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Remote Sensing
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Remote Sensing and Lidar Data for Forest Monitoring
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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 4181

Special Issue Editors

Prof. Dr. Ioannis Gitas

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: 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

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
Dr. Patricia Oliva

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 (3 papers)

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Research

23 pages, 11056 KiB  
Article
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 312
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 [...] Read more.
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 (R2 = 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 (R2 = 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)
►▼ Show Figures

Figure 1

Figure 1
<p>Location of the study area. (<b>a</b>) The administrative boundary of the State of California and the location of the study area within the state (i.e., where the red box is); (<b>b</b>) ground elevation distribution of the study area; (<b>c</b>) local distribution of GEDI footprints.</p> Full article ">Figure 2
<p>Technology road map of this study.</p> Full article ">Figure 3
<p>Filtered distribution of GEDI footprints: (<b>a</b>) urban area; (<b>b</b>) vegetation area. The green area represents vegetation, the purple area represents urban areas, and the blue area represents the ocean.</p> Full article ">Figure 4
<p>The correlation between the features and forest AGB, with all significance levels of the selected features at 0.01.</p> Full article ">Figure 5
<p>Feature selection outcomes. (<b>a</b>) Features selected using the stepwise method. (<b>b</b>) Features selected using the random forest method.</p> Full article ">Figure 6
<p>Scatter plots between reference AGB and predicted AGB derived from the combined optical and GEDI data. The red dotted lines represent the fitted trend-lines.</p> Full article ">Figure 7
<p>Histograms of AGB: (<b>a</b>) ALS-derived and (<b>b</b>) interpolation-method-derived.</p> Full article ">Figure 8
<p>Boxplot of AGB distribution. IQR = Q3 − Q1, where Q1 is the first quartile and Q3 is the third quartile.</p> Full article ">Figure 9
<p>Forest AGB mapping results. (<b>a</b>) Satellite images of the study area. (<b>b</b>) Forest AGB map derived from the co-kriging interpolation model. The red line represents the boundary of the study area (i.e., Sonoma County).</p> Full article ">Figure 10
<p>Comparison of interpolation accuracy for different covariate combinations.</p> Full article ">Figure 11
<p>Accuracy assessment of the wall-to-wall forest AGB products. (<b>a</b>) The predicted AGB derived from co-kriging interpolation. (<b>b</b>) The predicted AGB derived from the regression method. The red dotted lines represent the fitted trend-lines.</p> Full article ">
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 1113
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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Figure 1

Figure 1
<p>(<b>a</b>–<b>e</b>) A diagram of the procedure for fabricating the IOF filter; (<b>f</b>) the IOF component compared with a coin.</p> Full article ">Figure 2
<p>(<b>a</b>) The optical schematic and (<b>b</b>) system setup of the 6-channel IOF-HSL; (<b>c</b>) shows the details of the structure of the IOF and the APD detector.</p> Full article ">Figure 3
<p>The echo waveforms from an SRB-60% collected by the (<b>a</b>) AOTF-HSL and (<b>b</b>) IOF-HSL at distances of 6 m, 7 m, 8 m, and 9 m; (<b>c</b>,<b>d</b>) are the corresponding reflectance calibrated by an SRB-99%. The straight line in (<b>a</b>,<b>b</b>) is the linear fitting result.</p> Full article ">Figure 4
<p>The waveforms from a paper box, SRB-99%, and white wall at different distances.</p> Full article ">Figure 5
<p>(<b>a</b>) An orange and carrot, the arrows are the rotating direction; (<b>b</b>) the echo waveforms and (<b>c</b>) reflectance of each spectral channel, and the solid lines in (<b>b</b>,<b>c</b>) are the spectral profiles obtained by the AOTF-HSL.</p> Full article ">Figure 6
<p>(<b>a</b>) three apples with different appearances, the arrows are the rotating direction; (<b>b</b>) the echo waveform and (<b>c</b>) the reflectance of each spectral channel.</p> Full article ">Figure 7
<p>(<b>a</b>) Green/dry leaves and three kinds of dry wood, (<b>b</b>) the waveforms and (<b>c</b>) reflectance of each target, and (<b>d</b>) the Normalized Difference Vegetation Index (NDVI) parameters based on spectral profiles are also given.</p> Full article ">Figure 8
<p>(<b>a</b>) three kinds of ores irradiated with the laser and (<b>b</b>) the corresponding spectral profiles collected by the IOF-HSL prototype.</p> Full article ">Figure 9
<p>(<b>a</b>) HSL based on single-element APD, and IOF-HSL prototype employing (<b>b</b>) single-element APD with strip IOF; (<b>c</b>) array detector with strip IOF; and (<b>d</b>) array detector with array IOF.</p> Full article ">
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 5 | Viewed by 1808
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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Figure 1

Figure 1
<p>Vegetation map of the study area, including the sampling plots and road network.</p> Full article ">Figure 2
<p>Canopy height model of Pertouli University Forest generated from ALS data showing the sampled trees: (<b>a</b>–<b>f</b>) areas employed for non-destructive sampling (i.e., measurement of DBH, H, canopy base height and crown radii for crown biomass estimation using allometric equations); (<b>g</b>) specific trees and area of interest for destructive sampling.</p> Full article ">Figure 3
<p>Flowchart of the workflow for the single-tree dead branches, needles and branch biomass (DB, NB, and BB, respectively) estimations using destructive sampling and multispectral ALS data in a multilayered fir forest.</p> Full article ">Figure 4
<p>Scatterplots of the observed versus the predicted dead branches biomass (DB) for the best performing algorithm in each point cloud, based on 46 testing samples. The blue line represents the fitted line, and the red line represents the 1:1 line.</p> Full article ">Figure 5
<p>Demonstration of the relative importance (<b>a</b>–<b>c</b>) of the predictors for the best-performing algorithm for DB estimation in each point cloud based on the random forest algorithm.</p> Full article ">Figure 6
<p>Scatterplots of the observed versus the predicted needles biomass (NB) for the best performing algorithm in each point cloud, based on 46 training samples. The blue line represents the fitted line, and the red line represents the 1:1 line.</p> Full article ">Figure 7
<p>Demonstration of the relative importance (<b>a</b>–<b>c</b>) of the predictors for the best-performing algorithm for NB estimation in each point cloud based on the random forest algorithm.</p> Full article ">Figure 8
<p>Scatterplots of the observed versus predicted branches biomass (BB) for the best-performing algorithm in each point cloud based on 46 testing samples. The blue line represents the fitted line and the red line represents the 1:1 line.</p> Full article ">Figure 9
<p>Demonstration of the relative importance (<b>a</b>–<b>c</b>) of the predictors for the best-performing algorithm for BB estimation in each point cloud based on the random forest algorithm.</p> Full article ">
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