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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: 1 February 2025 | Viewed by 16918

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Dr. Zhen Zhen

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Guest Editor

Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forest University, Harbin 150040, China
Interests: biomass; carbon neutral; machine learning algorithms; temporal and spatial modeling; LiDAR; hyperspectral data

Dr. Tao Liu

E-Mail Website
Guest Editor

College of Forest Resources and Environmental Science, Michigan Tech, Houghton, MI 49931, USA
Interests: LiDAR point processing; deep learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Lin Cao

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Guest Editor

College of Forestry, Nanjing Forestry University, Nanjing, China
Interests: forest resource monitoring; forest phenotyping; biodiversity; LiDAR; UAV; satellite images
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest ecosystems play critical roles in global carbon sequestration, sustainable development, and climate change mitigation. Since the global agreement (“Paris Agreement”) was reached at the 2015 United Nations Climate Change Conference (COP21), many countries (e.g., China, France, Japan, South Korea, United States) have announced the exact years of peak carbon emissions and carbon neutrality (i.e., ‘dual carbon goals’). The accurate estimation and assessment of forest biomass/carbon stock are fundamental and vital for modeling the global carbon cycle, quantifying carbon fluxes from land use and land cover change, and achieving ‘dual carbon goals’. In recent years, the increasing availability of multisource remote sensing data (e.g., multispectral, hyperspectral, LiDAR, and SAR) with various platforms (e.g., satellite, airborne, unmanned aerial vehicle, and terrestrial) and advanced artificial intelligence (e.g., machine learning, deep learning, and transfer learning) provide unprecedented potential and opportunity to accurately estimate and assess forest biomass/carbon.

This Special Issue will provide a platform for cutting-edge research on accurately assessing and monitoring forest biomass/carbon stock towards carbon neutrality using multi-source remote sensing data. Well-prepared, unpublished submissions that address one or more of the following topics are solicited (but not limited to this list):

high-resolution and large-scale mapping, monitoring, and modeling of the dynamics of forest biomass/carbon
deep learning or innovative artificial intelligence algorithms for forest biomass/carbon stock estimation
multiscale estimation and its spatial uncertainty of forest biomass/carbon stock
the development of individual tree species classification or forest classification models using artificial intelligence approaches
estimation of tree-level structural parameters and biophysical properties that are significant for forest biomass/carbon stock
monitoring and modeling carbon fluxes in forest ecosystems
the impact of climate change on the carbon source and carbon sink distribution of forests
responses of forests to extreme weather events (e.g., heavy precipitation, drought, sand and dust storms) or disturbances (e.g., wildfire, insects)
impact of forest mortality on carbon flux
forest growth modeling using remote sensing data

Dr. Zhen Zhen
Dr. Tao Liu
Prof. Dr. Lin Cao
Guest Editors

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Keywords

AGB
AGC
carbon neutral
carbon flux
artificial intelligence
carbon source/sink
deep learning

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Published Papers (9 papers)

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Research

18 pages, 11081 KiB

Open AccessArticle

Mapping Forest Carbon Stock Distribution in a Subtropical Region with the Integration of Airborne Lidar and Sentinel-2 Data

by Xiaoyu Sun, Guiying Li, Qinquan Wu, Jingyi Ruan, Dengqiu Li and Dengsheng Lu

Remote Sens. 2024, 16(20), 3847; https://doi.org/10.3390/rs16203847 - 16 Oct 2024

Viewed by 763

Abstract

Forest carbon stock is an important indicator reflecting a forest ecosystem’s structures and functions. Its spatial distribution is valuable for managing natural resources, protecting ecosystems and biodiversity, and further promoting sustainability, but accurately mapping the forest carbon stock distribution in a large area is a challenging task. This study selected Changting County, Fujian Province, as a case study to explore a method to map the forest carbon stock distribution using the integration of airborne Lidar, Sentinel-2, and ancillary data in 2022. The Bayesian hierarchical modeling approach was used to estimate the local forest carbon stock based on airborne Lidar data and field measurements, and then the random forest approach was used to develop a regional forest carbon stock estimation model based on the Sentinel-2 and ancillary data. The results indicated that the Lidar-based carbon stock distribution effectively provided sample plots with good spatial representativeness for modeling regional carbon stock with a coefficient of determination (R²) of 0.7 and root mean square error (RMSE) of 12.94 t/ha. The average carbon stocks were 48.55 t/ha, 55.51 t/ha, and 57.04 t/ha for Masson pine, Chinese fir, and broadleaf forests, respectively. The carbon stock in non-conservation regions was 15.2–16.1 t/ha higher than that in conservation regions. This study provides a promising method through the use of airborne Lidar data as a linkage between sample plots and Sentinel-2 data to map the regional carbon stock distribution in those subtropical regions where serious soil erosion has led to a relatively sparse forest canopy density. The results are valuable for local government to make scientific decisions for promoting ecosystem restoration due to water and soil erosion. Full article

(This article belongs to the Special Issue Forest Biomass/Carbon Monitoring towards Carbon Neutrality)

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19 pages, 9003 KiB

Open AccessArticle

Estimating Forest Aboveground Biomass Using a Combination of Geographical Random Forest and Empirical Bayesian Kriging Models

by Zhenjiang Wu, Fengmei Yao, Jiahua Zhang and Haoyu Liu

Remote Sens. 2024, 16(11), 1859; https://doi.org/10.3390/rs16111859 - 23 May 2024

Cited by 1 | Viewed by 1498

Abstract

Accurately estimating forest aboveground biomass (AGB) is imperative for comprehending carbon cycling, calculating carbon budgets, and formulating sustainable forest management plans. Currently, random forest (RF) and other machine learning models are widely used to estimate forest AGB, as they can effectively handle nonlinear relationships. However, by constructing a global model using all the samples collected from a study area, these models fail to account for the spatial heterogeneity in the AGB and cannot correct the prediction biases, thereby constraining the estimation accuracy. To overcome these limitations, we proposed a novel approach termed geographical random forest and empirical Bayesian kriging (GRFEBK). This hybrid model combines the localized modeling capability of geographical random forest (GRF) with the bias correction strength of empirical Bayesian kriging (EBK). GRF adapts RF to account for the spatial heterogeneity of the AGB, while EBK utilizes the spatial autocorrelation of residuals to correct the prediction deviations. This study was conducted in Hainan Island, utilizing spectral bands, vegetation indices, tasseled cap components derived from Landsat-8 imagery, backscattering coefficients from ALOS-2 synthetic aperture radar, topographic features, and the forest canopy height as the explanatory variables. A total of 195 forest aboveground biomass (AGB) samples were collected for modeling and assessing the predictive accuracy. The results demonstrate that, among the tested models, including GRFEBK, RF, support vector machine (SVM), k-nearest neighbor (KNN), geographically weighted regression (GWR), GRF, and EBK, GRFEBK attains the highest

R^{2}

(0.78) and the lowest RMSE (36.04 Mg/ha) and RRMSE (22.87%), significantly outperforming the conventional models and using GRF or EBK alone. These results demonstrate that by accounting for local non-stationarity in AGB and correcting prediction biases, GRFEBK achieves significantly higher accuracy than conventional RF and other models. While the results are promising, the computational cost of GRFEBK and its performance under varying geographical conditions warrant further investigation at larger scales to assess its broader applicability. Nevertheless, GRFEBK provides an innovative and more reliable approach for accurate forest AGB estimation with great potential to support global forest resource monitoring. Full article

(This article belongs to the Special Issue Forest Biomass/Carbon Monitoring towards Carbon Neutrality)

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21 pages, 7672 KiB

Open AccessArticle

Characterizing the Accelerated Global Carbon Emissions from Forest Loss during 1985–2020 Using Fine-Resolution Remote Sensing Datasets

by Wendi Liu, Xiao Zhang, Hong Xu, Tingting Zhao, Jinqing Wang, Zhehua Li and Liangyun Liu

Remote Sens. 2024, 16(6), 978; https://doi.org/10.3390/rs16060978 - 11 Mar 2024

Cited by 2 | Viewed by 1577

Abstract

Previous studies on global carbon emissions from forest loss have been marked by great discrepancies due to uncertainties regarding the lost area and the densities of different carbon pools. In this study, we employed a new global 30 m land cover dynamic dataset (GLC_FCS30D) to improve the assessment of forest loss areas; then, we combined multi-sourced carbon stock products to enhance the information on carbon density. Afterwards, we estimated the global carbon emissions from forest loss over the period of 1985–2020 based on the method recommended by the Intergovernmental Panel on Climate Change Guidelines (IPCC). The results indicate that global forest loss continued to accelerate over the past 35 years, totaling about 582.17 Mha and leading to total committed carbon emissions of 35.22 ± 9.38 PgC. Tropical zones dominated global carbon emissions (~2/3) due to their higher carbon density and greater forest loss. Furthermore, global emissions more than doubled in the period of 2015–2020 (1.77 ± 0.44 PgC/yr) compared to those in 1985–2000 (0.69 ± 0.21 PgC/yr). Notably, the forest loss at high altitudes (i.e., above 1000 m) more than tripled in mountainous regions, resulting in more pronounced carbon emissions in these areas. Therefore, the accelerating trend of global carbon emissions from forest loss indicates that great challenges still remain for achieving the COP 26 Declaration to halt forest loss by 2030. Full article

(This article belongs to the Special Issue Forest Biomass/Carbon Monitoring towards Carbon Neutrality)

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22 pages, 6029 KiB

Open AccessArticle

Estimation of Forest Residual Biomass for Bioelectricity Utilization towards Carbon Neutrality Based on Sentinel-2A Multi-Temporal Images: A Case Study of Aizu Region of Fukushima, Japan

by Tana Qian, Makoto Ooba, Minoru Fujii, Takanori Matsui, Chihiro Haga, Akiko Namba and Shogo Nakamura

Remote Sens. 2024, 16(4), 706; https://doi.org/10.3390/rs16040706 - 17 Feb 2024

Cited by 2 | Viewed by 1621

Abstract

Forest biomass is expected to remain a key part of the national energy portfolio mix, yet residual forest biomass is currently underused. This study aimed to estimate the potential availability of waste woody biomass in the Aizu region and its energy potential for local bioelectricity generation as a sustainable strategy. The results showed that the available quantity of forest residual biomass for energy production was 191,065 tons, with an average of 1.385 t/ha in 2018, of which 72% (146,976 tons) was from logging residue for commercial purposes, and 28% (44,089 tons) was from thinning operations for forest management purposes. Forests within the biomass–collection radius of a local woody power plant can provide 45,925 tons of residual biomass, supplying bioelectricity at 1.6 times the plant’s capacity, which could avoid the amount of 65,246 tons of CO₂ emission per year by replacing coal-fired power generation. The results highlight the bioelectricity potential and carbon-neutral capacity of residual biomass. This encourages government initiatives or policy inclinations to sustainably boost the production of bioenergy derived from residual biomass. Full article

(This article belongs to the Special Issue Forest Biomass/Carbon Monitoring towards Carbon Neutrality)

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34 pages, 9060 KiB

Open AccessArticle

A New Method for Reconstructing Tree-Level Aboveground Carbon Stocks of Eucalyptus Based on TLS Point Clouds

by Guangpeng Fan, Feng Lu, Huide Cai, Zhanyong Xu, Ruoyoulan Wang, Xiangquan Zeng, Fu Xu and Feixiang Chen

Remote Sens. 2023, 15(19), 4782; https://doi.org/10.3390/rs15194782 - 30 Sep 2023

Cited by 2 | Viewed by 1264

Abstract

Eucalyptus plantation forests in southern China provide not only the economic value of producing timber, but also the ecological value service of absorbing carbon dioxide and releasing oxygen. Based on the theory of spatial colonial modeling, this paper proposes a new method for 3D reconstruction of tree terrestrial LiDAR point clouds for determining the aboveground carbon stock of eucalyptus monocotyledons, which consists of the main steps of tree branch and trunk separation, skeleton extraction and optimization, 3D reconstruction, and carbon stock calculation. The main trunk and branches of the tree point clouds are separated using a layer-by-layer judgment and clustering method, which avoids errors in judgment caused by sagging branches. By optimizing and adjusting the skeleton to remove small redundant branches, the near-parallel branches belonging to the same tree branch are fused. The missing parts of the skeleton point clouds were complemented using the cardinal curve interpolation algorithm, and finally a real 3D structural model was generated based on the complemented and smoothed tree skeleton expansion. The bidirectional Hausdoff distance, average Hausdoff distance, and F distance were used as evaluation indexes, which were reduced by 0.7453 m, 0.0028 m, and 0.0011 m, respectively, and the improved spatial colonization algorithm enhanced the accuracy of the reconstructed tree 3D structural model. To verify the accuracy of our method to determine the carbon stock and its related parameters, we cut down 41 eucalyptus trees and destructively sampled the measurement data as reference values. The R² of the linear fit between the reconstructed single-tree aboveground carbon stock estimates and the reference values was 0.96 with a CV(RMSE) of 16.23%, the R² of the linear fit between the trunk volume estimates and the reference values was 0.94 with a CV(RMSE) of 19.00%, and the R² of the linear fit between the branch volume estimates and the reference values was 0.95 with a CV(RMSE) of 38.84%. In this paper, a new method for reconstructing eucalyptus carbon stocks based on TLS point clouds is proposed, which can provide decision support for forest management and administration, forest carbon sink trading, and emission reduction policy formulation. Full article

(This article belongs to the Special Issue Forest Biomass/Carbon Monitoring towards Carbon Neutrality)

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26 pages, 6667 KiB

Open AccessArticle

Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests

by Ye Ma, Lianjun Zhang, Jungho Im, Yinghui Zhao and Zhen Zhen

Remote Sens. 2023, 15(18), 4364; https://doi.org/10.3390/rs15184364 - 5 Sep 2023

Cited by 3 | Viewed by 1762

Abstract

Identifying important factors (e.g., features and prediction models) for forest aboveground biomass (AGB) estimation can provide a vital reference for accurate AGB estimation. This study proposed a novel feature of the canopy height distribution (CHD), a function of canopy height, that is useful for describing canopy structure for AGB estimation of natural secondary forests (NSFs) by fitting a bimodal Gaussian function. Three machine learning models (Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (Xgboost)) and three deep learning models (One-dimensional Convolutional Neural Network (1D-CNN4), 1D Visual Geometry Group Network (1D-VGG16), and 1D Residual Network (1D-Resnet34)) were applied. A completely randomized design was utilized to investigate the effects of four feature sets (original CHD features, original LiDAR features, the proposed CHD features fitted by the bimodal Gaussian function, and the LiDAR features selected by the recursive feature elimination algorithm) and models on estimating the AGB of NSFs. Results revealed that the models were the most important factor for AGB estimation, followed by the features. The fitted CHD features significantly outperformed the other three feature sets in most cases. When employing the fitted CHD features, the 1D-Renset34 model demonstrates optimal performance (R² = 0.80, RMSE = 9.58 Mg/ha, rRMSE = 0.09), surpassing not only other deep learning models (e.g.,1D-VGG16: R² = 0.65, RMSE = 18.55 Mg/ha, rRMSE = 0.17) but also the best machine learning model (RF: R² = 0.50, RMSE = 19.42 Mg/ha, rRMSE = 0.16). This study highlights the significant role of the new CHD features fitting a bimodal Gaussian function and the effects between the models and the CHD features, which provide the sound foundations for effective estimation of AGB in NSFs. Full article

(This article belongs to the Special Issue Forest Biomass/Carbon Monitoring towards Carbon Neutrality)

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23 pages, 3916 KiB

Open AccessArticle

Improving the Potential of Coniferous Forest Aboveground Biomass Estimation by Integrating C- and L-Band SAR Data with Feature Selection and Non-Parametric Model

by Yifan Hu, Yonghui Nie, Zhihui Liu, Guoming Wu and Wenyi Fan

Remote Sens. 2023, 15(17), 4194; https://doi.org/10.3390/rs15174194 - 25 Aug 2023

Cited by 8 | Viewed by 1577

Abstract

Forests play a significant role in terrestrial ecosystems by sequestering carbon, and forest biomass is a crucial indicator of carbon storage potential. However, the single-frequency SAR estimation of forest biomass often leads to saturation issues. This research aims to improve the potential for estimating forest aboveground biomass (AGB) by feature selection based on a scattering mechanism and sensitivity analysis and utilizing a non-parametric model that combines the advantage of dual-frequency SAR data. By employing GF-3 and ALOS-2 data, this study explores the scattering mechanism within a coniferous forest by using results of target decomposition and the pixel statistics method. By selecting an appropriate feature (backscatter coefficients and polarization parameters) and using stepwise regression models and a non-parametric model (the random forest adaptive genetic algorithm (RF-AGA)), the results revealed that the RF-AGA model with feature selection exhibited excellent AGB estimation performance without obvious saturation (RMSE = 10.42 t/ha, R² = 0.93, leave-one-out cross validation). The

σ_{H V}

σ_{V H}

, Pauli three-component decomposition, Yamaguchi three-component decomposition, and VanZyl3 component decomposition of thee C-band and

σ_{H V}

σ_{V H}

σ_{H H},

Yamaguchi three-component decomposition, and VanZyl3 component decomposition of the L-band are suited for estimating the AGB of coniferous forests. Volume scattering was the dominant mechanism, followed by surface scattering, while double-bounce scattering had the smallest proportion. This study highlights the potential of investigating scattering mechanisms, sensitivity factors, and parameter selection in the C- and L-band SAR data for improved forest AGB estimation. Full article

(This article belongs to the Special Issue Forest Biomass/Carbon Monitoring towards Carbon Neutrality)

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19 pages, 2905 KiB

Open AccessArticle

Comparison of Different Important Predictors and Models for Estimating Large-Scale Biomass of Rubber Plantations in Hainan Island, China

by Xin Li, Xincheng Wang, Yuanfeng Gao, Jiuhao Wu, Renxi Cheng, Donghao Ren, Qing Bao, Ting Yun, Zhixiang Wu, Guishui Xie and Bangqian Chen

Remote Sens. 2023, 15(13), 3447; https://doi.org/10.3390/rs15133447 - 7 Jul 2023

Cited by 4 | Viewed by 1988

Abstract

Rubber (Hevea brasiliensis Muell.) plantations are among the most critical agricultural ecosystems in tropical regions, playing a vital role in regional carbon balance. Accurate large-scale biomass estimation for these plantations remains a challenging task due to the severe signal saturation problem. Recent advances in remote sensing big data, cloud platforms, and machine learning have facilitated the precise acquisition of key physiological variables, such as stand age (A) and canopy height (H), which are critical parameters for biomass estimation but have been underutilized in prior studies. Using Hainan Island—the second-largest rubber planting base in China—as a case study, we integrated extensive ground surveys, maps of stand age and canopy height, remote sensing indicators (RSIs), and geographical and climate indicators (ECIs) to ascertain the optimal method for estimating rubber plantation biomass. We compared different inputs and estimation approaches (direct and indirect) using the random forest algorithm and analyzed the spatiotemporal characteristics of rubber plantation biomass on Hainan Island. The results indicated that the traditional model (RSIs + ECIs) had low accuracy and significant estimation bias (R² = 0.24, RMSE = 38.36 mg/ha). The addition of either stand age or canopy height considerably enhance model accuracy (R² = 0.77, RMSE ≈ 21.12 mg/ha). Moreover, incorporating the DBH obtained through indirect inversion yielded even greater predictive accuracy (R² = 0.97, RMSE = 7.73 mg/ha), outperforming estimates derived from an allometric equation model input with the DBH (R² = 0.67, RMSE = 25.43 mg/ha). However, augmenting the model with stand age, canopy height, or their combination based on RSIs, ECIs, and DBH only marginally improved the accuracy. Consequently, it is not recommended in scenarios with limited data and computing resources. Employing the optimal model, we generated biomass maps of rubber plantations on Hainan Island for 2016 and 2020, revealing that the spatiotemporal distribution pattern of the biomass is closely associated with the establishment year of the rubber plantations. While average biomass in a few areas has undergone slight decreases, total biomass has exhibited significant growth, reaching 5.46 × 10⁷ mg by the end of 2020, underscoring its considerable value as a carbon sink. Full article

(This article belongs to the Special Issue Forest Biomass/Carbon Monitoring towards Carbon Neutrality)

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25 pages, 9022 KiB

Open AccessArticle

Evaluating Carbon Sink Potential of Forest Ecosystems under Different Climate Change Scenarios in Yunnan, Southwest China

by Fucheng Lü, Yunkun Song and Xiaodong Yan

Remote Sens. 2023, 15(5), 1442; https://doi.org/10.3390/rs15051442 - 4 Mar 2023

Cited by 6 | Viewed by 3160

Abstract

Nature-based Solutions (NbS) can undoubtedly play a significant role in carbon neutrality strategy. Forests are a major part of the carbon budget in terrestrial ecosystems. The possible response of the carbon balance of southwestern forests to different climate change scenarios was investigated through a series of simulations using the forest ecosystem carbon budget model for China (FORCCHN), which clearly represents the influence of climate factors on forest carbon sequestration. Driven by downscaled global climate model (GCM) data, the FORCCHN evaluates the carbon sink potential of southwestern forest ecosystems under different shared socioeconomic pathways (SSPs). The results indicate that, first, gross primary productivity (GPP), ecosystem respiration (ER) and net primary productivity (NPP) of forest ecosystems are expected to increase from 2020 to 2060. Forest ecosystems will maintain a carbon sink, but net ecosystem productivity (NEP) will peak and begin to decline in the 2030s. Second, not only is the NEP in the SSP1-2.6 scenario higher than in the other climate change scenarios for 2025–2035 and 2043–2058, but the coefficient of variation of the NEP is also narrower than in the other scenarios. Third, in terms of spatial distribution, the carbon sequestration potential of northwest and central Yunnan is significantly higher than that of other regions, with a slight upward trend in NEP in the future. Finally, GPP and ER are significantly positively correlated with temperature and insignificantly correlated with precipitation, and the increasing temperature will have a negative and unstable impact on forest carbon sinks. This study provides a scientific reference for implementing forest management strategies and achieving sustainable development. Full article

(This article belongs to the Special Issue Forest Biomass/Carbon Monitoring towards Carbon Neutrality)

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