Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania |
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| Institution: | 1. Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432, Ås, Norway;2. Department of Forest Resources, University of Minnesota, Saint Paul, Minnesota, 55108, USA;3. The Food and Agriculture Organization of the United Nations (FAO), 00152 Rome, Italy;4. NASA-Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA;5. Gamma Remote Sensing, Worbstrasse 225, Gümligen, Switzerland;6. Norwegian Computing Center, Gaustadalléen 23A, P.O. Box 114, NO-0314, Oslo, Norway;7. Department of Forest Resources Assessment and Management, Sokoine University of Agriculture, P.O. Box 3013, Chuo Kikuu, Morogoro, United Republic of Tanzania |
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| Abstract: | Field surveys are often a primary source of aboveground biomass (AGB) data, but plot-based estimates of parameters related to AGB are often not sufficiently precise, particularly not in tropical countries. Remotely sensed data may complement field data and thus help to increase the precision of estimates and circumvent some of the problems with missing sample observations in inaccessible areas. Here, we report the results of a study conducted in a 15,867 km² area in the dry miombo woodlands of Tanzania, to quantify the contribution of existing canopy height and biomass maps to improving the precision of canopy height and AGB estimates locally. A local and a global height map and three global biomass maps, and a probability sample of 513 inventory plots were subject to analysis. Model-assisted sampling estimators were used to estimate mean height and AGB across the study area using the original maps and then with the maps calibrated with local inventory plots. Large systematic map errors – positive or negative – were found for all the maps, with systematic errors as great as 60–70 %. The maps contributed nothing or even negatively to the precision of mean height and mean AGB estimates. However, after being calibrated locally, the maps contributed substantially to increasing the precision of both mean height and mean AGB estimates, with relative efficiencies (variance of the field-based estimates relative to the variance of the map-assisted estimates) of 1.3–2.7 for the overall estimates. The study, although focused on a relatively small area of dry tropical forests, illustrates the potential strengths and weaknesses of existing global forest height and biomass maps based on remotely sensed data and universal prediction models. Our results suggest that the use of regional or local inventory data for calibration can substantially increase the precision of map-based estimates and their applications in assessing forest carbon stocks for emission reduction programs and policy and financial decisions. |
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| Keywords: | Biomass maps Model-assisted estimation Systematic map errors Dry tropical forests |
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