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Forests
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Remote Sensing Application in Forest Biomass and Carbon Cycle
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Remote Sensing Application in Forest Biomass and Carbon Cycle

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: closed (31 August 2023) | Viewed by 16411

Special Issue Editors

Dr. Qisheng He

E-Mail Website
Guest Editor
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: quantitative remote sensing; vegetation remote sensing; data fusion; data assimilation; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Wenmei Li

E-Mail Website
Guest Editor
School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Interests: microwave remote sensing; forest height; forest above-ground biomass; deep learning

Special Issue Information

Dear Colleagues,

Forests are the dominant terrestrial ecosystem of the earth, and they contain about 606 gigatonnes of living biomass and account for 80% of the earth's total plant biomass. As the main body of the terrestrial ecosystem, the change of forest biomass and carbon storage reflects the influence of forest succession, human activities, natural disturbance, and climate change, which is of great significance in the study of the global terrestrial ecosystem carbon cycle and climate change. Over the past few years, significant progress has been made in the remote sensing monitoring of forest above-ground biomass and the carbon cycle. Multi-resource remote sensing including airborne/spaceborne multi-and hyperspectral, LiDAR (e.g., the new spaceborne GEDI and ICESat-2), interferometric SAR, and polarimetric interferometric SAR (PolInSAR) can generate regional to global maps of forest above-ground biomass. Meanwhile, novel approaches have improved the accuracy of forest biomass estimation, such as a combination of lidar data and mechanistic models, fusion of multispectral and lidar, and the application of machine learning and deep learning.

This Special Issue focuses on the application of remote sensing in forest above-ground biomass and carbon cycles, all original research findings and perspectives relative to forest biomass estimation are welcomed

Dr. Qisheng He
Dr. Wenmei Li
Guest Editors

Manuscript Submission Information

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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

  • forest above-ground biomass
  • carbon cycle
  • LiDAR
  • interferometric SAR
  • polarimetric SAR interferometry
  • machine learning
  • deep learning

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Published Papers (6 papers)

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Research

17 pages, 4222 KiB  
Article
Estimation of Aboveground Carbon Stocks in Forests Based on LiDAR and Multispectral Images: A Case Study of Duraer Coniferous Forests
by Rina Su, Wala Du, Hong Ying, Yu Shan and Yang Liu
Forests 2023, 14(5), 992; https://doi.org/10.3390/f14050992 - 11 May 2023
Cited by 4 | Viewed by 2773
Abstract
The correct estimation of forest aboveground carbon stocks (AGCs) allows for an accurate assessment of the carbon sequestration potential of forest ecosystems, which is important for in-depth studies of the regional ecological environment and global climate change. How to estimate forest AGCs quickly [...] Read more.
The correct estimation of forest aboveground carbon stocks (AGCs) allows for an accurate assessment of the carbon sequestration potential of forest ecosystems, which is important for in-depth studies of the regional ecological environment and global climate change. How to estimate forest AGCs quickly and accurately and realize dynamic monitoring has been a hot topic of research in the forestry field worldwide. LiDAR and remote sensing optical imagery can be used to monitor forest resources, enabling the simultaneous acquisition of forest structural properties and spectral information. A high-density LiDAR-based point cloud cannot only reveal stand-scale forest parameters but can also be used to extract single wood-scale forest parameters. However, there are multiple forest parameter estimation model problems, so it is especially important to choose appropriate variables and models to estimate forest AGCs. In this study, we used a Duraer coniferous forest as the study area and combined LiDAR, multispectral images, and measured data to establish multiple linear regression models and multiple power regression models to estimate forest AGCs. We selected the best model for accuracy evaluation and mapped the spatial distribution of AGC density. We found that (1) the highest accuracy of the multiple multiplicative power regression model was obtained for the estimated AGC (R2 = 0.903, RMSE = 10.91 Pg) based on the LiDAR-estimated DBH; the predicted AGC values were in the range of 4.1–279.12 kg C. (2) The highest accuracy of the multiple multiplicative power regression model was obtained by combining the normalized vegetation index (NDVI) with the predicted AGC based on the DBH estimated by LiDAR (R2 = 0.906, RMSE = 10.87 Pg); the predicted AGC values were in the range of 3.93–449.07 kg C. (3) The LiDAR-predicted AGC values and the combined LiDAR and optical image-predicted AGC values agreed with the field AGCs. Full article
(This article belongs to the Special Issue Remote Sensing Application in Forest Biomass and Carbon Cycle)
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16 pages, 3197 KiB  
Article
Rapid Estimation of Decameter FPAR from Sentinel-2 Imagery on the Google Earth Engine
by Yiting Wang, Yinggang Zhan, Donghui Xie, Jinghao Liu, Haiyang Huang, Dan Zhao, Zihang Xiao and Xiaode Zhou
Forests 2022, 13(12), 2122; https://doi.org/10.3390/f13122122 - 11 Dec 2022
Cited by 1 | Viewed by 2230
Abstract
As a direct indicator of vegetation photosynthesis, the fraction of absorbed photosynthetically active radiation (FPAR) serves as a critical input in a series of land surface models. While existing satellite FPAR products are generally at coarse resolutions ranging from 250 m to 1 [...] Read more.
As a direct indicator of vegetation photosynthesis, the fraction of absorbed photosynthetically active radiation (FPAR) serves as a critical input in a series of land surface models. While existing satellite FPAR products are generally at coarse resolutions ranging from 250 m to 1 km, operational FPAR products at fine resolution are urgently needed in studying land surface processes at the plot scale. However, existing methods for estimating fine-resolution FPAR were mainly designed for Landsat data, and few studies have attempted to develop algorithms for Sentinel-2 data. In particular, the operational estimation of decameter FPAR has a higher requirement for the algorithms in terms of generalizability, efficiency, accuracy, and adaptability to Sentinel-2 data. In this paper, we developed a retrieval chain on the Google Earth Engine (GEE) platform to estimate FPAR by learning the relationship between MODIS FPAR and Sentinel-2 surface reflectance. Scale-consistent multilinear models were used to model the relationship between MODIS FPAR and Sentinel-2 surface reflectance, and the model coefficients were regressed from the selected training samples. To account for the spectral and spatial characteristics of the Sentinel-2 data, we designed criteria for selecting training samples and compared different band combinations. Three strategies for band combination were used: (1) green, red, and near infrared (NIR) bands at 10 m resolution (i.e., three bands); (2) green, red, NIR, and red edge (RE) 1, RE2, and RE3 bands at 20 m resolution (i.e., five bands); and (3) green, red, NIR, RE1, RE2, RE3, shortwave infrared1 (SWIR1) and SWIR2 bands at 20 m resolution (i.e., eight bands). Meanwhile, the official Sentinel Application Platform (SNAP) method has also been implemented to estimate the Sentinel FPAR at 10 m and 20 m resolutions for comparison. Both methods were applied to the western Guanzhong area, Shaanxi Province, China, for FPAR estimation of all cloud-free Sentinel-2 images in 2021. The results show that the scaling-based method using five bands at 20 m resolution was the most accurate compared to the in situ measurements (RMSE = 0.076 and R² = 0.626), which outperformed the SNAP method at 10 m and 20 m resolutions and the scaling-based method using other strategies. The results of the scaling-based method using all three strategies were highly consistent with the MODIS FPAR product, while the SNAP method systematically underestimated FPAR values compared to the MODIS FPAR products. The proposed method is more ready-to-use and more efficient than SNAP software. Considering that the service of the MODIS sensor is overdue, the proposed method can be extended to alternatives to MODIS products, such as VIIRS and Sentinel-3 data. Full article
(This article belongs to the Special Issue Remote Sensing Application in Forest Biomass and Carbon Cycle)
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17 pages, 3462 KiB  
Article
Remote Sensing Estimation of Forest Aboveground Biomass Based on Lasso-SVR
by Ping Wang, Sanqing Tan, Gui Zhang, Shuang Wang and Xin Wu
Forests 2022, 13(10), 1597; https://doi.org/10.3390/f13101597 - 29 Sep 2022
Cited by 12 | Viewed by 1916
Abstract
With the Lutou Forest Farm as the research area, the Lasso algorithm was used for characteristic selection, and the optimal combination of variables was input into the support vector regression (SVR) model. The most suitable SVR model was selected to estimate the aboveground [...] Read more.
With the Lutou Forest Farm as the research area, the Lasso algorithm was used for characteristic selection, and the optimal combination of variables was input into the support vector regression (SVR) model. The most suitable SVR model was selected to estimate the aboveground biomass of the forest through the comparison of the kernel function and optimal parameters, and the spatial distribution map of the aboveground biomass in the study area was drawn. The significance analysis of special variables showed good correlations between forest aboveground biomass and each vegetation index. There was a more significant correlation with some remote sensing bands, a less significant correlation with some texture features, and a strong correlation with DEM in the terrain features. When the parameters C is 2 and g is 0.01, the SVR model has the highest precision, which can illustrate 73% of the forest aboveground biomass, with the validation set R2 being 0.62. The statistical analysis of the results shows that the total aboveground biomass of the Lutou Forest Farm is 4.82×105 t. The combination of Lasso with the SVR model can improve the estimation accuracy of forest aboveground biomass, and the model has a strong generalization ability. Full article
(This article belongs to the Special Issue Remote Sensing Application in Forest Biomass and Carbon Cycle)
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18 pages, 4286 KiB  
Article
Comparison of Multiple Machine Learning Models for Estimating the Forest Growing Stock in Large-Scale Forests Using Multi-Source Data
by Huajian Huang, Dasheng Wu, Luming Fang and Xinyu Zheng
Forests 2022, 13(9), 1471; https://doi.org/10.3390/f13091471 - 13 Sep 2022
Cited by 10 | Viewed by 2390
Abstract
The forest growing stock is one of the key indicators in monitoring forest resources, and its quantitative estimation is of great significance. Based on multi-source data, including Sentinel-1 radar remote sensing data, Sentinel-2 optical remote sensing data, digital elevation model (DEM), and inventory [...] Read more.
The forest growing stock is one of the key indicators in monitoring forest resources, and its quantitative estimation is of great significance. Based on multi-source data, including Sentinel-1 radar remote sensing data, Sentinel-2 optical remote sensing data, digital elevation model (DEM), and inventory data for forest management planning and design, the Lasso feature selection method was used to remove the non-significant indicators, and three machine learning algorithms, GBDT, XGBoost, and CatBoost, were used to estimate forest growing stock. In addition, four category features, forest population, dominant tree species, humus thickness, and slope direction, were involved in estimating forest growing stock. The results showed that the addition of category features significantly improved the performance of the models. To a certain extent, radar remote sensing data also could improve estimating accuracy. Among the three models, the CatBoost model (R2 = 0.78, MSE = 0.62, MAE = 0.59, MAPE = 16.20%) had the highest estimating accuracy, followed by XGBoost (R2 = 0.75, MSE = 0.71, MAE = 0.62, MAPE = 18.28%) and GBDT (R2 = 0.72, MSE = 0.78, MAE = 0.68, MAPE = 20.28%). Full article
(This article belongs to the Special Issue Remote Sensing Application in Forest Biomass and Carbon Cycle)
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20 pages, 14435 KiB  
Article
Identification of Urban Green Space Types and Estimation of Above-Ground Biomass Using Sentinel-1 and Sentinel-2 Data
by Jue Xiao, Longqian Chen, Ting Zhang, Long Li, Ziqi Yu, Ran Wu, Luofei Bai, Jianying Xiao and Longgao Chen
Forests 2022, 13(7), 1077; https://doi.org/10.3390/f13071077 - 8 Jul 2022
Cited by 7 | Viewed by 3353
Abstract
High-quality urban green space supports the healthy functioning of urban ecosystems. This study aimed to rapidly assess the distribution, and accurately estimate the above-ground biomass, of urban green space using remote sensing methods, thus providing a better understanding of the urban ecological environment [...] Read more.
High-quality urban green space supports the healthy functioning of urban ecosystems. This study aimed to rapidly assess the distribution, and accurately estimate the above-ground biomass, of urban green space using remote sensing methods, thus providing a better understanding of the urban ecological environment in Xuzhou for more effective management. We performed urban green space classifications and compared the performance of Sentinel-2 MSI data and Sentinel-1 SAR data and combinations, for estimating above-ground biomass, using field data from Xuzhou, China. The results showed the following: (1) incorporating an object-oriented method and random forest algorithm to extract urban green space information was effective; (2) compared with stepwise regression models with single-source data, biomass estimation models based on multi-source data provide higher estimation accuracy (R2 = 0.77 for coniferous forest, R2 = 0.76 for shrub-grass vegetation, R2 = 0.75 for broadleaf forest); and (3) from 2016 to 2021, urban green space coverage in Xuzhou decreased, while the total above-ground biomass increased, with higher average above-ground biomass in broadleaf forests (133.71 tons/ha) compared to coniferous forests (92.13 tons/ha) and shrub-grass vegetation (21.65 tons/ha). Our study provides an example of automated classification and above-ground biomass mapping for urban green space using multi-source data and facilitates urban eco-management. Full article
(This article belongs to the Special Issue Remote Sensing Application in Forest Biomass and Carbon Cycle)
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22 pages, 2712 KiB  
Article
Comparison of Variable Selection Methods among Dominant Tree Species in Different Regions on Forest Stock Volume Estimation
by Gengsheng Fang, Luming Fang, Laibang Yang and Dasheng Wu
Forests 2022, 13(5), 787; https://doi.org/10.3390/f13050787 - 18 May 2022
Cited by 9 | Viewed by 2641
Abstract
The forest stock volume (FSV) is one of the crucial indicators to reflect the quality of forest resources. Variable selection methods are usually used for FSV estimated models. However, few studies have explored which variable selection methods can make the selected data set [...] Read more.
The forest stock volume (FSV) is one of the crucial indicators to reflect the quality of forest resources. Variable selection methods are usually used for FSV estimated models. However, few studies have explored which variable selection methods can make the selected data set have better explanatory and robustness for the same dominant tree species in different regions after the feature variables were filtered by the feature selection methods. In this study, we chose six dominant tree species from Lin’an District, Anji County, and a part of Longquan City. The tree species include broad-leaved, coniferous, Masson pine, Chinese fir, coniferous and broad-leaved mixed forest, and all tree species which include the above five groups of tree species. The last two tree species were represented by mixed and all, respectively. Then, the satellite images, terrain factors, and forest inventory data were selected by six variable selection methods (least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), stepwise regression (Step-Reg), permutation importance (PI), mean decrease impurity (MDI), and SelectFromModel based on LightGBM (SFM)), according to different dominant tree types in different regions. The selected variables were formed into a new dataset divided by different dominant trees. Besides, extreme gradient boosting (XGBoost) was used, combined with variable selection methods to estimate the FSV. The performed results are as follows: In the feature selection of coniferous, RFE performed better both in the average and in the separate regions. In the feature selection of Chinese fir and all, PI performed better both in the average and in the separate regions. In the feature selection of Masson pine, MDI performed better both in the average and in the separate regions. In the feature selection of mixed, MDI performed better in the average while RFE performed better in the separate regions comprehensively. The results showed that not only in separate regions, but the average result two factors, RFE, MDI, and PI all performed well to select variables to estimate the FSV. Furthermore, we selected the top five high feature-importance factors of different tree types, and the results showed that tree age and canopy density were both of great importance to the estimation of FSV. Besides, in the exhibited results of feature selection methods, compared with no variable selection, the research also found that variable selection can improve the performance of the model. Additionally, from the results of different tree types in different regions, we also found that small-scale and diversity of dominant tree types may lead to the instability and unreliability of experimental results. The study provides some insight into the application the optimal variable selection methods of the same dominant tree type in different regions. This study will help the development of variable selection methods to estimate FSV. Full article
(This article belongs to the Special Issue Remote Sensing Application in Forest Biomass and Carbon Cycle)
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