|Paper title||Use of tree cover and tree height datasets to estimate global forest above-ground biomass|
|Form of presentation||Poster|
Forest above-ground biomass (AGB) is identified as an essential climate variable (ECV) by the Global Climate Observing System (GCOS). Monitoring its spatial distribution and temporal variations is therefore a necessity to improve our understanding of climate change and increase our ability to predict its impacts.
In this study, we develop a novel approach to estimate AGB by using the TC×H variable, i.e. the product of percent tree cover (TC) and forest height (H) variables. To do so, we have used already available global datasets of TC and H. Percent tree cover is estimated from optical imagery, and we have retained the following products: a) the Global 2010 Tree Cover at 30m resolution derived from Landsat (Hansen et al., 2013) and b) the 2019 Tree Cover Fraction at 100m resolution derived from Proba-V (Buchhorn et al., 2020). Forest height is estimated from spaceborne lidar data and spatially extrapolated with optical imagery, and we have used the following products: a) the 2005 Global Forest Heights dataset at 1km resolution based on ICESAT-GLAS (Simard et al., 2011) and b) the 2019 Global Forest Canopy Height dataset at 30m resolution based on GEDI (Potapov et al., 2021). The spatial resolution of the datasets is degraded to 1km resolution to produce two TC×H layers: one for epoch 2005-2010 using the Hansen tree cover and the Simard height, and one for epoch 2019 using the Buchhorn tree cover and the Potapov height.
Relationships between TC×H and AGB were established using reference AGB estimates obtained from airborne Lidar datasets available within the ESA Climate Change Initiative Biomass project in the form of 100m resolution layers in Brazil, Indonesia, Australia, and the United States. The rationale behind the choice of the TC×H variable is that it constitutes a proxy of the vegetation volume, which itself is related to the AGB through the wood volumetric density. When the spatial resolution is degraded to 1 km, it is expected that the wood volumetric density can be considered almost uniform at the biome level. Therefore, we have aimed at establishing biome-specific AGB/TC×H relationships that are used to produce global estimates of AGB at 1km resolution for epochs 2005-2010 and 2019. These relationships are established through regressions based on a 3-parameter model, with the parameters estimated at each epoch (2005-2010 and 2019) and biome (temperate and boreal, wet tropical, and dry tropical). The inversion of these relationships provides global AGB estimates at 1km resolution at the two epochs. The AGB difference between the two epochs can be used to estimate the AGB change at a decadal scale.
This new approach can provide a low-cost and accurate alternative for the production of AGB maps at the kilometric scale. The validation of the AGB estimates is on-going and the first analysis results are promising. A quantitative comparison with the existing global AGB datasets (in particular the recently released CCI Biomass datasets) will be presented, in order to evaluate the strengths and weaknesses of each approach and identify the complementarity between methods.
Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N.-E., Herold, M., Fritz, S., 2020. Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe. https://doi.org/10.5281/zenodo.3939050
Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., Townshend, J.R.G., 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850–853. https://doi.org/10.1126/science.1244693
Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M.C., Kommareddy, A., Pickens, A., Turubanova, S., Tang, H., Silva, C.E., Armston, J., Dubayah, R., Blair, J.B., Hofton, M., 2021. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165. https://doi.org/10.1016/j.rse.2020.112165
Simard, M., Pinto, N., Fisher, J.B., Baccini, A., 2011. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosciences 116. https://doi.org/10.1029/2011JG001708