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Environmental Monitoring and Mapping Using 3D Elevation Program Data

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 32612

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Special Issue Editors

Dr. Jason Stoker

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Guest Editor

United States Geological Survey, Fort Collins, CO 80526, USA
Interests: Lidar; remote sensing; GIS

Dr. Tyson Lee Swetnam

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Guest Editor

University of Arizona, Tucson, AZ 85721, USA
Interests: lidar remote sensing; small unmanned aerial systems; cyberinfrastructure; reproducible research

Mr. Christopher Crosby

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Guest Editor

UNAVCO / OpenTopography, Boulder, CO 80301-5394, USA
Interests: lidar remote sensing; geodetic imaging; cyberinfrastructure; terrestrial laser scanning
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Dear Colleagues,

The use of lidar remote sensing has grown tremendously, becoming one of the most ubiquitous remote sensing tools for understanding the earth in three dimensions with incredibly high resolution and accuracy. To respond to growing needs for high quality elevation data, the 3D Elevation Program (3DEP) is attempting to collect lidar for the entire United States (IfSAR in Alaska) by 2023. This baseline of elevation data, provided to the public for free and in open formats, is hoped to improve operational work as well as foster research.

With over 67% of the United States collected or in progress with Quality Level 2 or better lidar (and 100% of Alaska with IfSAR), and an available public repository of over 15 trillion lidar points (and growing) spread over 1100 projects covering almost 20 years, a wealth of public lidar data is available for environmental monitoring and mapping applications. Although most of these projects were collected and have been used for operational needs, this repository offers tremendous opportunities for research needs, and research using these data can help improve future directions of 3DEP. At present, 3DEP data are available via FTP and in the cloud, and connected to various computing services, such as OpenTopography and CyVerse.

The purpose of this Special Issue is to demonstrate the value of 3DEP data for research applications. Whereas preference will be given to environmental monitoring and mapping applications, any papers and research contributions using 3DEP data will be considered. Contributions covering the following subtopics are welcome:

Use of 3DEP lidar point clouds to extract and understand vegetation information
Extraction of new features from 3DEP source data
Use of 3DEP DEMs for understanding environmental processes
Use of 3DEP data to identify and understand natural hazards and associated processes
Use of 3DEP data in combination with other sources of topographic data to understand landscape change associated with natural or anthropogenic processes
Continental scale uses of 3DEP seamless DEMs
New methodologies to understand the quality and accuracy of 3DEP data for environmental applications
Synergies and fusion of 3DEP data (DEMs and/or point clouds) with multi- and hyperspectral imagery
Synergies and fusion of 3DEP data with global data, such as ICESat-2, GEDI, and others
Machine learning and artificial intelligence applications using 3DEP data
Big data processing of 3DEP data using cloud, high performance computing and other cyberinfrastructure platforms, such as OpenTopography and CyVerse

Dr. Jason Stoker
Dr. Tyson Lee Swetnam
Mr. Christopher Crosby
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Remote sensing
Mapping
Lidar
GEDI
ICESat-2
Degradation
Fire
Tools
Forest restoration
Forest management
UAV
Artificial intelligence
Machine learning
Cyberinfrastructure
Big data
Cloud computing
High performance computing

Published Papers (6 papers)

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Research

18 pages, 7744 KiB

Open AccessArticle

Absolute Accuracy Assessment of Lidar Point Cloud Using Amorphous Objects

by Minsu Kim, Jason Stoker, Jeffrey Irwin, Jeffrey Danielson and Seonkyung Park

Remote Sens. 2022, 14(19), 4767; https://doi.org/10.3390/rs14194767 - 23 Sep 2022

Cited by 5 | Viewed by 2550

Abstract

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. Full article

(This article belongs to the Special Issue Environmental Monitoring and Mapping Using 3D Elevation Program Data)

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24 pages, 4163 KiB

Open AccessArticle

The Accuracy and Consistency of 3D Elevation Program Data: A Systematic Analysis

by Jason Stoker and Barry Miller

Remote Sens. 2022, 14(4), 940; https://doi.org/10.3390/rs14040940 - 15 Feb 2022

Cited by 22 | Viewed by 5567

Abstract

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. Full article

(This article belongs to the Special Issue Environmental Monitoring and Mapping Using 3D Elevation Program Data)

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19 pages, 8856 KiB

Open AccessArticle

Statewide USGS 3DEP Lidar Topographic Differencing Applied to Indiana, USA

by Chelsea Phipps Scott, Matthew Beckley, Minh Phan, Emily Zawacki, Christopher Crosby, Viswanath Nandigam and Ramon Arrowsmith

Remote Sens. 2022, 14(4), 847; https://doi.org/10.3390/rs14040847 - 11 Feb 2022

Cited by 7 | Viewed by 11427

Abstract

Differencing multi-temporal topographic data (radar, lidar, or photogrammetrically derived point clouds or digital elevation models—DEMs) measures landscape change, with broad applications for scientific research, hazard management, industry, and urban planning. The United States Geological Survey’s 3D Elevation Program (3DEP) is an ambitious effort to collect light detection and ranging (lidar) topography over the United States’ lower 48 and Interferometric Synthetic Aperture Radar (IfSAR) in Alaska by 2023. The datasets collected through this program present an important opportunity to characterize topography and topographic change at regional and national scales. We present Indiana statewide topographic differencing results produced from the 2011–2013 and 2016–2020 lidar collections. We discuss the insights, challenges, and lessons learned from conducting large-scale differencing. Challenges include: (1) designing and implementing an automated differencing workflow over 94,000 km² of high-resolution topography data, (2) ensuring sufficient computing resources, and (3) managing the analysis and visualization of the multiple terabytes of data. We highlight observations including infrastructure development, vegetation growth, and landscape change driven by agricultural practices, fluvial processes, and natural resource extraction. With 3DEP and the U.S. Interagency Elevation Inventory data, at least 37% of the Contiguous 48 U.S. states are already covered by repeat, openly available, high-resolution topography datasets, making topographic differencing possible. Full article

(This article belongs to the Special Issue Environmental Monitoring and Mapping Using 3D Elevation Program Data)

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27 pages, 10619 KiB

Open AccessArticle

Extensibility of U-Net Neural Network Model for Hydrographic Feature Extraction and Implications for Hydrologic Modeling

by Lawrence V. Stanislawski, Ethan J. Shavers, Shaowen Wang, Zhe Jiang, E. Lynn Usery, Evan Moak, Alexander Duffy and Joel Schott

Remote Sens. 2021, 13(12), 2368; https://doi.org/10.3390/rs13122368 - 17 Jun 2021

Cited by 17 | Viewed by 3636

Abstract

Accurate maps of regional surface water features are integral for advancing ecologic, atmospheric and land development studies. The only comprehensive surface water feature map of Alaska is the National Hydrography Dataset (NHD). NHD features are often digitized representations of historic topographic map blue lines and may be outdated. Here we test deep learning methods to automatically extract surface water features from airborne interferometric synthetic aperture radar (IfSAR) data to update and validate Alaska hydrographic databases. U-net artificial neural networks (ANN) and high-performance computing (HPC) are used for supervised hydrographic feature extraction within a study area comprised of 50 contiguous watersheds in Alaska. Surface water features derived from elevation through automated flow-routing and manual editing are used as training data. Model extensibility is tested with a series of 16 U-net models trained with increasing percentages of the study area, from about 3 to 35 percent. Hydrography is predicted by each of the models for all watersheds not used in training. Input raster layers are derived from digital terrain models, digital surface models, and intensity images from the IfSAR data. Results indicate about 15 percent of the study area is required to optimally train the ANN to extract hydrography when F1-scores for tested watersheds average between 66 and 68. Little benefit is gained by training beyond 15 percent of the study area. Fully connected hydrographic networks are generated for the U-net predictions using a novel approach that constrains a D-8 flow-routing approach to follow U-net predictions. This work demonstrates the ability of deep learning to derive surface water feature maps from complex terrain over a broad area. Full article

(This article belongs to the Special Issue Environmental Monitoring and Mapping Using 3D Elevation Program Data)

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20 pages, 7004 KiB

Open AccessFeature PaperArticle

Positional Accuracy Assessment of Lidar Point Cloud from NAIP/3DEP Pilot Project

by Minsu Kim, Seonkyung Park, Jeffrey Irwin, Collin McCormick, Jeffrey Danielson, Gregory Stensaas, Aparajithan Sampath, Mark Bauer and Matthew Burgess

Remote Sens. 2020, 12(12), 1974; https://doi.org/10.3390/rs12121974 - 19 Jun 2020

Cited by 10 | Viewed by 4880

Abstract

The Leica Geosystems CountryMapper hybrid system has the potential to collect data that satisfy the U.S. Geological Survey (USGS) National Geospatial Program (NGP) and 3D Elevation Program (3DEP) and the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) requirements in a single collection. This research will help 3DEP determine if this sensor has the potential to meet current and future 3DEP topographic lidar collection requirements. We performed an accuracy analysis and assessment on the lidar point cloud produced from CountryMapper. The boresighting calibration and co-registration by georeferencing correction based on ground control points are assumed to be performed by the data provider. The scope of the accuracy assessment is to apply the following variety of ways to measure the accuracy of the delivered point cloud to obtain the error statistics. Intraswath uncertainty from a flat surface was computed to evaluate the point cloud precision. Intraswath difference between opposite scan directions and the interswath overlap difference were evaluated to find boresighting or any systematic errors. Absolute vertical accuracy over vegetated and non-vegetated areas were also assessed. Both horizontal and vertical absolute errors were assessed using the 3D absolute error analysis methodology of comparing conjugate points derived from geometric features. A three-plane feature makes a single unique intersection point. Intersection points were computed from ground-based lidar and airborne lidar point clouds for comparison. The difference between two intersection points form one error vector. The geometric feature-based error analysis was applied to intraswath, interswath, and absolute error analysis. The CountryMapper pilot data appear to satisfy the accuracy requirements suggested by the USGS lidar specification, based upon the error analysis results. The focus of this research was to demonstrate various conventional accuracy measures and novel 3D accuracy techniques using two different error computation methods on the CountryMapper airborne lidar point cloud. Full article

(This article belongs to the Special Issue Environmental Monitoring and Mapping Using 3D Elevation Program Data)

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16 pages, 6154 KiB

Open AccessArticle

Evaluating Elevation Change Thresholds between Structure-from-Motion DEMs Derived from Historical Aerial Photos and 3DEP LiDAR Data

by Peter Chirico, Jessica DeWitt and Sarah Bergstresser

Remote Sens. 2020, 12(10), 1625; https://doi.org/10.3390/rs12101625 - 19 May 2020

Cited by 3 | Viewed by 2698

Abstract

This study created digital terrain models (DTMs) from historical aerial images using Structure from Motion (SfM) for a variety of image dates, resolutions, and photo scales. Accuracy assessments were performed on the SfM DTMs, and they were compared to the United States Geological Survey’s three-dimensional digital elevation program (3DEP) light detection and ranging (LiDAR) DTMs to evaluate geomorphic change thresholds based on vertical accuracy assessments and elevation change methodologies. The results of this study document a relationship between historical aerial photo scales and predicted vertical accuracy of the resultant DTMs. The results may be used to assess geomorphic change thresholds over multi-decadal timescales depending on spatial scale, resolution, and accuracy requirements. This study shows that if elevation changes of approximately ±1 m are to be mapped, historical aerial photography collected at 1:20,000 scale or larger would be required for comparison to contemporary LiDAR derived DTMs. Full article

(This article belongs to the Special Issue Environmental Monitoring and Mapping Using 3D Elevation Program Data)

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