Mapping high-resolution forest aboveground biomass of China using multisource remote sensing data

Yang, Qiuli; Niu, Chunyue; Liu, Xiaoqiang; Feng, Yuhao; Ma, Qin; Wang, Xuejing; Tang, Hao

doi:10.1080/15481603.2023.2203303

Cited by 28 publications

(15 citation statements)

References 53 publications

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“…This research, together with our previous works 23 , 24 , 36 , 62 , 63 , revealed the dynamics of China’s planted forests and their C storage. However, some uncertainties remain in the results, mainly associated with mapped planted forest area and C density derived for vegetation types.…”

Section: Methodssupporting

confidence: 69%

“…To demonstrate the reasonableness of the calculated C storage in the planted forests, we used publicly accessible field survey samples created by Xu, et al 61 (download from the National Ecological Data Center resource sharing service platform of http://www.nesdc.org.cn/ ) to validate the estimated C storage in 2010, because these samples were collected around in 2010. Additionally, we downloaded China’s forest biomass data for the year 2019 generated by Yang, et al 62 ( https://www.3decology.org/2023/08/02/china-forest-agb-map2019/ ) and the Global dataset of forest aboveground biomass for the year 2020 ( https://climate.esa.int/en/projects/biomass/data/ ) to validate the estimated C storage in 2020. The comparison results indicated an R 2 ranging from 0.50 to 0.68(Supplementary Fig.…”

Section: Methodsmentioning

confidence: 99%

“…National forest inventory data are widely recognized as the most authoritative source 1 , 58 . Field samples were collected by forestry experts from various provinces, and the accuracy of their labels is generally reliable and commonly used for remote sensing mapping 62 . However, the crowdsourced data collection method presented challenges for our classification process.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Carbon storage through China’s planted forest expansion

Cheng,

Yang,

Tao

et al. 2024

Nat Commun

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China’s extensive planted forests play a crucial role in carbon storage, vital for climate change mitigation. However, the complex spatiotemporal dynamics of China’s planted forest area and its carbon storage remain uncaptured. Here we reveal such changes in China’s planted forests from 1990 to 2020 using satellite and field data. Results show a doubling of planted forest area, a trend that intensified post-2000. These changes lead to China’s planted forest carbon storage increasing from 675.6 ± 12.5 Tg C in 1990 to 1,873.1 ± 16.2 Tg C in 2020, with an average rate of ~ 40 Tg C yr−1. The area expansion of planted forests contributed ~ 53% (637.2 ± 5.4 Tg C) of the total above increased carbon storage in planted forests compared with planted forest growth. This proactive policy-driven expansion of planted forests has catalyzed a swift increase in carbon storage, aligning with China’s Carbon Neutrality Target for 2060.

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Section: Methodssupporting

confidence: 69%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Carbon storage through China’s planted forest expansion

Cheng,

Yang,

Tao

et al. 2024

Nat Commun

Self Cite

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“…In this study, a total of 26 features related to forest AGB [14,36,37] were gathered from RS imagery and published research, serving as explanatory variables for AGB estimation. Among these, six spectral bands, eleven vegetation indices, and three components of brightness, greenness, and wetness obtained through the tasseled cap transformation (TCT) were extracted from Landsat-8 (L8) images.…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

“…TCT is an RS data processing technique that transforms the raw multispectral bands into feature components with ecological significance [39]. These feature components are capable of characterizing the soil and vegetation conditions on the ground surface [37,40], thereby providing supplementary land surface information for forest biomass estimation.…”

Section: Landsat-8-based Datamentioning

confidence: 99%

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

Wu,

Yao,

Zhang

et al. 2024

Remote Sensing

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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 R2 (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.

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Estimation of above ground biomass in tropical heterogeneous forests in India using GEDI

Indirabai,

Nilsson

2024

Ecological Informatics

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Mapping high-resolution forest aboveground biomass of China using multisource remote sensing data

Cited by 28 publications

References 53 publications

Carbon storage through China’s planted forest expansion

Carbon storage through China’s planted forest expansion

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

Estimation of above ground biomass in tropical heterogeneous forests in India using GEDI

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