Airborne Laser Scanning Datasets Research Articles

Exploring Digital Surface Models from Nine Different Sensors for Forest Monitoring and Change Detection

Digital surface models (DSMs) derived from spaceborne and airborne sensors enable the monitoring of the vertical structures for forests in large areas. Nevertheless, due to the lack of an objective performance assessment for this task, it is difficult to select the most appropriate data source for DSM generation. In order to fill this gap, this paper performs change detection analysis including forest decrease and tree growth. The accuracy of the DSMs is evaluated by comparison with measured tree heights from inventory plots (field data). In addition, the DSMs are compared with LiDAR data to perform a pixel-wise quality assessment. DSMs from four different satellite stereo sensors (ALOS/PRISM, Cartosat-1, RapidEye and WorldView-2), one satellite InSAR sensor (TanDEM-X), two aerial stereo camera systems (HRSC and UltraCam) and two airborne laser scanning datasets with different point densities are adopted for the comparison. The case study is a complex central European temperate forest close to Traunstein in Bavaria, Germany. As a major experimental result, the quality of the DSM is found to be robust to variations in image resolution, especially when the forest density is high. The forest decrease results confirm that besides aerial photogrammetry data, very high resolution satellite data, such as WorldView-2, can deliver results with comparable quality as the ones derived from LiDAR, followed by TanDEM-X and Cartosat DSMs. The quality of the DSMs derived from ALOS and Rapid-Eye data is lower, but the main changes are still correctly highlighted. Moreover, the vertical tree growth and their relationship with tree height are analyzed. The major tree height in the study site is between 15 and 30 m and the periodic annual increments (PAIs) are in the range of 0.30–0.50 m.

Application of 3D triangulations of airborne laser scanning data to estimate boreal forest leaf area index

We propose 3D triangulations of airborne Laser Scanning (ALS) point clouds as a new approach to derive 3D canopy structures and to estimate forest canopy effective LAI (LAIe). Computational geometry and topological connectivity were employed to filter the triangulations to yield a quasi-optimal relationship with the field measured LAIe. The optimal filtering parameters were predicted based on ALS height metrics, emulating the production of maps of LAIe and canopy volume for large areas. The LAIe from triangulations was validated with field measured LAIe and compared with a reference LAIe calculated from ALS data using logarithmic model based on Beer’s law. Canopy transmittance was estimated using All Echo Cover Index (ACI), and the mean projection of unit foliage area (β) was obtained using no-intercept regression with field measured LAIe. We investigated the influence species and season on the triangulated LAIe and demonstrated the relationship between triangulated LAIe and canopy volume. Our data is from 115 forest plots located at the southern boreal forest area in Finland and for each plot three different ALS datasets were available to apply the triangulations. The triangulation approach was found applicable for both leaf-on and leaf-off datasets after initial calibration. Results showed the Root Mean Square Errors (RMSEs) between LAIe from triangulations and field measured values agreed the most using the highest pulse density data (RMSE=0.63, the coefficient of determination (R2)=0.53). Yet, the LAIe calculated using ACI-index agreed better with the field measured LAIe (RMSE=0.53 and R2=0.70). The best models to predict the optimal alpha value contained the ACI-index, which indicates that within-crown transmittance is accounted by the triangulation approach. The cover indices may be recommended for retrieving LAIe only, but for applications which require more sophisticated information on canopy shape and volume, such as radiative transfer models, the triangulation approach may be preferred.

Classification of Urban Point Clouds: A Robust Supervised Approach With Automatically Generating Training Data

To reduce the cost of manually annotating training data for parsing outdoor scenes, we propose a supervised approach with automatically generating training data for classifying 3-D point clouds of large-scale urban scenes. In this approach, the input point cloud is aggregated into point clusters, and the disjoint set union issue is combined with geometric attributes of each point cluster to obtain object segments. The prior knowledge among different classes is used to label the segments by using the decision-tree model. Then, the initialized training samples are generated automatically. The confidence estimation for the labeling is employed to filter the mislabeled training samples. With the generated training data, we train a random forest classifier to create the initial classification of the 3-D scene on the set of descriptors for each 3-D point. The classification results are further optimized by multilabel conditional Random Fields. Experimental results on five urban point clouds captured by different types of scanners (i.e., terrestrial laser scanning, vehicle laser scanning, and airborne laser scanning datasets) demonstrate that the proposed approach achieves a competitive classification performance.

Multitemporales, flugzeuggetragenes Laserscanning für die Forstwirtschaft

Multi-temporal airborne laserscanning data for forestry applications During the last decade airborne laserscanning (ALS) data has been established as a suitable data source for three-dimensional description of forests and for deriving forest parameters. For the study area Vorarlberg, Austria, the potential of multi-temporal ALS data (data from 2004 and 2011) was analyzed for deriving the amount of harvests in terms of area and stem volume. The data were used as well to assess the site index with the aid of the changes in top heights. The analyses have shown that harvested areas could be detected with a minimum mapping unit of 20 m2, which corresponds to harvested single trees from the dominant canopy layer. The average amount of harvested stem volume could be estimated with an overall accuracy of 96.4%. The derived changes in top heights clearly reflect the local growth conditions. For the study area Liechtenstein two ALS data sets (leaf-on and leaf-off) were used for the differentiation between coniferous and deciduous forests. Here, an overall accuracy of 92% could be achieved.

Monitoring gully change: A comparison of airborne and terrestrial laser scanning using a case study from Aratula, Queensland

Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) technologies capture spatially detailed estimates of surface topography and when collected multi-temporally can be used to assess geomorphic change. The sensitivity and repeatability of ALS measurements to characterise geomorphic change in topographically complex environments such as gullies; however, remains an area lacking quantitative research. In this study, we captured coincident ALS and TLS datasets to assess their ability and synergies to detect geomorphic change for a gully located in Aratula, southeast Queensland, Australia. We initially used the higher spatial density and ranging accuracy of TLS to provide an assessment of the Digital Elevation Models (DEM) derived from ALS within a gully environment. Results indicated mean residual errors of 0.13 and 0.09m along with standard deviation (SD) of residual errors of 0.20 and 0.16m using pixel sizes of 0.5 and 1.0m, respectively. The positive mean residual errors confirm that TLS data consistently detected deeper sections of the gully than ALS. We also compared the repeatability of ALS and TLS for characterising gully morphology. This indicated that the sensitivity to detect change using ALS is substantially lower than TLS, as expected, and that the ALS survey characteristics influence the ability to detect change. Notably, we found that using one ALS transect (mean density of 5 points/m2) as opposed to three transects increased the SD of residual error by approximately 30%. The supplied classification of ALS ground points was also demonstrated to misclassify gully features as non-ground, with minimum elevation filtering found to provide a more accurate DEM of the gully. The number and placement of terrestrial laser scans were also found to influence the derived DEMs. Furthermore, we applied change detection using two ALS data captures over a four year period and four TLS field surveys over an eight month period. This demonstrated that ALS can detect large scale erosional changes with head cutting of gully branches migrating approximately 10m upslope. In comparison, TLS captured smaller scale intra-annual erosional patterns largely undetectable by the ALS dataset with a large rainfall event coinciding with the highest volumetric change (net change>46m3). Overall, these findings reaffirm the importance of quantifying DEM errors and demonstrate that ALS is unlikely to detect subtle geomorphic changes (<0.45m) potentially missing significant sediment change. TLS was able to detect more subtle intra-annual changes but was limited in its spatial coverage. This suggests TLS and ALS surveys are complementary technologies and when used together can provide a more detailed understanding of gully processes at different temporal and spatial scales, provided the inherent errors are taken into account.

Unmanned aerial systems for precision forest inventory purposes: A review and case study

Unmanned Aerial Systems (UAS) are capable of improving the efficiency of acquisition and providing fine spatial scale data for sustainable resource management. In this paper we begin by describing differences between UAS airframes, their successes and limitations, and list contemporary research applications. UAS compatible sensor technologies are discussed, including passive and active sensors. Finally, we detail a case study where UAS updated an Enhanced Forest Inventory (EFI) for a study area in interior British Columbia. Airborne Laser Scanning (ALS) from 2013 and Digital Aerial Photogrammetric (DAP) point clouds acquired using a UAS from 2015 were used to estimate individual tree height and volume increments. A total of 246 trees were detected using Canopy Height Models (CHMs) with 70% of these trees being matched in the ALS and DAP data sets. Mean tree growth between 2013 and 2015 from the CHM and 95th percentile of height (P95) was estimated at 0.68 ± 0.05 and 0.50 m ± 0.05 m, respectively. Similarl...

High performance filtering for big datasets from Airborne Laser Scanning with CUDA technology

There are many studies on the problems of processing big datasets provided by Airborne Laser Scanning (ALS). The processing of point clouds is often executed in stages or on the fragments of the measurement set. Therefore, solutions that enable the processing of the entire cloud at the same time in a simple, fast, efficient way are the subject of many researches. In this paper, authors propose to use General-Purpose computation on Graphics Processing Units (GPGPUs) to process the big datasets obtained from ALS. GPGPU handles computation for computer graphics using GPUs (Graphic Processing Units). This study was based on programming model Compute Unified Device Architecture (CUDA), which facilitates the development of applications in GPUs. CUDA programming was used to carry out the filtration based on adaptive TIN model method in the initial stage of the processing of big ALS dataset. Results of the analysis showed that GPGPU can be used for the filtration of ALS point clouds and significantly speeds up calculations for big dataset.

Key structural features of Boreal forests may be detected directly using L-moments from airborne lidar data

This article introduces a novel methodology for automated classification of forest areas from airborne laser scanning (ALS) datasets based on two direct and simple rules: L-coefficient of variation Lcv=0.5 and L-skewness Lskew=0, thresholds based on descriptors of the mathematical properties of ALS height distributions. We observed that, while Lcv>0.5 may represent forests with large tree size inequality, Lskew>0 can be an indicator for areas lacking a closed dominant canopy. Lcv=0.5 discriminated forests with trees of approximately equal sizes (even tree size classes) from those with large tree size inequality (uneven tree size classes) with kappa κ=0.48 and overall accuracy OA=92.4%, while Lskew=0 segregated oligophotic and euphotic zones with κ=0.56 and OA=84.6%. We showed that a supervised classification could only marginally improve some of these accuracy results. The rule-based approach presents a simple method for detecting structural properties key to tree competition and potential for natural regeneration. The study was carried out with low-density datasets from the national program on ALS surveying of Finland, which shows potential for replication with the ALS datasets typically acquired at nation-wide scales. Since the presented method was based on deductive mathematical rules for describing distributions, it stands out from inductive supervised and unsupervised classification methods which are more commonly used in remote sensing. Therefore, it presents an opportunity for deducing physical relations which could partly eliminate the need for supporting ALS applications with field plot data for training and modelling, at least in Boreal forest ecosystems.

A Semantic Modelling Framework-Based Method for Building Reconstruction from Point Clouds

Over the past few years, there has been an increasing need for semantic information in automatic city modelling. However, due to the complexity of building structure, the semantic reconstruction of buildings is still a challenging task because it is difficult to extract architectural rules and semantic information from the data. To improve the insufficiencies, we present a semantic modelling framework-based approach for automated building reconstruction using the semantic information extracted from point clouds or images. In this approach, a semantic modelling framework is designed to describe and generate the building model, and a workflow is established for extracting the semantic information of buildings from an unorganized point cloud and converting the semantic information into the semantic modelling framework. The technical feasibility of our method is validated using three airborne laser scanning datasets, and the results are compared with other related works comprehensively, which indicate that our approach can simplify the reconstruction process from a point cloud and generate 3D building models with high accuracy and rich semantic information.

WIDE-AREA MAPPING OF FOREST WITH NATIONAL AIRBORNE LASER SCANNING AND FIELD INVENTORY DATASETS

Abstract. Airborne laser scanning (ALS) remote sensing data are now available for entire countries such as Switzerland. Methods for the estimation of forest parameters from ALS have been intensively investigated in the past years. However, the implementation of a forest mapping workflow based on available data at a regional level still remains challenging. A case study was implemented in the Canton of Valais (Switzerland). The national ALS dataset and field data of the Swiss National Forest Inventory were used to calibrate estimation models for mean and maximum height, basal area, stem density, mean diameter and stem volume. When stratification was performed based on ALS acquisition settings and geographical criteria, satisfactory prediction models were obtained for volume (R2 = 0.61 with a root mean square error of 47 %) and basal area (respectively 0.51 and 45 %) while height variables had an error lower than 19%. This case study shows that the use of nationwide ALS and field datasets for forest resources mapping is cost efficient, but additional investigations are required to handle the limitations of the input data and optimize the accuracy.

Classification of forest development stages from national low-density lidar datasets: a comparison of machine learning methods

&lt;p&gt;Los métodos de estimación por áreas son ya habituales para el uso de escaneo láser aerotransportado (ALS) en la predicción de atributos forestales descritos por variables continuas: biomasa, volumen, densidad, etc. No obstante, apenas se ha prestado atención a los métodos de clasificación por aprendizaje automático, que por otro lado son frecuentes en diversos campos de la teledetección, como la identificación de coberturas del suelo por sensores multiespectrales. En este artículo hemos centrado la atención en métodos estadísticos destinados a predecir variables discretas, para obtener clasificaciones supervisadas de datos ALS. Varios métodos han sido revisados: clasificadores de análisis discriminante (DA) –máxima verosimilitud, elipsoide de volumen mínimo y Bayesiano ingenuo–, máquinas de vector soporte (SVM), redes neuronales artificiales (ANN), selvas aleatorias (RF), y vecino más próximo (NN). La comparación se ha realizado en el contexto de una clasificación en las etapas de desarrollo (ED) utilizadas en la gestión forestal de los bosques de Finlandia, utilizando para ello datos de baja densidad de su vuelo nacional. RF y NN produjeron las matrices de error más equilibradas, con predicciones por validación cruzada no sesgadas para todas las EDs. Aunque SVM y ANN mostraron las mayores precisiones, obtuvieron resultados muy dispares entre las distintas EDs, siendo óptimas sólo para algunas concretas. DA obtuvo los peores resultados, y sólo se observó que pudieran ser beneficiosas en la predicción de rodales en la etapa de diseminado. Nuestros resultados muestran que, además de la confirmada capacidad del ALS para predecir variables de cuantificación de las masas forestales, también existe potencial en la clasificación de variables categóricas en general, y tipologías forestales en particular. En conclusión, consideramos que estas metodologías se pueden también adaptar a las clases naturales de edad y tipos estructurales que sean relevantes en ecosistemas mediterráneos, abriendo un abanico de posibilidades para investigación en ALS, con mucho potencial.&lt;/p&gt;

An automated method to register airborne and terrestrial laser scanning point clouds

Laser scanning techniques have been widely used to capture three-dimensional (3D) point clouds of various scenes (e.g. urban scenes). In particular, airborne laser scanning (ALS) and mobile laser scanning (MLS), terrestrial laser scanning (TLS) are effective to capture point clouds from top or side view. Registering the complimentary point clouds captured by ALS and MLS/TLS provides an aligned data source for many purposes (e.g. 3D reconstruction). Among these MLS can be directly geo-referenced to ALS according to the equipped position systems. For small scanning areas or dense building areas, TLS is used instead of MLS. However, registering ALS and TLS datasets suffers from poor automation and robustness because of few overlapping areas and sparse corresponding geometric features. A robust method for the registration of TLS and ALS datasets is proposed, which has four key steps. (1) extracts building outlines from TLS and ALS data sets independently; (2) obtains the potential matching pairs of outlines according to the geometric constraints between building outlines; (3) constructs the Laplacian matrices of the extracted building outlines to model the topology between the geometric features; (4) calculates the correlation coefficients of the extracted geometric features by decomposing the Laplacian matrices into the spectral space, providing correspondences between the extracted features for coarse registration. Finally, the multi-line adjustment strategy is employed for the fine registration. The robustness and accuracy of the proposed method are verified using field data, demonstrating a reliable and stable solution to accurately register ALS and TLS datasets.

Integration of LiDAR data for the assessment of activity in diachronic landslides: a case study in the Betic Cordillera (Spain)

In the procedures to minimize diachronic landslides, data on their temporal evolution and destructive capacities are necessary. For that purpose, remote-detection techniques proved to be highly useful for quantifying the ongoing change in the relief, as well as in comparisons between digital terrain models achieved by Light Detection and Ranging. The methodology presented in this paper includes the supervised merging and comparison of sequential scans, acquired within nearly annual intervals from an irregular terrain, which improves the quality of the results highlighting ground changes. This approach is based on the processing of digital terrain models from point clouds acquired by Terrestrial Laser Scanning to quantify and interpret the landslide displacements. In parallel, it is supported by Global Navigation Satellite Systems, the use of artificial targets and a refined data processing to minimize the uncertainty and improve the precision of the results. This is applied to a large translational slide affecting phyllite rocks in a IV-V degree of weathering settled on the southern slope of Sierra Nevada (south-eastern Spain). During the monitoring period (2008–2010), the slide remained inactive until 2009, followed by a reactivation with displacements in the range −1.80 to 1.20 m along the period 2009–2010, where negative values are downwards from the reference model (2009). The accumulated relative standard deviation between data sets was on the order of 7.5 cm, whereas the threshold to determine a terrain displacement (also avoiding changes due to erosion-accumulation processes) was of 10 cm. When applying this methodology to Airborne Laser Scanning datasets for the years 2008 and 2010, covering zones hidden to the line of sight of the terrestrial technique, a reactivation with similar deformation pattern was found useful to validate the findings, although the detail of changes and quantitative results did not match exactly due to the different accuracy and resolution of both techniques. The reactivation of the slide coincided with a period of intense rains, pointing to this as the triggering factor, with a precipitation threshold of roughly 1000 mm in a period of 4 months, only reached on one occasion throughout in the historical record.

Inventory estimates of stem volume using nine sampling methods in thinned Pinus radiata stands, New Zealand

Simulation is an established tool for examining the efficacy of forestry sampling designs yet there is little empirical information on the effect that spatial layout of a sample has on stand-level inventory of managed, even-aged stands. This simulation study examines the performance of nine different sampling methods in terms of bias and reliability. Data sets, derived from five stands of radiata pine, consisted of census lists of every stem, including the location of each stem, breast-height diameter over-bark (DBH), height and derived volume. In four small stands, stems had been geo-located using ground-based methods, whereas the data for a larger stand were derived from an Airborne Laser Scanning data set. Nine sampling methods (random, stand-boundary, quasi-random, Zigzag transects, grid-based plots of four sizes and single-point) were simulated and applied repeatedly to each stand, and the bias and reliability of the estimate of mean stem volume calculated. Sampling the stand boundary produced a biased estimate, averaging a 12% over-estimate for the four stands aged 22 years or more. The other sampling methods generally showed little bias with most estimates within ±0.5% of the population mean, although the Single-point method was considerably less accurate. The Stand-boundary, Grid-plot (>0.02 ha), and Single-point methods produced unreliable confidence intervals. Most sampling methods showed little bias and good reliability when analysed as simple random samples. Sampling plots in the range of 0.02 to 0.04 ha, located systematically on a grid with random orientation and origin, produced some of the most unbiased and reliable estimates. However, the Zigzag method may be appropriate in small stands as it produces little bias, good reliability and is likely to be operationally efficient.

Classification of vegetation in an open landscape using full-waveform airborne laser scanner data

Airborne laser scanning (ALS) is increasingly being used for the mapping of vegetation, although the focus so far has been on woody vegetation, and ALS data have only rarely been used for the classification of grassland vegetation. In this study, we classified the vegetation of an open alkali landscape, characterized by two Natura 2000 habitat types: Pannonic salt steppes and salt marshes and Pannonic loess steppic grasslands. We generated 18 variables from an ALS dataset collected in the growing (leaf-on) season. Elevation is a key factor determining the patterns of vegetation types in the landscape, and hence 3 additional variables were based on a digital terrain model (DTM) generated from an ALS dataset collected in the dormant (leaf-off) season. We classified the vegetation into 24 classes based on these 21 variables, at a pixel size of 1 m. Two groups of variables with and without the DTM-based variables were used in a Random Forest classifier, to estimate the influence of elevation, on the accuracy of the classification. The resulting classes at Level 4, based on associations, were aggregated at three levels — Level 3 (11 classes), Level 2 (8 classes) and Level 1 (5 classes) — based on species pool, site conditions and structure, and the accuracies were assessed. The classes were also aggregated based on Natura 2000 habitat types to assess the accuracy of the classification, and its usefulness for the monitoring of habitat quality. The vegetation could be classified into dry grasslands, wetlands, weeds, woody species and man-made features, at Level 1, with an accuracy of 0.79 (Cohen’s kappa coefficient, κ). The accuracies at Levels 2–4 and the classification based on the Natura 2000 habitat types were κ: 0.76, 0.61, 0.51 and 0.69, respectively. Levels 1 and 2 provide suitable information for nature conservationists and land managers, while Levels 3 and 4 are especially useful for ecologists, geologists and soil scientists as they provide high resolution data on species distribution, vegetation patterns, soil properties and on their correlations. Including the DTM-based variables increased the accuracy (κ) from 0.73 to 0.79 for Level 1. These findings show that the structural and spectral attributes of ALS echoes can be used for the classification of open landscapes, especially those where vegetation is influenced by elevation, such as coastal salt marshes, sand dunes, karst or alluvial areas; in these cases, ALS has a distinct advantage over other remotely sensed data.

Application of the Msplit method for filtering airborne laser scanning data-sets to estimate digital terrain models

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.

Comparison of field and airborne laser scanning based crown cover estimates across land cover types in Kenya

Abstract. Tree crown cover (CC) provides means for the continuous land cover characterization of complex tropical landscapes with multiple land uses and variable degrees of degradation. It is also a key parameter in the international forest definitions that are basis for monitoring global forest cover changes. Recently, airborne laser scanning (ALS) has emerged as a practical method for accurate CC mapping, but ALS derived CC estimates have rarely been assessed with field data in the tropics. Here, our objective was to compare the various field and ALS based CC estimates across multiple land cover types in the Taita Hills, Kenya. The field data was measured from a total of 178 sample plots (0.1 ha) in 2013 and 2014. The most accurate field measurement method, line intersect sampling using Cajanus tube, was used in 37 plots. Other methods included CC estimate based on the tree inventory data (144 plots), crown relascope (43 plots) and hemispherical photography (30 plots). Three ALS data sets, including two scanners and flying heights, were acquired concurrently with the field data collection. According to the results, the first echo cover index (FCI) from ALS data had good agreement with the most accurate field based CC estimates (RMSD 7.1% and 2.7% depending on the area and scan). The agreement with other field based methods was considerably worse. Furthermore, we observed that ALS cover indices were robust between the different scans in the overlapping area. In conclusion, our results suggest that ALS provides a reliable method for continuous CC mapping across tropical land cover types although dense shrub layer and tree-like herbaceous plants can cause overestimation of CC.

Optimization of legacy lidar data sets for measuring near‐field earthquake displacements

AbstractAirborne lidar (light detection and ranging) topography, acquired before and after an earthquake, can provide an estimate of the coseismic surface displacement field by differencing the preevent and postevent lidar point clouds. However, estimated displacements can be contaminated by the presence of large systematic errors in either of the point clouds. We present three‐dimensional displacements obtained by differencing airborne lidar point clouds collected before and after the El Mayor–Cucapah earthquake, a Mw 7.2 earthquake that occurred in 2010. The original surface displacement estimates contained large, periodic artifacts caused by systematic errors in the preevent lidar data. Reprocessing the preevent data, detailed herein, removed a majority of these systematic errors that were largely due to misalignment between the scanning mirror and the outgoing laser beam. The methodology presented can be applied to other legacy airborne laser scanning data sets in order to improve change estimates from temporally spaced lidar acquisitions.

Monitoring cultural heritage by comparing DEMs derived from historical aerial photographs and airborne laser scanning

Abstract This paper presents results from a study where identification and documentation of landscape changes using a combination of historical aerial photographs and newer airborne laser scanning (ALS) data were examined. The study was based on remotely sensed data covering a Norwegian protected cultural environment consisting of several pebble-stone built grave cairns. Georeferenced digital elevation models (DEMs) were generated using historical air photographs from the years 1968, 1979 and 1999. In addition ALS datasets from 2008 and 2010 were used in the study. Altogether seven difference models were generated as a result of conducting automated change detections between the different epochs. In this way detailed information about changes that occurred in the landscape and to individual monuments for almost the last 50 years was obtained. Further, the incidents that caused the identified changes were interpreted based on documents from archives. Using this approach the dynamic character of the studied protected site was proven. The study demonstrates the importance of historical aerial photographs as a valuable source that makes possible retrospective monitoring of past landscape changes on a detailed scale.

Calibrated Full-Waveform Airborne Laser Scanning for 3D Object Segmentation

Segmentation of urban features is considered a major research challenge in the fields of photogrammetry and remote sensing. However, the dense datasets now readily available through airborne laser scanning (ALS) offer increased potential for 3D object segmentation. Such potential is further augmented by the availability of full-waveform (FWF) ALS data. FWF ALS has demonstrated enhanced performance in segmentation and classification through the additional physical observables which can be provided alongside standard geometric information. However, use of FWF information is not recommended without prior radiometric calibration, taking into account all parameters affecting the backscatter energy. This paper reports the implementation of a radiometric calibration workflow for FWF ALS data, and demonstrates how the resultant FWF information can be used to improve segmentation of an urban area. The developed segmentation algorithm presents a novel approach which uses the calibrated backscatter cross-section as a weighting function to estimate the segmentation similarity measure. The normal vector and the local Euclidian distance are used as criteria to segment the point clouds through a region growing approach. The paper demonstrates the potential to enhance 3D object segmentation in urban areas by integrating the FWF physical backscattered energy alongside geometric information. The method is demonstrated through application to an interest area sampled from a relatively dense FWF ALS dataset. The results are assessed through comparison to those delivered from utilising only geometric information. Validation against a manual segmentation demonstrates a successful automatic implementation, achieving a segmentation accuracy of 82%, and out-performs a purely geometric approach.