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Differencing digital terrain models (DTMs) generated from multitemporal airborne light detection and ranging (lidar) data provide accurate and detailed information about three-dimensional (3D) changes on the Earth. However, noticeable spurious errors along flight paths are often included in the differencing results, hindering the accurate analysis of the topographic changes. This paper proposes a new scalable method to alleviate the problematic systematic errors with a high degree of automation in consideration of the practical limitations raised when processing the rapidly increasing amount of large-scale lidar datasets. The proposed method focused on estimating the displacements caused by vertical positioning errors, which are the most critical error source, and adjusting the DTMs already produced as basic lidar products without access to the point cloud and raw data from the laser scanner. The feasibility and effectiveness of the proposed method were evaluated with experiments with county-level multitemporal airborne lidar datasets in Indiana, USA. The experimental results demonstrated that the proposed method could estimate the vertical displacement reasonably along the flight paths and improve the county-level lidar differencing results by reducing the problematic errors and increasing consistency across the flight paths. The improved differencing results presented in this paper are expected to provide more consistent information about topographic changes in Indiana. In addition, the proposed method can be a feasible solution to upcoming problems induced by rapidly increasing large-scale multitemporal lidar given recent active government-driven lidar data acquisition programs, such as the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP). [ABSTRACT FROM AUTHOR]

The state of Indiana maintains approximately 120 salt storage facilities strategically distributed across the state for winter operations. In April 2023, those facilities contained approximately 217,000 tons of salt with an estimated value of USD 21 million. Accurate inventories at each facility during the winter season are important for scheduling re-supply so the facilities do not run out of salt. Inventories are also important at the end of the season for restocking to provide balanced inventories. This paper describes the implementation of a portable pole-mounted LiDAR system to measure salt stockpile inventory at 120 salt storage facilities in Indiana. Using two INDOT staff members, the end-of-season inventory took 9 working days, with volumetric inventories provided within 24 h of data collection. To provide an independent evaluation of the methodologies, the Hovermap ST backpack was used at selected facilities to provide control volumes. This system has a range of 100 m and an accuracy of ±3 cm, which reduces the occlusion to less than 8%. The pre-season facility capacity ranged from 0% to 100%, with an average of 66% full across all facilities. The post-season facility percentage ranged from 3% to 100%, with an average of 70% full. In addition, permanent roof-mounted LiDAR systems were deployed at two facilities to evaluate the effectiveness of monitoring salt stockpile inventories during winter operation activities. Plans are now underway to install fixed LiDAR systems at 15 additional facilities for the 2023–2024 winter season. [ABSTRACT FROM AUTHOR]

Transportation agencies in northern environments spend a considerable amount of their budget on salt for winter operations. For example, in the state of Indiana, there are approximately 140 salt storage facilities distributed throughout the state and the state expends between USD 30 M and USD 60 M on inventory and delivery each year. Historical techniques of relying on visual estimates of salt stockpiles can be inaccurate and do not scale well for managing the supply chain during the winter or planning for re-supply during summer months. This paper describes the implementation of a portable pole mounted LiDAR system that can be used to inventory a large barn in under 15 min and describes how this system has been deployed over 90 times at 30 facilities. A quick and easy accuracy test, based upon conservation of volume, was used to provide an independent check on the system performance by repositioning portions of the salt pile. Those tests indicated stockpile volumes can be estimated with an accuracy of approximately 0.1%. The paper concludes by discussing how this technology can be permanently installed near the roof for systematic monitoring throughout the year. [ABSTRACT FROM AUTHOR]

The recent record-breaking Laurentian Great Lakes water levels have led to severe erosion along many shorelines, but quantification of this erosion has not yet been widely reported. In this paper, novel shoreline measurements of Lake Michigan erosion, using a LiDAR-equipped unmanned aerial vehicle (UAV). The vehicle surveyed two beaches, Dune Acres and Beverly Shores, along the Indiana shoreline of southern Lake Michigan over the period 2018–2019, which are compared to existing aircraft-based LiDAR surveys. The UAV surveys reveal extensive shoreline erosion and recession at both beaches since the low water conditions of 2012–2013, with both beaches losing 70.7 and 64.8 m3/m of dry beach volume. Shoreline recession over this period was nearly 35 m for both beaches. In the period 2018–2019, the UAV measurements reveal continued beach erosion and the transition of beach topography to a steep, actively eroding foredune. Storm erosion resolved by short-interval surveys highlight the episodic nature of shoreline erosion, with 8 m3/m of material lost over a stormy two-week period at Dune Acres. While large, the erosion and recession observed at both beaches is in keeping with limited historical observations of shoreline change during high water episodes. However, the observed overall shoreline recession slopes of 1:29 (water level rise : recession) are much less than historical slopes, suggesting that the beaches are not yet in equilibrium with the high water levels and that erosion will continue in the foreseeable future while lake levels remain high. • A novel LiDAR-equipped unmanned aerial vehicle (UAV) provides timely estimates of shoreline erosion at two Lake Michigan beaches during period of rapid, large water level increase. • Shoreline recession at both beaches between 2013 and 2019 is nearly 35 m, with 70 m3/m of material lost to submergence and erosion. • The presently-observed shoreline recession slope of 1:29 suggests that at the time of the 2019 surveys both beaches were not yet in equilibrium with the elevated water levels. • While the observed erosion is significant, it is consistent with Lake Michigan beach erosion during similar periods of water level increase. [ABSTRACT FROM AUTHOR]