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Remote Sensing and Lidar Data for Forest Monitoring

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 3066

Special Issue Editors


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Guest Editor
Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, P.O. Box 248, 54124 Thessaloniki, Greece
Interests: forest fires; land use/land cover mapping; pre-fire planning and post-fire assessment; remote sensing; GIS; forest management
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, P.O. Box 248, 54124 Thessaloniki, Greece
Interests: fuzzy systems; machine learning; land use/land cover mapping; wildfires; remote sensing; GIS; image processing; burned area mapping
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Research Group in Environmental Remote Sensing, Department of Geology, Geography and Environment, Universidad de Alcalá, Colegios 2, 28801 Alcalá de Henares, Spain
Interests: active fire detection; burned area mapping; burn severity estimation; hyperspectral methods; radiative transfer models; remote sensing; forest health assessment; estimation of fire emissions

Special Issue Information

Dear Colleagues,

In the past, remote sensing has been shown to contribute significantly to a better understanding of both the natural and built environment. With LiDAR remote sensing making it possible to collect 3D coordinates of objects with extremely high accuracy, many fields such as geosciences, urban studies, and vegetation mapping have been given the opportunity to develop further.

LiDAR sensors onboard different platforms (e.g., terrestrial, airborne, UAV, satellite, backpack, and handheld) have been widely used in various biomes, especially over large and remote areas. So far, one of the main applications of LiDAR data is to provide a reliable estimation of biomass and carbon stock as well as information related to different forest parameters (e.g., diameter at breast height and basal area, tree height, and canopy base height), resulting in significant contributions to sustainable forest management and climate change mitigation.

Recent developments in forest research include the integration of LiDAR with other remote sensing data at different scales, as well as the use of machine learning and deep learning to extract semantic information about different forest attributes.

This Special Issue on “Remote Sensing and LiDAR Data for Forest Monitoring” welcomes papers focusing on remote sensing applications based on LiDAR data for forest ecosystem monitoring. The scope of topics to be discussed includes but is not limited to the following:

  • LiDAR-based approaches for forest ecology and management.
  • Forest biomass estimation using LiDAR data or multisource approaches (including LiDAR).
  • New methods in LiDAR processing for forest attribute retrieval.
  • Machine learning and deep-learning approaches for forest information retrieval from LiDAR data.
  • Multisensor approaches and data fusion for forest ecosystem monitoring.
  • Multitemporal LiDAR approaches for forest change monitoring.
  • New approaches in forest damage detection methods employing LiDAR data.

Prof. Dr. Ioannis Gitas
Dr. Dimitris Stavrakoudis
Dr. Patricia Oliva
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

  • forest management
  • forest remote sensing
  • forest biomass
  • forest ecosystems
  • forest inventory
  • LiDAR
  • data fusion

Published Papers (2 papers)

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Research

12 pages, 3256 KiB  
Article
Miniaturizing Hyperspectral Lidar System Employing Integrated Optical Filters
by Haibin Sun, Yicheng Wang, Zhipei Sun, Shaowei Wang, Shengli Sun, Jianxin Jia, Changhui Jiang, Peilun Hu, Haima Yang, Xing Yang, Mika Karjalnen, Juha Hyyppä and Yuwei Chen
Remote Sens. 2024, 16(9), 1642; https://doi.org/10.3390/rs16091642 - 4 May 2024
Viewed by 709
Abstract
Hyperspectral LiDAR (HSL) has been utilized as an efficacious technique in object classification and recognition based on its unique capability to obtain ranges and spectra synchronously. Different kinds of HSL prototypes with varied structures have been promoted and measured its performance. However, almost [...] Read more.
Hyperspectral LiDAR (HSL) has been utilized as an efficacious technique in object classification and recognition based on its unique capability to obtain ranges and spectra synchronously. Different kinds of HSL prototypes with varied structures have been promoted and measured its performance. However, almost all of these HSL prototypes employ complex and large spectroscopic devices, such as an Acousto-Optic Tunable Filter and Liquid-Crystal Tunable Filter, which makes this HSL system bulky and expensive, and then hinders its extensive application in many fields. In this paper, a smart and smaller spectroscopic component, an intergraded optical filter (IOF), is promoted to miniaturize these HSL systems. The system calibration, range precision, and spectral profile experiments were carried out to test the HSL prototype. Although the IOF employed here only covered a wavelength range of 699–758 nm with a six-channel passband and showed a transmittance of less than 50%, the HSL prototype showed excellent performance in ranging and spectral profile collecting. The spectral profiles collected are well in accordance with those acquired based on the AOTF. The spectral profiles of the fruits, vegetables, plants, and ore samples collected by the HSL based on an IOF can effectively reveal the status of the plants, the component materials, and ore species. Finally, we also showed the integrated design of the HSL based on a three-dimensional IOF and combined with a detector. The performance and designs of this HSL system based on an IOF show great potential for miniaturizing in some specific applications. Full article
(This article belongs to the Special Issue Remote Sensing and Lidar Data for Forest Monitoring)
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23 pages, 9596 KiB  
Article
Estimating Crown Biomass in a Multilayered Fir Forest Using Airborne LiDAR Data
by Nikos Georgopoulos, Ioannis Z. Gitas, Lauri Korhonen, Konstantinos Antoniadis and Alexandra Stefanidou
Remote Sens. 2023, 15(11), 2919; https://doi.org/10.3390/rs15112919 - 3 Jun 2023
Cited by 3 | Viewed by 1632
Abstract
The estimation of individual biomass components within tree crowns, such as dead branches (DB), needles (NB), and branch biomass (BB), has received limited attention in the scientific literature despite their significant contribution to forest biomass. This study aimed to assess the potential of [...] Read more.
The estimation of individual biomass components within tree crowns, such as dead branches (DB), needles (NB), and branch biomass (BB), has received limited attention in the scientific literature despite their significant contribution to forest biomass. This study aimed to assess the potential of multispectral LiDAR data for estimating these biomass components in a multi-layered Abies borissi-regis forest. Destructive (i.e., 13) and non-destructive (i.e., 156) field measurements were collected from Abies borisii-regis trees to develop allometric equations for each crown biomass component and enrich the reference data with the non-destructively sampled trees. A set of machine learning regression algorithms, including random forest (RF), support vector regression (SVR) and Gaussian process (GP), were tested for individual-tree-level DB, NB and BB estimation using LiDAR-derived height and intensity metrics for different spectral channels (i.e., green, NIR and merged) as predictors. The results demonstrated that the RF algorithm achieved the best overall predictive performance for DB (RMSE% = 17.45% and R2 = 0.89), NB (RMSE% = 17.31% and R2 = 0.93) and BB (RMSE% = 24.09% and R2 = 0.85) using the green LiDAR channel. This study showed that the tested algorithms, particularly when utilizing the green channel, accurately estimated the crown biomass components of conifer trees, specifically fir. Overall, LiDAR data can provide accurate estimates of crown biomass in coniferous forests, and further exploration of this method’s applicability in diverse forest structures and biomes is warranted. Full article
(This article belongs to the Special Issue Remote Sensing and Lidar Data for Forest Monitoring)
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