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Forests
Special Issues
Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation
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Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 2277

Special Issue Editors

Dr. Yongjie Ji

E-Mail Website
Guest Editor
College of Soil and Water Conservation, Southwest Forestry University, Kunming 650224, China
Interests: forests remote sensing; forest aboveground biomass (AGB); synthetic aperture radar (SAR); light detection and ranging (LiDAR); wall-to-wall forest AGB mapping
Dr. Yanqiu Xing

E-Mail Website
Guest Editor
Centre for Forest Operations and Environment, College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Interests: forests remote sensing; light detection and ranging (LiDAR); forest aboveground biomass (AGB); ecology remote sensing
Dr. Armando Marino

E-Mail Website
Guest Editor
Department of Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA, UK
Interests: remote sensing; synthetic aperture radar (SAR); polarimetric SAR; forest aboveground biomass; polarimetric target detector
Special Issues, Collections and Topics in MDPI journals
Dr. Jiangping Long

E-Mail Website
Guest Editor
College of Advanced Interdisciplinary studies, Central South University of Forestry and Technology, Changsha 410018, China
Interests: forests remote sensing; polarimetric SAR; artificial intelligence (AI); forest aboveground biomass

Special Issue Information

Dear Colleagues,

It is universally recognized that forests play an important role in the main terrestrial carbon sink. Forest aboveground biomass (AGB) is related to the global carbon cycle and can slow down the trend of global climate change. Rapid and accurate acquisition of spatiotemporal information on forest AGB is the basis for evaluating forest carbon sequestration capacity. Remote sensing technology can obtain real-time and large-scale information on the distribution, structure, dynamic changes, and processes of ground forest resources at different temporal and spatial scales, providing a powerful tool for investigating forest AGB. The features of sensor data from different remote sensing mechanisms can greatly represent the horizontal and vertical structural information of forests, thereby greatly improving the inversion accuracy and saturation point of forest AGB. Optical remote sensing data can extract spectral indices and texture information that are strongly correlated with the horizontal structure of forest vegetation; light detection and ranging (LiDAR) data can extract discrete forest density and height information; and synthetic aperture radar (SAR) data can obtain polarization and interference information for large-scale forests. By utilizing such remote sensing features that are highly correlated with forest structure, combined with machine learning and deep learning algorithms, the accuracy and saturation points of forest AGB inversion can be greatly extracted. In light of these advantages, we organized this Special Issue, entitled “Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation”. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  1. Fusion method for forest AGB inversion using different combinations of remote sensing data sources;
  2. Construction and analysis of inversion models for forest AGB;
  3. Wall-to-wall mapping of forest AGB at large-scale;
  4. Temporal and spatial analysis of regional forest AGB products;
  5. Uncertainty analysis of forest AGB inversion.

We look forward to receiving your contributions.

Dr. Yongjie Ji
Dr. Yanqiu Xing
Dr. Armando Marino
Dr. Jiangping Long
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. Forests is an international peer-reviewed open access monthly 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 2600 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

  • remote sensing
  • forest AGB
  • feature optimization
  • machine learning
  • deep learning
  • physical model
  • forest mapping
  • uncertainty analysis

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Published Papers (3 papers)

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Research

22 pages, 5059 KiB  
Article
Developing a Method to Estimate Above-Ground Carbon Stock of Forest Tree Species Pinus densata Using Remote Sensing and Climatic Data
by Kai Luo, Yafei Feng, Yi Liao, Jialong Zhang, Bo Qiu, Kun Yang, Chenkai Teng and Tangyan Yin
Forests 2024, 15(11), 2023; https://doi.org/10.3390/f15112023 - 16 Nov 2024
Viewed by 544
Abstract
Forest above-ground carbon stock (AGCS) is one of the primary ecological evaluation indicators, so it is crucial to estimate the AGCS accurately. In this research, we added the climatic and topographic factors to the estimation process by a remote sensing approach to explore [...] Read more.
Forest above-ground carbon stock (AGCS) is one of the primary ecological evaluation indicators, so it is crucial to estimate the AGCS accurately. In this research, we added the climatic and topographic factors to the estimation process by a remote sensing approach to explore their impact and to achieve more precise estimations. We hope to develop a more accurate estimation method for AGCS based on remote sensing data and climate data. The random forest (RF) method has good robustness and wide applicability. Therefore, we modeled and predicted the AGCS by RF based on sixty field sample plots of Pinus densata pure forests in southwest China and the factors extracted from Landsat 8 OLI images (source I), Sentinel-2A images (source II), and combined Landsat 8 OLI and Sentinel-2A images (source III). We added the topographic and climatic factors to establish the AGCS estimation model and compared the results. The topographic factors contain elevation, slope, and aspect. Climatic factors contain mean annual temperature, annual precipitation, annual potential evapotranspiration, and monthly mean potential evapotranspiration. It was found that the R2 and RMSE of the model based on source III were better than the R2 and RMSE of the models based on source I and source II. Compared to the models based on source I and source II, the model based on source III improved R2 by up to 0.08, reduced RMSE by up to 2.88 t/ha, and improved P by up to 4.29%. Among the models without adding factors, the model based on source III worked the best, with an R2 of 0.87, an RMSE of 10.81 t/ha, an rRMSE of 23.19%, and a P of 79.71%. Among the models that added topographic factors, the model based on source III worked best after adding elevation, with an R2 of 0.89, an RMSE of 10.01 t/ha, an rRMSE of 21.47%, and a P of 82.17%. Among the models that added climatic factors, the model that added the annual precipitation factor had the best modeling result, with an R2 of 0.90, an RMSE of 9.53 t/ha, an rRMSE of 20.59%, and a P of 83.00%. The prediction result exhibited that the AGCS of the Pinus densata forest in 2021 was 9,737,487.52 t. The combination of Landsat 8 OLI and Sentinel-2A could improve the prediction accuracy of the AGCS. The addition of annual precipitation can effectively improve the accuracy of AGCS estimation. Higher resolution of climate data is needed to enhance the modeling in future work. Full article
(This article belongs to the Special Issue Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation)
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16 pages, 6859 KiB  
Article
Estimation of the Aboveground Biomass of Forests in Complex Mountainous Areas Using Regression Kriging
by Yining Luo, Lihui Yan, Zhongfa Zhou, Denghong Huang, Lu Cai, Shuanglong Du, Yue Yang, Youyan Huang and Qianxia Li
Forests 2024, 15(10), 1734; https://doi.org/10.3390/f15101734 - 30 Sep 2024
Viewed by 678
Abstract
The forest area in China’s plateaus and mountainous regions accounts for as much as 43% of the country’s total forest area. Accurately estimating the aboveground biomass (AGB) in these plateau and mountain forests is significant for global carbon sink assessment and climate change. [...] Read more.
The forest area in China’s plateaus and mountainous regions accounts for as much as 43% of the country’s total forest area. Accurately estimating the aboveground biomass (AGB) in these plateau and mountain forests is significant for global carbon sink assessment and climate change. However, the complexity of the natural environment poses significant challenges to the accurate estimation of forests’ aboveground biomass (AGB), and the accuracy of both AGB estimation and spatial mapping needs further improvement. This study utilized support vector regression, backpropagation neural networks, and random forests to predict trends in AGB and establish an optimal original model for forest AGB estimation. Further calibration was performed using regression kriging on the optimal model. The results indicated that (1) random forests achieved the highest coefficient of determination (R2 for cypress = 0.63, R2 for fir = 0.66, R2 for cryptomeria = 0.64, and R2 for mixed forest = 0.54), showing greater potential in predicting AGB in complex mountainous mixed forests; (2) the residual kriging method significantly improved the estimation accuracy, increasing the R2 values of the original RF model by 25%, 24%, and 22%, and improving the accuracy of mixed plot estimates from 54% to 81%; and (3) the residual kriging method effectively addressed the underestimation of high values and overestimation of low values in AGB estimates, broadening the range of AGB values and allowing for a more detailed spatial distribution of forests’ aboveground biomass. Full article
(This article belongs to the Special Issue Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation)
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20 pages, 15739 KiB  
Article
A Novel Method for Extracting DBH and Crown Base Height in Forests Using Small Motion Clips
by Shuhang Yang, Yanqiu Xing, Boqing Yin, Dejun Wang, Xiaoqing Chang and Jiaqi Wang
Forests 2024, 15(9), 1635; https://doi.org/10.3390/f15091635 - 16 Sep 2024
Viewed by 663
Abstract
The diameter at breast height (DBH) and crown base height (CBH) are important indicators in forest surveys. To enhance the accuracy and convenience of DBH and CBH extraction for standing trees, a method based on understory small motion clips (a series of images [...] Read more.
The diameter at breast height (DBH) and crown base height (CBH) are important indicators in forest surveys. To enhance the accuracy and convenience of DBH and CBH extraction for standing trees, a method based on understory small motion clips (a series of images captured with slight viewpoint changes) has been proposed. Histogram equalization and quadtree uniformization algorithms are employed to extract image features, improving the consistency of feature extraction. Additionally, the accuracy of depth map construction and point cloud reconstruction is improved by minimizing the variance cost function. Six 20 m × 20 m square sample plots were selected to verify the effectiveness of the method. Depth maps and point clouds of the sample plots were reconstructed from small motion clips, and the DBH and CBH of standing trees were extracted using a pinhole imaging model. The results indicated that the root mean square error (RMSE) for DBH extraction ranged from 0.60 cm to 1.18 cm, with relative errors ranging from 1.81% to 5.42%. Similarly, the RMSE for CBH extraction ranged from 0.08 m to 0.21 m, with relative errors ranging from 1.97% to 5.58%. These results meet the accuracy standards required for forest surveys. The proposed method enhances the efficiency of extracting tree structural parameters in close-range photogrammetry (CRP) for forestry. A rapid and accurate method for DBH and CBH extraction is provided by this method, laying the foundation for subsequent forest resource management and monitoring. Full article
(This article belongs to the Special Issue Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation)
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