The Highest Peaks of the Mountains: Comparing the Use of GNSS, LiDAR Point Clouds, DTMs, Databases, Maps, and Historical Sources

Advances in remote data acquisition techniques have contributed to the flooding of society with spatial data sets and information. Widely available spatial data sets, including digital terrain models (DTMs) from aerial laser scanning (ALS) data, are finding more and more new applications. The article analyses and compares the heights of the 14 highest peaks of the Polish Carpathians derived from different data sources. Global navigation satellite system (GNSS) geodetic measurements were used as reference. The comparison primarily involves ALS data, and selected peaks’ GNSS measurements carried out with Xiaomi Mi 8 smartphones were also compared. Recorded raw smartphone GNSS measurements were used for calculations in post-processing mode. Other data sources were, among others, global and local databases and models and topographic maps (modern and old). The article presents an in-depth comparison of Polish and Slovak point clouds for two peaks. The results indicate the possible use of large-area laser scanning in determining the maximum heights of mountain peaks and the need to use geodetic GNSS measurements for selected peaks. For the Polish peak of Rysy, the incorrect classification of point clouds causes its height to be overestimated. The conclusions presented in the article can be used in the dissemination of knowledge and to improve positioning methods.

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Big LIDAR Data in Digital Earth: Ways Out of Dead End

10.51130/graphicon-2020-2-3-46 ◽

2020 ◽

pp. paper46-1-paper46-10

Author(s):

Ilya Rylskiy

Keyword(s):

Data Storage ◽

Spatial Data ◽

Laser Scanning ◽

Three Dimensional ◽

Alternative Methods ◽

Measuring Instruments ◽

Lidar Data ◽

Data Sets ◽

Spatial Data Sets ◽

Limited Precision

During past 25 years, laser scanning has evolved from an experimental method into a fully autonomous family of Earth remote sensing methods. Now this group of methods provides the most accurate and detailed spatial data sets, while the cost of data is constantly falling, the number of measuring instruments (laser scanners) is constantly growing. The volumes of data that will be obtained during the surveys in the coming decades will allow the creation of the first sub-global coverage of the planet. However, the flip side of high accuracy and detail is the need to store fantastically large volumes of three-dimensional data without loss of accuracy. At the same time, the ability to work with the specified data in both 2D and 3D mode should be improved. Standard storage methods (file method, geodatabases, archiving, etc) solve the problem only partially. At the same time, there are some other alternative methods that can remove current restrictions and lead to the emergence of more flexible and functional spatial data infrastructures. One of the most flexible and promising ways of laser data storage and processing are quadtree and octree-based approaches. Of course, these approaches are more complicated than typical file data structures, that are commonly used for LIDAR data storage, but they allow users to solve some typical negative features of point datasets (processing speed, non-topological spatial structure, limited precision, etc.).

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WebGL Visualisation of 3D Environmental Models Based on Finnish Open Geospatial Data Sets

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-3-163-2014 ◽

2014 ◽

Vol XL-3 ◽

pp. 163-169 ◽

Cited By ~ 3

Author(s):

A. Krooks ◽

J. Kahkonen ◽

L. Lehto ◽

P. Latvala ◽

M. Karjalainen ◽

...

Keyword(s):

Spatial Data ◽

Laser Scanning ◽

National Level ◽

Image Data ◽

Point Clouds ◽

Open Pit ◽

Data Sets ◽

Environmental Models ◽

3D City Model ◽

Recent Developments

Recent developments in spatial data infrastructures have enabled real time GIS analysis and visualization using open input data sources and service interfaces. In this study we present a new concept where metric point clouds derived from national open airborne laser scanning (ALS) and photogrammetric image data are processed, analyzed, finally visualised a through open service interfaces to produce user-driven analysis products from targeted areas. The concept is demonstrated in three environmental applications: assessment of forest storm damages, assessment of volumetric changes in open pit mine and 3D city model visualization. One of the main objectives was to study the usability and requirements of national level photogrammetric imagery in these applications. The results demonstrated that user driven 3D geospatial analyses were possible with the proposed approach and current technology, for instance, the landowner could assess the amount of fallen trees within his property borders after a storm easily using any web browser. On the other hand, our study indicated that there are still many uncertainties especially due to the insufficient standardization of photogrammetric products and processes and their quality indicators.

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THE CONCEPT OF LIDAR DATA QUALITY ASSESSMENT IN THE CONTEXT OF BIM MODELING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-1-w2-61-2019 ◽

2019 ◽

Vol XLII-1/W2 ◽

pp. 61-66 ◽

Cited By ~ 3

Author(s):

A. Warchoł

Keyword(s):

Spatial Data ◽

Point Clouds ◽

Lidar Data ◽

Data Sets ◽

Density Parameter ◽

Large Area ◽

3D Data ◽

Data Quality Assessment ◽

Other Information ◽

Architectural Detail

<p><strong>Abstract.</strong> LiDAR technology has revolutionized the area of 3D data acquisition. It is possible to obtain in a very fast and accurate way geometric and other information for a large area . Along with the development of design technology, LiDAR point clouds are often used to create inventory models of building objects and installations. This paper presents the possibilities of assessing LiDAR data for BIM modeling. The areas in which the assessment and description of obtained TLS data is important are presented. In addition to the attributes for assessing the quality of spatial data contained in the ISO 19157 standard, a density parameter was proposed. Examples of point clouds with different density for the architectural detail are presented in the final part of the work. For the attributes describing LiDAR data sets the levels of importance have been proposed for.</p>

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Spatial data-sets for geographical referencing

10.3403/00334343 ◽

1994 ◽

Keyword(s):

Spatial Data ◽

Data Sets ◽

Spatial Data Sets

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Spatial data-sets for geographical referencing

10.3403/02124652 ◽

2000 ◽

Keyword(s):

Spatial Data ◽

Data Sets ◽

Spatial Data Sets

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Spatial data-sets for geographical referencing

10.3403/02503338 ◽

2002 ◽

Keyword(s):

Spatial Data ◽

Data Sets ◽

Spatial Data Sets

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An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level

Open Geosciences ◽

10.1515/geo-2020-0159 ◽

2020 ◽

Vol 12 (1) ◽

pp. 580-597

Author(s):

Mohamad Hamzeh ◽

Farid Karimipour

Keyword(s):

Spatial Data ◽

Spatial Models ◽

Model Performance ◽

Essential Elements ◽

Petroleum System ◽

Petroleum Exploration ◽

System Level ◽

Data Sets ◽

Petroleum Potential ◽

Spatial Data Sets

AbstractAn inevitable aspect of modern petroleum exploration is the simultaneous consideration of large, complex, and disparate spatial data sets. In this context, the present article proposes the optimized fuzzy ELECTRE (OFE) approach based on combining the artificial bee colony (ABC) optimization algorithm, fuzzy logic, and an outranking method to assess petroleum potential at the petroleum system level in a spatial framework using experts’ knowledge and the information available in the discovered petroleum accumulations simultaneously. It uses the characteristics of the essential elements of a petroleum system as key criteria. To demonstrate the approach, a case study was conducted on the Red River petroleum system of the Williston Basin. Having completed the assorted preprocessing steps, eight spatial data sets associated with the criteria were integrated using the OFE to produce a map that makes it possible to delineate the areas with the highest petroleum potential and the lowest risk for further exploratory investigations. The success and prediction rate curves were used to measure the performance of the model. Both success and prediction accuracies lie in the range of 80–90%, indicating an excellent model performance. Considering the five-class petroleum potential, the proposed approach outperforms the spatial models used in the previous studies. In addition, comparing the results of the FE and OFE indicated that the optimization of the weights by the ABC algorithm has improved accuracy by approximately 15%, namely, a relatively higher success rate and lower risk in petroleum exploration.

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Leveraging TLS as a Calibration and Validation Tool for MLS and ULS Mapping of Savanna Structure and Biomass at Landscape-Scales

Remote Sensing ◽

10.3390/rs13020257 ◽

2021 ◽

Vol 13 (2) ◽

pp. 257 ◽

Cited By ~ 1

Author(s):

Shaun R. Levick ◽

Tim Whiteside ◽

David A. Loewensteiner ◽

Mitchel Rudge ◽

Renee Bartolo

Keyword(s):

Remote Sensing ◽

Vegetation Structure ◽

Laser Scanning ◽

Canopy Cover ◽

Structural Diversity ◽

Point Clouds ◽

Large Area ◽

Calibration And Validation ◽

Savanna Vegetation ◽

Landscape Scales

Savanna ecosystems are challenging to map and monitor as their vegetation is highly dynamic in space and time. Understanding the structural diversity and biomass distribution of savanna vegetation requires high-resolution measurements over large areas and at regular time intervals. These requirements cannot currently be met through field-based inventories nor spaceborne satellite remote sensing alone. UAV-based remote sensing offers potential as an intermediate scaling tool, providing acquisition flexibility and cost-effectiveness. Yet despite the increased availability of lightweight LiDAR payloads, the suitability of UAV-based LiDAR for mapping and monitoring savanna 3D vegetation structure is not well established. We mapped a 1 ha savanna plot with terrestrial-, mobile- and UAV-based laser scanning (TLS, MLS, and ULS), in conjunction with a traditional field-based inventory (n = 572 stems > 0.03 m). We treated the TLS dataset as the gold standard against which we evaluated the degree of complementarity and divergence of structural metrics from MLS and ULS. Sensitivity analysis showed that MLS and ULS canopy height models (CHMs) did not differ significantly from TLS-derived models at spatial resolutions greater than 2 m and 4 m respectively. Statistical comparison of the resulting point clouds showed minor over- and under-estimation of woody canopy cover by MLS and ULS, respectively. Individual stem locations and DBH measurements from the field inventory were well replicated by the TLS survey (R2 = 0.89, RMSE = 0.024 m), which estimated above-ground woody biomass to be 7% greater than field-inventory estimates (44.21 Mg ha−1 vs 41.08 Mg ha−1). Stem DBH could not be reliably estimated directly from the MLS or ULS, nor indirectly through allometric scaling with crown attributes (R2 = 0.36, RMSE = 0.075 m). MLS and ULS show strong potential for providing rapid and larger area capture of savanna vegetation structure at resolutions suitable for many ecological investigations; however, our results underscore the necessity of nesting TLS sampling within these surveys to quantify uncertainty. Complementing large area MLS and ULS surveys with TLS sampling will expand our options for the calibration and validation of multiple spaceborne LiDAR, SAR, and optical missions.

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