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Advances in Remote Sensing of Vegetation Structure Analysis and Forest...
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Advances in Remote Sensing of Vegetation Structure Analysis and Forest Ecosystems Monitoring

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

Deadline for manuscript submissions: 30 May 2025 | Viewed by 3160

Special Issue Editors

Prof. Dr. Carlos José Novillo Camacho

E-Mail Website
Guest Editor
Department of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, Madrid, Spain
Interests: remote sensing; GIS; forests; wildfire; land-cover change
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Arrogante-Funes Patricia

E-Mail Website
Guest Editor
Departamento de Tecnología Química y Ambiental, ESCET, Universidad Rey Juan Carlos, 28933 Móstoles, Madrid, Spain
Interests: remote sensing applications; temporal series analysis; protected areas; climate influence on forest ecosystems; forest ecosystems monitoring

Special Issue Information

Dear Colleagues,

Forests cover one-third of the earth's land and represent one of the most sophisticated ecosystems. Forests provide a large number of ecosystem services and play a crucial role in mitigating global climate change. Vegetation structural data are significant for assessing biophysical processes and changes and promoting sustainable forest management. It is one of the important data sources for forest ecosystem monitoring. Multi-source remote-sensing technologies and data can be further mined and applied to forest remote sensing. Satellite imagery has been used for land cover change detection, landscape dynamics mapping, vegetation parameters and structure analysis, and climate change monitoring. Spaceborne lidars could be applied in detailed measurements of vegetation vertical structures. These remote sensing technologies have had a significant effect on the monitoring of forest ecosystems.

This Special Issue is aimed at studies covering the application of advanced remote sensing techniques to vegetation structure analysis, with the aim of supporting forest ecosystem monitoring.

Articles may address, but are not limited to, the following topics:

  1. Vegetation structural characteristics
  2. Land cover and landscape change
  3. LiDAR point cloud processing in forests
  4. Landslide Susceptibility Assessment
  5. SAR imaging for forest applications
  6. Forest Ecosystems and Forest Management

Prof. Dr. Carlos José Novillo Camacho
Prof. Dr. Arrogante-Funes Patricia
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

  • LiDAR remote sensing
  • forest monitoring
  • aerial photogrammetry
  • vegetation structure
  • time series vegetation index
  • land cover change

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

26 pages, 5460 KiB  
Article
Assessing Methods to Measure Stem Diameter at Breast Height with High Pulse Density Helicopter Laser Scanning
by Matthew J. Sumnall, Ivan Raigosa-Garcia, David R. Carter, Timothy J. Albaugh, Otávio C. Campoe, Rafael A. Rubilar, Bart Alexander, Christopher W. Cohrs and Rachel L. Cook
Remote Sens. 2025, 17(2), 229; https://doi.org/10.3390/rs17020229 - 10 Jan 2025
Viewed by 929
Abstract
Technological developments have allowed helicopter airborne laser scanning (HALS) to produce high-density point clouds below the forest canopy. We present a tree stem classification method that combines linear shape detection and model-based clustering, using four discrete methods to estimate stem diameter. Stem horizontal [...] Read more.
Technological developments have allowed helicopter airborne laser scanning (HALS) to produce high-density point clouds below the forest canopy. We present a tree stem classification method that combines linear shape detection and model-based clustering, using four discrete methods to estimate stem diameter. Stem horizontal size was estimated every 25 cm below the living crown, and a cubic spline was used to estimate where there were gaps. Individual stem diameter at breast height (DBH) was estimated for 77% of field-measured trees. The root mean square error (RMSE) of DBH estimates was 7–12 cm using stem circle fitting. Adapting the approach to use an existing stem taper model reduced the RMSE of estimates (<1 cm). In contrast, estimates that were produced from a previously existing DBH estimation method (PREV) could be achieved for 100% of stems (DBH RMSE 6 cm), but only after location-specific error was corrected. The stem classification method required comparatively little development of statistical models to provide estimates, which ultimately had a similar level of accuracy (RMSE < 1 cm) to PREV. HALS datasets can measure broad-scale forest plantations and reduce field efforts and should be considered an important tool for aiding in inventory creation and decision-making within forest management. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Vegetation Structure Analysis and Forest Ecosystems Monitoring)
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30 pages, 10784 KiB  
Article
Phenology and Plant Functional Type Link Optical Properties of Vegetation Canopies to Patterns of Vertical Vegetation Complexity
by Duncan Jurayj, Rebecca Bowers and Jessica V. Fayne
Remote Sens. 2024, 16(14), 2577; https://doi.org/10.3390/rs16142577 - 13 Jul 2024
Viewed by 1429
Abstract
Vegetation vertical complexity influences biodiversity and ecosystem productivity. Rapid warming in the boreal region is altering patterns of vertical complexity. LiDAR sensors offer novel structural metrics for quantifying these changes, but their spatiotemporal limitations and their need for ecological context complicate their application [...] Read more.
Vegetation vertical complexity influences biodiversity and ecosystem productivity. Rapid warming in the boreal region is altering patterns of vertical complexity. LiDAR sensors offer novel structural metrics for quantifying these changes, but their spatiotemporal limitations and their need for ecological context complicate their application and interpretation. Satellite variables can estimate LiDAR metrics, but retrievals of vegetation structure using optical reflectance can lack interpretability and accuracy. We compare vertical complexity from the airborne LiDAR Land Vegetation and Ice Sensor (LVIS) in boreal Canada and Alaska to plant functional type, optical, and phenological variables. We show that spring onset and green season length from satellite phenology algorithms are more strongly correlated with vegetation vertical complexity (R = 0.43–0.63) than optical reflectance (R = 0.03–0.43). Median annual temperature explained patterns of vegetation vertical complexity (R = 0.45), but only when paired with plant functional type data. Random forest models effectively learned patterns of vegetation vertical complexity using plant functional type and phenological variables, but the validation performance depended on the validation methodology (R2 = 0.50–0.80). In correlating satellite phenology, plant functional type, and vegetation vertical complexity, we propose new methods of retrieving vertical complexity with satellite data. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Vegetation Structure Analysis and Forest Ecosystems Monitoring)
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