Airborne lidar change detection: An overview of Earth sciences applications

Okyay, U.; Telling, J. W.; Glennie, C. L.; Dietrich, W. E.

doi:10.1016/j.earscirev.2019.102929

Cited by 97 publications

(52 citation statements)

References 153 publications

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“…On the one hand, such multi-temporal measurements make it possible to check on a profile-by-profile basis whether unevenness in the base has an effect on the evenness of the manufactured surface. On the other hand, volume-based analysis or change detection analysis [38] can also be carried out. Hereby, the quality of the spatial data synchronization plays a decisive role in the derivation of absolute differences between temporally offset data recordings.…”

Section: D Analysis Of the Concrete Paving Thickness Using Multitempmentioning

confidence: 99%

Process Evaluation for Smart Concrete Road Construction:Road Surface and Thickness Evaluation Using High-Speed LiDAR Technology

Skalecki

Sesselmann²,

Rechkemmer

et al. 2021

Automation

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The enhancement of new quality criteria in highway construction is a key aspect to improving the construction process and lifetime of road. In particular, mobile laser scanning systems are nowadays able to provide realistic 3D elevation profiles of a road to detect anomalies. In this context, this study utilizes a high-accuracy high-speed mobile mapping vehicle and evaluates a weighted longitudinal profile as an improved measure for evenness analysis. For comparison a classical method with a rolling straight edge was evaluated on the same road section and observed effects are discussed. The second focus is the areal reconstruction of the road thickness. For this purpose, a modern method was developed to spatially synchronize two high-speed laser scans using reference boxes next to the road, to transfer the point clouds into a surface model and to calculate the layer thickness. This procedure was conceptually validated by some pointwise measurements of the layer thickness. With this information, imperfections in the base layer could be detected automatically over a wide area at an early stage and countermeasures might be initiated before constructing the highway.

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Section: D Analysis Of the Concrete Paving Thickness Using Multitempmentioning

confidence: 99%

Process Evaluation for Smart Concrete Road Construction:Road Surface and Thickness Evaluation Using High-Speed LiDAR Technology

Skalecki

Sesselmann²,

Rechkemmer

et al. 2021

Automation

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“…Round about the same time airborne LiDAR systems became available [70] which were able to map surfaces at very high resolution from the local to the regional scale. Today, these systems are arguably the most commonly used systems in geomorphic-relevant applications [71]. Other systems are airborne and spaceborne SAR (synthetic aperture RADAR) and InSAR systems (interferometric SAR, [72]) that enable geomorphology to be monitored with accuracy levels to the mm.…”

Section: Remote Sensing Techniques For Monitoring Geomorphology-terramentioning

confidence: 99%

“…There are many different types of LiDARs [71] installed on various RS platforms: the ground-based LiDAR (TLS-terrestrial laser scanner, [120]) and the MLS-mobile laser scanner, the airborne-based LiDAR (ALS-airborne laser scanner, installed on UAVs [121], microlights, and airplanes [114]), and even satellite-based LiDAR (SLS-satellite laser scanner, LiDAR-GEDI-LiDAR [45,122,123], and ICESat−2; [124], Figure 7). Comparatively simple LiDARs are limited to one or two returns per shot, usually the first and last return which typically represent the top of the canopy (first) and the ground (last).…”

Section: Lidar and Radar Altimetersmentioning

confidence: 99%

“…DiffInSAR (in areas with no or sparse vegetation) PSI (essentially in urban areas, dense time series available almost globally since end of 2014) C-band [317,321] RADARSAT- 2 3 DiffInSAR (in areas with no or sparse vegetation) PSI (essentially in urban areas, dense time series rarely available) C-band [322,323] DiffInSAR (in non-forested areas) PSI (essentially in urban areas, long and dense time series rarely available) L-band [325] Airborne LiDAR 2 , e.g., Optech ALTM Gemini LiDAR (four return, echo intensity recording), for changes in the order of dm or more One laser, max. 167,000 shots/s [71,319,326,327] UAV photogrammetry 1 , e.g., Octocopter X8000 (platform, gimbal, various camera systems)…”

Section: Cosmo Skymedmentioning

confidence: 99%

See 1 more Smart Citation

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

Lausch

Schaepman

Skidmore³

et al. 2020

Remote Sensing

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The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.

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“…However, two-dimensional land cover products cannot represent the vertical differentiation of geo-objects. Thus, airborne laser scanning (ALS), which can directly acquire three-dimensional geometry information of geo-objects, has been introduced into urban morphology and ecology research [4,5], such as change detection [6,7] and carbon storage mapping [8,9], Unfortunately, a lot of different types of geo-objects, such as buildings, vegetation, and cars, may appear in a small urban local neighborhood [10,11], and it is difficult to automatically extract all geo-objects in the urban environment from raw ALS geometry information. Consequently, many researchers only extracted the ground [12,13], the buildings [14,15], or the powerlines [16,17].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Classification of Urban ALS Data by Using Geometry and Intensity Information

Liu

Chen

et al. 2019

Sensors

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Airborne laser scanning (ALS) can acquire both geometry and intensity information of geo-objects, which is important in mapping a large-scale three-dimensional (3D) urban environment. However, the intensity information recorded by ALS will be changed due to the flight height and atmospheric attenuation, which decreases the robustness of the trained supervised classifier. This paper proposes a hierarchical classification method by separately using geometry and intensity information of urban ALS data. The method uses supervised learning for stable geometry information and unsupervised learning for fluctuating intensity information. The experiment results show that the proposed method can utilize the intensity information effectively, based on three aspects, as below. (1) The proposed method improves the accuracy of classification result by using intensity. (2) When the ALS data to be classified are acquired under the same conditions as the training data, the performance of the proposed method is as good as the supervised learning method. (3) When the ALS data to be classified are acquired under different conditions from the training data, the performance of the proposed method is better than the supervised learning method. Therefore, the classification model derived from the proposed method can be transferred to other ALS data whose intensity is inconsistent with the training data. Furthermore, the proposed method can contribute to the hierarchical use of some other ALS information, such as multi-spectral information.

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Airborne lidar change detection: An overview of Earth sciences applications

Cited by 97 publications

References 153 publications

Process Evaluation for Smart Concrete Road Construction:Road Surface and Thickness Evaluation Using High-Speed LiDAR Technology

Process Evaluation for Smart Concrete Road Construction:Road Surface and Thickness Evaluation Using High-Speed LiDAR Technology

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

Hierarchical Classification of Urban ALS Data by Using Geometry and Intensity Information

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