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Spatial-Temporal Monitoring of Environmental and Ecological Processes Using LiDAR

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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 (10 December 2022) | Viewed by 6029

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Special Issue Editors

Dr. Yingkui Li

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Guest Editor

Department of Geograhpy, Univeristy of Tennessee, Knoxville, TN 37996, USA
Interests: LiDAR/UAS and earth surface processes; climate and environmental change; human impacts on environment; GIS and spatial analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Qingwu Hu

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Guest Editor

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: remote sensing; LiDAR; mobile mapping; SLAM; 3D mapping
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Dr. Haidong Li

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Guest Editor

Research Center for Ecological Civilization Construction, Nanjing Institute of Environmental Sciences (NIES), Ministry of Ecology and Environment (MEE), Nanjing 210042, China
Interests: ecological restoration assessment; LiDAR; mine areas monitoring; land dersertfication control; revegetation process; biodiversity conservation
Special Issues, Collections and Topics in MDPI journals

Dr. Robert Washington-Allen

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Guest Editor

Department of Agriculture, Veterinary and Rangeland Sciences, University of Nevada, Reno, NV 89557, USA
Interests: dryland ecology; LiDAR; remote sensing; rangeland management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advantages of LiDAR (light detection and ranging) technology provide unique opportunities to monitor spatial–temporal changes in environmental and ecological processes. LiDAR sensors can be implemented in ground-, mobile-, aerial-, and space-based platforms with a variety of spatial and temporal resolutions. Although more and more studies have been conducted, there is still a need to develop novel methods and best practices in processing LiDAR data and effectively quantifying environmental and ecological processes. This Special Issue invites submissions of both research and review papers on innovative applications using various LiDAR sensors to monitor spatial and temporal changes in environmental and ecological processes. The following are a list of potential topics:

Novel methods and best practices in LiDAR data processing, such as point cloud registration, point cloud classification, noise filtering, data fusion, changing detection, and error propagation
Spatial–temporal monitoring of hillslope processes, such as rill erosion, gully erosion, and landslides
Spatial–temporal monitoring of fluvial processes, such as streambank erosion, stream migration, and flooding
Spatial–temporal monitoring of coastal processes, such as beach erosion; deposition; and the impacts of hurricanes, tides, and sea level change on shorelines.
Spatial–temporal monitoring of aeolian processes and revegetation, such as sand dune movement, wind erosion, and the impacts of topography on revegetation
Spatial–temporal monitoring of cryosphere processes, such as glacial advance/retreat, ice sheet dynamics, glacial landform extraction and mapping, and permafrost changes
Spatial–temporal monitoring of karst landforms and processes, such as caves, sinkholes, and their related hazards
Spatial–temporal monitoring of tectonic landforms and processes, such as active faults, volcanoes, earthquakes, and their related hazards
Spatial–temporal monitoring of human–environmental interaction processes, such as construction monitoring, urban structure, green infrastructure, and stream restoration
Spatial–temporal monitoring of ecosystem services and ecological processes, such as revegetation effectiveness; grassland degradation; forest and shrub structures; and canopy, biomass, and carbon estimations.

You may choose our Joint Special Issue in Geomatics.

Dr. Yingkui Li
Dr. Qingwu Hu
Dr. Haidong Li
Dr. Robert Washington-Allen
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

terrestrial laser scanning
mobile laser scanning
UAV-based LiDAR
airborne LiDAR
spaceborne LiDAR
environmental and ecological processes
change detection
monitoring

Related Special Issue

Spatial-Temporal Monitoring of Environmental and Ecological Processes Using LiDAR in Geomatics (1 article)

Published Papers (3 papers)

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Research

20 pages, 14857 KiB

Open AccessArticle

Comparison of Ground Point Filtering Algorithms for High-Density Point Clouds Collected by Terrestrial LiDAR

by Gene Bailey, Yingkui Li, Nathan McKinney, Daniel Yoder, Wesley Wright and Hannah Herrero

Remote Sens. 2022, 14(19), 4776; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194776 - 24 Sep 2022

Cited by 6 | Viewed by 1678

Abstract

Terrestrial LiDAR (light detection and ranging) has been used to quantify micro-topographic changes using high-density 3D point clouds in which extracting the ground surface is susceptible to off-terrain (OT) points. Various filtering algorithms are available in classifying ground and OT points, but additional research is needed to choose and implement a suitable algorithm for a given surface. This paper assesses the performance of three filtering algorithms in classifying terrestrial LiDAR point clouds: a cloth simulation filter (CSF), a modified slope-based filter (MSBF), and a random forest (RF) classifier, based on a typical use-case in quantifying soil erosion and surface denudation. A hillslope plot was scanned before and after removing vegetation to generate a test dataset of ground and OT points. Each algorithm was then tested against this dataset with various parameters/settings to obtain the highest performance. CSF produced the best classification with a Kappa value of 0.86, but its performance is highly influenced by the ‘time-step’ parameter. MSBF had the highest precision of 0.94 for ground point classification but the highest Kappa value of only 0.62. RF produced balanced classifications with the highest Kappa value of 0.75. This work provides valuable information in optimizing the parameters of the filtering algorithms to improve their performance in detecting micro-topographic changes. Full article

(This article belongs to the Special Issue Spatial-Temporal Monitoring of Environmental and Ecological Processes Using LiDAR)

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15 pages, 8354 KiB

Open AccessArticle

Las2DoD: Change Detection Based on Digital Elevation Models Derived from Dense Point Clouds with Spatially Varied Uncertainty

by Gene Bailey, Yingkui Li, Nathan McKinney, Daniel Yoder, Wesley Wright and Robert Washington-Allen

Remote Sens. 2022, 14(7), 1537; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071537 - 22 Mar 2022

Cited by 9 | Viewed by 1897

Abstract

The advances of remote sensing techniques allow for the generation of dense point clouds to detect detailed surface changes up to centimeter/millimeter levels. However, there is still a need for an easy method to derive such surface changes based on digital elevation models generated from dense point clouds while taking into consideration spatial varied uncertainty. We present a straightforward method, Las2DoD, to quantify surface change directly from point clouds with spatially varied uncertainty. This method uses a cell-based Welch’s t-test to determine whether each cell of a surface experienced a significant elevation change based on the points measured within the cell. Las2DoD is coded in Python with a simple graphic user interface. It was applied in a case study to quantify hillslope erosion on two plots: one dominated by rill erosion, and the other by sheet erosion, in southeastern United States. The results from the rilled plot indicate that Las2DoD can estimate 90% of the total measured sediment, in comparison to 58% and 70% from two other commonly used methods. The Las2DOD-derived result is less accurate (65%) but still outperforms the other two methods (30% and 48%) for the plot dominated by sheet erosion. Las2DoD captures more low-magnitude changes and is particularly useful where surface changes are small but contribute significantly to the total surface change when summed. Full article

(This article belongs to the Special Issue Spatial-Temporal Monitoring of Environmental and Ecological Processes Using LiDAR)

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Figure 1

19 pages, 3288 KiB

Open AccessArticle

Microtopographic Controls on Erosion and Deposition of a Rilled Hillslope in Eastern Tennessee, USA

by Yingkui Li, Xiaoyu Lu, Robert A. Washington-Allen and Yanan Li

Remote Sens. 2022, 14(6), 1315; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061315 - 09 Mar 2022

Cited by 5 | Viewed by 1292

Abstract

Topography plays an important role in shaping the patterns of sediment erosion and deposition of different landscapes. Studies have investigated the role of topography at basin scales, whereas little work has been conducted on hillslopes, partially due to the lack of high-resolution topographic data. We monitored detailed topographic changes of a rilled hillslope in the southeastern United States using terrestrial laser scanning and investigated the influences of various microtopographic factors on erosion and deposition. The results suggest that the contributing area is the most important factor for both rill erosion and deposition. Rills with large contributing areas tend to have high erosion and deposition. Slope is positively related to erosion but negatively related to deposition. Roughness, on the other hand, is positively related to deposition but negatively related to erosion. Higher erosion and lower deposition likely occur on north-facing aspects, possibly because of higher soil moisture resulting from less received solar insolation. Similarly, soil moisture is likely higher in areas with higher terrain wetness index values, leading to higher erosion. This work provides important insight into the sediment dynamic and its microtopographic controls on hillslopes. Full article

(This article belongs to the Special Issue Spatial-Temporal Monitoring of Environmental and Ecological Processes Using LiDAR)

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