STATISTICAL OUTLIER DETECTION METHOD FOR AIRBORNE LIDAR DATA

Direct and automatic measurements of stem curve and volume using a high-resolution airborne laser scanning system

This dissertation addresses the problem of automated vector extraction from Airborne Laser Scanner (ALS) data in urban areas. The recent popularity of Geographic Information Systems (GIS) has stimulated research on automated object extraction in order to simplify the data acquisition and update process. By automatically generating GIS inputs and updates from a single data source, the cost of data acquisition and processing will be kept to a minimum. Compared with other remote sensing techniques, extraction of objects from ALS data is in its infancy. ALS sensor technology has evolved rapidly and now allows the acquisition of very dense point clouds in a short period of time. ALS data is unique in that it explicitly contains 3D information and is acquired from an active sensor. As such, there are several benefits that can be immediately realised by using an ALS only data approach; data acquisition will not be limited to daylight hours as with other sensors and accurate height information is contained in the data. This means that registration of different data sources is not required and as only one data source is used, acquisition costs are minimised. Apart from these facts, ALS data has some other unique properties that have not been utilised to their full potential. An ALS sensor can record both height and intensity information from multiple returns in a single swath. To date, all the available information has rarely been used. For example, the intensity of a laser return has regularly been dismissed as it was considered under-sampled and noisy. This data still contains usable information and until this information is used, the optimum object recognition results will not be achieved; thus the use of as much of this information as possible is a major focus of this thesis. As ALS is an explicit 3D data source, the early stages of development were primarily focused on topographic mapping of terrain in forested areas in order to generate Digital Terrain Models (DTMs). As sensor technology has improved, so has the achievable resolution of point clouds from ALS data, and methods to extract objects from stand-alone ALS data have emerged. Attempts have been made to create city models and maps from ALS data that included buildings, roads, trees and powerlines. Each of these spatial object types has unique attributes, which means that automatic map creation is not an easy task. For example, buildings in general are easily detected in ALS data but the building outline is not easily delineated. Another difficulty with building detection is the separation of buildings and bridges as they have many of the same properties as observed by an ALS system. Bridges can usually be found in a road network but road extraction techniques typically produce poor detection rates and often require existing data and / or user interaction in semi-automatic techniques. These simple examples highlight the complexity of automatically generating vectorised maps from ALS data or in fact any data source. In this thesis, new methods are presented for the automatic creation of vectorised maps from ALS data. A two-step processing paradigm is adopted for this purpose, namely the classification of the ALS data and the vectorisation of the classification results. A classification strategy is introduced that creates a hierarchy for object detection with respect to the ALS data itself. This approach develops an ontology between the spatial object classes and the ALS data. The hierarchical framework highlights the fact that one object might not be discernable within the data without considering another. New classification algorithms are then presented within this framework. Each algorithm attempts to exploit the attributes of the data that are consistent within the spatial object class being considered as described by the classification framework. New algorithms for the classification of roads, trees and powerlines are all introduced whilst an extension to existing building classification methods is presented. Once classification is complete, a vectorisation process specific to the task at hand can be employed to yield vectorised results. These developed vectorisation processes are new and include an algorithm that has been generalised to allow the vectorisation of thick lines in images by detecting the centreline, direction and width. The primary goal of this thesis is to present a framework of new algorithms that will allow automatic spatial object detection and vectorisation whilst providing results of an acceptable quality. The algorithms presented rely solely on ALS data and require minimal operator knowledge. Each algorithm has been designed to exploit the way in which the object exhibits itself in the data. The new algorithms are integrated into a software package called JTD (Join The Dots) that will facilitate the effective automatic processing of ALS data. The results of the new algorithms have been evaluated over four 2 x 2 km areas that have been sampled with medium resolution ALS data. The results for each area are displayed and analysed to show the applicability of the whole process in an exemplary way.

A critical component of the forest ecosystem, the understorey supports the vast majority of wildlife habitat and total ecosystem floristic diversity. Remote sensing data have been developed to provide information at different scales for surveys of forest ecosystems, but obtaining information about the understorey remains a challenge. As rapid and efficient tools for forest structure attribute estimation, Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) have attracted much attention. We examine the relationship between ALS and TLS data and detect changes in the forest understorey caused by forest-tending events in the study area. We conducted trials in five plots within a young Khasi pine (Pinus kesiya Royle ex Gord.) plantation in Yunnan province, China, before and after forest tending. We collected bi-temporal ALS data in this area and TLS data from 10 scanning stations. Canopy height profiles were retrieved from ALS and TLS data, and understorey material volume retrieved from filled TLS voxels volume agreed well with the understorey point clouds percentile distribution (PD) obtained from ALS data. The PD value for the understorey from ALS multiplied by the percentage of ALS return points in the overstorey had a stronger correlation (R2 = 0.90) with the TLS-derived understorey material volume than did the ALS PD value for the understorey only (R2 = 0.71). Taking the effect of the overstorey into consideration will improve evaluations of forest understorey parameters from ALS data. This study confirmed the potential of TLS as a validation tool to assess the accuracy of forest understorey material volume estimation at large scales using ALS data.

Modelling top height growth and site index using repeated laser scanning data

Airborne laser scanning (ALS) has demonstrated utility for forestry applications and has renewed interest in other forms of remotely sensed data, especially those that capture three-dimensional (3-D) forest characteristics. One such data source results from the advanced processing of high spatial resolution digital stereo imagery (DSI) to generate 3-D point clouds. From the derived point cloud, a digital surface model and forest vertical information with similarities to ALS can be generated. A key consideration is that when developing forestry related products such as a canopy height model (CHM), a high spatial resolution digital terrain model (DTM), typically from ALS, is required to normalize DSI elevations to heights above ground. In this paper we report on our investigations into the use of DSI-derived vertical information for capturing variations in forest structure and compare these results to those acquired using ALS. An ALS-derived DTM was used to provide the spatially detailed ground surface elevations to normalize DSI-derived heights. Similar metrics were calculated from the vertical information provided by both DSI and ALS. Comparisons revealed that ALS metrics provided a more detailed characterization of the canopy surface including canopy openings. Both DSI and ALS metrics had similar levels of correlation with forest structural attributes (e.g., height, volume, and biomass). DSI-based models predicted height, diameter, basal area, stem volume, and biomass with root mean square (RMS) accuracies of 11.2%, 21.7%, 23.6%, 24.5%, and 23.7%, respectively. The respective accuracies for the ALS-based predictions were 7.8%, 19.1%, 17.8%, 17.9%, and 17.5%. Change detection between ALS-derived CHM (time 1) and DSI-derived CHM (time 2) provided change estimates that demonstrated good agreement (r = 0.71) with two-date, ALS only, change outputs. For the single-layered, even-aged stands under investigation in this study, the DSI-derived vertical information is an appropriate and cost-effective data source for estimating and updating forest information. The accuracy of DSI information is based on a capability to measure the height of the upper canopy envelope with performance analogous to ALS. Forest attributes that are well captured and subsequently modeled from height metrics are best suited to estimation from DSI metrics, whereas ALS is more suitable for capturing stand density. Further investigation is required to better understand the performance of DSI-derived height products in more complex forest environments. Furthermore, the difference in variance captured between ALS and DSI-derived CHM also needs to be better understood in the context of change detection and inventory update considerations.

Unmanned aerial systems digital aerial photogrammetry (UAS-DAP) is an emerging technology that has the capacity to generate dense three-dimensional point clouds similar to airborne laser scanning (ALS). Over forested areas, these point clouds can be used to model forest attributes using the area-based approach (ABA). However, with DAP point clouds, canopy occlusion contributes to larger gaps in terrain registration from UAS-DAP compared to ALS point-clouds. Few studies have investigated the terrain modelling and forest inventory capacity of UAS-DAP over complex coniferous forests. In this study, we applied common terrain surface-interpolation routines using an established set of optimal UAS-DAP ground points and analysed how these routines influenced the prediction accuracy of forest stand attributes. Interpolation routines included inverse-distance weighted (IDW), natural neighbour (NATN), triangulated irregular network (TIN), and spline with tension (SPLT). The forest attributes of interest included mean tree height (H mean), Lorey’s height (H Lorey) and stem volume per hectare (V stem). Models were developed using metrics calculated from the vertical distribution of the UAS-DAP point cloud normalized by the different UAS-DAP terrain surfaces in addition to a reference surface generated from commercially provided ALS ground points. Results showed no significant difference between predictions derived from different terrain surfaces for all three dependent variables; however, the IDW method produced a distribution of wall-to-wall predictions most similar to those from the ALS-DEM. The best performing forest attribute models for H mean, H Lorey and V stem yielded mean RMSE values of 1.19 m (7.29%), 0.92 m (5.04%) and 54.55 m3 ha−1 (26.66%) respectively across the four UAS-DAP terrain surfaces generated. Model performance was higher yet comparable when using the ALS-DEM for point cloud height normalization with RMSE values of 0.73 m (4.43%), 0.59 m (3.24%) and 37.31 m3 ha−1 (18.24%).

Various methods have been developed to measure the physical presence of objects in a landscape with high positional accuracy. A new method that has been gaining popularity is Airborne Laser Scanning (ALS). ALS works by scanning a landscape (the collection of ground, buildings, vegetation, etc.,) in multiple passes. In each scan pulses of laser light are emitted from an airborne platform and their return time is measured, thus enabling the range from the point of emission to the landscape to be determined. The product of airborne laser scanning is a cloud of points in 3D space. ALS is capable of delivering very dense and accurate point clouds of a landscape in a relatively short time. However, in spite of the ability to measure objects with high positional accuracy, the automatic detection and interpretation of individual objects in landscapes remains a challenge. An example of just such a challenge is the classification of point clouds produced by ALS. The classification of ALS point clouds consists firstly in the labeling of points as either object or bare earth. The labeled object points are then further labeled as either building or vegetation. As a measurement technique ALS holds great promise and motivated by the desire to promote it, research has been conducted here to automate the detection of bare earth, buildings and vegetation in ALS point clouds.

In the paper the authors decided to present the capabilities of the application of the technology of mobile and airborne laser scanning both in surveying and modeling of railway infrastructure. First of all, they presented the possibility of using the laser scanning technology and in particular mobile laser scanning in terms of data collection and analysis for the tasks associated with the elaboration and diagnostics of railway infrastructure. For the mobile laser scanning the object of the study was the railway station in Slomniki, on a section of the railway line No. 8, and length of about 550 meters. The cloud of points together with photographic documentation was obtained using Riegl VMX - 450 system. Data from the airborne laser scanning included two different fragments of the railway line. The first one is located in Strzalkowo in the Wielkopolskie Voivodship (railway tracks along with the station building and the second one in Bochnia in the Malopolskie Voivodship. The section in Strzalkowo was about 470m and in Bochnia 302m. For example, the density of point cloud in Strzalkowo was 10 points/m2, while in Bochnia 17 points/m2.

Abstract. Monitoring of engineering structures is important for ensuring safety of operation. Traditional surveying methods have proven to be reliable; however, the advent of new point cloud technologies such as terrestrial laser scanning (TLS) and small unmanned aerial systems (sUAS) have provided an unprecedented wealth of data. Furthermore, simultaneous localization and mapping (SLAM) is now able to facilitate the collection of registered point clouds on the fly. SLAM is most successful when applied to indoor environments where the algorithm can identify primitives (points, planes, lines) for registration, but it can be problematic in outdoor settings where there is absence of constructed features. This work includes the collection of SLAM-based LiDAR data along a levee for the purpose of inspection and monitoring. Due to the outdoor setting and absence of man-made features, the resulting point cloud was considerably distorted due to erroneous drift in sensor orientation. A correction algorithm is proposed that relies on reference TLS point cloud data to remove drift distortions identified in the SLAM LiDAR. Results indicate an alignment between the corrected SLAM LiDAR and TLS data of around ±10cm, which is sufficient for general inspection and multi-epoch monitoring of levees. The algorithm is based on common points identified in the TLS and SLAM data and necessitates that the SLAM LiDAR is collected in individual, one-way lines to allow correction of distortions as a function of distance from the starting point. This approach increases the efficiency of LiDAR-based levee monitoring by reducing the time required to survey the levees.

Active remote sensing technologies, including airborne laser scanning (LIDAR), interferometric radar (InSAR) have the capability to provide accurate information relating to three-dimensional forest canopy structure over extensive areas of the landscape. Detailed mapping of forests height and terrain features is now possible through 3D remote sensing. Consequently, forest inventory parameters are evaluated through point cloud and normalized surface models. Practically, forest resource mapping is carried out through Airborne Laser Scanner (ALS). In the modern age, ALS achieve considerable popularity in alternative 3-D techniques along with the apace-borne and air-borne sensors. Point clouds are generated through radargrammetry, photogrammetry and interferometry. Photogrammetric point clouds are derived through airborne stereo imagery while radargrammetry and interferometry utilize very-high-resolution Synthetic Aperture Radar (SAR) data. Similarly, high resolution optical data is also used for forest inventory. ALS has the ability of mapping tree heights and terrain in even mixed forest scenarios, which is advantageous over SAR data or aerial imagery. To put it simply, in the forest scenarios, height of the canopy, single tree heights and canopy density can be mapped. Forest zoning, crown density, crown height and forest area has been mapped out using these different datasets in comparison. In this paper, first we review experiences of the use of ALS data in estimation of forest inventory parameters in Lubrecht forest in California, and we compare techniques to SAR and optical imagery. In addition, on the basis of our experiences, we aim to present new implications.

More than 50% of the national lands in Japan have been surveyed by airborne laser scanning (ALS) data with different point densities; and developing an effective approach to take full advantage of these ALS data for forest management has thus become an urgent topic of study. This study attempted to assess the utility of ALS data for individual tree detection and species classification in a mixed forest with a high canopy density. For comparison, two types of tree tops and tree crowns in the study area were delineated by the individual tree crown (ITC) approach using the green band of the orthophoto imagery and the digital canopy height model (DCHM) derived from the ALS data, respectively. Then, the two types of tree crowns were classified into four classes—Pinus densiflora (Pd), Chamaecyparis obtusa (Co), Larix kaempferi (Lk), and broadleaved trees (Bl)—by a crown-based classification approach using different combinations of the three orthophoto bands with intensity and slope maps as follows: RGB (red, green and blue); RGB and intensity (RGBI); RGB and slope (RGBS); and RGB, intensity and slope (RGBIS). Finally, the tree tops were annotated with species attributes from the two best-classified tree crown maps, and the number of different tree species in each compartment was counted for comparison with the field data. The results of our study suggest that the combination of RGBIS yielded greater classification accuracy than the other combinations. In the tree crown classifications delineated by the green band and DCHM data, the improvements in the overall accuracy compared to the RGB ranged from 5.7% for the RGBS to 9.0% for the RGBIS and from 8.3% for the RGBS to 11.8% for the RGBIS. The laser intensity and slope derived from the ALS data may be valuable sources of information for tree species classification, and in terms of distinguishing species for the detection of individual trees, the findings of this study demonstrate the advantages of using DCHM instead of optical data to delineate tree crowns. In conclusion, the synthesis of individual tree delineation using DCHM data and species classification using the RGBIS combination is recommended for interpreting forest resources in the study area. However, the usefulness of this approach must be verified in future studies through its application to other forests.

The United States Forest Service Forest Inventory and Analysis (FIA) Program provides a diverse selection of data used to assess the status of the nation’s forests using sample locations dispersed throughout the country. Airborne laser scanning (ALS) systems are capable of producing accurate measurements of individual tree dimensions and also possess the ability to characterize forest structure in three dimensions. This study investigates the potential of discrete return ALS data for modeling forest aboveground biomass (AGBM) and gross volume (gV) at FIA plot locations in the Malheur National Forest, eastern Oregon utilizing three analysis levels: (1) individual subplot (r = 7.32 m); (2) plot, comprising four clustered subplots; and (3) hectare plot (r = 56.42 m). A methodology for the creation of three point cloud-based airborne LiDAR metric sets is presented. Models for estimating AGBM and gV based on LiDAR-derived height metrics were built and validated utilizing FIA estimates of AGBM and gV derived using regional allometric equations. Simple linear regression models based on the plot-level analysis out performed subplot-level and hectare-level models, producing R2 values of 0.83 and 0.81 for AGBM and gV, utilizing mean height and the 90th height percentile as predictors, respectively. Similar results were found for multiple regression models, where plot-level analysis produced models with R2 values of 0.87 and 0.88 for AGBM and gV, utilizing multiple height percentile metrics as predictor variables. Results suggest that the current FIA plot design can be used with dense airborne LiDAR data to produce area-based estimates of AGBM and gV, and that the increased spatial scale of hectare plots may be inappropriate for modeling AGBM of gV unless exhaustive tree tallies are available. Overall, this study demonstrates that ALS data can be used to create models that describe the AGBM and gV of Pacific Northwest FIA plots and highlights the potential of estimates derived from ALS data to augment current FIA data collection procedures by providing a temporary intermediate estimation of AGBM and gV for plots with outdated field measurements.

Reliable information concerning stand volume is fundamental to making strategic decisions in sustainable forest management. A variety of remotely sensed data and different inventory methods have been used for the estimation of forest biometric parameters. Particularly, airborne laser scanning (ALS) point clouds are widely used for the estimation of stand volume and forest biomass using an area-based approach (ABA) framework. This method relies on the reference measurements of field plots with the necessary prerequisite of a precise co-registration between ground reference plots and the corresponding ALS samples. In this research, the allometric area-based approach (AABA) is proposed in the context of stand volume estimation of Scots pine (Pinus sylvestris L.) stands. The proposed method does not require detailed information about the coordinates of the field plots. We applied Polish National Forest Inventory data from 9400 circular field plots (400 m2) to develop a plot level stand volume allometric model using two independent variables: top height (TH) and relative spacing index (RSI). The model was developed using the multiple linear regression method with a log–log transformation of variables. The hypothesis was that, the field measurements of TH and RSI could be replaced with corresponding ALS-derived metrics. It was assumed that TH could be represented by the maximum height of the ALS point cloud, while RSI can be calculated based on the number of tree crowns delineated within the ALS-derived canopy height model. Performance of the developed AABA model was compared with the semi-empirical ABASE (with two predictors: TH and RSI) and empirical ABAE (several point cloud metrics as predictors). The models were validated at the plot level using 315 forest management inventory plots (400 m2) and at the stand level using the complete field measurements from 42 Scots pine dominated forest stands in the Milicz forest district (Poland). The AABA model showed a comparable accuracy to the traditional ABA models with relatively high accuracy at the plot (relative root mean square error (RMSE) = 22.8 per cent; R2 = 0.63) and stand levels (RMSE = 17.8 per cent, R2 = 0.65). The proposed novel approach reduces time- and cost-consuming field work required for the classic ABA method, without a significant reduction in the accuracy of stand volume estimations. The AABA is potentially applicable in the context of forest management inventory without the necessity for field measurements at local scale. The transportability of the approach to other species and more complex stands needs to be explored in future studies.

Combining airborne and terrestrial laser scanning for quantifying erosion and deposition by a debris flow event

ALS point cloud filtering involves the separation of observations representing the physical terrain surface from those representing terrain details. A digital terrain model (DTM) is created from a subset of points representing the ground surface. The accuracy of the generated DTM is influenced by several factors, including the survey method used, the accuracy of the source data, the applied DTM generation algorithm, and the survey conditions.This article proposes the use of a new estimation method in the filtering of point clouds obtained from airborne laser scanning (ALS), provisionally called Msplit-estimation. The application of Msplit-estimation in ALS data filtering requires the determination of the appropriate functional model for the surface, which will be used in the filtering of the set of points. A polynomial terrain surface model was selected for this purpose. Two methods of filtering using the Msplit method are presented. The first is based on the estimated parameters of the polynomial describing the surface (called the ‘quality’ approach in the article). The second method (provisionally called the ‘quantity’ method) is carried out in two stages. The first stage is point cloud filtering, which results in two subsets being created. One of these is the subset of points intended for DTM creation, while the other contains the remaining points. The second stage of the approach is the creation of a DTM from the first subset.Since the Msplit method has an analytical character, the ATIN method was selected to verify the correct operation of the method. The ATIN method is based on computational geometry and uses repeated Delaunay triangulation and statistical evaluation of the geometric parameters. Comparison of Msplit with a method based on different principles mitigates errors arising from similarly functioning methods belonging to the same group of filters. The choice of the ATIN method was also dictated by its established position among filtering algorithms. The method is well-known, documented, and verified and this ensures that filtering by this method provides a reliable result that can serve as a reference for comparison with the proposed new filtering method.The theoretical discussion presented in this article was verified with two practical examples. The results obtained from computation by the Msplit method with appropriate terrain models encourage more detailed theoretical and empirical tests of this method for the filtering and segmentation of ALS data-sets.