method is both robust and efficient and has the potential to be used especially in coniferous forests to extract the structural parameters of individual trees for forest management, carbon stock estimation, and habitat mapping.

A Feature-Level Point Cloud Fusion Method for Timber Volume of Forest Stands Estimation

Accurate diameter at breast height (DBH) and tree height (H) information can be acquired through terrestrial laser scanning (TLS) and airborne LiDAR scanner (ALS) point cloud, respectively. To utilize these two features simultaneously but avoid the difficulties of point cloud fusion, such as technical complexity and time-consuming and laborious efforts, a feature-level point cloud fusion method (FFATTe) is proposed in this paper. Firstly, the TLS and ALS point cloud data in a plot are georeferenced by differential global navigation and positioning system (DGNSS) technology. Secondly, point cloud processing and feature extraction are performed for the georeferenced TLS and ALS to form feature datasets, respectively. Thirdly, the feature-level fusion of LiDAR data from different data sources is realized through spatial join according to the tree trunk location obtained from TLS and ALS, that is, the tally can be implemented at a plot. Finally, the individual tree parameters are optimized based on the tally results and fed into the binary volume model to estimate the total volume (TVS) in a large area (whole study area). The results show that the georeferenced ALS and TLS point cloud data using DGNSS RTK/PPK technology can achieve coarse registration (mean distance ≈ 40 cm), which meets the accuracy requirements for feature-level point cloud fusion. By feature-level fusion of the two point cloud data, the tally can be achieved quickly and accurately in the plot. The proposed FFATTe method achieves high accuracy (with error of 3.09%) due to its advantages of combining different LiDAR data from different sources in a simple way, and it has strong operability when acquiring TVS over large areas.

Information extraction system for urban planning and governance using LiDAR based 3D repository

ABSTRACT In rapidly expanding cities, an accurate land-cover information system can assist in making decisions for sustainable urban planning and governance. Point cloud procured using Airborne LiDAR Scanning (ALS) can be harnessed by creating an adaptable urban semantic information layout with querying capability. We propose a novel methodology that isolates spatial objects and integrates them with attribute information to enable object-based spatio-semantic queries. Experiments were carried out to demonstrate proposed methodology based on queries of different complexities. Results indicate that proposed approach is scalable, flexible, and hence opens a wide application perspective in urban planning, and sustainable smart city development.

Forest Structure Measurement in Tropical Rainforest using Laser Scanning Technology

Numerous measurements can be made to extract the forest structure. Forest structure is one of the main aspects of forest management. The precise estimation of forest structure is vital for some forestry applications. Thus, this study presents a novel non-destructive approach for the measurement of forest structure using laser scanning technologies of airborne LiDAR and Terrestrial Laser Scanning (TLS). The study area was located at the forest campus of the Forest Research Institute of Malaysia (FRIM), Kepong, Selangor. The elements of forest structure that were measured: were canopy height; plant density and basal area. The field survey was conducted over 3 plots of 25 m radius circular shape with a total of 60 trees with diameter at breast height (DBH) of 10 cm and above. The tree canopy height was estimated based on canopy height model (CHM) of LiDAR data. For plant density, it was estimated based on crown delineation created from CHM. While, for TLS data, the extraction of individual trees was done using Cyclone algorithm. The forest structure measurement obtained from laser scanning technologies is proven to be reliable with the root mean square error (RMSE) of 5.415, t-test of 3.011, and p-value of 0.004 for canopy height. For basal area, the mean of RMSE, t-test and p-value was 0.22589, 0.620 and 0.324 for the overall 3 plots, respectively. The result obtained for plant density was one tree per meter². The final outputs were presented as the map of CHM, plant density and basal area map. In conclusion, laser scanning measurement improves and provides a precise technique for forest structure measurement.

Suitable LiDAR Platform for Measuring the 3D Structure of Mangrove Forests

Investigating the three-dimensional structure of mangrove forests is critical for their conservation and restoration. However, mangrove forests are difficult to survey in the field, and their 3D structure is poorly understood. Light detection and ranging (LiDAR) is considered an accurate and dependable method of measuring the 3D structure of mangrove forests. This study aimed to find a suitable LiDAR platform for obtaining attributes such as breast height diameter and canopy area, as well as for measuring a digital terrain model (DTM), the base data for hydrological analysis. A mangrove forest near the mouth of the Oura River in Aza-Oura, Nago City, Okinawa Prefecture, Japan, was studied. We used data from terrestrial LiDAR scanning “TLS” and unmanned aerial vehicle (UAV) LiDAR scanning “ULS” as well as data merged from TLS and ULS “Merge”. By interpolating point clouds of the ground surface, DTMs of 5 cm × 5 cm were created. DTMs obtained from ULS could not reproduce the heaps of Thalassina anomala or forest floor microtopography compared with those obtained from TLS. Considering that ULS had a few point clouds in the forest, automatic trunk identification could not be used to segment trees. TLS could segment trees by automatically identifying trunks, but the number of trees identified roughly doubled that of the visual identification results. The number of tree crowns identified using TLS and ULS was approximately one quarter of those identified visually, and many of them were larger in area than the visually traced crowns. The accuracy of tree segmentation using the canopy height model (CHM) was low. The number of canopy trees identified using Merge produced the best results, accounting for 61% of the visual identification results. Results of tree segmentation by CHM suggest that combining TLS and ULS measurements may improve tree canopy identification. Although ULS is a promising new technology, its applications are clearly limited, at least in mangrove forests such as the Oura River, where Bruguiera gymnorhiza is dominant. Depending on the application, using different LiDAR platforms, such as airborne LiDAR scanning, UAV LiDAR scanning, and TLS, is important. Merging 3D point clouds acquired by different platforms, as proposed in this study, is an important option in this case.

Biomass Estimation of Subtropical Arboreal Forest at Single Tree Scale Based on Feature Fusion of Airborne LiDAR Data and Aerial Images

Low-cost UAV aerial photogrammetry and airborne lidar scanning have been widely used in forest biomass survey and mapping. However, the feature dimension after multisource remote sensing fusion is too high and screening key features to achieve feature dimension reduction is of great significance for improving the accuracy and efficiency of biomass estimation. In this study, UAV image and point cloud data were combined to estimate and map the biomass of subtropical forests. Firstly, a total of 173 dimensions of visible light vegetation index, texture, point cloud height, intensity, density, canopy, and topographic features were extracted as variables. Secondly, the Kendall Rank correlation coefficient and permutation importance (PI) index were used to identify the key features of biomass estimation among different tree species. The random forest (RF) model and XGBoost model finally were used to compare the accuracy of biomass estimation with different variable sets. The experimental results showed that the point cloud height, canopy features, and topographic factors were identified as the key parameters of the biomass estimate, which had a significant influence on the biomass estimation of the three dominant tree species in the study area. In addition, the differences in the importance of characteristics among the tree species were discussed. The fusion features combined with the PI index screening and RF model achieved the best estimation accuracy, the R2 of 0.7356, 0.8578, and 0.6823 were obtained for the three tree species, respectively.

Extracting Highway Cross Slopes From Airborne and Mobile LiDAR Point Clouds

Adequate water pavement surface drainage on highways is crucial in minimizing the potential of hydroplaning. Highway cross slope has a significant effect of draining water laterally from the pavement surface. Currently, field surveying techniques and other manual measurement methods are used to collect cross slope data on a limited basis in most states, despite these methods being labor intensive and exposing personnel to traffic. Furthermore, field surveying techniques cannot provide continuous data and can only be conducted at sample-based locations. This study conducted a technical evaluation of the effectiveness of airborne LiDAR (light detection and ranging) scanning and mobile terrestrial LiDAR scanning systems in measuring pavement cross slopes. Cross slope data were extracted from the LiDAR point cloud at five selected test sections using two different methods: (i) end-to-end method using elevations only from the pavement edge lines to generate the cross slope; and (ii) 0.2 ft interval point extraction along the cross-section and using a fitted linear regression line as the basis for the cross slope. Cross slopes were also measured at test section locations using conventional surveying methods and compared with LiDAR-extracted cross slopes. Results demonstrate that LiDAR methods are reliable for collecting accurate pavement cross slopes and should be considered for the purpose of cross slope verification on a braod scale such as statewide to address cross slope and pavement surface drainage issues proactively.

Forest mapping and monitoring in Africa using Sentinel-2 data and deep learning

We propose and investigate a method for creating large scale forest height maps at 10 m resolution from Sentinel-2 data using deep neural networks. In addition, we demonstrate how clear-cutting events can be detected in a time series of the resulting forest height maps. The network architecture is a convolutional neural network based on the U-Net architecture. The 13 Sentinel-2 spectral bands are resampled to 10 m spatial resolution and input to the U-Net, which outputs a map with per-pixel forest height estimates. The network is trained with ground truth data acquired from airborne lidar scanning surveys from three different geographical regions. They cover different types of forests: lowland tropical rainforest in the Democratic Republic of Congo, Miombo woodlands (dry forest) in Liwale, Tanzania, and submontane tropical rainforest in Amani, Tanzania. We demonstrate that the trained network generalizes to new geographical regions within the African continent with a mean average error of 4.6 m. This is on-par with a previously published method’s ability to generalize to new geographical regions within the same country. Clear-cutting events are detected using a t-test. The null-hypothesis of the t-test is that the forest height has not changed after any given point in time in the forest height time-series.

Mapping tree mortality rate in a tropical moist forest using multi-temporal LiDAR

Several studies have shown an increase in tree mortality in intact tropical forests in recent decades. However, most studies are based on networks of field plots whose representativeness is debated. We examine the potential of repeated Airborne LiDAR Scanning data to map forest structure change over large areas with high spatial resolution and to detect tree mortality patterns at landscape level.

Strategies for 3D Modelling of Buildings from Airborne Laser Scanner and Photogrammetric Data Based on Free-Form and Model-Driven Methods: The Case Study of the Old Town Centre of Bordeaux (France)

This paper presents a data-driven free-form modelling method dedicated to the parametric modelling of buildings with complex shapes located in particularly valuable Old Town Centres, using Airborne LiDAR Scanning (ALS) data and aerial imagery. The method aims to reconstruct and preserve the input point cloud based on the relative density of the data. The method is based on geometric operations, iterative transformations between point clouds, meshes, and shape identification. The method was applied on a few buildings located in the Old Town Centre of Bordeaux (France). The 3D model produced shows a mean distance to the point cloud of 0.058 m and a standard deviation of 0.664 m. In addition, the incidence of building footprint segmentation techniques in automatic and interactive model-driven modelling was investigated and, in order to identify the best approach, six different segmentation methods were tested. The segmentation was performed based on the footprints derived from Digital Surface Model (DSM), point cloud, nadir images, and OpenStreetMap (OSM). The comparison between the models shows that the segmentation that produces the most accurate and precise model is the interactive segmentation based on nadir images. This research also shows that in modelling complex structures, the model-driven method can achieve high levels of accuracy by including an interactive editing phase in building 3D models.

Tree Extraction from Airborne Laser Scanning Data in Urban Areas

Tree information in urban areas plays a significant role in many fields of study, such as ecology and environmental management. Airborne LiDAR scanning (ALS) excels at the fast and efficient acquisition of spatial information in urban-scale areas. Tree extraction from ALS data is an essential part of tree structural studies. Current raster-based methods that use canopy height models (CHMs) suffer from the loss of 3D structure information, whereas the existing point-based methods are non-robust in complex environments. Aiming at making full use of the canopy’s 3D structure information that is provided by point cloud data, and ensuring the method’s suitability in complex scenes, this paper proposes a new point-based method for tree extraction that is based on 3D morphological features. Considering the elevation deviations of the ALS data, we propose a neighborhood search method to filter out the ground and flat-roof points. A coarse extraction method, combining planar projection with a point density-filtering algorithm is applied to filter out distracting objects, such as utility poles and cars. After that, a Euclidean cluster extraction (ECE) algorithm is used as an optimization strategy for coarse extraction. In order to verify the robustness and accuracy of the method, airborne LiDAR data from Zhangye, Gansu, China and unmanned aircraft vehicle (UAV) LiDAR data from Xinyang, Henan, China were tested in this study. The experimental results demonstrated that our method was suitable for extracting trees in complex urban scenes with either high or low point densities. The extraction accuracy obtained for the airborne LiDAR data and UAV LiDAR data were 99.4% and 99.2%, respectively. In addition, a further study found that the aberrant vertical structure of the artificially pruned canopy was the main cause of the error. Our method achieved desirable results in different scenes, with only one adjustable parameter, making it an easy-to-use method for urban area studies.

Leveraging Airborne LiDAR Data and Gradient Boosting for Mapping the Density of Different Sized Trees

Information on the distribution of trees with different diameters at breast height (DBH) is needed to inform management programs aimed at achieving conservation objectives in high stem density forest stands. This article explored the feasibility of mapping the density of trees with different DBHs using airborne LiDAR data. Experiments were conducted in the largest river red gum forest in the world, located in the southeast of Australia. Field measured data on trees with different DBHs were used for the supervised learning of airborne LiDAR scans with a pulse density of 5.92 pulses/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Specifically, the hyperparameters of gradient boosting and random forest regressors were tuned to produce a viable solution for mapping the density of different sized trees at the plot level. Our results indicate that the total tree density (DBH > 0 cm; height > 1.37 m) can be mapped using airborne LiDAR data with the coefficient of determination R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of up to 0.67, with gradient boosting outperforming random forest. However, the accuracy of mapping the density of saplings (DBH ≤ 10 cm), small trees (10 cm <; DBH ≤ 50 cm), and large trees (DBH > 50 cm) differed with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.65, 0.60, and 0.42, respectively. These results show that the airborne LiDAR data can provide a viable solution for mapping the density of small trees (DBH ≤ 50 cm) over large areas and has the potential for mapping the density of large trees (DBH > 50 cm).

Stand Characterization of Eucalyptus spp. Plantations in Uruguay Using Airborne Lidar Scanner Technology

Airborne lidar scanner (ALS) technology is used in a variety of applications, including forestry. ALS has enormous potential for the estimation of relevant biometric parameters in forest plantations. This study investigates the use of an object-oriented semi-automated segmentation algorithm for stands delineation, based on modeling ALS data, in plantations of Eucalyptus grandis and E. dunnii in Uruguay. The results show that non-parametric methods delivered more accurate and less biased results for total volume (TV) with R2 0.93, RMSE 20.04 m3 h−1 for E. grandis and R2 0.93, RMSE 18.43 m3 h−1 for E.dunnii; and above ground biomass (AGB) with R2 0.95, RMSE 70.2 kg h−1 for E. grandis and R2 0.96, RMSE: 71.2 Kg h−1 for E. dunnii. Parametric methods performed better for dominant height (Ho) with R2 0.98, RMSE 0.67 m and R2: 0.96, RMSE: 0.8 m for E. grandis and E. dunnii, respectively. The most informative ALS metrics for the estimation of AGB and TV were metrics related to the elevation in parametric models (Elev.70 and Elev.75), while for the non-parametric models (k-NN) they were Elev.75 and canopy density. For Ho, the ALS metrics selected were also related to elevation both in the parametric (Elev.90 and Elev.99) and random forest models (Elev.max and Elev.75). The segmentation methodology proposed here matched closely the segments delineated by human operators, and provides a low-cost, cost-effective, easy to apply and update model aimed at generating AGB or TV maps for harvest tasks, based on rasters derived from ALS metrics. The present research shows the capacity of ALS metrics to improve extensive strategic inventories; validating and promoting the adoption of ALS technology for inventory forest stands of Eucalyptus spp. in Uruguay.

Robust processing of airborne laser scans to plant area density profiles

Abstract. We present a new algorithm for the estimation of the plant area density (PAD) profiles and plant area index (PAI) for forested areas based on data from airborne lidar. The new element in the algorithm is to scale and average returned lidar intensities for each lidar pulse, whereas other methods do not use the intensity information at all, use only average intensity values, or do not scale the intensity information, which can cause problems for heterogeneous vegetation. We compare the performance of the new algorithm to three previously published algorithms over two contrasting types of forest: a boreal coniferous forest with a relatively open structure and a dense beech forest. For the beech forest site, both summer (full-leaf) and winter (bare-tree) scans are analyzed, thereby testing the algorithm over a wide spectrum of PAIs. Whereas all tested algorithms give qualitatively similar results, absolute differences are large (up to 400 % for the average PAI at one site). A comparison with ground-based estimates shows that the new algorithm performs well for the tested sites. Specific weak points regarding the estimation of the PAD from airborne lidar data are addressed including the influence of ground reflections and the effect of small-scale heterogeneity, and we show how the effect of these points is reduced in the new algorithm, by combining benefits of earlier algorithms. We further show that low-resolution gridding of the PAD will lead to a negative bias in the resulting estimate according to Jensen's inequality for convex functions and that the severity of this bias is method dependent. As a result, the PAI magnitude as well as heterogeneity scales should be carefully considered when setting the resolution for the PAD gridding of airborne lidar scans.

AUTOMATIC BUILDING CHANGE DETECTION USING MULTI-TEMPORAL AIRBORNE LIDAR DATA

Abstract. The automatic detection of building changes is an essential process for urban area monitoring, urban planning, and database update. In this context, 3D information derived from multi-temporal airborne LiDAR scanning is one effective alternative. Despite several works in the literature, the separation of change areas in building and non-building remains a challenge. In this sense, it is proposed a new method for building change detection, having as the main contribution the use of height entropy concept to identify the building change areas. The experiments were performed considering multi-temporal airborne LiDAR data from 2012 and 2014, both with average density around 5 points/m2. Qualitative and quantitative analyses indicate that the proposed method is robust in building change detection, having the potential to identify small changes (larger than 20 m2). In general, the change detection method presented average completeness and correctness around 97% and 71%, respectively.