4D objects-by-change: Spatiotemporal segmentation of geomorphic surface change from LiDAR time series

Anders, Katharina; Winiwarter, Lukas; Lindenbergh, Roderik; Williams, Jack G.; Vos, Sander; Höfle, Bernhard

doi:10.1016/j.isprsjprs.2019.11.025

Cited by 24 publications

(15 citation statements)

References 45 publications

(61 reference statements)

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“…With increased acquisition frequencies and a trend towards 4D point cloud analyses using more than 2 epochs (e.g. Anders et al, 2020), these considerations become increasingly important.…”

Section: Discussionmentioning

confidence: 99%

Influence of Ranging Uncertainty of Terrestrial Laser Scanning on Change Detection in Topographic 3d Point Clouds

Winiwarter

Anders

Wujanz³

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Terrestrial laser scanners are commonly used for remotely sensing natural surfaces into 3D point clouds. Time series of such 3D point clouds can be analysed to gain information of surface changes that are induced by Earth surface shaping processes. The atomic unit in time series analysis is a bitemporal change detection and quantification. This should involve an estimation of the minimum quantifiable change, the Level of Detection, to separate signal from noise, e.g. stemming from the measurement. To enable such an estimation through error propagation, a model of the sensing instrument’s measurement uncertainty is required. In this work, we present an investigation on the ranging component of terrestrial laser scanning on this uncertainty and its influence on 3D distances between point clouds of two epochs. Specifically, we analyse the effects of incidence angle, intensity and range for different object materials, and make additional considerations with respect to waveform information returned by the sensor. We estimate a model for the rangefinder uncertainty of a terrestrial laser scanner and apply it on experimental data. The results show that using a sensor-specific model of ranging uncertainty allows an appropriate estimation of the Level of Detection. At a range of 60 m and a rotational displacement of 10°, this Level of Detection ranges between 0.1 mm to 1 mm for a white and a grey surface and up to 5 mm for a black surface. The completeness of the detection of significant change ranges from 60.2 % (black) to 89.8 % (grey) for the proposed method and from 65.5 % to 88.9 % for the baseline, when compared to tachymeter measurements. The similarity between the results is expected and suggests the validity of error propagation for the derivation of the Level of Detection.

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“…With increased acquisition frequencies and a trend towards 4D point cloud analyses using more than 2 epochs (e.g. Anders et al, 2020), these considerations become increasingly important.…”

Section: Discussionmentioning

confidence: 99%

Influence of Ranging Uncertainty of Terrestrial Laser Scanning on Change Detection in Topographic 3d Point Clouds

Winiwarter

Anders

Wujanz³

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…A similar approach would be to use our clustering results and identified change patterns as input to the region-growing approach of Anders et al (2020). In this way we could combine advantages of both approaches by making the identification of the corresponding regions for each distinct deformation pattern more exact, without having to define possible deformation patterns in advance.…”

Section: Clustering Methodsmentioning

confidence: 99%

Coastal Change Patterns from Time Series Clustering of Permanent Laser Scan Data

Kuschnerus¹,

Lindenbergh²,

Vos³

2020

Preprint

Self Cite

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Abstract. Sandy coasts are constantly changing environments governed by complex interacting processes. Permanent laser scanning is a promising technique to monitor such coastal areas and support analysis of geomorphological deformation processes. This novel technique delivers 3D representations of a part of the coast at hourly temporal and centimetre spatial resolution and allows to observe small scale changes in elevation over extended periods of time. These observations have the potential to improve understanding and modelling of coastal deformation processes. However, to be of use to coastal researchers and coastal management, an efficient way to find and extract deformation processes from the large spatio-temporal data set is needed. In order to allow data mining in an automated way, we extract time series in elevation or range and use unsupervised learning algorithms to derive a partitioning of the observed area according to change patterns. We compare three well known clustering algorithms, k-means, agglomerative clustering and DBSCAN, and identify areas that undergo similar evolution during one month. We test if they fulfil our criteria for a suitable clustering algorithm on our exemplary data set. The three clustering methods are applied to time series of 30 epochs (during one month) extracted from a data set of daily scans covering a part of the coast at Kijkduin, the Netherlands. A small section of the beach, where a pile of sand was accumulated by a bulldozer is used to evaluate the performance of the algorithms against a ground truth. The k-means algorithm and agglomerative clustering deliver similar clusters, and both allow to identify a fixed number of dominant deformation processes in sandy coastal areas, such as sand accumulation by a bulldozer or erosion in the intertidal area. The DBSCAN algorithm finds clusters for only about 44 % of the area and turns out to be more suitable for the detection of outliers, caused for example by temporary objects on the beach. Our study provides a methodology to efficiently mine a spatio-temporal data set for predominant deformation patterns with the associated regions, where they occur.

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“…One of the greatest challenges in aeolian monitoring using RS is the spatio-temporal recording and delimitation of highly dynamic dune migration as well as subtle changes that occur on the surface due to transported sand. The implementation and the connection of airborne, spaceborne (LiDAR, optical and RADAR) with high-frequency spatial and temporal close-range terrestrial laser scanning (TLS), as well as in-situ measurements will enable an almost continuous monitoring and assessment of 3D-4DD dune dynamics and morphology, their interactions and geomorphic activity, helping to understand continuous surfaces over longer periods of time [165].…”

Section: Aeolian Landformsmentioning

confidence: 99%

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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4D objects-by-change: Spatiotemporal segmentation of geomorphic surface change from LiDAR time series

Cited by 24 publications

References 45 publications

Influence of Ranging Uncertainty of Terrestrial Laser Scanning on Change Detection in Topographic 3d Point Clouds

Influence of Ranging Uncertainty of Terrestrial Laser Scanning on Change Detection in Topographic 3d Point Clouds

Coastal Change Patterns from Time Series Clustering of Permanent Laser Scan Data

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

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