Terrestrial laserscanning of tidal flats—a case study in Jiangsu Province, China

Potential and limitations of LiDAR altimetry in archaeological survey. Copper Age and Bronze Age settlements in southern Iberia

Digital elevation model (DEM) has been frequently used for the reduction and management of flood risk. Various classification methods have been developed to extract DEM from point clouds. However, the accuracy and computational efficiency need to be improved. The objectives of this study were as follows: (1) to determine the suitability of a new method to produce DEM from unmanned aerial vehicle (UAV) and light detection and ranging (LiDAR) data, using a raw point cloud classification and ground point filtering based on deep learning and neural networks (NN); (2) to test the convenience of rebalancing datasets for point cloud classification; (3) to evaluate the effect of the land cover class on the algorithm performance and the elevation accuracy; and (4) to assess the usability of the LiDAR and UAV structure from motion (SfM) DEM in flood risk mapping. In this paper, a new method of raw point cloud classification and ground point filtering based on deep learning using NN is proposed and tested on LiDAR and UAV data. The NN was trained on approximately 6 million points from which local and global geometric features and intensity data were extracted. Pixel-by-pixel accuracy assessment and visual inspection confirmed that filtering point clouds based on deep learning using NN is an appropriate technique for ground classification and producing DEM, as for the test and validation areas, both ground and non-ground classes achieved high recall (>0.70) and high precision values (>0.85), which showed that the two classes were well handled by the model. The type of method used for balancing the original dataset did not have a significant influence in the algorithm accuracy, and it was suggested not to use any of them unless the distribution of the generated and real data set will remain the same. Furthermore, the comparisons between true data and LiDAR and a UAV structure from motion (UAV SfM) point clouds were analyzed, as well as the derived DEM. The root mean square error (RMSE) and the mean average error (MAE) of the DEM were 0.25 m and 0.05 m, respectively, for LiDAR data, and 0.59 m and –0.28 m, respectively, for UAV data. For all land cover classes, the UAV DEM overestimated the elevation, whereas the LIDAR DEM underestimated it. The accuracy was not significantly different in the LiDAR DEM for the different vegetation classes, while for the UAV DEM, the RMSE increased with the height of the vegetation class. The comparison of the inundation areas derived from true LiDAR and UAV data for different water levels showed that in all cases, the largest differences were obtained for the lowest water level tested, while they performed best for very high water levels. Overall, the approach presented in this work produced DEM from LiDAR and UAV data with the required accuracy for flood mapping according to European Flood Directive standards. Although LiDAR is the recommended technology for point cloud acquisition, a suitable alternative is also UAV SfM in hilly areas.

Calibration and validation of aboveground biomass (AGB) (AGB) products retrieved from satellite-borne sensors require accurate AGB estimates across hectare scales (1 to 100ha). Recent studies recommend making use of non-destructive terrestrial laser scanning (TLS) based techniques for individual tree AGB estimation that provide unbiased AGB predictors. However, applying these techniques across large sites and landscapes remains logistically challenging. Unoccupied aerial vehicle laser scanning (UAV-LS) has the potential to address this through the collection of high density point clouds across many hectares, but estimation of individual tree AGB based on these data has been challenging so far, especially in dense tropical canopies. In this study, we investigated how TLS and UAV-LS can be used for this purpose by testing different modelling strategies with data availability and modelling framework requirements. The study included data from four forested sites across three biomes: temperate, wet tropical, and tropical savanna. At each site, coincident TLS and UAV-LS campaigns were conducted. Diameter at breast height (DBH) and tree height were estimated from TLS point clouds. Individual tree AGB was estimated for ≥170 trees per site based on TLS tree point clouds and quantitative structure modelling (QSM), and treated as the best available, non-destructive estimate of AGB in the absence of direct, destructive measurements. Individual trees were automatically segmented from the UAV-LS point clouds using a shortest-path algorithm on the full 3D point cloud. Predictions were evaluated in terms of individual tree root mean square error (RMSE) and population bias, the latter being the absolute difference between total tree sample population TLS QSM estimated AGB and predicted AGB. The application of global allometric scaling models (ASM) at local scale and across data modalities, i.e., field-inventory and light detection and ranging LiDAR metrics, resulted in individual tree prediction errors in the range of reported studies, but relatively high population bias. The use of adjustment factors should be considered to translate between data modalities. When calibrating local models, DBH was confirmed as a strong predictor of AGB, and useful when scaling AGB estimates with field inventories. The combination of UAV-LS derived tree metrics with non-parametric modelling generally produced high individual tree RMSE, but very low population bias of ≤5% across sites starting from 55 training samples. UAV-LS has the potential to scale AGB estimates across hectares with reduced fieldwork time. Overall, this study contributes to the exploitation of TLS and UAV-LS for hectare scale, non-destructive AGB estimation relevant for the calibration and validation of space-borne missions targeting AGB estimation.

The roughness spectrum (i.e., the power spectral density) is a derivative of digital terrain models (DTMs) that is used as a surface roughness descriptor in many geomorphological and physical models. Although light detection and ranging (LiDAR) has become one of the main data sources for DTM calculation, it is still unknown how roughness spectra are affected when calculated from different LiDAR point clouds, or when they are processed differently. In this paper, we used three different LiDAR point clouds of a 1 m × 10 m gravel plot to derive and analyze the roughness spectra from the interpolated DTMs. The LiDAR point clouds were acquired using terrestrial laser scanning (TLS), and laser scanning from both an unmanned aerial vehicle (ULS) and an airplane (ALS). The corresponding roughness spectra are derived first as ensemble averaged periodograms and then the spectral differences are analyzed with a dB threshold that is based on the 95% confidence intervals of the periodograms. The aim is to determine scales (spatial wavelengths) over which the analyzed spectra can be used interchangeably. The results show that one TLS scan can measure the roughness spectra for wavelengths larger than 1 cm (i.e., two times its footprint size) and up to 10 m, with spectral differences less than 0.65 dB. For the same dB threshold, the ULS and TLS spectra can be used interchangeably for wavelengths larger than about 1.2 dm (i.e., five times the ULS footprint size). However, the interpolation parameters should be optimized to make the ULS spectrum more accurate at wavelengths smaller than 1 m. The plot size was, however, too small to draw particular conclusions about ALS spectra. These results show that novel ULS data has a high potential to replace TLS for roughness spectrum calculation in many applications.

Light detection and ranging (LIDAR) technology enables rapid and accurate data collection for many applications such as engineering design, geoscience studies, and cultural heritage. LIDAR systems have become important resources to produce highly detailed digital terrain models (DTMs). Hence, professionals in many disciplines need to understand how to process, handle, and analyze such data sets. For this article, “LIDAR” refers generically to airborne, mobile, and/or terrestrial LIDAR systems unless explicitly stated. Terrestrial laser scanning (TLS) refers exclusively to ground-based LIDAR systems. Several recent articles have addressed surveying education. Soler (2010) describes the current limitations in and needs of surveying education, particularly within engineering, and discusses the need for more rigorous education to allow engineers and surveyors to analyze modern geospatial data. Schultz (2007) comments on academic barriers creating low enrollments in geomatics. Yu et al. (2010) asserts that academia and industry have been slow to address the challenge of effectively utilizing new, emerging technologies in surveying, such as LIDAR. As such, fewer students have been entering into geospatial fields, and those that do may not receive enough exposure to these new technologies and/or a strong enough theoretical background to properly prepare for careers in the rapidly evolving geomatics field. Overall, there is a lack of LIDAR course offerings, and most available education is typically performed through short training sessions, conference workshops, and so on. Recently, Evergreen Valley College in San Jose, California, was funded by the National Science Foundation (NSF) to develop a TLS course in its land-surveying curriculum. (Yu et al. 2010). This course discussed important topics of TLS including background information, field operations, and postprocessing techniques. This course provides a model course to help fill this knowledge gap, with resources available to help other instructors develop similar courses. In addition to a lack of LIDAR courses, development of computer-programming skills within surveying and civil engineering programs are generally limited, whereas students are typically exposed to few (if any) courses in basic programming. Scripting and programming are becoming increasingly important in geospatial technology and data processing in which multiple data sources need to be fused together efficiently. Programming skills can allow students and professionals to spend less time performing rudimentary data processing and conversion tasks and more time in analysis. Furthermore, use of a geographic information system (GIS) by surveyors enables easier data integration, documentation, and transfer. To address these shortcomings, a digital terrain modeling course was recently created at Oregon State University (OSU) that provides students with the opportunity to collect and process LIDAR data, develop programming skills, and utilize GIS to analyze LIDAR datasets. OSU is currently expanding its Accreditation Board for Engineering and Technology–Engineering Accreditation Commission (ABET-EAC) geomatics program within the School of Civil and Construction Engineering. This expansion involves additional new faculty, graduate students, research projects, and course offerings. The new courses expose students to the latest technologies in three-dimensional (3D) geospatial data. Currently, there are approximately 1,000 students within OSU’s School of Civil and Construction Engineering, which provides many students with exposure to geomatics. This integration also allows students to prepare for dual licensure as surveyors and engineers. The interested reader is referred to a recent article (Olsen 2010) for more information about the expansion of the OSU geomatics program. An important part of this growth was the formation of a partnership between OSU, Leica Geosystems, and David Evans and Associates (DEA). The main objectives of this partnership are to

Digital elevation models (DEMs) are becoming increasingly important components in national and regional spatial data infrastructure. High-quality DEMs can now be derived directly from airborne light detection and ranging (LiDAR) point-cloud data of high spatial density if the derivation process can be verified. However, LiDAR is relatively new compared with other technologies for terrain data collection, and, although offering the potential for providing better spatial resolution than those that have been routinely available before, will not diffuse among DEM users until the results of meeting the verification challenge are favourable enough to inspire re-organisation of spatial data in decision support for catchment management and other third-tier-of-government authorities. By way of exemplification, the research presented in this thesis concerned ways of improving the processing of the airborne LiDAR data for high-quality DEM generation in terms of both accuracy and efficiency, and explored the applications of LiDAR-derived DEMs in the region of the Corangamite Catchment Management Authority, Victoria, Australia. This thesis begins with a review of the traditional technologies for terrain data collection and DEM generation and compares them with the LiDAR technology. Accordingly, a review of the recently-reported advances in LiDAR data deployment for DEM generation is followed by reports of experiments designed to improve selection and deployment of LiDAR data filtering, modelling methods and data reduction, and the achievement of vertical accuracy for different land covers. Also reported are results of deployment of LiDAR data for ground truthing, and application of LiDAR data for the extraction of drainage networks on an area of deranged drainage: the Victorian Volcanic Plain. The show that: (a) the issues of filtering, modelling techniques, interpolation methods, DEM resolution, and data reduction are critical and must be considered carefully when using LiDAR data for a high-quality DEM generation; (b) it is efficient to use survey marks for the accuracy assessment of LiDAR data. Normal distribution must be tested in order to select a suitable measure for the accuracy assessment of LiDAR data over different land covers; (c) LiDAR data reduction can improve the terrain production efficiency without compromising the product quality. The deployment of breaklines made a significant contribution to improving the accuracy of terrain models while allowing for data reduction; (d) it demonstrated the practical feasibility of applying ground control points from LiDAR intensity image and LiDAR-derived DEM in image orthorectification. The resultant orthoimage accuracy was shown to be superior to that achieved by using (lower accuracy) data sources such as those from Vicmap data; and (e) the LiDAR-derived DEM offers the capability of extracting and delineating the drainage networks in much more detail in low¬relief terrain, including areas in which drainage is barely coherent; The advantages of using LiDAR-derived DEM over the lower-accuracy DEM emerge in terms of stream order, stream number and stream length.

High-Resolution Topography (HRT) data sets are becoming increasingly available, improving our ability and opportunities to monitor geomorphic changes through multi-temporal Digital Terrain Models (DTMs). The use of repeated topographic surveys enables inferring the sediment dynamics of hazardous geomorphic processes such as floods, debris flows, and landslides, and allows us to derive important information on the risks often associated with these processes. The topographic surveying platforms, georeferencing systems, and processing tools have seen important developments in the last two decades, in particular Light Detection And Ranging (LiDAR) technology used in Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Moreover, HRT data, produced through these techniques, changed a lot in terms of point cloud density, accuracy and precision over time. Therefore, old “legacy” data sets and recent surveys can often show comparison problems, especially when multi-temporal data are not homogeneous in terms of quality and uncertainties. In this context, data co-registration should be used to guarantee the coherence among multi-temporal surveys, minimizing, on stable areas, the distance between corresponding points acquired at different epochs. Although several studies highlight that this process is fundamental to properly compare multi-temporal DTMs, it is often not addressed in LiDAR post-processing workflows. In this paper we focus on the alignment of multi-temporal surveys in a topographically complex and rugged environment as the Moscardo debris-flow catchment (Eastern Italian Alps), testing various co-registration methods to align multi-temporal ALS point clouds (i.e. years 2003, 2009 and 2013) and the derived DTMs. In particular, we tested the pairwise registration with manual correspondences, the Iterative Closest Point (ICP) algorithm and a mathematical model that allows aligning simultaneously a generic number of point clouds, the so-called Generalized Procrustes Analysis (GPA), also in its GPA-ICP variant. Then, to correct the possible small inaccuracies generated from the gridding interpolation process, a custom-developed DTM co-registration tool (GRD-CoReg) was used to align gridded data. Both alignment phases (i.e. at point cloud and DTM level) proved to be fundamental and allowed us to obtain proper and reliable DTMs of Difference (DoDs), useful to quantify the debris mobilized and to detect the spatial and temporal patterns of catchment-scale erosion and deposition. The consistency of DoDs data was verified through the comparison between the erosion estimate of DoDs and the volumes of debris-flow events measured by the monitoring station close to the Moscardo torrent catchment outlet. The GPA-ICP algorithm followed by the GRD-CoReg tool proved to be the most effective solution for improving DoDs results with a decrease of systematic trend due to vertical and horizontal uncertainties between surveys, especially at steep slopes. The net volume difference (i.e. the sediment output from the catchment) of the 2003–2013 period changed from 3,237,896 m3 to 135,902 m3 in DoDs obtained from not co-registered and co-registered DTMs. The volume of debris flows measured at the catchment outlet during the same time interval amounts to 169,660 m3. The comparison with debris-flow volume measures at the monitoring station shows, therefore, that the DTMs obtained from the co-registration processes generate more reliable DoDs than those obtained from the raw DTMs (without the alignment).

Light detection and ranging (LiDAR) and terrestrial laser scanning (TLS) sensors are powerful tools for characterizing vegetation structure and for constructing three-dimensional (3D) models of trees, also known as quantitative structural models (QSM). 3D models and structural traits derived from them provide valuable information for biodiversity conservation, forest management, and fire behavior modeling. However, vegetation studies and 3D modeling methodologies often only focus on the forest canopy, with little attention given to understory vegetation. In particular, 3D structural information of shrubs is limited or not included in fire behavior models. Yet, understory vegetation is an important component of forested ecosystems, and has an essential role in determining fire behavior. In this dissertation, I explored the use of TLS data and quantitative structure models to model shrub architecture in three related studies. In the first study, I present a semi-automated methodology for reconstructing architecturally different shrubs from TLS LiDAR. By investigating shrubs with different architectures and point cloud densities, I showed that occlusion, shrub complexity, and shape greatly affect the accuracy of shrub models. In my second study, I assessed the 3D architectural drivers of understory flammability by evaluating the use of architectural metrics derived from the TLS point cloud and 3D reconstructions of the shrubs. I focused on eight species common in the understory of the fire-prone longleaf pine forest ecosystem of the state of Florida, USA. I found a general tendency for each species to be associated with a unique combination of flammability and architectural traits. Novel shrub architectural traits were found to be complementary to the direct use of TLS data and improved flammability predictions. The inherent complexity of shrub architecture and uncertainty in the TLS point cloud make scaling up from an individual shrub to a plot level a challenging task. Therefore, in my third study, I explored the effects of lidar uncertainty on vegetation parameter prediction accuracy. I developed a practical workflow to create synthetic forest stands with varying densities, which were subsequently scanned with simulated terrestrial lidar. This provided data sets quantitatively similar to those created by real-world LiDAR measurements, but with the advantage of exact knowledge of the forest plot parameters, The results showed that the lidar scan location had a large effect on prediction accuracy. Furthermore, occlusion is strongly related to the sampling density and plot complexity. The results of this study illustrate the potential of non-destructive lidar approaches for quantifying shrub architectural

Candi Gunung Gangsir, salah satu warisan bangunan cagar budaya yang memiliki nilai budaya dan ilmu pengetahuan yang penting. Candi ini perlu dilestarikan dengan cara pendokumentasian secara digital. Perkembangan teknologi memungkinkan dalam bentuk pemodelan 3D menggunakan sensor berupa LiDAR (Light Detection and Ranging). Telah ada teknologi terkini berupa smartphone Phone 12 Pro Max dengan tambahan built-in sensor LiDAR. Sensor LiDAR pada iPhone tersebut lebih terjangkau jika dibandingkan dengan alat TLS (Terrestrial Laser Scanner). Akuisisi data dengan iPhone 12 Pro Max sebagai Low-Cost LiDAR dan Leica RTC360 sebagai TLS. Low-Cost LiDAR melakukan pemindaian secara bergerak sehingga menghasilkan 8.234.112 point cloud, sementara TLS menggunakan pemindaian statik menghasilkan 1.359.463.159 point cloud. Secara visual model 3D yang dihasilkan oleh TLS menampilkan detail yang lebih baik daripada hasil dari Low-Cost LiDAR. Diperoleh RMS Error untuk Low-Cost LiDAR sebesar 19,3 cm dan TLS sebesar 0,5 cm, sehingga hanya TLS yang memenuhi batas toleransi yang telah ditetapkan. Pengujian akurasi point cloud menggunakan 3 parameter eigen value based yaitu planarity, linearity, dan sphericity menunjukkan data yang dihasilkan oleh Low-Cost LiDAR tidak memiliki perbedaan yang signifikan dengan data yang dihasilkan oleh TLS secara uji statistik.

In forestry research, for forest inventories or other applications which require accurate 3D information on the forest structure, a Terrestrial Laser Scanner (TLS) is an efficient tool for vegetation structure estimation. Light Detection and Ranging (LiDAR) can even provide high-resolution information in tree canopies due to its high penetration capability. Depending on the forest plot size, tree density, and structure, multiple TLS scans are acquired to cover the forest plot in all directions to avoid any voids in the dataset that are generated. However, while increasing the number of scans, we often tend to increase the data redundancy as we keep acquiring data for the same region from multiple scan positions. In this research, an extensive qualitative analysis was carried out to examine the capability and efficiency of TLS to generate canopy top points in six different scanning combinations. A total of nine scans were acquired for each forest plot, and from these nine scans, we made six different combinations to evaluate the 3D vegetation structure derived from each scan combination, such as Center Scans (CS), Four Corners Scans (FCS), Four Corners with Center Scans (FCwCS), Four Sides Center Scans (FSCS), Four Sides Center with Center Scans (FSCwCS), and All Nine Scans (ANS). We considered eight forest plots with dimensions of 25 m × 25 m, of which four plots were of medium tree density, and the other four had a high tree density. The forest plots are located in central Slovakia; European beech was the dominant tree species with a mixture of European oak, Silver fir, Norway spruce, and European hornbeam. Altogether, 487 trees were considered for this research. The quantification of tree canopy top points obtained from a TLS point cloud is very crucial as the point cloud is used to derive the Digital Surface Model (DSM) and Canopy Height Model (CHM). We also performed a statistical evaluation by calculating the differences in the canopy top points between ANS and the five other combinations and found that the most significantly different combination was FSCwCS respective to ANS. The Root Mean Squared Error (RMSE) of the deviations in tree canopy top points obtained for plots TLS_Plot1 and TLS_Plot2 ranged from 0.89 m to 14.98 m and 0.61 m to 7.78 m, respectively. The relative Root Mean Squared Error (rRMSE) obtained for plots TLS_Plot1 and TLS_Plot2 ranged from 0.15% to 2.48% and 0.096% to 1.22%, respectively.

Light detection and ranging (LiDAR) is a widely used technology for the acquisition of three-dimensional (3D) information about a wide variety of physical objects and environments. However, before conducting a campaign, a test is typically conducted to assess the potential of the utilized algorithm for information retrieval. It might not be a real campaign but rather a simulation to save time and costs. Here, a multi-platform LiDAR simulation model considering the location, direction, and wavelength of each emitted laser pulse was developed based on the large-scale remote sensing (RS) data and image simulation framework (LESS) model, which is a 3D radiative transfer model for simulating passive optical remote sensing signals using the ray tracing algorithm. The LESS LiDAR simulator took footprint size, returned energy, multiple scattering, and multispectrum LiDAR into account. The waveform and point similarity were assessed with the LiDAR module of the discrete anisotropic radiative transfer (DART) model. Abstract and realistic scenes were designed to assess the simulated LiDAR waveforms and point clouds. A waveform comparison in the abstract scene with the DART LiDAR module showed that the relative error was lower than 1%. In the realistic scene, airborne and terrestrial laser scanning were simulated by LESS and DART LiDAR modules. Their coefficients of determination ranged from 0.9108 to 0.9984. Their mean was 0.9698. The number of discrete returns fitted well and the coefficient of determination was 0.9986. A terrestrial point cloud comparison in the realistic scene showed that the coefficient of determination between the two sets of data could reach 0.9849. The performance of the LESS LiDAR simulator was also compared with the DART LiDAR module and HELIOS++. The results showed that the LESS LiDAR simulator is over three times faster than the DART LiDAR module and HELIOS++ when simulating terrestrial point clouds in a realistic scene. The proposed LiDAR simulator offers two modes for simulating point clouds: single-ray and multi-ray modes. The findings demonstrate that utilizing a single-ray simulation approach can significantly reduce the simulation time, by over 28 times, without substantially affecting the overall point number or ground pointswhen compared to employing multiple rays for simulations. This new LESS model integrating a LiDAR simulator has great potential in terms of simultaneously simulating LiDAR data and optical images based on the same 3D scene and parameters. As a proof of concept, the normalized difference vegetation index (NDVI) results from multispectral images and the vertical profiles from multispectral LiDAR waveforms were simulated and analyzed. The results showed that the proposed LESS LiDAR simulator can fulfill its design goals.

Anti-aliasing filters for deriving high-accuracy DEMs from TLS data: A case study from Freeport, Texas

In recent years, LIght Detection And Ranging (LiDAR) and especially Terrestrial Laser Scanning (TLS) systems have shown the potential to revolutionise forest structural characterisation by providing unprecedented 3D data. However, manned Airborne Laser Scanning (ALS) requires costly campaigns and produces relatively low point density, while TLS is labour intense and time demanding. Unmanned Aerial Vehicle (UAV)-borne laser scanning can be the way in between. In this study, we present first results and experiences with the RIEGL RiCOPTER with VUX-1UAV ALS system and compare it with the well tested RIEGL VZ-400 TLS system. We scanned the same forest plots with both systems over the course of two days. We derived Digital Terrain Models (DTMs), Digital Surface Models (DSMs) and finally Canopy Height Models (CHMs) from the resulting point clouds. ALS CHMs were on average 11.5 higher in five plots with different canopy conditions. This showed that TLS could not always detect the top of canopy. Moreover, we extracted trunk segments of 58 trees for ALS and TLS simultaneously, of which 39 could be used to model Diameter at Breast Height (DBH). ALS DBH showed a high agreement with TLS DBH with a correlation coefficient of 0.98 and root mean square error of 4.24 . We conclude that RiCOPTER has the potential to perform comparable to TLS for estimating forest canopy height and DBH under the studied forest conditions. Further research should be directed to testing UAV-borne LiDAR for explicit 3D modelling of whole trees to estimate tree volume and subsequently Above-Ground Biomass (AGB).

Information on a crop’s three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R2 (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R2 (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets.

Abstract. This work uses the canopy height model (CHM) based workflow for individual tree crown delineation and 3D feature extraction approach (Overwatch Geospatial's proprietary algorithm) for building feature delineation from high-density light detection and ranging (LiDAR) point cloud data in an urban environment and evaluates its accuracy by using very high-resolution panchromatic (PAN) (spatial) and 8-band (multispectral) WorldView-2 (WV-2) imagery. LiDAR point cloud data over San Francisco, California, USA, recorded in June 2010, was used to detect tree and building features by classifying point elevation values. The workflow employed includes resampling of LiDAR point cloud to generate a raster surface or digital terrain model (DTM), generation of a hill-shade image and an intensity image, extraction of digital surface model, generation of bare earth digital elevation model (DEM) and extraction of tree and building features. First, the optical WV-2 data and the LiDAR intensity image were co-registered using ground control points (GCPs). The WV-2 rational polynomial coefficients model (RPC) was executed in ERDAS Leica Photogrammetry Suite (LPS) using supplementary *.RPB file. In the second stage, ortho-rectification was carried out using ERDAS LPS by incorporating well-distributed GCPs. The root mean square error (RMSE) for the WV-2 was estimated to be 0.25 m by using more than 10 well-distributed GCPs. In the second stage, we generated the bare earth DEM from LiDAR point cloud data. In most of the cases, bare earth DEM does not represent true ground elevation. Hence, the model was edited to get the most accurate DEM/ DTM possible and normalized the LiDAR point cloud data based on DTM in order to reduce the effect of undulating terrain. We normalized the vegetation point cloud values by subtracting the ground points (DEM) from the LiDAR point cloud. A normalized digital surface model (nDSM) or CHM was calculated from the LiDAR data by subtracting the DEM from the DSM. The CHM or the normalized DSM represents the absolute height of all aboveground urban features relative to the ground. After normalization, the elevation value of a point indicates the height from the ground to the point. The above-ground points were used for tree feature and building footprint extraction. In individual tree extraction, first and last return point clouds were used along with the bare earth and building footprint models discussed above. In this study, scene dependent extraction criteria were employed to improve the 3D feature extraction process. LiDAR-based refining/ filtering techniques used for bare earth layer extraction were crucial for improving the subsequent 3D features (tree and building) feature extraction. The PAN-sharpened WV-2 image (with 0.5 m spatial resolution) was used to assess the accuracy of LiDAR-based 3D feature extraction. Our analysis provided an accuracy of 98 % for tree feature extraction and 96 % for building feature extraction from LiDAR data. This study could extract total of 15143 tree features using CHM method, out of which total of 14841 were visually interpreted on PAN-sharpened WV-2 image data. The extracted tree features included both shadowed (total 13830) and non-shadowed (total 1011). We note that CHM method could overestimate total of 302 tree features, which were not observed on the WV-2 image. One of the potential sources for tree feature overestimation was observed in case of those tree features which were adjacent to buildings. In case of building feature extraction, the algorithm could extract total of 6117 building features which were interpreted on WV-2 image, even capturing buildings under the trees (total 605) and buildings under shadow (total 112). Overestimation of tree and building features was observed to be limiting factor in 3D feature extraction process. This is due to the incorrect filtering of point cloud in these areas. One of the potential sources of overestimation was the man-made structures, including skyscrapers and bridges, which were confounded and extracted as buildings. This can be attributed to low point density at building edges and on flat roofs or occlusions due to which LiDAR cannot give as much precise planimetric accuracy as photogrammetric techniques (in segmentation) and lack of optimum use of textural information as well as contextual information (especially at walls which are away from roof) in automatic extraction algorithm. In addition, there were no separate classes for bridges or the features lying inside the water and multiple water height levels were also not considered. Based on these inferences, we conclude that the LiDAR-based 3D feature extraction supplemented by high resolution satellite data is a potential application which can be used for understanding and characterization of urban setup.