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MODIS Based Estimation of Forest Aboveground Biomass in China.

Yin G, Zhang Y, Sun Y, Wang T, Zeng Z, Piao S - PLoS ONE (2015)

Bottom Line: The mean forest aboveground biomass density is 56.1 Mg C ha-1, with high values observed in temperate humid regions.The responses of forest aboveground biomass density to mean annual temperature are closely tied to water conditions; that is, negative responses dominate regions with mean annual precipitation less than 1300 mm y-1 and positive responses prevail in regions with mean annual precipitation higher than 2800 mm y-1.During the 2000s, the forests in China sequestered C by 61.9 Tg C y-1, and this C sink is mainly distributed in north China and may be attributed to warming climate, rising CO2 concentration, N deposition, and growth of young forests.

View Article: PubMed Central - PubMed

Affiliation: College of Urban and Environmental Sciences, Peking University, Beijing, China.

ABSTRACT
Accurate estimation of forest biomass C stock is essential to understand carbon cycles. However, current estimates of Chinese forest biomass are mostly based on inventory-based timber volumes and empirical conversion factors at the provincial scale, which could introduce large uncertainties in forest biomass estimation. Here we provide a data-driven estimate of Chinese forest aboveground biomass from 2001 to 2013 at a spatial resolution of 1 km by integrating a recently reviewed plot-level ground-measured forest aboveground biomass database with geospatial information from 1-km Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset in a machine learning algorithm (the model tree ensemble, MTE). We show that Chinese forest aboveground biomass is 8.56 Pg C, which is mainly contributed by evergreen needle-leaf forests and deciduous broadleaf forests. The mean forest aboveground biomass density is 56.1 Mg C ha-1, with high values observed in temperate humid regions. The responses of forest aboveground biomass density to mean annual temperature are closely tied to water conditions; that is, negative responses dominate regions with mean annual precipitation less than 1300 mm y-1 and positive responses prevail in regions with mean annual precipitation higher than 2800 mm y-1. During the 2000s, the forests in China sequestered C by 61.9 Tg C y-1, and this C sink is mainly distributed in north China and may be attributed to warming climate, rising CO2 concentration, N deposition, and growth of young forests.

No MeSH data available.


Comparison of observed AGBD (Mg C ha-1) against predicted AGBD using MTE algorithm.The blue dots indicate the training samples (R2 = 0.57, RMSE = 22.4 Mg C ha-1), and the red ones refer to the validation samples (R2 = 0.46, RMSE = 22.7 Mg C ha-1).
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pone.0130143.g002: Comparison of observed AGBD (Mg C ha-1) against predicted AGBD using MTE algorithm.The blue dots indicate the training samples (R2 = 0.57, RMSE = 22.4 Mg C ha-1), and the red ones refer to the validation samples (R2 = 0.46, RMSE = 22.7 Mg C ha-1).

Mentions: A machine-learning technique Model Tree Ensembles (MTE) [25] is used to predict grid-scale forest aboveground biomass density based on ground-measured AGBD and remote sensing data in China. In this study, the MTE was trained with ground-measured AGBD as the dependent variable and the set of AGBD explanatory variables listed in Table 1 as inputs. 90% of the ground measured AGBD data is used in the training phase and the rest 10% is used for validation. As shown in Fig 2, the AGBD prediction using MTE explained half of the AGBD variation (R2 = 0.57, RMSE = 22.4 Mg C ha-1 for the training data, and R2 = 0.46, RMSE = 22.7 Mg C ha-1 for the validation data). We then extended the trained MTE to the whole China. For each forest pixel (1 km in our case), AGBD is estimated from the trained MTE based on satellite-derived reflectance and vegetation indices (Table 1) during the period from 2001 to 2013.


MODIS Based Estimation of Forest Aboveground Biomass in China.

Yin G, Zhang Y, Sun Y, Wang T, Zeng Z, Piao S - PLoS ONE (2015)

Comparison of observed AGBD (Mg C ha-1) against predicted AGBD using MTE algorithm.The blue dots indicate the training samples (R2 = 0.57, RMSE = 22.4 Mg C ha-1), and the red ones refer to the validation samples (R2 = 0.46, RMSE = 22.7 Mg C ha-1).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4482713&req=5

pone.0130143.g002: Comparison of observed AGBD (Mg C ha-1) against predicted AGBD using MTE algorithm.The blue dots indicate the training samples (R2 = 0.57, RMSE = 22.4 Mg C ha-1), and the red ones refer to the validation samples (R2 = 0.46, RMSE = 22.7 Mg C ha-1).
Mentions: A machine-learning technique Model Tree Ensembles (MTE) [25] is used to predict grid-scale forest aboveground biomass density based on ground-measured AGBD and remote sensing data in China. In this study, the MTE was trained with ground-measured AGBD as the dependent variable and the set of AGBD explanatory variables listed in Table 1 as inputs. 90% of the ground measured AGBD data is used in the training phase and the rest 10% is used for validation. As shown in Fig 2, the AGBD prediction using MTE explained half of the AGBD variation (R2 = 0.57, RMSE = 22.4 Mg C ha-1 for the training data, and R2 = 0.46, RMSE = 22.7 Mg C ha-1 for the validation data). We then extended the trained MTE to the whole China. For each forest pixel (1 km in our case), AGBD is estimated from the trained MTE based on satellite-derived reflectance and vegetation indices (Table 1) during the period from 2001 to 2013.

Bottom Line: The mean forest aboveground biomass density is 56.1 Mg C ha-1, with high values observed in temperate humid regions.The responses of forest aboveground biomass density to mean annual temperature are closely tied to water conditions; that is, negative responses dominate regions with mean annual precipitation less than 1300 mm y-1 and positive responses prevail in regions with mean annual precipitation higher than 2800 mm y-1.During the 2000s, the forests in China sequestered C by 61.9 Tg C y-1, and this C sink is mainly distributed in north China and may be attributed to warming climate, rising CO2 concentration, N deposition, and growth of young forests.

View Article: PubMed Central - PubMed

Affiliation: College of Urban and Environmental Sciences, Peking University, Beijing, China.

ABSTRACT
Accurate estimation of forest biomass C stock is essential to understand carbon cycles. However, current estimates of Chinese forest biomass are mostly based on inventory-based timber volumes and empirical conversion factors at the provincial scale, which could introduce large uncertainties in forest biomass estimation. Here we provide a data-driven estimate of Chinese forest aboveground biomass from 2001 to 2013 at a spatial resolution of 1 km by integrating a recently reviewed plot-level ground-measured forest aboveground biomass database with geospatial information from 1-km Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset in a machine learning algorithm (the model tree ensemble, MTE). We show that Chinese forest aboveground biomass is 8.56 Pg C, which is mainly contributed by evergreen needle-leaf forests and deciduous broadleaf forests. The mean forest aboveground biomass density is 56.1 Mg C ha-1, with high values observed in temperate humid regions. The responses of forest aboveground biomass density to mean annual temperature are closely tied to water conditions; that is, negative responses dominate regions with mean annual precipitation less than 1300 mm y-1 and positive responses prevail in regions with mean annual precipitation higher than 2800 mm y-1. During the 2000s, the forests in China sequestered C by 61.9 Tg C y-1, and this C sink is mainly distributed in north China and may be attributed to warming climate, rising CO2 concentration, N deposition, and growth of young forests.

No MeSH data available.