| Terrestrial ecosystem carbon cycle is a crucial part of the global carbon cycle, and plays an important role in the global climate change. Forest, the main body of the terrestrial ecosystem, stores50%to60%of the carbon in the terrestrial ecosystem and makes significant contributions to the carbon cycle exchange between the terrestrial biosphere and the atmosphere. Forest aboveground biomass (AGB) is an important indicator of forest ecosystem function. How to estimate the forest AGB quickly and accurately becomes one of the hot spots in forest ecosystem and global climate change studies recently, and is also of great importance for not only researches about global carbon cycle, but also for deeply understanding global climate change.This study takes Daxing’an Mountains in Inner Mongolia as the study area, studies on Updating forest aboveground biomass (AGB) based on remote sensing data and the BEPS ecological model. High spatial and temporal resolution time series LAI were retrieved with4-scale geometric optical model and fusion algorithm based on multi-source remote sensing data. With the generated LAI, meteorological data, soil data, land cover data and estimated AGB data, the BEPS ecological model was driven to simulate daily NPP at a resolution of30m during the period from2005to2012. Then the simulated NPP was used to update forest AGB, and the updated result was validated and analyzed with in situ measured AGB and AGB estimated based on airborne Lidar data. The main findings and conclusions of this study could be drawn as follows: 1) Retriving long term time series of high spatial and temporal resolution LAI and processing Airborne Lidar dataEmpirical model and physical model are used to retrieve LAI. Firstly, empirical model was used to calculate30m leaf area index (LAI) in the growing season of each year during the period from2005-2012. Then, MODIS reflectance and land cover data were employed to drive the4-scale geometric optical model to invert500m LAI time series data. Finally, these two sets of data were fused together to generate30m LAI time series data during the period from2005-2012. Ground data and vegetation data were classified from airborne Lidar data. Ground data was utilized to generate DEM through triangulated irregular network (TIN). Then the canopy height model (CHM) was created after the vegetation points normalized by DEM. Multiple Variables could be extracted from CHM to construct AGB estimation model.2) Estimating forest aboveground biomass based on remote sensing dataThere were good correlations between LAI and AGB of different forest types. The AGB estimated by estimation model constructed based on LAI has good consistency with the in situ measured data. Especially, estimated AGB of mixed forest has better precision, the R2of estimated AGB was0.62and RMSE was1.4kg/m2, followed by the estimated AGB of coniferous forest (R2=0.60, RMSE=1.4kg/m2) and broadleaf forest (R2=0.54, RMSE=1.6kg/m2). Most of the variables extracted from airborne Lidar data correlated significantly with in situ measured AGB, the correlation coefficient were more than0.85. The estimation model established with average canopy height (hm) and observed AGB has the R2reaches0.81, and RMSE was1.1kg/m2. It showes that airborne Lidar data could estimate AGB more effectively than LAI do.3) Simulating forest NPP and updating forest aboveground biomassThe BEPS model was improved to simulate GPP, NPP and ABI of the study area. It was found that the GPP, NPP and ABI of different forest types all showed obvious increasing trend from2006to2012, and the fastest increasing years are2009and2011.With NPP simulated by BEPS model was used to update AGB of2012. Validation test shows that the updated AGB has good agreement with in situ measured AGB, AGB estimated by airborne Lidar data and AGB estimated by TM/ETM+images in2012. This indicates that the method based on remote sensing data and the BEPS ecological model to update forest aboveground biomass is feasible. |
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