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Multi-scale simulation and accuracy assessment of forest carbon using Landsat and MODIS data

Forest ecosystems have a great potential in mitigation of carbon concentration in the atmosphere. Thus, generating its spatially explicit estimates at national, regional and global scale becomes very important. In Southern China, mapping forest carbon is often conducted by combining ground plot data from national forest inventory and remotely sensed images from Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) with variable spatial resolutions. However, the inconsistency of sample plot sizes with spatial resolutions of images will lead to a great challenge for mapping and accuracy assessment of forest carbon. In this study, an image based spatial co-simulation was used to map forest carbon by directly combining a total of 56 sample plots with plot size of 25.8 m × 25.8 m and a Landsat Thematic Mapper™ image at a spatial resolution of 30 m × 30 m for You County of Hunan with a area of 4.82×105 hm2. An image based spatial block co-simulation was then employed to combine and scale up the plot and TM image data to 225 m × 225 m, 450 m × 450 m and 900 m × 900 m to create forest carbon maps at multiple spatial resolutions. Moreover, MODIS images, including MOD13Q1, MOD09A1 and MOD15A2 with three spatial resolutions corresponding to those above, were applied to map forest carbon for this County. The obtained map from TM image at the spatial resolution of 30 m × 30 m was validated using a dataset of 26 sample plots that were not used for simulation, while the accuracy of the MODIS derived maps was assessed using the higher resolution TM image derived estimates. The results showed that the coefficient of determination between the TM image derived forest carbon estimates and the observations was 0.81 with a root mean square error of 8.8 T/ha. The determination coefficient between the TM and MODIS derived estimates varied from 0.78 to 0.82. Moreover, all the TM and MODIS derived estimates had similar spatial distributions and patterns to those of the sample plot data. But, compared with the maps from TM image, the MODIS derived forest carbon maps were more smoothed. This study, to some extent, overcame some of significant gaps that currently exists in mapping and accuracy assessment of forest carbon using remotely sensed data when the ground data have different spatial resolutions from used images.