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LiDAR and Terrestrial Laser Scanning for Environmental Monitoring, Agricultural Analysis, and Environmental Infrastructure Analytics

A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 466

Editor

Dr. Mostafa Arastounia

E-Mail Website
Guest Editor
Department of Geography and Anthropology, Kennesaw State University, Marietta, GA 30060, USA
Interests: point cloud; laser scanning; LiDAR; object recognition; segmentation; modeling; object extraction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of LiDAR and laser scanning technology has fundamentally transformed numerous industries by driving unprecedented progress in data volume, spatial resolution, processing methodologies, and deliverables visualization. By deploying these sensors across a diverse spectrum of platforms, researchers and practitioners now possess unparalleled capabilities for mass data acquisition. These technologies have become central to real-time decision-making in environmental conservation, precision agriculture, and engineering. This paradigm shift has moved the discipline beyond traditional data collection towards the generation of autonomous, high-fidelity digital twins. 
Prospective authors are invited to contribute to this Special Issue of Remote Sensing by submitting an original manuscript of their latest research results. Original and innovative contributions may include, but are not limited to, the following:
1. Monitoring ecosystems, environmental, and forestry analysis:
     a. Above-ground biomass (AGB) and carbon sequestration.
     b. Forest regeneration monitoring.
     c. Tree morphology to measure diameter at breast height (DBH), crown dimensions, and wood-leaf classification.
2. Agricultural analysis and precision farming:
     a. Precision phenotyping.
     b. Crop structure and yield prediction.
3. Soil and geomorphic analysis:
     a. Soil erosion modeling.
     b. Sediment management.
4. Environmental engineering applications:
     a. Digital twinning and automated surveillance of environmental facilities.
     b. Automated surveying, mapping, structural health, and resilience monitoring of environmental infrastructure.

Dr. Mostafa Arastounia
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 
  • LiDAR
  • ecosystem and environment monitoring
  • agricultural analysis
  • change detection
  • forest resource assessment
  • tree morphology and classification
  • infrastructure analytics
  • surveying mapping and monitoring
  • digital twin

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Published Papers (1 paper)

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Research

47 pages, 14117 KB  
Article
A Unified Framework for Individual Tree Segmentation and Forest Biometrics Derivation from LiDAR Point Clouds Captured by Different Platforms in Diverse Forest Environments
by Hazem Hanafy, Sangyoon Park, Songlin Fei and Ayman Habib
Remote Sens. 2026, 18(17), 3059; https://doi.org/10.3390/rs18173059 - 7 Sep 2026
Viewed by 258
Abstract
Light Detection and Ranging (LiDAR)-based forest inventory increasingly relies on diverse platforms, ranging from proximal systems including BackPack, All-Terrain Vehicle (ATV), and terrestrial laser scanning (TLS) to near-proximal systems such as uncrewed aerial vehicles (UAVs). However, differences in point density, viewing geometry, and [...] Read more.
Light Detection and Ranging (LiDAR)-based forest inventory increasingly relies on diverse platforms, ranging from proximal systems including BackPack, All-Terrain Vehicle (ATV), and terrestrial laser scanning (TLS) to near-proximal systems such as uncrewed aerial vehicles (UAVs). However, differences in point density, viewing geometry, and occlusions among these acquisition systems pose challenges for processing heterogeneous LiDAR datasets using a common workflow. Traditional geometric approaches often rely on parameter tuning. On the other hand, deep learning (DL) approaches can be constrained by domain shift when applied to different sensors or forest environments. This study proposes a forest inventory pipeline for individual tree segmentation and the derivation of key forest biometrics including tree location and diameter at breast height (DBH) across heterogeneous LiDAR datasets. The pipeline uses a confidence-guided, multi-stage quality control framework that evaluates agreement between complementary tree location estimates to reduce common segmentation errors. In addition, a semi-automated procedure is developed to generate reference data for datasets lacking field measurements. The proposed workflow was evaluated using eight diverse datasets representing different platforms, sensors, acquisition patterns, and forest environments and was compared with 3DFIN, TreeLearn, and ForestFormer3D. Field reference measurements were available for a natural forest site, while the remaining datasets were evaluated using semi-automatically generated and manually refined reference data. The proposed tree detection pipeline achieved Precision ranging from 86.44% to 100%, Recall from 74.17% to 100%, and F1-scores from 81.82% to 100% across the evaluated datasets. For the Martell–BackPack dataset with independent field reference measurements, Precision, Recall, and F1-score were 97.55%, 96.95%, and 97.25%, respectively. For correctly detected trees by the proposed approach in the natural forest dataset with field measurements, DBH estimates achieved an RMSE of 2.5 cm with the total basal area underestimated by 1.88%, compared with DBH RMSE and reduction in basal area of 4.0 cm and 3.67%, respectively, for 3DFIN. Although the proposed pipeline did not achieve the highest performance in every test case, it maintained strong and generally consistent tree detection performance for the evaluated datasets. The main limitation of the proposed pipeline is its dependence on sufficient lower-stem visibility, which reduced tree detection accuracy in sparsely sampled areas. The proposed framework provides a practical workflow for LiDAR-based individual tree segmentation and DBH estimation using a fixed parameter configuration for all datasets captured by a given acquisition system. Full article
(This article belongs to the Special Issue LiDAR and Terrestrial Laser Scanning for Environmental Monitoring, Agricultural Analysis, and Environmental Infrastructure Analytics)
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