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.

现场调查通常是地上生物量（AGB）数据的主要来源，但是与AGB相关的参数的基于图的估计通常不够精确，尤其是在热带国家。遥感数据可以补充实地数据，从而有助于提高估计的准确性，并避免在无法接近的区域缺少样本观测值而造成的一些问题。在这里，我们报告了在坦桑尼亚干燥的Miombo林地的15867平方公里区域内进行的一项研究的结果，以量化现有树冠高度和生物量图对提高树冠高度的精度和当地AGB估算的贡献。对局部和全局高度图以及三个全局生物量图以及513个库存图的概率样本进行了分析。使用模型辅助的抽样估算器，使用原始地图，然后通过本地库存图校准的地图，估算研究区域的平均高度和AGB。在所有地图上都发现了较大的系统误差（正或负），系统误差高达60–70％。这些地图对平均身高和平均AGB估算的准确性没有任何贡献，甚至没有产生负面影响。但是，在局部校准后，这些地图极大地提高了平均高度和平均AGB估算值的精度，相对效率（基于字段的估算值相对于地图辅助估算值的方差）为1.3-2.7进行整体估算。这项研究虽然侧重于相对较小的干燥热带森林地区，根据遥感数据和通用预测模型，说明现有全球森林高度和生物量图的潜在优势和劣势。我们的结果表明，使用区域或本地清单数据进行校准可以大大提高基于地图的估算及其在评估森林碳储量以用于减排计划以及政策和财务决策中的应用的准确性。