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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: closed (31 October 2024) | Viewed by 12302

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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
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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 (7 papers)

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Research

29 pages, 12160 KiB

Open AccessArticle

Integration of UAS and Backpack-LiDAR to Estimate Aboveground Biomass of Picea crassifolia Forest in Eastern Qinghai, China

by Junejo Sikandar Ali, Long Chen, Bingzhi Liao, Chongshan Wang, Fen Zhang, Yasir Ali Bhutto, Shafique A. Junejo and Yanyun Nian

Remote Sens. 2025, 17(4), 681; https://doi.org/10.3390/rs17040681 - 17 Feb 2025

Viewed by 632

Abstract

Precise aboveground biomass (AGB) estimation of forests is crucial for sustainable carbon management and ecological monitoring. Traditional methods, such as destructive sampling, field measurements of Diameter at Breast Height with height (DBH and H), and optical remote sensing imagery, often fall short in capturing detailed spatial heterogeneity in AGB estimation and are labor-intensive. Recent advancements in remote sensing technologies, predominantly Light Detection and Ranging (LiDAR), offer potential improvements in accurate AGB estimation and ecological monitoring. Nonetheless, there is limited research on the combined use of UAS (Uncrewed Aerial System) and Backpack-LiDAR technologies for detailed forest biomass. Thus, our study aimed to estimate AGB at the plot level for Picea crassifolia forests in eastern Qinghai, China, by integrating UAS-LiDAR and Backpack-LiDAR data. The Comparative Shortest Path (CSP) algorithm was employed to segment the point clouds from the Backpack-LiDAR, detect seed points and calculate the DBH of individual trees. After that, using these initial seed point files, we segmented the individual trees from the UAS-LiDAR data by employing the Point Cloud Segmentation (PCS) method and measured individual tree heights, which enabled the calculation of the observed/measured AGB across three specific areas. Furthermore, advanced regression models, such as Random Forest (RF), Multiple Linear Regression (MLR), and Support Vector Regression (SVR), are used to estimate AGB using integrated data from both sources (UAS and Backpack-LiDAR). Our results show that: (1) Backpack-LiDAR extracted DBH compared to field extracted DBH shows about (R² = 0.88, RMSE = 0.04 m) whereas UAS-LiDAR extracted height achieved the accuracy (R² = 0.91, RMSE = 1.68 m), which verifies the reliability of the abstracted DBH and height obtained from the LiDAR data. (2) Individual Tree Segmentation (ITS) using a seed file of X and Y coordinates from Backpack to UAS-LiDAR, attaining a total accuracy F-score of 0.96. (3) Using the allometric equation, we obtained AGB ranges from 9.95–409 (Mg/ha). (4) The RF model demonstrated superior accuracy with a coefficient of determination (R²) of 89%, a relative Root Mean Square Error (rRMSE) of 29.34%, and a Root Mean Square Error (RMSE) of 33.92 Mg/ha compared to the MLR and SVR models in AGB prediction. (5) The combination of Backpack-LiDAR and UAS-LiDAR enhanced the ITS accuracy for the AGB estimation of forests. This work highlights the potential of integrating LiDAR technologies to advance ecological monitoring, which can be very important for climate change mitigation and sustainable environmental management in forest monitoring practices. Full article

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

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18 pages, 6460 KiB

Open AccessArticle

Understory Terrain Estimation by Synergizing Ice, Cloud, and Land Elevation Satellite-2 and Multi-Source Remote Sensing Data

by Jiapeng Huang and Yang Yu

Remote Sens. 2024, 16(24), 4770; https://doi.org/10.3390/rs16244770 - 21 Dec 2024

Viewed by 731

Abstract

Forest ecosystems are incredibly valuable, and understory terrain is crucial for estimating various forest structure parameters. As the demand for monitoring forest ecosystems increases, quickly and accurately understanding the spatial distribution patterns of understory terrain has become a new challenge. This study used ICESat-2 data as a reference and validation basis, integrating multi-source remote sensing data (including Landsat 8, ICESat-2, and SRTM) and applying machine learning methods to accurately estimate the sub-canopy topography of the study area. The results from the random forest model show a significant improvement in accuracy compared to traditional SRTM products, with an R² of 0.99, ME of 0.22 m, RMSE of 3.59 m, and STD of 3.59 m. In addition, we assessed the accuracy of understory topography estimates for different landforms, canopy heights, forest cover types, and forest coverage. The results demonstrate that the estimation results are minimally impacted by ground elevation, forest cover type, and forest coverage, indicating good stability. This approach holds promise for accurately estimating understory terrain at regional and global scales, providing crucial support for monitoring and protecting forest ecosystems. Full article

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

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23 pages, 10173 KiB

Open AccessArticle

Aboveground Carbon Stock Estimation Based on Backpack LiDAR and UAV Multispectral Imagery at the Forest Sample Plot Scale

by Rina Su, Wala Du, Yu Shan, Hong Ying, Wu Rihan and Rong Li

Remote Sens. 2024, 16(21), 3927; https://doi.org/10.3390/rs16213927 - 22 Oct 2024

Cited by 4 | Viewed by 1475

Abstract

Aboveground carbon stocks (AGCs) in forests play an important role in understanding carbon cycle processes. The global forestry sector has been working to find fast and accurate methods to estimate forest AGCs and implement dynamic monitoring. The aim of this study was to explore the effects of backpack LiDAR and UAV multispectral imagery on AGC estimation for two tree species (Larix gmelinii and Betula platyphylla) and to emphasize the accuracy of the models used. We estimated the AGC of Larix gmelinii and B. platyphylla forests using multivariate stepwise linear regression and random forest regression models using backpack LiDAR data and multi-source remote sensing data, respectively, and compared them with measured data. This study revealed that (1) the diameter at breast height (DBH) extracted from backpack LiDAR and vegetation indices (RVI and GNDVI) extracted from UAV multispectral imagery proved to be extremely effective in modeling for estimating AGCs, significantly improving the accuracy of the model. (2) Random forest regression models estimated AGCs with higher precision (Xing’an larch R² = 0.95, RMSE = 3.99; white birch R² = 0.96, RMSE = 3.45) than multiple linear regression models (Xing’an larch R² = 0.92, RMSE = 6.15; white birch R² = 0.96, RMSE = 3.57). (3) After combining backpack LiDAR and UAV multispectral data, the estimation accuracy of AGCs for both tree species (Xing’an larch R² = 0.95, white birch R² = 0.96) improved by 2% compared to using backpack LiDAR alone (Xing’an larch R² = 0.93, white birch R² = 0.94). Full article

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

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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

Cited by 2 | Viewed by 996

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 2212

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 AccessEditor’s ChoiceArticle

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 2 | Viewed by 2101

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 7 | Viewed by 2521

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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