Uncertainty assessment and probabilistic change detection using terrestrial and airborne lidar

Jalobeanu, A.; Kim, Angela M.; Runyon, Scott C.; Olsen, R. C.; Kruse, Fred A.

doi:10.1117/12.2049611

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…ICP after removing the ground points would work for the trees, but may result in worse co-registration at ground level. Aligning the flight lines of the QM8 and VLP16 systems using a Bayesian method as described by Jalobeanu et al [30] would reduce errors associated with misalignment. Figure 6 displays the point distribution height profiles, subdivided for the return number.…”

Section: Point Cloudsmentioning

confidence: 99%

Evaluating Data Inter-Operability of Multiple UAV–LiDAR Systems for Measuring the 3D Structure of Savanna Woodland

Bartholomeus

Calders

Whiteside³

et al. 2022

Remote Sensing

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For vegetation monitoring, it is crucial to understand which changes are caused by the measurement setup and which changes are true representations of vegetation dynamics. UAV–LiDAR offers great possibilities to measure vegetation structural parameters; however, UAV–LiDAR sensors are undergoing rapid developments, and the characteristics are expected to keep changing over the years, which will introduce data inter-operability issues. Therefore, it is important to determine whether datasets acquired by different UAV–LiDAR sensors can be interchanged and if changes through time can accurately be derived from UAV–LiDAR time series. With this study, we present insights into the magnitude of differences in derived forest metrics in savanna woodland when three different UAV–LiDAR systems are being used for data acquisition. Our findings show that all three systems can be used to derive plot characteristics such as canopy height, canopy cover, and gap fractions. However, there are clear differences between the metrics derived with different sensors, which are most apparent in the lower parts of the canopy. On an individual tree level, all UAV–LiDAR systems are able to accurately capture the tree height in a savanna woodland system, but significant differences occur when crown parameters are measured with different systems. Less precise systems result in underestimations of crown areas and crown volumes. When comparing UAV–LiDAR data of forest areas through time, it is important to be aware of these differences and ensure that data inter-operability issues do not influence the change analysis. In this paper, we want to stress that it is of utmost importance to realise this and take it into consideration when combining datasets obtained with different sensors.

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Section: Point Cloudsmentioning

confidence: 99%

Evaluating Data Inter-Operability of Multiple UAV–LiDAR Systems for Measuring the 3D Structure of Savanna Woodland

Bartholomeus

Calders

Whiteside³

et al. 2022

Remote Sensing

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“…Then, least square fit residual statistics are used to build an empirical uncertainty estimate, within a nonparametric regression [24] framework. In [9], such an approach is used, and a local Student-t [25] pdf for each grid height is estimated from a set of independent LiDAR points.…”

Section: A Promise Of Multiviewmentioning

confidence: 99%

“…For flood applications, we need to know the probability of a given elevation to exceed a given threshold. For change detection, we need to determine the probability of a height difference to exceed a predefined confidence interval, as explained in [9], which depends on a preset false-alarm rate and a combination of local height uncertainties. Clearly, a single error measure for an entire model (RMS error, or global accuracy rating) is not sufficient for such situations, as the quality and information content of the input images have no reason to be spatially uniform.…”

Section: Introductionmentioning

confidence: 99%

The Unknown Spatial Quality of Dense Point Clouds Derived From Stereo Images

Jalobeanu¹,

Gonçalves

2015

IEEE Geosci. Remote Sensing Lett.

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Is it possible to use stereo images to generate point clouds and to compute rigorous uncertainty maps? Currently, neither modern commercial photogrammetric software nor state-of-the-art algorithms are able to provide a spatial distribution of uncertainty. In this letter, we explain why this is the case, despite a high demand from the user community. Many applications would indeed benefit from the availability of error bars on each point, as uncertainties on derived models and quantities could be accurately predicted. For instance, change detection could be performed rigorously since the statistical significance of observed changes could be computed. In this letter, we focus on dense stereo methods. We first explain that it is not possible to derive reliable predictive uncertainties mainly due to matching and modeling errors. Our research shows that both intrinsic and practical limitations of the algorithms lead to unpredictable artifacts. Then, we focus on the use of empirical errors, showing that, despite the redundancy brought by multiview stereo, there is a fundamental limitation due to the unknown density of independent measurements. We think that these problems will represent a big challenge for the future, as these limitations cannot be addressed by algorithmic design, computational power, or imaging sensor technology.Index Terms-Error analysis, image matching, image processing, point clouds, probability, spatial quality, uncertainty. 1545-598X

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“…This paper only describes the NPS Remote Sensing Center research effort and selected results from that research. The full project included many other aspects, some of which are summarized in other papers presented at SPIE Defense and Security 2014 [22][23][24][25]. Partners in the research who contributed significantly to the overall project include the NPS Virtualization and Cloud Computing Lab, the NPS Hastily Formed Networks (HFN) group, the NPS CORE Lab, the NPS Center for Asymmetric Warfare (CAW), NOAA/NGDC, and San Diego State University.…”

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confidence: 99%

Multispectral, hyperspectral, and LiDAR remote sensing and geographic information fusion for improved earthquake response

et al. 2014

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The Naval Postgraduate School (NPS) Remote Sensing Center (RSC) and research partners have completed a remote sensing pilot project in support of California post-earthquake-event emergency response. The project goals were to dovetail emergency management requirements with remote sensing capabilities to develop prototype map products for improved earthquake response. NPS coordinated with emergency management services and first responders to compile information about essential elements of information (EEI) requirements. A wide variety of remote sensing datasets including multispectral imagery (MSI), hyperspectral imagery (HSI), and LiDAR were assembled by NPS for the purpose of building imagery baseline data; and to demonstrate the use of remote sensing to derive ground surface information for use in planning, conducting, and monitoring post-earthquake emergency response. Worldview-2 data were converted to reflectance, orthorectified, and mosaicked for most of Monterey County; CA. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired at two spatial resolutions were atmospherically corrected and analyzed in conjunction with the MSI data. LiDAR data at point densities from 1.4 pts/ m 2 to over 40 points/ m 2 were analyzed to determine digital surface models. The multimodal data were then used to develop change detection approaches and products and other supporting information. Analysis results from these data along with other geographic information were used to identify and generate multi-tiered products tied to the level of post-event communications infrastructure (internet access + cell, cell only, no internet/cell). Technology transfer of these capabilities to local and state emergency response organizations gives emergency responders new tools in support of post-disaster operational scenarios.

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Uncertainty assessment and probabilistic change detection using terrestrial and airborne lidar

Cited by 3 publications

References 8 publications

Evaluating Data Inter-Operability of Multiple UAV–LiDAR Systems for Measuring the 3D Structure of Savanna Woodland

Evaluating Data Inter-Operability of Multiple UAV–LiDAR Systems for Measuring the 3D Structure of Savanna Woodland

The Unknown Spatial Quality of Dense Point Clouds Derived From Stereo Images

Multispectral, hyperspectral, and LiDAR remote sensing and geographic information fusion for improved earthquake response

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