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Jason M. Stoker

Publications and source records attributed to Jason M. Stoker.

At least 73 records · Page 4Linked to original sources
USGS2025

Evaluating Three-Dimensional Elevation Program lidar consistency and accuracy at scale using cloud-native, open-source methods

The U.S. Geological Survey three-dimensional elevation program (3DEP) has significantly expanded national lidar coverage, necessitating scalable, reproducible methods for assessing data quality across diverse terrains and acquisition conditions. This study introduces a cloud-native, open-source workflow designed to evaluate the geometric accuracy and consistency of 3DEP lidar data sets at a national scale. Leveraging tools such as the Point Data Abstraction Library, Open3D, and Amazon Web Services infrastructure, the workflow integrates global navigation satellite system‐surveyed ground control points and terrestrial laser scanning data to validate airborne lidar collections. Two case studies demonstrate the application of this process. In Puerto Rico, the process identified vertical biases and inconsistencies in vegetated areas, while in Iowa and Arizona, the process confirmed high vertical accuracy with minimal bias. The results underscore the effectiveness of combining cloud computing with open-source tools to perform large-scale lidar data quality assessments. This process offers a reproducible, efficient solution for nationwide validation of 3DEP data sets, supporting enhanced decision-making in geospatial applications.

Photogrammetric Engineering and Remote Sensing↗
USGS2025

The 3D National Topography Model Call for Action—Part 2: The Next Generation 3D Elevation Program

The three-dimensional (3D) National Topography Model initiative to integrate elevation and hydrography data includes the next generation of hydrography data from the 3D Hydrography Program and the next generation of elevation data from the 3D Elevation Program (3DEP). The first-ever collection of light detection and ranging (lidar) data for the nation (IfSAR for Alaska) provides a critical baseline reference, and the addition of multiple repeat elevation mapping projects as part of the next generation of 3DEP would substantially expand analysis capabilities. As the U.S. Geological Survey (USGS) is closing in on our goal of complete coverage with 3DEP data available or in progress for 98.3 percent of the Nation at the end of fiscal year 2024, the USGS is already transitioning to the next generation of 3DEP. Based on the 3D Nation Study results and input from a broad range of stakeholders, the USGS National Geospatial Program has finalized a new design for 3DEP that provides increased lidar quality levels and refresh rates. The new program is designed with more flexibility to meet changing user needs and take advantage of improvements in mapping technologies. The program will aim to expand the level of interagency coordination for topobathymetric lidar acquisition for inland rivers. The next generation of 3DEP will also aim to emphasize research, including advancing program design, products, and services and engaging and leveraging the evolving 3D industry. Research goals also include becoming more flexible in meeting user needs and taking advantage of evolving remote-sensing technologies. The program also plans to move from focusing on producing standard products to producing a concept of a 3D Nation Ecosystem with a variety of inputs, products, and services.

Circular↗
USGS2024

Assessing the vertical accuracy of digital elevation models by quality level and land cover

The vertical accuracy of elevation data in coastal environments is critical because small variations in elevation can affect an area’s exposure to waves, tides, and storm-related flooding. Elevation data contractors typically quantify the vertical accuracy of lidar-derived digital elevation models (DEMs) on a per-project basis to gauge whether the datasets meet quality and accuracy standards. Here, we collated over 5200 contractor elevation checkpoints along the Atlantic and Gulf of Mexico coasts of the United States that were collected for project-level analyses produced for assessing DEMs acquired for the U.S. Geological Survey’s Three-Dimensional Elevation Program. We used land cover data to quantify non-vegetated vertical accuracy and vegetated vertical accuracy statistics (overall and by point spacing bins) and assessed elevation error by land cover class. We found the non-vegetated vertical accuracy had an overall root mean square error of 6.9 cm and vegetated areas had a 95th percentile vertical error of 22.3 cm. Point spacing was generally positively correlated to elevation accuracy, but sample size limited the ability to interpret results from accuracy by land cover, particularly in wetlands. Based on the specific questions a researcher may be asking, use of literature or fieldwork could assist with enhancing error statistics in underrepresented classes.

Remote Sensing Letters↗
USGS2024

Enhancing assessments of coastal wetland migration potential with sea-level rise: Accounting for uncertainty in elevation data, tidal data, and future water levels

Sea-level rise rates are predicted to surpass rates of wetland vertical adjustment in the coming decades in many areas, increasing the potential for wetland submergence. Information on where wetland migration is possible can help natural resource managers for planning land acquisition or enhancing habitat connectivity to bolster adaptation of coastal wetlands to rising seas. Elevation-based models of wetland migration are often hampered with uncertainty associated with ground surface elevation, current water levels (i.e., tides and extreme water levels), and future water levels from sea-level rise. Here, we developed an approach that involved digital elevation model error reduction and the use of Monte Carlo simulations that utilize uncertainty assumptions regarding elevation error, contemporary water levels, and future sea levels to identify potential wetland migration areas. Our analyses were developed for Duvall and Nassau Counties in northeastern Florida (USA). We focus on the migration of regularly oceanic-flooded wetlands (i.e., flooded by oceanic water daily) and irregularly oceanic-flooded wetlands (i.e., flooded by oceanic water less frequently than daily). For two relative sea-level rise scenarios based on the 0.5 m and the 1.5 m global mean sea-level rise scenarios, we quantified migration by wetland flooding frequency class and identified land cover and land use types that are vulnerable to future exposure to oceanic waters. The variability in total coverage and relative coverage of wetland migration from our results highlights how topography and accelerated sea-level rise interact. Our wetland migration results communicate uncertainty by showing flooding frequency class as probabilistic outputs.

Florida↗
USGS2023

Elevation-based probabilistic mapping of irregularly flooded wetlands along the northern Gulf of Mexico coast

Irregularly flooded wetlands are found above the mean high water tidal datum and are exposed to tides and saltwater less frequently than daily. These wetlands provide important ecosystem services, such as providing habitat for fish and wildlife, enhancing water quality, ameliorating flooding impacts, supporting coastal food webs, and protecting upslope areas from erosion. Mapping irregularly flooded wetlands is challenging given their expansive coverage and dynamic nature. Furthermore, coastal wetlands are expected to change over the coming century due to sea-level rise and changes in the frequency and intensity of extreme storms. Consequently, coastal managers need baseline information on the spatial distribution of wetlands along with efficient and repeatable methods for observing changes. In this study, we used coastal wetlands from existing land use land cover data, best available lidar-derived digital elevation models, and Monte Carlo simulations to incorporate elevation uncertainty to create a probabilistic map of irregularly flooded wetlands along the northern Gulf of Mexico coast (USA). Our approach integrated findings from a review of coastal wetland elevation error in lidar datasets and an analysis of spatial autocorrelations of wetland elevation. We found a positive correlation ( r = 0.563, p < 0.0001) when comparing the probability estimated from a digital elevation model and in situ elevation observations. The differences in probability had a mean bias error of −0.04 (i.e., digital elevation model-based probability tends to be slightly lower), a mean absolute error of 0.20, and a root mean square error of 0.26. Beyond this overall validation, we explored error metrics for land cover classes and lidar collection details. To quantify areal coverage of the probabilistic output, we classified the probability values into equal bins using an interval of 0.33. The areal coverage of the lowest probability bin (“unlikely”; probability ≤0.33) was separated into the upper and lower portions of the irregularly flooded wetland zone. Of the coastal wetlands along the northern Gulf of Mexico coast about 38% were classified as unlikely and low with the greatest coverage in south Louisiana and the Everglades and around 33% were classified as unlikely and high with the greatest coverage in the Everglades and Texas. The relative coverage within the highest probability bin (“likely”; probability >0.66) covered around 13%, with the greatest coverage in south Florida, south Louisiana, and Texas. The framework developed in this study can be transferred to other coastal wetland areas and updated to observe changes with sea-level rise.

Gulf of Mexico coastline↗
USGS2023

Evaluating and mitigating the impact of systematic geolocation error on canopy height measurement performance of GEDI

NASA's Global Ecosystem Dynamics Investigation (GEDI) is designed to provide high-resolution measurements of forest structure and topography between 52° N and S. However, current geolocation accuracy may limit further science applications of footprint-level products as early adopters have found it difficult to align with in-situ forestry inventory data and high-resolution imagery for calibration and validation purpose. Here we developed a new means to rapidly evaluate and mitigate the impact of systematic geolocation error on the performance of GEDI's forest height estimates in the US. By integrating nationwide high-resolution airborne lidar data collected through the 3D Elevation Program of the USGS, we provided optimal geolocation adjustments of GEDI at per beam level and tracked their performances over the first 18-mo. Our results suggest that the first release of GEDI product (R01) can have large systematic geolocation errors at beam level (i.e., 50.5% of beams with an error > 20 m). Its impact on canopy height measurement could drastically vary in space and time, which in turn also offers a separate indirect method to evaluate and track geolocation performance. The second release of GEDI data (R02) has achieved a much-improved systematic geolocation accuracy which is shown to meet the mission requirement (0.2% beams >20 m and 80.8% beams <10 m) and should be able to meet requirements from many practical science applications tolerant to moderate geolocation errors. In sum, our approach has provided a short-term solution for an enhanced Cal/Val strategy for GEDI. While further improvements will certainly be made in future releases, it can potentially create an alternative pathway to generate and validate biomass products by linking GEDI footprint samples directly with in-situ data collections.

Remote Sensing of Environment↗
USGS2022

Absolute accuracy assessment of lidar point cloud using amorphous objects

The accuracy assessment of airborne lidar point cloud typically estimates vertical accuracy by computing RMSEz (root mean square error of the z coordinate) from ground check points (GCPs). Due to the low point density of the airborne lidar point cloud, there is often not enough accurate semantic context to find an accurate conjugate point. To advance the accuracy assessment in full three-dimensional (3D) context, geometric features, such as the three-plane intersection point or two-line intersection point, are often used. Although the point density is still low, geometric features are mathematically modeled from many points. Thus, geometric features provide a robust determination of the intersection point, and the point is considered as a GCP. When no regular built objects are available, we describe the process of utilizing features of irregular shape called amorphous natural objects, such as a tree or a rock. When scanned to a high-density point cloud, an amorphous natural object can be used as ground truth reference data to estimate 3D georeferencing errors of the airborne lidar point cloud. The algorithm to estimate 3D accuracy is the optimization that minimizes the sum of the distance between the airborne lidar points to the ground scanned data. The search volume partitioning was the most important procedure to improve the computational efficiency. We also performed an extensive study to address the external uncertainty associated with the amorphous object method. We describe an accuracy assessment using amorphous objects (108 trees) spread over the project area. The accuracy results for ∆ x , ∆ y , and ∆ z obtained using the amorphous object method were 3.1 cm, 3.6 cm, and 1.7 cm RMSE, along with a mean error of 0.1 cm, 0.1 cm, and 4.5 cm, respectively, satisfying the accuracy requirement of U.S. Geological Survey lidar base specification. This approach shows strong promise as an alternative to geometric feature methods when artificial targets are scarce. The relative convenience and advantages of using amorphous targets, along with its good performance shown here, make this amorphous object method a practical way to perform 3D accuracy assessment.

Remote Sensing↗
USGS2022

The accuracy and consistency of 3D Elevation Program data: A systematic analysis

The 3D Elevation Program (3DEP) has created partnership opportunities to increase the collection of high-resolution elevation data across the United States, eventually leading to complete coverage of high-resolution, three-dimensional (3D) information from light detection and ranging (lidar) data across the entire country (interferometric synthetic aperture radar in Alaska). While 3DEP data are collected at different times and by varying producers, the assumption is that the use of the 3DEP Lidar Base Specification will provide standardized and consistent data across data collections. Another assumption is that the integration of lidar data into the seamless digital elevation models increases the accuracy of the derived products. This study tests these assumptions and updates some of the accuracy metrics that were done on previous versions of the standard products.

Remote Sensing↗
USGS2020

Defining technology operational readiness for the 3D Elevation Program—A plan for investment, incubation, and adoption

The 3D Elevation Program (3DEP) is an acquisition strategy that uses data from commercial remote sensing technologies to create three-dimensional maps of the United States and U.S. territories. Currently, light detection and ranging and interferometric synthetic aperture radar are the two commercial technologies being used to provide three-dimensional information to meet the program’s operational requirements. This is because there is not a well-established process for vendors of new and novel instruments to know when and how 3DEP will accept their technologies into the 3DEP portfolio. The purpose of this plan is to provide a strategy and rules for communication between 3DEP and commercial partners interested in proposing their modalities for use in the program. To accomplish this, 3DEP will also consider how it invests in new technologies and how it disseminates data to and categorizes data for the broader community and the public.

Open-File Report↗
USGS2020

The 3D Elevation Program and energy for the Nation

High-resolution light detection and ranging (lidar) data are used in energy infrastructure siting, design, permitting, construction, and monitoring to promote public safety through the reduction of risks. For example, lidar data are used to identify safe locations for energy infrastructure by analyzing terrain parameters and identifying and evaluating geologic hazards (for example, landslide and fault locations) and their potential public safety effects on the location or design of infrastructure. Increasingly, engineering companies and regulatory agencies are using lidar and other remote sensing techniques as an efficient method to collect accurate, comprehensive data while reducing risks to field personnel. The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) is collecting lidar data nationwide (interferometric synthetic aperture radar [IfSAR] data in Alaska) to support a wide range of applications, including projects related to energy infrastructure construction and safety. Renewable energy resources, resource mining, and oil and gas resources were identified by the National Enhanced Elevation Assessment as business uses requiring three-dimensional (3D) elevation data. Elevation data are critical in assessing potential sites for energy infrastructure, such as pipelines, refineries and other facilities, to mitigate risks from natural hazards. For example, the Federal Energy Regulatory Commission (FERC), an independent agency that regulates the interstate transmission of electricity, natural gas, and oil, uses enhanced elevation data to conduct National Environmental Policy Act (NEPA) compliance assessments. The acquisition of high-resolution lidar data by the USGS 3DEP initiative helps the FERC and NEPA permit applicants by providing accurate and consistent data for hazards analysis. The use of these data accelerates the application and review process and avoids the much higher costs of acquiring elevation data along proposed energy facility locations and pipeline corridors.

Fact Sheet↗
USGS2019

General external uncertainty models of three-plane intersection point for 3D absolute accuracy assessment of lidar point cloud

The traditional practice to assess accuracy in lidar data involves calculating RMSEz (root mean square error of the vertical component). Accuracy assessment of lidar point clouds in full 3D (dimension) is not routinely performed. The main challenge in assessing accuracy in full 3D is how to identify a conjugate point of a ground-surveyed checkpoint in the lidar point cloud with the smallest possible uncertainty value. Relatively coarse point-spacing in airborne lidar data makes it challenging to determine a conjugate point accurately. As a result, a substantial unwanted error is added to the inherent positional uncertainty of the lidar data. Unless we keep this additional error small enough, the 3D accuracy assessment result will not properly represent the inherent uncertainty. We call this added error “external uncertainty,” which is associated with conjugate point identification. This research developed a general external uncertainty model using three-plane intersections and accounts for several factors (sensor precision, feature dimension, and point density). This method can be used for lidar point cloud data from a wide range of sensor qualities, point densities, and sizes of the features of interest. The external uncertainty model was derived as a semi-analytical function that takes the number of points on a plane as an input. It is a normalized general function that can be scaled by smooth surface precision (SSP) of a lidar system. This general uncertainty model provides a quantitative guideline on the required conditions for the conjugate point based on the geometric features. Applications of external uncertainty model was demonstrated using various lidar point cloud data from US Geological Survey (USGS) 3D Elevation Program (3DEP) library to determine the valid conditions for a conjugate point from three-plane.

Remote Sensing↗
USGS2019

An integrated framework for ecological drought across riverscapes of North America

Climate change is increasing the severity and extent of extreme droughts events, posing a critical threat to freshwater ecosystems, particularly with increasing human demands for diminishing water supplies. Despite the importance of drought as a significant driver of ecological and evolutionary dynamics, current understanding of drought consequences for freshwater biodiversity is very limited. We describe key barriers that hinder integrative drought research and monitoring across riverscapes. The primary constraint limiting understanding of ecological drought is an existing monitoring framework focused on human water consumption and flood risk in mainstem rivers. This approach is misaligned with escalating needs for research and data collection that illuminate exposure, sensitivity, and adaptive capacity (i.e., vulnerability) of biota to drought across entire riverscapes. We present a hierarchical framework for integrated ecological drought monitoring and research that addresses drought vulnerability across riverscapes, and describe how this approach can directly inform natural-resource management.

BioScience↗
USGS2018

Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions

Long-term, interdisciplinary studies of relations between climate and ecological conditions on wetland-upland landscapes have been lacking, especially studies integrated across scales meaningful for adaptive resource management. We collected data in situ at individual wetlands, and via satellite for surrounding 4-km 2 landscape blocks, to assess relations between annual weather dynamics, snow duration, phenology, wetland surface-water availability, amphibian presence and calling activity, greenness, and evapotranspiration in four U.S. conservation areas from 2008 to 2012. Amid recent decades of relatively warm growing seasons, 2012 and 2010 were the first and second warmest seasons, respectively, dating back to 1895. Accordingly, we observed the earliest starts of springtime biological activity during those two years. In all years, early-season amphibians first called soon after daily mean air temperatures were ≥ 0°C and snow had mostly melted. Similarly, satellite-based indicators suggested seasonal leaf-out happened soon after snowmelt and temperature thresholds for plant growth had occurred. Daily fluctuations in weather and water levels were related to amphibian calling activity, including decoupling the timing of the onset of calling at the start of season from the onset of calling events later in the season. Within-season variation in temperature and precipitation also was related to vegetation greenness and evapotranspiration, but more at monthly and seasonal scales. Wetland water levels were moderately to strongly associated with precipitation and early or intermittent wetland drying likely reduced amphibian reproduction success in some years, even though Pseudacris crucifer occupied sites at consistently high levels. Notably, satellite-based indicators of landscape water availability did not suggest such consequential, intra-seasonal variability in wetland surface-water availability. Our cross-disciplinary data show how temperature and precipitation interacted to affect key ecological relations and outcomes on our study landscapes. These results demonstrate the value of multi-year studies and the importance of scale for understanding actual climate-related effects in these areas.

Minnesota, Wisconsin↗
USGS2017

The 3D Elevation Program national indexing scheme

The 3D Elevation Program (3DEP) of the U.S. Geological Survey (USGS) acquires high-resolution elevation data for the Nation. This program has been operating under an opportunity-oriented approach, acquiring light detection and ranging (lidar) projects of varying sizes scattered across the United States. As a result, the national 3DEP elevation layer is subject to data gaps or unnecessary overlap between adjacent collections. To mitigate this problem, 3DEP is adopting a strategic, systematic approach to national data acquisition that will create efficiencies in efforts to achieve nationwide elevation data coverage and help capture additional Federal and non-Federal investments resulting from advance awareness of proposed acquisitions and partnership opportunities. The 3DEP Working Group, an interagency group managed by the USGS, has agreed that all future 3DEP collections within the lower 48 States should be coordinated by using a 1-kilometer by 1-kilometer tiling scheme for the conterminous United States. Fiscal Year 2018 is being considered a transition year, and in Fiscal Year 2019 the national indexing scheme will be fully implemented, so that all 3DEP-supported projects will be acquired and delivered in the national indexing scheme and projected into the Albers Equal Area projection.

Fact Sheet↗
USGS2017

USGS integrated drought science

Project Need and Overview Drought poses a serious threat to the resilience of human communities and ecosystems in the United States (Easterling and others, 2000). Over the past several years, many regions have experienced extreme drought conditions, fueled by prolonged periods of reduced precipitation and exceptionally warm temperatures. Extreme drought has far-reaching impacts on water supplies, ecosystems, agricultural production, critical infrastructure, energy costs, human health, and local economies (Milly and others, 2005; Wihlite, 2005; Vörösmarty and others, 2010; Choat and others, 2012; Ledger and others, 2013). As global temperatures continue to increase, the frequency, severity, extent, and duration of droughts are expected to increase across North America, affecting both humans and natural ecosystems (Parry and others, 2007). The U.S. Geological Survey (USGS) has a long, proven history of delivering science and tools to help decision-makers manage and mitigate effects of drought. That said, there is substantial capacity for improved integration and coordination in the ways that the USGS provides drought science. A USGS Drought Team was formed in August 2016 to work across USGS Mission Areas to identify current USGS drought-related research and core capabilities. This information has been used to initiate the development of an integrated science effort that will bring the full USGS capacity to bear on this national crisis.

Circular↗
USGS2017

Status of the 3D Elevation Program, 2015

The 3D Elevation Program (3DEP) is a cooperative activity to collect light detection and ranging (lidar) data for the conterminous United States, Hawaii, and U.S. territories; and interferometric synthetic aperture radar (IfSAR) elevation data for Alaska during an 8-year period. The U.S. Geological Survey (USGS) and partner organizations acquire high-quality three-dimensional elevation data for the United States and its territories that support requirements beyond what could be realized if agencies independently pursued lidar and IfSAR data collection activities. Data collection rates have been increasing as a growing number of State and Federal agencies participate in cooperative data acquisition projects. USGS and partner agencies expanded data collection, completed the initial product delivery systems and implemented changes to the program governance to include a restructuring of the 3DEP working group and formalizing the relationship to the Federal Geographic Data Committee during the final year (2015) of program preparation.

Open-File Report↗