A Review of Standards for Airborne LiDAR Data Acquisition, Processing, QA/QC, and Delivery

Lohani, Bharat; Ghosh, S. K.; Dashora, Ajay

doi:10.1007/978-981-13-2330-0_22

Cited by 6 publications

(5 citation statements)

References 3 publications

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“…However, we still lack convenient and widespread access to high quality DEM. Quantification of the accuracy of these products is still under active development [52], and may not be applicable across broad scales. Thus, channel boundary accuracy from passive imagery still remains an issue of concern, especially given the higher cost required to acquire lidar-derived DEMs [53].…”

Section: Importance Of Considering Channel Delineation Accuracymentioning

confidence: 99%

Identifying Factors That Influence Accuracy of Riparian Vegetation Classification and River Channel Delineation Mapped Using 1 m Data

Quackenbush

Stehman

2021

Remote Sensing

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Riparian vegetation delineation includes both the process of delineating the riparian zone and classifying vegetation within that zone. We developed a holistic framework to assess riparian vegetation delineation that includes evaluating channel boundary delineation accuracy using a combination of pixel- and object-based metrics. We also identified how stream order, riparian zone width, riparian land use, and image shadow influenced the accuracy of delineation and classification. We tested the framework by evaluating vegetation vs. non-vegetation riparian zone maps produced by applying random forest classification to aerial photographs with a 1 m pixel size. We assessed accuracy of the riparian vegetation classification and channel boundary delineation for two rivers in the northeastern United States. Overall accuracy for the channel boundary delineation was generally above 80% for both sites, while object-based accuracy revealed that 50% of delineated channel was less than 5 m away from the reference channel. Stream order affected channel boundary delineation accuracy while land use and image shadows influenced riparian vegetation classification accuracy; riparian zone width had little impact on observed accuracy. The holistic approach to quantification of accuracy that considers both channel boundary delineation and vegetation classification developed in this study provides an important tool to inform riparian management.

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Section: Importance Of Considering Channel Delineation Accuracymentioning

confidence: 99%

Identifying Factors That Influence Accuracy of Riparian Vegetation Classification and River Channel Delineation Mapped Using 1 m Data

Quackenbush

Stehman

2021

Remote Sensing

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“…The use of national datasets for regional to global scale modelling is often hampered by their fragmentary availability, varying data acquisition and treatment methods and possibly limited data access (e.g. Höfle and Rutzinger, 2011;Lohani et al, 2018). Such lack of harmonization can introduce additional important uncertainties.…”

Section: Model Input Data At the Continental Scalementioning

confidence: 99%

“…Numerous countries nowadays produce national DEMs based on airborne LiDAR surveys down to submeter pixel size (e.g. Lohani et al, 2018). Here we focus on DEMs having a (nearly) global or European coverage (Table 4).…”

Section: Topographymentioning

confidence: 99%

Measuring, modelling and managing gully erosion at large scales: A state of the art

Vanmaercke

Panagos

Vanwalleghem

et al. 2021

Earth-Science Reviews

157

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“…Ai et al used Light Detection and Ranging (LiDAR) technology to detect characteristics in sidewalks including width, grade, and slopes [ 5 ]. LiDAR instruments have been used to extract the landscape characteristics [ 21 , 22 , 23 , 24 ], however based on a study in the US [ 24 ], the accuracy of LiDAR is typically 15–20 cm (~6 in) and 1/3–1 m for vertical and horizontal measurements, respectively. Therefore, this technology does not provide enough accuracy to detect small cracks and their depths.…”

Section: Literature Reviewmentioning

confidence: 99%

A Vision-Based Approach for Sidewalk and Walkway Trip Hazards Assessment

Cohen

Fernie

Fekr

2020

IJERPH

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Tripping hazards on the sidewalk cause many falls annually, and the inspection and repair of these hazards cost cities millions of dollars. Currently, there is not an efficient and cost-effective method to monitor the sidewalk to identify any possible tripping hazards. In this paper, a new portable device is proposed using an Intel RealSense D415 RGB-D camera to monitor the sidewalks, detect the hazards, and extract relevant features of the hazards. This paper first analyzes the effects of environmental factors contributing to the device’s error and compares different regression techniques to calibrate the camera. The Gaussian Process Regression models yielded the most accurate predictions with less than 0.09 mm Mean Absolute Errors (MAEs). In the second phase, a novel segmentation algorithm is proposed that combines the edge detection and region-growing techniques to detect the true tripping hazards. Different examples are provided to visualize the output results of the proposed method.

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A Review of Standards for Airborne LiDAR Data Acquisition, Processing, QA/QC, and Delivery

Cited by 6 publications

References 3 publications

Identifying Factors That Influence Accuracy of Riparian Vegetation Classification and River Channel Delineation Mapped Using 1 m Data

Identifying Factors That Influence Accuracy of Riparian Vegetation Classification and River Channel Delineation Mapped Using 1 m Data

Measuring, modelling and managing gully erosion at large scales: A state of the art

A Vision-Based Approach for Sidewalk and Walkway Trip Hazards Assessment

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