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Advances in Estimating Aboveground Biomass Based on Multi-Source Remote Sensing Data

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Dr. Shaobo Sun

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Guest Editor

Institute of Surface-Earth System Science, Tianjin University, Tianjin, China
Interests: remote sensing in ecology; carbon fluxes; GEE; ecosystem carbon stocks

Prof. Dr. Xufeng Wang

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Guest Editor

Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: carbon cycle; vegetation phenology; remote sensing; carbon fluxes observation
Special Issues, Collections and Topics in MDPI journals

Dr. Kai Liu

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Guest Editor

School of Geography and Planning, Center of Integrated Geographic Information Analysis, Sun Yat-sen University, Guangzhou 510275, China
Interests: UAV; LiDAR; machine learning; vegetation index; leaf area index; mangrove forests; WorldView-2 imagery; red-edge band; variable importance

Special Issue Information

Dear Colleagues,

Above-ground biomass（AGB) is a critical proxy for productivity and the most dynamic terrestrial carbon pool. The accurate determination of AGB is critical in monitoring plants’ growth and assessing terrestrial carbon budgets. Remote sensing provides the most practical and effective approach for estimating AGB from a local to a global scale. In particular, the emerging open and high spatiotemporal resolution microwave and optical sensors (such as the Sentinel sensors) and the development of LiDAR and unmanned aerial vehicles (UAVs) allows an unprecedented opportunity for the accurate estimation of AGB. Considerable amounts of multi-source remote sensing data and popular cloud computing platforms such as Google Earth Engine (GEE) make it easy to rapidly monitor AGB in different ecosystems at low costs. AGB estimates based on multi-source remote sensing data, new algorithms and well-calibrated models have a wide range of applications, such as assessing terrestrial carbon budgets, monitoring crop growth, and examining the impacts of climate change and human activities on vegetation.

The Special Issue aims to call for papers on recent advances to estimate the AGB of different ecosystems (such as forests, grasslands, croplands, and wetlands) using multi-source remote sensing data at different scales

Topics may include but are not limited to model and algorithm developments, the assessment of different methods, responses of the AGB to climate change and human activities, and applications of remotely sensed AGB data.

Dr. Shaobo Sun
Prof. Dr. Xufeng Wang
Dr. Kai Liu
Guest Editors

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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. 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

above ground biomass
LiDAR
machine learning
multi-source remote sensing
vegetation carbon stocks
unmanned aerial vehicle (UAV)

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19 pages, 5297 KiB

Open AccessArticle

Estimating the Aboveground Biomass of Various Forest Types with High Heterogeneity at the Provincial Scale Based on Multi-Source Data

by Tianbao Huang, Guanglong Ou, Yong Wu, Xiaoli Zhang, Zihao Liu, Hui Xu, Xiongwei Xu, Zhenghui Wang and Can Xu

Remote Sens. 2023, 15(14), 3550; https://doi.org/10.3390/rs15143550 - 14 Jul 2023

Cited by 6 | Viewed by 1656

Abstract

It is important to improve the accuracy of models estimating aboveground biomass (AGB) in large areas with complex geography and high forest heterogeneity. In this study, k-nearest neighbors (k-NN), gradient boosting machine (GBM), random forest (RF), quantile random forest (QRF), regularized random forest (RRF), and Bayesian regularization neural network (BRNN) machine learning algorithms were constructed to estimate the AGB of four forest types based on environmental factors and the variables selected by the Boruta algorithm in Yunnan Province and using integrated Landsat 8 OLI and Sentinel 2A images. The results showed that (1) DEM was the most important variable for estimating the AGB of coniferous forests, evergreen broadleaved forests, deciduous broadleaved forests, and mixed forests; while the vegetation index was the most important variable for estimating deciduous broadleaved forests, the climatic factors had a higher variable importance for estimating coniferous and mixed forests, and texture features and vegetation index had a higher variable importance for estimating evergreen broadleaved forests. (2) In terms of specific model performance for the four forest types, RRF was the best model both in estimating the AGB of coniferous forests and mixed forests; the R² and RMSE for coniferous forests were 0.63 and 43.23 Mg ha⁻¹, respectively, and the R² and RMSE for mixed forests were 0.56 and 47.79 Mg ha⁻¹, respectively. BRNN performed the best in estimating the AGB of evergreen broadleaved forests; the R² was 0.53 and the RMSE was 68.16 Mg ha⁻¹. QRF was the best in estimating the AGB of deciduous broadleaved forests, with R² of 0.43 and RMSE of 45.09 Mg ha⁻¹. (3) RRF was the best model for the four forest types according to the mean values, with R² and RMSE of 0.503 and 52.335 Mg ha⁻¹, respectively. In conclusion, different variables and suitable models should be considered when estimating the AGB of different forest types. This study could provide a reference for the estimation of forest AGB based on remote sensing in complex terrain areas with a high degree of forest heterogeneity. Full article

(This article belongs to the Special Issue Advances in Estimating Aboveground Biomass Based on Multi-Source Remote Sensing Data)

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Advances in Estimating Aboveground Biomass Based on Multi-Source Remote Sensing Data

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