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Remote Sensing and Lidar Data for Forest Monitoring

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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 6080

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Special Issue Editors

Prof. Dr. Ioannis Gitas

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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

Dr. Dimitris Stavrakoudis

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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: 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

Dr. Patricia Oliva

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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

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Published Papers (4 papers)

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Research

14 pages, 3793 KiB

Open AccessArticle

Large-Scale Mapping of Complex Forest Typologies Using Multispectral Imagery and Low-Density Airborne LiDAR: A Case Study in Pinsapo Fir Forests

by Antonio Jesús Ariza-Salamanca, Pablo González-Moreno, José Benedicto López-Quintanilla and Rafael María Navarro-Cerrillo

Remote Sens. 2024, 16(17), 3182; https://doi.org/10.3390/rs16173182 - 28 Aug 2024

Viewed by 552

Abstract

Climate change increases the vulnerability of relict forests. To address this problem, regional Forest Services require silvicultural and conservation actions to designate specific forest management alternatives. In this context, the main objective of this study was to develop a methodology to map complex Abies pinsapo forest typologies using multispectral and low-density airborne LiDAR data and machine learning. Stand density, species composition and cover were used to identify seven forest typologies. Random forest resulted as the more accurate model (OA = 0.62; Kappa = 0.43) to classify those types based on multispectral and LiDAR data, although showing a moderate model performance. Classification performance showed great differences between forest types with better results for the uneven-aged stands compared to the even-aged and two-aged stands. The developed typology was applied to supply local forest managers with more accurate forest maps that can be used to improve forest management plans. The typology proposed is easy to apply in forest management practices since it only uses as input the diameter at breast height, tree density and specific composition. The study demonstrated the potential of low-density LiDAR data combined with spectral information from high-resolution orthophotos to predict the structural characteristics of complex forest typologies. Full article

(This article belongs to the Special Issue Remote Sensing and Lidar Data for Forest Monitoring)

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23 pages, 11056 KiB

Open AccessArticle

Co-Kriging-Guided Interpolation for Mapping Forest Aboveground Biomass by Integrating Global Ecosystem Dynamics Investigation and Sentinel-2 Data

by Yingchen Wang, Hongtao Wang, Cheng Wang, Shuting Zhang, Rongxi Wang, Shaohui Wang and Jingjing Duan

Remote Sens. 2024, 16(16), 2913; https://doi.org/10.3390/rs16162913 - 9 Aug 2024

Viewed by 1062

Abstract

Mapping wall-to-wall forest aboveground biomass (AGB) at large scales is critical for understanding global climate change and the carbon cycle. In previous studies, a regression-based method was commonly used to map the spatially continuous distribution of forest AGB with the aid of optical images, which may suffer from the saturation effect. The Global Ecosystem Dynamics Investigation (GEDI) can collect forest vertical structure information with high precision on a global scale. In this study, we proposed a collaborative kriging (co-kriging) interpolation-based method for mapping spatially continuous forest AGB by integrating GEDI and Sentinel-2 data. First, by fusing spectral features from Sentinel-2 images with vertical structure features from GEDI, the optimal estimation model for footprint-level AGB was determined by comparing different machine-learning algorithms. Second, footprint-level predicted AGB was used as the main variable, with rh95 and B12 as covariates, to build a co-kriging guided interpolation model. Finally, the interpolation model was employed to map wall-to-wall forest AGB. The results showed the following: (1) For footprint-level AGB, CatBoost achieved the highest accuracy by fusing features from GEDI and Sentinel-2 data (

R^{2}

= 0.87, RMSE = 49.56 Mg/ha, rRMSE = 27.06%). (2) The mapping results based on the interpolation method exhibited relatively high accuracy and mitigated the saturation effect in areas with higher forest AGB (

R^{2}

= 0.69, RMSE = 81.56 Mg/ha, rRMSE = 40.98%, bias = −3.236 Mg/ha). The mapping result demonstrates that the proposed method based on interpolation combined with multi-source data can be a promising solution for monitoring spatially continuous forest AGB. Full article

(This article belongs to the Special Issue Remote Sensing and Lidar Data for Forest Monitoring)

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12 pages, 3256 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1366

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 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

Open AccessArticle

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 6 | Viewed by 1956

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 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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