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Geomatics
Special Issues
Spatial-Temporal Monitoring of Environmental and Ecological Processes Using LiDAR
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Spatial-Temporal Monitoring of Environmental and Ecological Processes Using LiDAR

A special issue of Geomatics (ISSN 2673-7418).

Deadline for manuscript submissions: closed (10 December 2022) | Viewed by 4875

Special Issue Editors

Dr. Yingkui Li

E-Mail Website
Chief 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

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: remote sensing; LiDAR; mobile mapping; SLAM; 3D mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Haidong Li

E-Mail Website
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

E-Mail Website
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 Remote Sensing.

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. Geomatics is an international peer-reviewed open access quarterly 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 1000 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

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Published Papers (1 paper)

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Research

23 pages, 17445 KiB  
Article
Transfer Learning for LiDAR-Based Lane Marking Detection and Intensity Profile Generation
by Ankit Patel, Yi-Ting Cheng, Radhika Ravi, Yi-Chun Lin, Darcy Bullock and Ayman Habib
Geomatics 2021, 1(2), 287-309; https://doi.org/10.3390/geomatics1020016 - 4 Jun 2021
Cited by 3 | Viewed by 4131
Abstract
Recently, light detection and ranging (LiDAR)-based mobile mapping systems (MMS) have been utilized for extracting lane markings using deep learning frameworks. However, huge datasets are required for training neural networks. Furthermore, with accurate lane markings being detected utilizing LiDAR data, an algorithm for [...] Read more.
Recently, light detection and ranging (LiDAR)-based mobile mapping systems (MMS) have been utilized for extracting lane markings using deep learning frameworks. However, huge datasets are required for training neural networks. Furthermore, with accurate lane markings being detected utilizing LiDAR data, an algorithm for automatically reporting their intensity information is beneficial for identifying worn-out or missing lane markings. In this paper, a transfer learning approach based on fine-tuning of a pretrained U-net model for lane marking extraction and a strategy for generating intensity profiles using the extracted results are presented. Starting from a pretrained model, a new model can be trained better and faster to make predictions on a target domain dataset with only a few training examples. An original U-net model trained on two-lane highways (source domain dataset) was fine-tuned to make accurate predictions on datasets with one-lane highway patterns (target domain dataset). Specifically, encoder- and decoder-trained U-net models are presented wherein, during retraining of the former, only weights in the encoder path of U-net were allowed to change with decoder weights frozen and vice versa for the latter. On the test data (target domain), the encoder-trained model (F1-score: 86.9%) outperformed the decoder-trained (F1-score: 82.1%). Additionally, on an independent dataset, the encoder-trained one (F1-score: 90.1%) performed better than the decoder-trained one (F1-score: 83.2%). Lastly, on the basis of lane marking results obtained from the encoder-trained U-net, intensity profiles were generated. Such profiles can be used to identify lane marking gaps and investigate their cause through RGB imagery visualization. Full article
(This article belongs to the Special Issue Spatial-Temporal Monitoring of Environmental and Ecological Processes Using LiDAR)
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