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1
Xiaoye Liu. "Airborne LiDAR for DEM generation: some critical issues." Progress in Physical Geography: Earth and Environment 32, no. 1 (February 2008): 31–49. http://dx.doi.org/10.1177/0309133308089496.
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Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for digital elevation model (DEM) generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the high-density characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented.
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Octariady, J., A. Hikmat, E. Widyaningrum, R. Mayasari, and M. K. Fajari. "VERTICAL ACCURACY COMPARISON OF DIGITAL ELEVATION MODEL FROM LIDAR AND MULTITEMPORAL SATELLITE IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 419–23. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-419-2017.
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Digital elevation model serves to illustrate the appearance of the earth's surface. DEM can be produced from a wide variety of data sources including from radar data, LiDAR data, and stereo satellite imagery. Making the LiDAR DEM conducted using point cloud data from LiDAR sensor. Making a DEM from stereo satellite imagery can be done using same temporal or multitemporal stereo satellite imagery. How much the accuracy of DEM generated from multitemporal stereo stellite imagery and LiDAR data is not known with certainty. The study was conducted using LiDAR DEM data and multitemporal stereo satellite imagery DEM. Multitemporal stereo satellite imagery generated semi-automatically by using 3 scene stereo satellite imagery with acquisition 2013–2014. The high value given each of DEM serve as the basis for calculating high accuracy DEM respectively. The results showed the high value differences in the fraction of the meter between LiDAR DEM and multitemporal stereo satellite imagery DEM.
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Cao, Hong, Zhao Pan, Qixin Chang, Aiguo Zhou, Xu Wang, and Ziyong Sun. "Stream Network Modeling Using Remote Sensing Data in an Alpine Cold Catchment." Water 13, no. 11 (June 4, 2021): 1585. http://dx.doi.org/10.3390/w13111585.
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The hydrological information derived from a digital elevation model is very important in distributed hydrological modeling. As part of alpine hydrological research on stream network modeling using remote sensing data in the northeast of the Tibetan Plateau, three digital elevation model (DEM) datasets were obtained for the purpose of hydrological features, mainly including channel network, watershed extent and terrain character. The data sources include the airborne light detection and ranging (LiDAR) with point spacing of 1 m, the High Mountain Asia (HMA) DEM and the Shuttle Radar Topography Mission (SRTM) DEM. Mapping of the watershed and stream network was conducted using each of the three DEM datasets. The modeled stream networks using the different DEMs were verified against the actual network mapped in the field. The results show that the stream network derived from the LiDAR DEM was the most accurate representation of the network mapped in the field. The SRTM DEM overestimated the basin hypsometry relative to the LiDAR watershed at the lowest elevation, while the HMA DEM underestimated the basin hypsometry relative to the LiDAR watershed at the highest elevation. This may be because, compared with the SRTM DEM and the HMA DEM, the LiDAR DEM has higher initial point density, accuracy and resolution. It can be seen that the LiDAR data have great potential for the application in hydrologic modeling and water resource management in small alpine catchments.
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Muhadi, Nur Atirah, Ahmad Fikri Abdullah, Siti Khairunniza Bejo, Muhammad Razif Mahadi, and Ana Mijic. "The Use of LiDAR-Derived DEM in Flood Applications: A Review." Remote Sensing 12, no. 14 (July 18, 2020): 2308. http://dx.doi.org/10.3390/rs12142308.
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Flood occurrence is increasing due to escalated urbanization and extreme climate change; hence, various studies on this issue and methods of flood monitoring and mapping are also increasing to reduce the severe impacts of flood disasters. The advancement of current technologies such as light detection and ranging (LiDAR) systems facilitated and improved flood applications. In a LiDAR system, a laser emits light that travels to the ground and reflects off objects like buildings and trees. The reflected light energy returns to the sensor, whereby the time interval is recorded. Since the conventional methods cannot produce high-resolution digital elevation model (DEM) data, which results in low accuracy of flood simulation results, LiDAR data are extensively used as an alternative. This review aims to study the potential and the applications of LiDAR-derived DEM in flood studies. It also provides insight into the operating principles of different LiDAR systems, system components, and advantages and disadvantages of each system. This paper discusses several topics relevant to flood studies from a LiDAR-derived DEM perspective. Furthermore, the challenges and future perspectives regarding DEM LiDAR data for flood mapping and assessment are also reviewed. This study demonstrates that LiDAR-derived data are useful in flood risk management, especially in the future assessment of flood-related problems.
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Tinkham, Wade T., Alistair M. S. Smith, Chad Hoffman, Andrew T. Hudak, Michael J. Falkowski, Mark E. Swanson, and Paul E. Gessler. "Investigating the influence of LiDAR ground surface errors on the utility of derived forest inventories." Canadian Journal of Forest Research 42, no. 3 (March 2012): 413–22. http://dx.doi.org/10.1139/x11-193.
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Light detection and ranging, or LiDAR, effectively produces products spatially characterizing both terrain and vegetation structure; however, development and use of those products has outpaced our understanding of the errors within them. LiDAR’s ability to capture three-dimensional structure has led to interest in conducting or augmenting forest inventories with LiDAR data. Prior to applying LiDAR in operational management, it is necessary to understand the errors in LiDAR-derived estimates of forest inventory metrics (i.e., tree height). Most LiDAR-based forest inventory metrics require creation of digital elevation models (DEM), and because metrics are calculated relative to the DEM surface, errors within the DEMs propagate into delivered metrics. This study combines LiDAR DEMs and 54 ground survey plots to investigate how surface morphology and vegetation structure influence DEM errors. The study further compared two LiDAR classification algorithms and found no significant difference in their performance. Vegetation structure was found to have no influence, whereas increased variability in the vertical error was observed on slopes exceeding 30°, illustrating that these algorithms are not limited by high-biomass western coniferous forests, but that slope and sensor accuracy both play important roles. The observed vertical DEM error translated into ±1%–3% error range in derived timber volumes, highlighting the potential of LiDAR-derived inventories in forest management.
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Kang, C. L., M. M. Zong, Y. Cheng, F. Wang, T. N. Lu, and G. Z. Liu. "RESEARCH ON CONSTRUCTING DEM WITH POINT CLOUD FILTERING ALGORITHM CONSIDERING SPECIAL TERRAIN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 211–14. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-211-2020.
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Abstract. With the development of airborne LiDAR, the use of LiDAR point cloud to construct DEM model is a hot topic in recent years. For the characteristics of time cloud filtering and poor validity, and the efficiency of non-ground point filtering is not high, the filtered point cloud has problems such as errors and leaks. This paper proposes a method of constructing DEM based on the point cloud filtering algorithm of airborne Lidar point cloud data considering special terrain. The experiment proves that the algorithm of this paper is effective for establishing DEM model, and the quality of DEM model is good.
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Jakovljevic, Gordana, Miro Govedarica, Flor Alvarez-Taboada, and Vladimir Pajic. "Accuracy Assessment of Deep Learning Based Classification of LiDAR and UAV Points Clouds for DTM Creation and Flood Risk Mapping." Geosciences 9, no. 7 (July 23, 2019): 323. http://dx.doi.org/10.3390/geosciences9070323.
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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.
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Szypuła, Bartłomiej. "Quality assessment of DEM derived from topographic maps for geomorphometric purposes." Open Geosciences 11, no. 1 (November 30, 2019): 843–65. http://dx.doi.org/10.1515/geo-2019-0066.
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Abstract Digital elevation models (DEMs) play a significant role in geomorphological research. For geomorphologists reconstructing landform and drainage structure is frequently as important as elevation accuracy. Consequently, large-scale topographic maps (with contours, height points and watercourses) constitute excellent material for creating models (here called Topo-DEM) in fine resolution. The purpose of the conducted analyses was to assess the quality of Topo-DEM against freely-available global DEMs and then to compare it with a reference model derived from laser scanning (LiDAR-DEM). The analysis also involved derivative maps of geomorphometric parameters (local relief, slope, curvature, aspect) generated on the basis of Topo-DEM and LiDAR-DEM. Moreover, comparative classification of landforms was carried out. It was indicated that Topo-DEM is characterised by good elevation accuracy (RMSE <2 m) and reflects the topography of the analyzed area surprisingly well. Additionally, statistical and percentage metrics confirm that it is possible to generate a DEM with very good quality parameters on the basis of a large-scale topographic map (1:10,000): elevation differences between Topo-DEM and: 1) topographic map amounted from−1.68 to +2.06 m,MAEis 0.10 m, RMSE 0.16 m; 2) LiDAR-DEM (MAE 1.13 m, RMSE 1.69 m, SD 1.83 m); 3) GPS RTK measurements amounted from−3.6 to +3.01 m, MAE is 0.72 m, RMSE 0.97 m, SD 0.97 m. For an area of several dozen km2 Topo-DEM with 10×10 m resolution proved more efficient than detailed (1×1 m) LiDAR-DEM.
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Brubaker, Kristen M., Wayne L. Myers, Patrick J. Drohan, Douglas A. Miller, and Elizabeth W. Boyer. "The Use of LiDAR Terrain Data in Characterizing Surface Roughness and Microtopography." Applied and Environmental Soil Science 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/891534.
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The availability of light detection and ranging data (LiDAR) has resulted in a new era of landscape analysis. For example, improvements in LiDAR data resolution may make it possible to accurately model microtopography over a large geographic area; however, data resolution and processing costs versus resulting accuracy may be too costly. We examined two LiDAR datasets of differing resolutions, a low point density (0.714 points/m2spacing) 1 m DEM available statewide in Pennsylvania and a high point density (10.28 points/m2spacing) 1 m DEM research-grade DEM, and compared the calculated roughness between both resulting DEMs using standard deviation of slope, standard deviation of curvature, a pit fill index, and the difference between a smoothed splined surface and the original DEM. These results were then compared to field-surveyed plots and transects of microterrain. Using both datasets, patterns of roughness were identified, which were associated with different landforms derived from hydrogeomorphic features such as stream channels, gullies, and depressions. Lowland areas tended to have the highest roughness values for all methods, with other areas showing distinctive patterns of roughness values across metrics. However, our results suggest that the high-resolution research-grade LiDAR did not improve roughness modeling in comparison to the coarser statewide LiDAR. We conclude that resolution and initial point density may not be as important as the algorithm and methodology used to generate a LiDAR-derived DEM for roughness modeling purposes.
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Gökgöz, Türkay, and Moustafa Baker. "Large Scale Landform Mapping Using Lidar DEM." ISPRS International Journal of Geo-Information 4, no. 3 (August 7, 2015): 1336–45. http://dx.doi.org/10.3390/ijgi4031336.
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Shan, Jie, and Sampath Aparajithan. "Urban DEM Generation from Raw Lidar Data." Photogrammetric Engineering & Remote Sensing 71, no. 2 (February 1, 2005): 217–26. http://dx.doi.org/10.14358/pers.71.2.217.
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Grau, Joan, Kang Liang, Jae Ogilvie, Paul Arp, Sheng Li, Bonnie Robertson, and Fan-Rui Meng. "Using Unmanned Aerial Vehicle and LiDAR-Derived DEMs to Estimate Channels of Small Tributary Streams." Remote Sensing 13, no. 17 (August 26, 2021): 3380. http://dx.doi.org/10.3390/rs13173380.
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Defining stream channels in a watershed is important for assessing freshwater habitat availability, complexity, and quality. However, mapping channels of small tributary streams becomes challenging due to frequent channel change and dense vegetation coverage. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to obtain a 3D Digital Surface Model (DSM) to estimate the total in-stream channel and channel width within grazed riparian pastures. We used two methods to predict the stream channel boundary: the Slope Gradient (SG) and Vertical Slope Position (VSP). As a comparison, the same methods were also applied using low-resolution DEM, obtained with traditional photogrammetry (coarse resolution) and two more LiDAR-derived DEMs with different resolution. When using the SG method, the higher-resolution, UAV-derived DEM provided the best agreement with the field-validated area followed by the high-resolution LiDAR DEM, with Mean Squared Errors (MSE) of 1.81 m and 1.91 m, respectively. The LiDAR DEM collected at low resolution was able to predict the stream channel with a MSE of 3.33 m. Finally, the coarse DEM did not perform accurately and the MSE obtained was 26.76 m. On the other hand, when the VSP method was used we found that low-resolution LiDAR DEM performed the best followed by high-resolution LiDAR, with MSE values of 9.70 and 11.45 m, respectively. The MSE for the UAV-derived DEM was 15.12 m and for the coarse DEM was 20.78 m. We found that the UAV-derived DEM could be used to identify steep bank which could be used for mapping the hydrogeomorphology of lower order streams. Therefore, UAVs could be applied to efficiently map small stream channels in order to monitor the dynamic changes occurring in these ecosystems at a local scale. However, the VSP method should be used to map stream channels in small watersheds when high resolution DEM data is not available.
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Ajayi, Oluibukun Gbenga, and Mark Palmer. "Modelling 3D Topography by Comparing Airborne Lidar Data with Unmanned Aerial System (UAS) Photogrammetry Under Multiple Imaging Conditions." Geoplanning: Journal of Geomatics and Planning 6, no. 2 (April 7, 2020): 122–38. http://dx.doi.org/10.14710/geoplanning.6.2.122-138.
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This study presents the effect of image data sources on the topographic modelling of part of the National Trust site located at Weston-Super-Mare, Bristol, United Kingdom, covering an approximate area of 1.82 hectares. The accuracy of the DEM generated from 1m resolution and 2m resolution LiDAR data together with the accuracy of the DEM generated from the UAV images acquired at different altitudes are analysed using the 1 m LiDAR DEM as reference for the accuracy assessment. Using the NSSDA methodology, the horizontal and vertical accuracy of the DEMs generated from each of the four sources were computed while the paired sample t-test was conducted to ascertain the existence of statistically significant difference between the means of the X, Y, and Z coordinates of the check points. The result obtained shows that with a RMSE of -0.0101499 and horizontal accuracy of -0.175674686m, the planimetric coordinates extracted from 2 m LiDAR DEM were more accurate than the planimetric coordinates extracted from the UAV based DEMs while the UAV based DEMs proved to be more accurate than the 2m LiDAR DEM in terms of altimetric coordinates, though the DEM generated from UAV images acquired at 50 m altitude gave the most accurate result when compared with the vertical accuracy obtained from the DEM generated from UAV images acquired at 30 m and 70 m flight heights. These findings are also consistent with the result of the statistical analysis at 95% confidence interval.
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Uysal, M., and N. Polat. "Investigating Performance Of Airborne Lidar Data Filtering With Triangular Irregular Network (TIN) Algorithm." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 199–202. http://dx.doi.org/10.5194/isprsarchives-xl-7-199-2014.
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Digital Elevation Model (DEM) is an important topographic product and essential demand for many applications. Traditional methods for creating DEM are very costly and time consuming because of land surveying. In time, Photogrammetry has become one of the major methods to generate DEM. Recently, airborne Light Detection and Ranging (LIDAR) system has become a powerful way to produce a DEM due to advantage of collecting three-dimensional information very effectively over a large area by means of precision and time. <br><br> Airborne LIDAR system collects information not only from land surface but also from every object between plane and terrain that can reflect the laser beam. So filtering out nonground points from raw point clouds is the major step of DEM generation. There are many filtering algorithm due to several factors that affect the filtering prosedures. The performanses of these filters change based on the topographic features of area.One of these algorithm is called Triangular Irregular Network (TIN). <br><br> In this study the TIN algorithm is used to filter Lidar point cloud that are collected from two different sites. While one of these sites is a rural area, the other site is an urban area; therefore these sites have different topographic features. In addition, the reference DEMs are available for these sites. In order to test the performance of TIN algorithm, the Lidar point clouds are filtered and used to generate DEM for the sites. Finally, the generated DEM are compared with the reference DEM for each site. The comparison results show that the TIN filtering algorithm perform more effectively in urban area than rural area in terms of correlations with reference DEMs.
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Ma, Ruijin. "DEM Generation and Building Detection from Lidar Data." Photogrammetric Engineering & Remote Sensing 71, no. 7 (July 1, 2005): 847–54. http://dx.doi.org/10.14358/pers.71.7.847.
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Bater, Christopher W., and Nicholas C. Coops. "Evaluating error associated with lidar-derived DEM interpolation." Computers & Geosciences 35, no. 2 (February 2009): 289–300. http://dx.doi.org/10.1016/j.cageo.2008.09.001.
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Mohamed, Mohamed Ali. "Classification of Landforms for Digital Soil Mapping in Urban Areas Using LiDAR Data Derived Terrain Attributes: A Case Study from Berlin, Germany." Land 9, no. 9 (September 9, 2020): 319. http://dx.doi.org/10.3390/land9090319.
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In this study, a knowledge-based fuzzy classification method was used to classify possible soil-landforms in urban areas based on analysis of morphometric parameters (terrain attributes) derived from digital elevation models (DEMs). A case study in the city area of Berlin was used to compare two different resolution DEMs in terms of their potential to find a specific relationship between landforms, soil types and the suitability of these DEMs for soil mapping. Almost all the topographic parameters were obtained from high-resolution light detection and ranging (LiDAR)-DEM (1 m) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-DEM (30 m), which were used as thresholds for the classification of landforms in the selected study area with a total area of about 39.40 km2. The accuracy of both classifications was evaluated by comparing ground point samples as ground truth data with the classification results. The LiDAR-DEM based classification has shown promising results for classification of landforms into geomorphological (sub)categories in urban areas. This is indicated by an acceptable overall accuracy of 93%. While the classification based on ASTER-DEM showed an accuracy of 70%. The coarser ASTER-DEM based classification requires additional and more detailed information directly related to soil-forming factors to extract geomorphological parameters. The importance of using LiDAR-DEM classification was particularly evident when classifying landforms that have narrow spatial extent such as embankments and channel banks or when determining the general accuracy of landform boundaries such as crests and flat lands. However, this LiDAR-DEM classification has shown that there are categories of landforms that received a large proportion of the misclassifications such as terraced land and steep embankments in other parts of the study area due to the increased distance from the major rivers and the complex nature of these landforms. In contrast, the results of the ASTER-DEM based classification have shown that the ASTER-DEM cannot deal with small-scale spatial variation of soil and landforms due to the increasing human impacts on landscapes in urban areas. The application of the approach used to extract terrain parameters from the LiDAR-DEM and their use in classification of landforms has shown that it can support soil surveys that require a lot of time and resources for traditional soil mapping.
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Mokhtar, N. M., N. Darwin, M. F. M. Ariff, Z. Majid, and K. M. Idris. "THE CAPABILITIES OF UNMANNED AERIAL VEHICLE FOR SLOPE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W16 (October 1, 2019): 451–59. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w16-451-2019.
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Abstract. Slope classification mapping is an important component of land suitability analysis for preventing landslides. This study aim to investigate the capabilities and application of Unmanned Aerial Vehicle (UAV) platform for slope classification. The objectives of this study such as investigating the capabilities of UAV for slope classification, generating Digital Elevation Model (DEM) and orthophoto from the image acquired and assessing the accuracy of DEM and orthophoto produced for slope classification. In this study, the aerial image was acquired using UAV at 60 m and 40 m altitude will then generates the DEM and orthophoto used to produce the slope map and classify the slope. The UAV data was validated with the check points observed from ground survey using GPS to obtain the Root Mean Square Error (RMSE) values. The RMSE value for UAV derived DEM at 60 m altitude is ±0.234 m and ±0.604 m for X and Y respectively. The average RMSE is ±0.279 m. The average RMSE value obtained from LiDAR derived DEM in previous research is ±0.616 m. The RMSE value for UAV derived DEM at 40 m altitude is ±0.596 m and ±0.405 for X and Y respectively. The average RMSE is ±0.334 m. The average RMSE value obtained from LiDAR derived DEM in previous research is ±0.450 m. In conclusion, it shows that the RMSE value obtained from UAV derived DEM is smaller than the RMSE value obtained from LiDAR derived DEM. Hence, UAV is capable for the generation of slope map and slope classification.
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Dayamit, O. M., M. F. Pedro, R. R. Ernesto, and B. L. Fernando. "DIGITAL ELEVATION MODEL FROM NON-METRIC CAMERA IN UAS COMPARED WITH LIDAR TECHNOLOGY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W4 (August 27, 2015): 411–14. http://dx.doi.org/10.5194/isprsarchives-xl-1-w4-411-2015.
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Digital Elevation Model (DEM) data as a representation of surface topography is highly demanded for use in spatial analysis and modelling. Aimed to that issue many methods of acquisition data and process it are developed, from traditional surveying until modern technology like LIDAR. On the other hands, in a past four year the development of Unamend Aerial System (UAS) aimed to Geomatic bring us the possibility to acquire data about surface by non-metric digital camera on board in a short time with good quality for some analysis. Data collectors have attracted tremendous attention on UAS due to possibility of the determination of volume changes over time, monitoring of the breakwaters, hydrological modelling including flood simulation, drainage networks, among others whose support in DEM for proper analysis. The DEM quality is considered as a combination of DEM accuracy and DEM suitability so; this paper is aimed to analyse the quality of the DEM from non-metric digital camera on UAS compared with a DEM from LIDAR corresponding to same geographic space covering 4 km2 in Artemisa province, Cuba. This area is in a frame of urban planning whose need to know the topographic characteristics in order to analyse hydrology behaviour and decide the best place for make roads, building and so on. Base on LIDAR technology is still more accurate method, it offer us a pattern for test DEM from non-metric digital camera on UAS, whose are much more flexible and bring a solution for many applications whose needs DEM of detail.
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Andersen, Mikkel Skovgaard, Áron Gergely, Zyad Al-Hamdani, Frank Steinbacher, Laurids Rolighed Larsen, and Verner Brandbyge Ernstsen. "Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment." Hydrology and Earth System Sciences 21, no. 1 (January 3, 2017): 43–63. http://dx.doi.org/10.5194/hess-21-43-2017.
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Abstract. The transition zone between land and water is difficult to map with conventional geophysical systems due to shallow water depth and often challenging environmental conditions. The emerging technology of airborne topobathymetric light detection and ranging (lidar) is capable of providing both topographic and bathymetric elevation information, using only a single green laser, resulting in a seamless coverage of the land–water transition zone. However, there is no transparent and reproducible method for processing green topobathymetric lidar data into a digital elevation model (DEM). The general processing steps involve data filtering, water surface detection and refraction correction. Specifically, the procedure of water surface detection and modelling, solely using green laser lidar data, has not previously been described in detail for tidal environments. The aim of this study was to fill this gap of knowledge by developing a step-by-step procedure for making a digital water surface model (DWSM) using the green laser lidar data. The detailed description of the processing procedure augments its reliability, makes it user-friendly and repeatable. A DEM was obtained from the processed topobathymetric lidar data collected in spring 2014 from the Knudedyb tidal inlet system in the Danish Wadden Sea. The vertical accuracy of the lidar data is determined to ±8 cm at a 95 % confidence level, and the horizontal accuracy is determined as the mean error to ±10 cm. The lidar technique is found capable of detecting features with a size of less than 1 m2. The derived high-resolution DEM was applied for detection and classification of geomorphometric and morphological features within the natural environment of the study area. Initially, the bathymetric position index (BPI) and the slope of the DEM were used to make a continuous classification of the geomorphometry. Subsequently, stage (or elevation in relation to tidal range) and a combination of statistical neighbourhood analyses (moving average and standard deviation) with varying window sizes, combined with the DEM slope, were used to classify the study area into six specific types of morphological features (i.e. subtidal channel, intertidal flat, intertidal creek, linear bar, swash bar and beach dune). The developed classification method is adapted and applied to a specific case, but it can also be implemented in other cases and environments.
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Lindsay, John B., Anthony Francioni, and Jaclyn M. H. Cockburn. "LiDAR DEM Smoothing and the Preservation of Drainage Features." Remote Sensing 11, no. 16 (August 17, 2019): 1926. http://dx.doi.org/10.3390/rs11161926.
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Fine-resolution Light Detection and Ranging (LiDAR) data often exhibit excessive surface roughness that can hinder the characterization of topographic shape and the modeling of near-surface flow processes. Digital elevation model (DEM) smoothing methods, commonly low-pass filters, are sometimes applied to LiDAR data to subdue the roughness. These techniques can negatively impact the representation of topographic features, most notably drainage features, such as headwater streams. This paper presents the feature-preserving DEM smoothing (FPDEMS) method, which modifies surface normals to smooth the topographic surface in a similar manner to approaches that were originally designed for de-noising three-dimensional (3D) meshes. The FPDEMS method has been optimized for application with raster DEM data. The method was compared with several low-pass filters while using a 0.5-m resolution LiDAR DEM of an agricultural area in southwestern Ontario, Canada. The findings demonstrated that the technique was better at removing roughness, when compared with mean, median, and Gaussian filters, while also preserving sharp breaks-in-slope and retaining the topographic complexity at broader scales. Optimal smoothing occurred with kernel sizes of 11–21 grid cells, threshold angles of 10°–20°, and 3–15 elevation-update iterations. These parameter settings allowed for the effective reduction in roughness and DEM noise and the retention of terrace scarps, channel banks, gullies, and headwater streams.
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Brown, Rebecca, Preston Hartzell, and Craig Glennie. "Evaluation of SPL100 Single Photon Lidar Data." Remote Sensing 12, no. 4 (February 22, 2020): 722. http://dx.doi.org/10.3390/rs12040722.
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Geiger-mode and single photon lidar sensors have recently emerged on the commercial market, advertising greater collection efficiency than the traditional linear mode lidar (LML) systems. Non-linear photon detection is a new technology for the geospatial community, and its performance characteristics for surveying and mapping are not yet well understood. Therefore, the geospatial quality of the data produced by one of these new sensors, the Leica SPL100, is examined by comparing the achieved lidar point cloud accuracy, precision, digital elevation model (DEM) generation, canopy penetration, and multiple return generation to a LML point cloud. We find the SPL100 has a lower ranging precision than linear mode lidar and that the precision is more negatively affected by surface properties such as low intensity and high incidence angle. The accuracy of the SPL100 point cloud, however, was found to be comparable to LML for smooth horizontal surfaces. A 1 m resolution SPL100 DEM was also comparable to a corresponding LML DEM, but the SPL100 was observed to have a reduced ability to resolve multiple returns through vegetation when compared to a LML sensor. In its current state, the SPL100 is likely best suited for applications in which the need for collection efficiency outweighs the need for maximum precision and canopy penetration and modeling.
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Miura, Hiroyuki. "Fusion Analysis of Optical Satellite Images and Digital Elevation Model for Quantifying Volume in Debris Flow Disaster." Remote Sensing 11, no. 9 (May 8, 2019): 1096. http://dx.doi.org/10.3390/rs11091096.
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Rapid identification of affected areas and volumes in a large-scale debris flow disaster is important for early-stage recovery and debris management planning. This study introduces a methodology for fusion analysis of optical satellite images and digital elevation model (DEM) for simplified quantification of volumes in a debris flow event. The LiDAR data, the pre- and post-event Sentinel-2 images and the pre-event DEM in Hiroshima, Japan affected by the debris flow disaster on July 2018 are analyzed in this study. Erosion depth by the debris flows is empirically modeled from the pre- and post-event LiDAR-derived DEMs. Erosion areas are detected from the change detection of the satellite images and the DEM-based debris flow propagation analysis by providing predefined sources. The volumes and their pattern are estimated from the detected erosion areas by multiplying the empirical erosion depth. The result of the volume estimations show good agreement with the LiDAR-derived volumes.
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Remmel, Tarmo K., Kenton W. Todd, and James Buttle. "A comparison of existing surficial hydrological data layers in a low-relief forested Ontario landscape with those derived from a LiDAR DEM." Forestry Chronicle 84, no. 6 (December 1, 2008): 850–65. http://dx.doi.org/10.5558/tfc84850-6.
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The current provincial-extent digital elevation model (DEM) and corresponding hydrological maps for Ontario have been produced using traditional photogrammetry and aerial photograph interpretation. This process is labour-intensive and requires visual interpretation of stereo image pairs. The ground surface and small hydrological features may be inaccurately delineated in areas where vegetation is dense or the ground is otherwise shielded from aerial view. In an effort to improve and automate delineation of hydrological features, we examined the behaviour and final products of the D8 flowrouting algorithm in 2 software environments (TAS and TauDEM for ArcGIS) operating on a high spatial resolution DEM derived using canopy-penetrating light detection and ranging (LiDAR) technology in a pilot study in the Romeo Malette Forest (41.25°N, 81.50°W). Filtered LiDAR data points (5-m spacing) were interpolated using IDW, TIN, and splines, each resulting in a 2.5-m spatial resolution DEM. Results demonstrate improved realism in the characterization of surficial hydrology by LIDAR derived products as compared to applying identical algorithms on existing coarser provincial data. Benefits include the ability to represent streams of lower Strahler order to define crisp watershed boundaries, and the more accurate identification of local depressions that form potentially wet sites. This approach identifies wet sites that should be avoided during forest operations (e.g., skidder traffic) and can provide additional information for trail layout, road planning, and water crossings. By increasing the number of uses of LiDAR, the capital investment in these data becomes increasingly palatable for forest companies interested in obtaining detailed plans of their forest holdings. Key words: LiDAR, DEM, OBM, spatial resolution, interpolation, Strahler stream order, flow routing, topographic wetness
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Hojo, Ai, Kentaro Takagi, Ram Avtar, Takeo Tadono, and Futoshi Nakamura. "Synthesis of L-Band SAR and Forest Heights Derived from TanDEM-X DEM and 3 Digital Terrain Models for Biomass Mapping." Remote Sensing 12, no. 3 (January 21, 2020): 349. http://dx.doi.org/10.3390/rs12030349.
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In this study, we compared the accuracies of above-ground biomass (AGB) estimated by integrating ALOS (Advanced Land Observing Satellite) PALSAR (Phased-Array-Type L-Band Synthetic Aperture Radar) data and TanDEM-X-derived forest heights (TDX heights) at four scales from 1/4 to 25 ha in a hemi-boreal forest in Japan. The TDX heights developed in this study included nine canopy height models (CHMs) and three model-based forest heights (ModelHs); the nine CHMs were derived from the three digital surface models (DSMs) of (I) TDX 12 m DEM (digital elevation model) product, (II) TDX 90 m DEM product and (III) TDX 5 m DSM, which we developed from two TDX–TSX (TerraSAR-X) image pairs for reference, and the three digital terrain models (DTMs) of (i) an airborne Light Detection and Ranging (LiDAR)-based DTM (LiDAR DTM), (ii) a topography-based DTM and (iii) the Shuttle Radar Topography Mission (SRTM) DEM; the three ModelHs were developed from the two TDX-TSX image pairs used in (III) and the three DTMs (i to iii) with the Sinc inversion model. In total, 12 AGB estimation models were developed for comparison. In this study, we included the C-band SRTM DEM as one of the DTMs. According to Walker et al. (2007), the SRTM DEM serves as a DTM for most of the Earth’s surface, except for the areas with extensive tree and/or shrub coverage, e.g., the boreal and Amazon regions. As our test site is located in a hemi-boreal zone with medium forest cover, we tested the ability of the SRTM DEM to serve as a DTM in our test site. This study especially aimed to analyze the capability of the two TDX DEM products (I and II) to estimate AGB in practice in the hemi-boreal region, and to examine how the different forest height creation methods (the simple DSM and DTM subtraction for the nine CHMs and the Sinc inversion model-based approach for the three ModelHs) and the different spatial resolutions of the three DSMs and three DTMs affected the AGB estimation results. We also conducted the slope-class analysis to see how the varying slopes influenced the AGB estimation accuracies. The results show that the combined use of the PALSAR data and the CHM derived from (I) TDX 12 m DEM and (i) LiDAR DTM achieved the highest AGB estimation accuracies across the scales (R2 ranged from 0.82 to 0.97), but the CHMs derived from (I) TDX 12 m DEM and another two DTMs, (ii) and (iii), showed low R2 values at any scales. In contrast, the two CHMs derived from (II) TDX 90 m DEM and both (i) LiDAR DTM and (iii) SRTM DEM showed high R2 values > 0.87 and 0.78, respectively, at the scales > 9.0 ha, but they yielded much lower R2 values at smaller scales. The three ModelHs gave the lowest R2 values across the scales (R2 ranged from 0.39 to 0.60). Analyzed by slope class at the 1.0 ha scale, however, all the 12 AGB estimation models yielded high R2 values > 0.66 at the lowest slope class (0° to 9.9°), including the three ModelHs (R2 ranged between 0.68 to 0.69). The two CHMs derived from (II) TDX 90 m DEM and both (i) LiDAR DTM and (iii) SRTM DEM showed R2 values of 0.80 and 0.71, respectively, at the lowest slope class, while the CHM derived from (I) TDX 12 m DEM and (i) LiDAR DTM showed high R2 values across the slope classes (R2 > 0.82). The results show that (I) TDX 12 m DEM had a high capability to estimate AGB, with a high accuracy across the scales and the slope classes in the form of CHM, but the use of (i) LiDAR DTM was required. On the other hand, (II) TDX 90 m DEM was able to achieve high AGB estimation accuracies not only with (i) LiDAR DTM, but also with (iii) SRTM DEM in the form of CHM, but it was limited to large scales > 9.0 ha; however, all the models developed in this study have the possibility to achieve higher AGB estimation accuracies at the 1.0 ha scale in flat terrains with slope < 10°. The analysis showed the strengths and limitations of each model, and it also indicates that the data creation methods, the spatial resolutions of datasets and topographic features affects the effective spatial scales for AGB mapping, and the optimal combinations of these features should be chosen to obtain high AGB estimation accuracies.
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Štular, Benjamin, Edisa Lozić, and Stefan Eichert. "Airborne LiDAR-Derived Digital Elevation Model for Archaeology." Remote Sensing 13, no. 9 (May 10, 2021): 1855. http://dx.doi.org/10.3390/rs13091855.
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The use of topographic airborne LiDAR data has become an essential part of archaeological prospection, and the need for an archaeology-specific data processing workflow is well known. It is therefore surprising that little attention has been paid to the key element of processing: an archaeology-specific DEM. Accordingly, the aim of this paper is to describe an archaeology-specific DEM in detail, provide a tool for its automatic precision assessment, and determine the appropriate grid resolution. We define an archaeology-specific DEM as a subtype of DEM, which is interpolated from ground points, buildings, and four morphological types of archaeological features. We introduce a confidence map (QGIS plug-in) that assigns a confidence level to each grid cell. This is primarily used to attach a confidence level to each archaeological feature, which is useful for detecting data bias in archaeological interpretation. Confidence mapping is also an effective tool for identifying the optimal grid resolution for specific datasets. Beyond archaeological applications, the confidence map provides clear criteria for segmentation, which is one of the unsolved problems of DEM interpolation. All of these are important steps towards the general methodological maturity of airborne LiDAR in archaeology, which is our ultimate goal.
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Leitão, João P., Matthew Moy de Vitry, Andreas Scheidegger, and Jörg Rieckermann. "Assessing the quality of digital elevation models obtained from mini unmanned aerial vehicles for overland flow modelling in urban areas." Hydrology and Earth System Sciences 20, no. 4 (April 29, 2016): 1637–53. http://dx.doi.org/10.5194/hess-20-1637-2016.
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Abstract. Precise and detailed digital elevation models (DEMs) are essential to accurately predict overland flow in urban areas. Unfortunately, traditional sources of DEM, such as airplane light detection and ranging (lidar) DEMs and point and contour maps, remain a bottleneck for detailed and reliable overland flow models, because the resulting DEMs are too coarse to provide DEMs of sufficient detail to inform urban overland flows. Interestingly, technological developments of unmanned aerial vehicles (UAVs) suggest that they have matured enough to be a competitive alternative to satellites or airplanes. However, this has not been tested so far. In this study we therefore evaluated whether DEMs generated from UAV imagery are suitable for urban drainage overland flow modelling. Specifically, 14 UAV flights were conducted to assess the influence of four different flight parameters on the quality of generated DEMs: (i) flight altitude, (ii) image overlapping, (iii) camera pitch, and (iv) weather conditions. In addition, we compared the best-quality UAV DEM to a conventional lidar-based DEM. To evaluate both the quality of the UAV DEMs and the comparison to lidar-based DEMs, we performed regression analysis on several qualitative and quantitative metrics, such as elevation accuracy, quality of object representation (e.g. buildings, walls and trees) in the DEM, which were specifically tailored to assess overland flow modelling performance, using the flight parameters as explanatory variables. Our results suggested that, first, as expected, flight altitude influenced the DEM quality most, where lower flights produce better DEMs; in a similar fashion, overcast weather conditions are preferable, but weather conditions and other factors influence DEM quality much less. Second, we found that for urban overland flow modelling, the UAV DEMs performed competitively in comparison to a traditional lidar-based DEM. An important advantage of using UAVs to generate DEMs in urban areas is their flexibility that enables more frequent, local, and affordable elevation data updates, allowing, for example, to capture different tree foliage conditions.
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Hogg, A. R., and J. Holland. "An evaluation of DEMs derived from LiDAR and photogrammetry for wetland mapping." Forestry Chronicle 84, no. 6 (December 1, 2008): 840–49. http://dx.doi.org/10.5558/tfc84840-6.
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The Ontario Ministry of Natural Resources (OMNR) and Ducks Unlimited Canada (DUC) have been engaged in developing an efficient and accurate methodology for inventorying wetlands. Their progress in this area has demonstrated that Digital Elevation Models (DEMs) are crucial input for wetland identification and boundary delineation. The provincial DEM, however, has known precision limitations in areas of minimal topographic relief that cause considerable mapping error. This study explored whether wetland mapping derived from bare-earth light detection and ranging (LiDAR) data would overcome the limitations of the provincial DEM. An automated wetland mapping approach was applied to the 2 elevation datasets and the results were compared using 2 methods of validation. One hundred aerial-photo-interpreted sample plots were used to quantitatively measure the ability of each source to separate upland from wetland. An overlay of wetland maps created from the 2 DEM sources was then qualitatively assessed to further clarify the magnitude of discrepancy between the 2 mapping sources. The study concluded that LiDAR showed a significant improvement at p = 0.05 over the provincial DEM for mapping wetlands, improving overall mapping accuracy from 76% to 84%. However, an overlay analysis and qualitative assessment showed the magnitude of this reported improvement is greater than was quantified by the accuracy assessment and that an assessment scheme with different sample units may further elucidate this discrepancy. Key words: LiDAR, DEM, wetland, mapping
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Guillaume, Annie S., Kevin Leempoel, Estelle Rochat, Aude Rogivue, Michel Kasser, Felix Gugerli, Christian Parisod, and Stéphane Joost. "Multiscale Very High Resolution Topographic Models in Alpine Ecology: Pros and Cons of Airborne LiDAR and Drone-Based Stereo-Photogrammetry Technologies." Remote Sensing 13, no. 8 (April 20, 2021): 1588. http://dx.doi.org/10.3390/rs13081588.
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The vulnerability of alpine environments to climate change presses an urgent need to accurately model and understand these ecosystems. Popularity in the use of digital elevation models (DEMs) to derive proxy environmental variables has increased over the past decade, particularly as DEMs are relatively cheaply acquired at very high resolutions (VHR; <1 m spatial resolution). Here, we implement a multiscale framework and compare DEM-derived variables produced by Light Detection and Ranging (LiDAR) and stereo-photogrammetry (PHOTO) methods, with the aim of assessing their relevance and utility in species distribution modelling (SDM). Using a case study on the arctic-alpine plant, Arabis alpina, in two valleys in the western Swiss Alps, we show that both LiDAR and PHOTO technologies can be relevant for producing DEM-derived variables for use in SDMs. We demonstrate that PHOTO DEMs, up to a spatial resolution of at least 1 m, rivalled the accuracy of LiDAR DEMs, largely owing to the customizability of PHOTO DEMs to the study sites compared to commercially available LiDAR DEMs. We obtained DEMs at spatial resolutions of 6.25 cm–8 m for PHOTO and 50 cm–32 m for LiDAR, where we determined that the optimal spatial resolutions of DEM-derived variables in SDM were between 1 and 32 m, depending on the variable and site characteristics. We found that the reduced extent of PHOTO DEMs altered the calculations of all derived variables, which had particular consequences on their relevance at the site with heterogenous terrain. However, for the homogenous site, SDMs based on PHOTO-derived variables generally had higher predictive powers than those derived from LiDAR at matching resolutions. From our results, we recommend carefully considering the required DEM extent to produce relevant derived variables. We also advocate implementing a multiscale framework to appropriately assess the ecological relevance of derived variables, where we caution against the use of VHR-DEMs finer than 50 cm in such studies.
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Smart, Graeme. "LiDAR resolution for catchment-inclusive hydrodynamic models." E3S Web of Conferences 40 (2018): 06031. http://dx.doi.org/10.1051/e3sconf/20184006031.
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Hydrodynamic models are usually based on LiDAR but there is little information on the LiDAR resolution required for appropriate model accuracy. In this study, algorithms to prepare DEM and roughness grids suitable for hydrodynamic modelling of both catchment and floodplain are applied to low, medium and high point-density LiDAR. The medium resolution LiDAR (9 points/m2) provided elevation and roughness grids sufficiently accurate for hydrodynamic flood mapping of urban and rural floodplains. Low-resolution LiDAR (3 points/m2) is considered adequate for hill catchments. Attention is required where narrow-crested control structures exist. Mapping and upscaling are discussed.
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Elaksher, Ahmed F. "Using LIDAR-based DEM to orthorectify Ikonos panchromatic images." Optics and Lasers in Engineering 47, no. 6 (June 2009): 629–35. http://dx.doi.org/10.1016/j.optlaseng.2009.01.005.
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Yang Shujuan, 杨书娟, 张珂殊 Zhang Keshu, and 邵永社 Shao Yongshe. "DEM Interpolation Algorithm of Data from Spiral Scanning Lidar." Chinese Journal of Lasers 45, no. 11 (2018): 1110006. http://dx.doi.org/10.3788/cjl201845.1110006.
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Teng, J., J. Vaze, D. Dutta, and S. Marvanek. "Rapid Inundation Modelling in Large Floodplains Using LiDAR DEM." Water Resources Management 29, no. 8 (March 14, 2015): 2619–36. http://dx.doi.org/10.1007/s11269-015-0960-8.
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Arnold, Neil, and Gareth Rees. "Effects of digital elevation model spatial resolution on distributed calculations of solar radiation loading on a High Arctic glacier." Journal of Glaciology 55, no. 194 (2009): 973–84. http://dx.doi.org/10.3189/002214309790794959.
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AbstractHigh-resolution airborne lidar data are used to produce digital elevation models (DEMs) of an arctic valley glacier (midre Lovénbreen, Svalbard) at resolutions of 2.5–2000 m, using three different interpolation schemes. These data are used in a distributed model of solar radiation loading for glaciers. When the mean of all lidar measurements within a DEM cell is used to calculate cell height, the differences between the finest- (2.5 m) and coarsest-resolution (2000 m) DEMs for the calculated annual whole-glacier spatial means of total potential direct-beam solar radiation, potential duration of direct-beam solar radiation, and intensity of potential direct-beam solar radiation are 20%, 56% and −23% of the 2.5 m DEM values respectively. A resolution change from 2.5 m to 200 m affects the whole-glacier spatial mean summer net solar radiative flux by an average of 5%, and the summer melt production from the glacier by an average of 3% compared with the 2.5 m DEM values, for the years 2001–03. These changes are largely driven by underestimation of shading by surrounding topography at coarser DEM resolutions. This dependency is reduced in the second and third interpolation schemes, especially at resolutions finer than 50 m, which use the maximum lidar height measurement in some or all DEM cells. These results suggest that resolutions of ∼50 m are the coarsest that should be adopted in high-resolution glacier surface energy-balance models for glaciers of similar size and in similar topographic situations to midre Lovénbreen, and that the impact of DEM resolution on calculated solar radiation receipts can be reduced by an appropriate choice of DEM interpolation scheme.
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Kamiński, Mirosław. "The Impact of Quality of Digital Elevation Models on the Result of Landslide Susceptibility Modeling Using the Method of Weights of Evidence." Geosciences 10, no. 12 (December 3, 2020): 488. http://dx.doi.org/10.3390/geosciences10120488.
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The paper discusses the impact that the quality of the digital elevation model (DEM) has on the final result of landslide susceptibility modeling (LSM). The landslide map was developed on the basis of the analysis of archival geological maps and the Light Detection and Ranging (LiDAR) digital elevation model. In addition, complementary field studies were conducted. In total, 92 landslides were inventoried and their degree of activity was assessed. An inventory of the landslides was prepared using a 1-m-LiDAR DEM and field research. Two digital photogrammetric elevation models with an elevation pixel resolution of 20 m were used for landslide susceptibility modeling. The first digital elevation model was obtained from a LiDAR point cloud (DEM–airborne laser scanning (ALS)), while the second model was developed based on archival digital stereo-pair aerial images (DEM–Land Parcel Identification System (LPIS)). Both models were subjected to filtration using a Gaussian low-pass filter to reduce errors in their elevation relief. Then, using ArcGIS software, a differential model was generated to illustrate the differences in morphology between the models. The maximum differences in topographic elevations between the DEM–ALS and DEM–LPIS models were calculated. The Weights-of-Evidence model is a geostatistical method used for the landslide susceptibility modeling. Six passive factors were employed in the process of susceptibility generation: elevation, slope gradient, exposure, topographic roughness index (TRI), distance from tectonic lines, and distance from streams. As a result, two landslide susceptibility maps (LSM) were obtained. The accuracy of the landslide susceptibility models was assessed based on the Receiver Operating Characteristic (ROC) curve index. The area under curve (AUC) values obtained from the ROC curve indicate that the accuracy of classification for the LSM–DEM–ALS model was 78%, and for the LSM–LPIS–DEM model was 73%.
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Serifoglu, C., O. Gungor, and V. Yilmaz. "PERFORMANCE EVALUATION OF DIFFERENT GROUND FILTERING ALGORITHMS FOR UAV-BASED POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 245–51. http://dx.doi.org/10.5194/isprsarchives-xli-b1-245-2016.
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Digital Elevation Model (DEM) generation is one of the leading application areas in geomatics. Since a DEM represents the bare earth surface, the very first step of generating a DEM is to separate the ground and non-ground points, which is called ground filtering. Once the point cloud is filtered, the ground points are interpolated to generate the DEM. LiDAR (Light Detection and Ranging) point clouds have been used in many applications thanks to their success in representing the objects they belong to. Hence, in the literature, various ground filtering algorithms have been reported to filter the LiDAR data. Since the LiDAR data acquisition is still a costly process, using point clouds generated from the UAV images to produce DEMs is a reasonable alternative. In this study, point clouds with three different densities were generated from the aerial photos taken from a UAV (Unmanned Aerial Vehicle) to examine the effect of point density on filtering performance. The point clouds were then filtered by means of five different ground filtering algorithms as Progressive Morphological 1D (PM1D), Progressive Morphological 2D (PM2D), Maximum Local Slope (MLS), Elevation Threshold with Expand Window (ETEW) and Adaptive TIN (ATIN). The filtering performance of each algorithm was investigated qualitatively and quantitatively. The results indicated that the ATIN and PM2D algorithms showed the best overall ground filtering performances. The MLS and ETEW algorithms were found as the least successful ones. It was concluded that the point clouds generated from the UAVs can be a good alternative for LiDAR data.
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Serifoglu, C., O. Gungor, and V. Yilmaz. "PERFORMANCE EVALUATION OF DIFFERENT GROUND FILTERING ALGORITHMS FOR UAV-BASED POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 245–51. http://dx.doi.org/10.5194/isprs-archives-xli-b1-245-2016.
Full textAbstract:
Digital Elevation Model (DEM) generation is one of the leading application areas in geomatics. Since a DEM represents the bare earth surface, the very first step of generating a DEM is to separate the ground and non-ground points, which is called ground filtering. Once the point cloud is filtered, the ground points are interpolated to generate the DEM. LiDAR (Light Detection and Ranging) point clouds have been used in many applications thanks to their success in representing the objects they belong to. Hence, in the literature, various ground filtering algorithms have been reported to filter the LiDAR data. Since the LiDAR data acquisition is still a costly process, using point clouds generated from the UAV images to produce DEMs is a reasonable alternative. In this study, point clouds with three different densities were generated from the aerial photos taken from a UAV (Unmanned Aerial Vehicle) to examine the effect of point density on filtering performance. The point clouds were then filtered by means of five different ground filtering algorithms as Progressive Morphological 1D (PM1D), Progressive Morphological 2D (PM2D), Maximum Local Slope (MLS), Elevation Threshold with Expand Window (ETEW) and Adaptive TIN (ATIN). The filtering performance of each algorithm was investigated qualitatively and quantitatively. The results indicated that the ATIN and PM2D algorithms showed the best overall ground filtering performances. The MLS and ETEW algorithms were found as the least successful ones. It was concluded that the point clouds generated from the UAVs can be a good alternative for LiDAR data.
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Leitão, J. P., M. Moy de Vitry, A. Scheidegger, and J. Rieckermann. "Assessing the quality of Digital Elevation Models obtained from mini-Unmanned Aerial Vehicles for overland flow modelling in urban areas." Hydrology and Earth System Sciences Discussions 12, no. 6 (June 12, 2015): 5629–70. http://dx.doi.org/10.5194/hessd-12-5629-2015.
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Abstract. Precise and detailed Digital Elevation Models (DEMs) are essential to accurately predict overland flow in urban areas. Unfortunately, traditional sources of DEM remain a bottleneck for detailed and reliable overland flow models, because the resulting DEMs are too coarse to provide DEMs of sufficient detail to inform urban overland flows. Interestingly, technological developments of Unmanned Aerial Vehicles (UAVs) suggest that they have matured enough to be a competitive alternative to satellites or airplanes. However, this has not been tested so far. In this this study we therefore evaluated whether DEMs generated from UAV imagery are suitable for urban drainage overland flow modelling. Specifically, fourteen UAV flights were conducted to assess the influence of four different flight parameters on the quality of generated DEMs: (i) flight altitude, (ii) image overlapping, (iii) camera pitch and (iv) weather conditions. In addition, we compared the best quality UAV DEM to a conventional Light Detection and Ranging (LiDAR)-based DEM. To evaluate both the quality of the UAV DEMs and the comparison to LiDAR-based DEMs, we performed regression analysis on several qualitative and quantitative metrics, such as elevation accuracy, quality of object representation (e.g., buildings, walls and trees) in the DEM, which were specifically tailored to assess overland flow modelling performance, using the flight parameters as explanatory variables. Our results suggested that, first, as expected, flight altitude influenced the DEM quality most, where lower flights produce better DEMs; in a similar fashion, overcast weather conditions are preferable, but weather conditions and other factors influence DEM quality much less. Second, we found that for urban overland flow modelling, the UAV DEMs performed competitively in comparison to a traditional LiDAR-based DEM. An important advantage of using UAVs to generate DEMs in urban areas is their flexibility that enables more frequent, local and affordable elevation data updates, allowing, for example, to capture different tree foliage conditions.
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Lo, Pai-Chiao, Wei Lo, Tai-Tien Wang, and Yu-Chung Hsieh. "Application of Geological Mapping Using Airborne-Based LiDAR DEM to Tunnel Engineering: Example of Dongao Tunnel in Northeastern Taiwan." Applied Sciences 11, no. 10 (May 12, 2021): 4404. http://dx.doi.org/10.3390/app11104404.
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The use of digital elevation models (DEMs) that use airborne-based light detection and the ranging technique (airborne-based LiDAR) to understand large-scale geological structures has become important in geological surveying and mapping. Taking the Dongao Tunnel area in northeastern Taiwan as the study area, this study used the airborne-based LiDAR DEM and related value-added maps to interpret the topographic and geomorphic features of the area and identify locations for geological investigation. The characteristics of the rock mass were observed on-site and revealed by excavation of the highway tunnel in the study area; they were compared with the interpreted topographic and geomorphic features to determine the potential of using 1 m-resolution LiDAR DEM in geological surveys and in the evaluation of engineering characteristics of underground rock masses. The results of this study demonstrated that the DEM accurately captured geomorphic features: the strata composed of slate and schist had distinct appearances in both the clinometric map and the hillshade map; the locations of faults, lineaments, and drainage were consistent with those observed on-site, and the positions of these features were captured more accurately than those on conventional maps. Evident microrelief features, including the distribution of scarps, erosion gullies, and mini-drainage systems provide an effective basis for interpreting a deep-seated gravitational deformation slope and for an on-site inspection for validation. The use of high-resolution LiDAR DEM to interpret geomorphic features along with geological surveys provides a more comprehensive understanding of the survey area, supporting surveys and geological mapping, revealing the locations of potential slope failures, and enabling the assessment of tunnel engineering risks.
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Ghuffar, Sajid. "DEM Generation from Multi Satellite PlanetScope Imagery." Remote Sensing 10, no. 9 (September 13, 2018): 1462. http://dx.doi.org/10.3390/rs10091462.
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Planet Labs have recently launched a large constellation of small satellites (3U cubesats) capable of imaging the whole Earth landmass everyday. These small satellites capture multiple images of an area on consecutive days or sometimes on the same day with a spatial resolution of 3–4 m. Planet Labs endeavors to operate the constellation in a nadir pointing mode, however, the view angle of these satellites currently varies within a few degrees from the nadir leading to varying B/H ratio for overlapping image pairs. Due to relatively small scene footprint and small off-nadir angle, the baseline to height ratio (B/H) of the overlapping PlanetScope images is often less than 1:10, which is not ideal for 3D reconstruction. Therefore, this paper explores the potential of Digital Elevation Model generation from this multi-date, multi-satellite PlanetScope imagery. The DEM generation from multiple PlanetScope images is achieved using a volumetric stereo reconstruction technique, which applies semi global matching in georeferenced object space. The results are evaluated using a LiDAR based DEM (5 m) over Mount Teide (3718 m) in Canary Islands and the ALOS (30 m) DEM on rugged terrain of the Nanga Parbat massif (8126 m) in the western Himalaya range. The proposed methodology is then applied on images from two PlanetScope satellites overpasses within a couple of minutes difference to compute the DEM of the Khurdopin glacier in the Karakoram range, known for its recent surge. The quantitative assessment of the generated elevation models is done by comparing statistics of the elevation differences between the reference LiDAR and ALOS DEM and the PlanetScope DEM. The Normalized Median of Absolute Deviation (NMAD) of the elevation differences between the computed PlanetScope DEM and LiDAR DEM is 4.1 m and the elevation differences for the ALOS DEM over stable terrain is 3.9 m. The results show that PlanetScope imagery can lead to sufficient quality DEM even with a small baseline to height ratio. Therefore, the daily PlanetScope imagery is a valuable data source and the DEM generated from this imagery can potentially be employed in numerous applications requiring multi temporal DEMs.
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Khalid, N. F., A. H. M. Din, K. M. Omar, M. F. A. Khanan, A. H. Omar, A. I. A. Hamid, and M. F. Pa’suya. "OPEN-SOURCE DIGITAL ELEVATION MODEL (DEMs) EVALUATION WITH GPS AND LiDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W1 (September 30, 2016): 299–306. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w1-299-2016.
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Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER GDEM), Shuttle Radar Topography Mission (SRTM), and Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) are freely available Digital Elevation Model (DEM) datasets for environmental modeling and studies. The quality of spatial resolution and vertical accuracy of the DEM data source has a great influence particularly on the accuracy specifically for inundation mapping. Most of the coastal inundation risk studies used the publicly available DEM to estimated the coastal inundation and associated damaged especially to human population based on the increment of sea level. In this study, the comparison between ground truth data from Global Positioning System (GPS) observation and DEM is done to evaluate the accuracy of each DEM. The vertical accuracy of SRTM shows better result against ASTER and GMTED10 with an RMSE of 6.054 m. On top of the accuracy, the correlation of DEM is identified with the high determination of coefficient of 0.912 for SRTM. For coastal zone area, DEMs based on airborne light detection and ranging (LiDAR) dataset was used as ground truth data relating to terrain height. In this case, the LiDAR DEM is compared against the new SRTM DEM after applying the scale factor. From the findings, the accuracy of the new DEM model from SRTM can be improved by applying scale factor. The result clearly shows that the value of RMSE exhibit slightly different when it reached 0.503 m. Hence, this new model is the most suitable and meets the accuracy requirement for coastal inundation risk assessment using open source data. The suitability of these datasets for further analysis on coastal management studies is vital to assess the potentially vulnerable areas caused by coastal inundation.
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Aditya, Sandi. "PEMBUATAN MODEL TIGA DIMENSI (3D) HASIL INTEGRASI DATA LiDAR DAN DATA SURVEI HIDROGRAFI." Seminar Nasional Geomatika 2 (February 9, 2018): 561. http://dx.doi.org/10.24895/sng.2017.2-0.454.
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<p>Visualisasi informasi geospasial tiga dimensi (3D) untuk penyajian data batimetri yang diperoleh dari hasil survei hidrografi terhitung jarang dilakukan. Hal ini dikarenakan kegiatan survei yang dilakukan pada Pusat Pemetaan Kelautan dan Lingkungan Pantai (PPKLP) Badan Informasi Geospasial (BIG) pada saat dahulu masih dilakukan pada skala menengah (1:50.000 dan 1:25.000). Seiring dengan perkembangan teknologi dan kebutuhan data pada skala yang lebih besar (1:10.000) terutama untuk mendukung program nasional tol laut di daerah pelabuhan-pelabuhan, maka PPKLP sudah mulai menerapkan metode dan peralatan survei yang lebih baik. Kualitas data hasil survei akan semakin terlihat pada skala besar dan dengan divisualisasikan pada bentuk 3D. Pada penelitian ini, penulis membentuk <em>Digital Elevation Model</em> (DEM) laut hasil survei hidrografi kemudian menggabungkan dengan DEM darat dari data LiDAR dalam satu referensi tinggi EGM 2008. Hasil penggabungan kedua DEM divisualisasikan dalam bentuk 3D yang dapat memerlihatkan DEM yang kontinu dari darat ke laut serta penggambaran posisi garis pantai pada saat pasang tertinggi, rata-rata, dan surut terendah.<strong></strong></p><strong>Kata kunci</strong>: 3 Dimensi, DEM, Batimetri, LiDAR
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Green, P. Corey, Harold E. Burkhart, John W. Coulston, Philip J. Radtke, and Valerie A. Thomas. "Auxiliary information resolution effects on small area estimation in plantation forest inventory." Forestry: An International Journal of Forest Research 93, no. 5 (June 1, 2020): 685–93. http://dx.doi.org/10.1093/forestry/cpaa012.
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Abstract In forest inventory, traditional ground-based resource assessments are often expensive and time-consuming forcing managers to reduce sample sizes to meet budgetary and logistical constraints. Small area estimation (SAE) is a class of statistical estimators that uses a combination of traditional survey data and linearly related auxiliary information to improve estimate precision. These techniques have been shown to improve the precision of stand-level inventory estimates in loblolly pine plantations using lidar height percentiles and thinning status as covariates. In this study, the effects of reduced lidar point-cloud densities and lower digital elevation model (DEM) spatial resolutions were investigated for total planted volume estimates using area-level SAE models. In the managed Piedmont pine plantation conditions evaluated, lower lidar point-cloud densities and DEM spatial resolutions were found to have minimal effects on estimates and precision. The results of this study are promising to those interested in incorporating SAE methods into forest inventory programs.
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Ismail, Z., M. F. Abdul Khanan, F. Z. Omar, M. Z. Abdul Rahman, and M. R. Mohd Salleh. "EVALUATING ERROR OF LIDAR DERIVED DEM INTERPOLATION FOR VEGETATION AREA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W1 (September 29, 2016): 141–50. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w1-141-2016.
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Light Detection and Ranging or LiDAR data is a data source for deriving digital terrain model while Digital Elevation Model or DEM is usable within Geographical Information System or GIS. The aim of this study is to evaluate the accuracy of LiDAR derived DEM generated based on different interpolation methods and slope classes. Initially, the study area is divided into three slope classes: (a) slope class one (0° – 5°), (b) slope class two (6° – 10°) and (c) slope class three (11° – 15°). Secondly, each slope class is tested using three distinctive interpolation methods: (a) Kriging, (b) Inverse Distance Weighting (IDW) and (c) Spline. Next, accuracy assessment is done based on field survey tachymetry data. The finding reveals that the overall Root Mean Square Error or RMSE for Kriging provided the lowest value of 0.727 m for both 0.5 m and 1 m spatial resolutions of oil palm area, followed by Spline with values of 0.734 m for 0.5 m spatial resolution and 0.747 m for spatial resolution of 1 m. Concurrently, IDW provided the highest RMSE value of 0.784 m for both spatial resolutions of 0.5 and 1 m. For rubber area, Spline provided the lowest RMSE value of 0.746 m for 0.5 m spatial resolution and 0.760 m for 1 m spatial resolution. The highest value of RMSE for rubber area is IDW with the value of 1.061 m for both spatial resolutions. Finally, Kriging gave the RMSE value of 0.790m for both spatial resolutions.
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Chu, Hone-Jay, Ruey-An Chen, Yi-Hsing Tseng, and Cheng-Kai Wang. "Identifying LiDAR sample uncertainty on terrain features from DEM simulation." Geomorphology 204 (January 2014): 325–33. http://dx.doi.org/10.1016/j.geomorph.2013.08.016.
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Garzón Barrero, Julián, Carlos Eduardo Cubides Burbano, and Gonzalo Jiménez-Cleves. "Quantifying the Effect of LiDAR Data Density on DEM Quality." Ciencia e Ingeniería Neogranadina 31, no. 2 (December 31, 2021): 149–69. http://dx.doi.org/10.18359/rcin.5776.
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LiDAR sensors capture three-dimensional point clouds with high accuracy and density; since they are regularly obtained, interpolation methods are required to generate a regular grid. Given the large size of its files, processing becomes a challenge for researchers with not very powerful computer stations. This work aims to balance the sampling density and the volume of data, preserving the sensitivity of representation of complex topographic shapes as a function of three surface descriptors: slope, curvature, and roughness. This study explores the effect of the density of LiDAR data on the accuracy of the Digital Elevation Model (DEM), using a ground point cloud of 32 million measurements obtained from a LiDAR flight over a complex topographic area of 156 ha. Digital elevation models with different relative densities to the total point dataset were produced (100, 75, 50, 25, 10, and 1 % and at different grid sizes 23, 27, 33, 46, 73, and 230cm). Accuracy was evaluated using the Inverse Distance Weighted and Kriging interpolation algorithms, obtaining 72 surfaces from which their error statistics were calculated: root mean square error, mean absolute error, mean square error, and prediction effectiveness index; these were used to evaluate the quality of the results in contrast with validation data corresponding to 10 % of the original sample. The results indicated that Kriging was the most efficient algorithm, reducing data to 1 % without statistically significant differences with the original dataset, and curvature was the morphometric parameter with the most significant negative impact on interpolation accuracy.
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Escobar Villanueva, Jairo R., Luis Iglesias Martínez, and Jhonny I. Pérez Montiel. "DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations." Sensors 19, no. 14 (July 20, 2019): 3205. http://dx.doi.org/10.3390/s19143205.
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Geospatial products, such as digital elevation models (DEMs), are important topographic tools for tackling local flood studies. This study investigates the contribution of LiDAR elevation data in DEM generation based on fixed-wing unmanned aerial vehicle (UAV) imaging for flood applications. More specifically, it assesses the accuracy of UAV-derived DEMs using the proposed LiDAR-derived control point (LCP) method in a Structure-from-Motion photogrammetry processing. Also, the flood estimates (volume and area) of the UAV terrain products are compared with a LiDAR-based reference. The applied LCP-georeferencing method achieves an accuracy comparable with other studies. In addition, it has the advantage of using semi-automatic terrain data classification and is readily applicable in flood studies. Lastly, it proves the complementarity between LiDAR and UAV photogrammetry at the local level.
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Nguyễn, Thị Thục Anh, Quốc Cương Tăng, and Thanh Tùng Đặng. "Phương pháp nắn trực giao ảnh vệ tinh độ phân giải cao sử dụng mô hình số bề mặt DSM Lidar." Tạp chí Khoa học Đo đạc và Bản đồ, no. 11 (March 1, 2012): 26–35. http://dx.doi.org/10.54491/jgac.2012.11.479.
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Ngày nay, ảnh viễn thám độ phân giải cao và dữ liệu Lidar đã trở thành hai trong các nguồn dữ liệu chính phục vụ việc xây dựng dữ liệu địa lý độ chính xác cao. Trong các dự án quét Lidar thường chụp ảnh máy bay đồng thời tuy nhiên nếu do điều kiện kỹ thuật (bay quét Lidar đêm hay do trục trặc với máy ảnh) không có ảnh máy bay chụp kèm, ảnh viễn thám độ phân giải cao sẽ là nguồn dữ liệu thay thế tương đối đồng bộ với dữ liệu Lidar về độ chi tiết, có thể kết hợp trong quá trình xử lý cũng như chiết tách thông tin. Việc nắn ảnh viễn thám bằng mô hình số bề mặt DSM cho phép tăng độ chính xác của ảnh trực giao, khử dịch chuyển do chênh cao của đối tượng với bề mặt DEM đặc biệt là cho khu vực đô thị nơi tập trung dân cư với mật độ nhà cao. Bài báo trình bày kết quả nghiên cứu về các phương pháp nắn ảnh trực giao, yêu cầu về điểm khống chế ảnh, đề xuất qui trình nắn ảnh dùng DSM từ Lidar. Kết quả thử nghiệm nắn ảnh WorldView1 độ phân giải 0.5m dùng DSM từ dữ liệu Lidar mật độ 2.5 điểm/m2 cho khu vực thành phố Bắc Giang được phân tích và so sánh với kết quả nắn bằng DEM. Kết luận và các khuyến cáo cho việc nắn ảnh vệ tính độ phân giải cao dùng DSM Lidar được đưa ra dựa trên kết quả thử nghiệm này.
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Jawak, S. D., S. N. Panditrao, and A. J. Luis. "Airborne LIDAR and high resolution satellite data for rapid 3D feature extraction." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 573–80. http://dx.doi.org/10.5194/isprsarchives-xl-8-573-2014.
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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.
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Amrullah, C., D. Suwardhi, and I. Meilano. "PRODUCT ACCURACY EFFECT OF OBLIQUE AND VERTICAL NON-METRIC DIGITAL CAMERA UTILIZATION IN UAV-PHOTOGRAMMETRY TO DETERMINE FAULT PLANE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-6 (June 7, 2016): 41–48. http://dx.doi.org/10.5194/isprsannals-iii-6-41-2016.
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This study aims to see the effect of non-metric oblique and vertical camera combination along with the configuration of the ground control points to improve the precision and accuracy in UAV-Photogrammetry project. The field observation method is used for data acquisition with aerial photographs and ground control points. All data are processed by digital photogrammetric process with some scenarios in camera combination and ground control point configuration. The model indicates that the value of precision and accuracy increases with the combination of oblique and vertical camera at all control point configuration. The best products of the UAV-Photogrammetry model are produced in the form of Digital Elevation Model (DEM) compared to the LiDAR DEM. Furthermore, DEM from UAV-Photogrammetry and LiDAR are used to define the fault plane by using cross-section on the model and interpretation to determine the point at the extreme height of terrain changes. The result of the defined fault planes indicate that two models do not show any significant difference.