From: Re-estimating the changes and ranges of forest biomass carbon in China during the past 40 years
Method | Feature | Period | Reference | ||
---|---|---|---|---|---|
1999–2003 | 2004–2008 | 2009–2013 | |||
Volume-derived | Stand-level linear volume-biomass equations, or continuous biomass expansion factor (CBEF), using variables of total area, volume per unit area. Data: NFIs. | 5.9 | 6.2 | ||
Volume-derived | Age-grouped, stand-level linear volume-biomass equations based. Data: 6th NFI. | 5.5 | Xu et al. (2007) | ||
Volume-derived | Mean ratio method, using variables of total volume and mean BEF. Data: 6th NFI. | 6.2 | Guo et al. (2010) | ||
Volume-derived | Tree-level allometric biomass equations with DBH and height predictors. Provincial BEFs were calculated based on DBH and height information of plots (660,000 permanent plots established in 7th NFI). Data: 7th NFI. | 6.7 | Li et al. (2011) | ||
Volume-derived | Stand-level allometric volume-biomass equations (power functions); removing upscaling error. Data: NFIs. | 4.9 | 5.4 | 6.0 | Zhou et al. (2016) |
Carbon density | Mean biomass density method, using variables of total area and mean biomass density. Data: 6th NFI. | 7.7 | Guo et al. (2010) | ||
Carbon density | Deriving carbon density from site measurements, and average carbon density from the provincial area-weighted average and its corresponding area; using Random Forest model to detail spatial patterns of carbon density. Data: Independent investigation, establishing total 7800 sites for field measurements in forests. | 10.4 | Tang et al. (2018) | ||
Remote sensing | Multiple spectral bands of MODIS and forest inventory data with an empirical statistical model. | 6.3 | 6.8 | Sun et al. (2015) | |
Remote sensing | A development of AGBmapping. Data: Geoscience Laser Altimeter System (GLAS)/Ice, Cloud, Land Elevation Satellite (ICESat) data, optical imagery, climate surfaces, and topographic data. | 10.2a | Su et al. (2016) |