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| 1 | Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors显示文摘Background: The increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest characteristics. Recently Light Detection and Ranging(LiDAR)-based maps of forest attributes have been developed in many countries and been well received by users due to their accurate spatial representation of forest resources. However, the correspondence between such mapping and model-based inference is seldom appreciated. In this study we applied hierarchical model-based inference to produce aboveground biomass maps as well as maps of the corresponding prediction uncertainties with the same spatial resolution. Further, an estimator of mean biomass at regional level, and its uncertainty, was developed to demonstrate how mapping and regional level assessment can be combined within the framework of model-based inference.Results: Through a new version of hierarchical model-based estimation, allowing models to be nonlinear, we accounted for uncertainties in both the individual tree-level biomass models and the models linking plot level biomass predictions with LiDAR metrics. In a 5005 km2 large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m×18 m map units was found to range between 9 and 447 Mg·ha^-1. The corresponding root mean square errors ranged between 10 and 162 Mg·ha^-1. For the entire study region, the mean aboveground biomass was 55 Mg·ha^-1 and the corresponding relative root mean square error 8%. At this level 75%of the mean square error was due to the uncertainty associated with tree-level models.Conclusions: Through the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level biomass models and models linking plot level biomass with LiDAR data are accounted for, both for the uncertainty maps and the overall estimates. The development of hierarchical model-based inference to handle nonlinear models was an important prerequisite for the study. | Svetlana Saarela AndréWästlund Emma Holmström Alex Appiah Mensah Sören Holm Mats Nilsson Jonas Fridman Göran Ståhl | 2020 | Forest Ecosystems2020，7，3： | 3 |
| 2 | Improved production of adenovirus vectors expressing apoptotie transgenes显示文摘 | Bruder J T Appiah A Kirkman W M | 2000 | Hum Gene Ther2000，，11： | 1 |
| 3 | Can family or replacement blood donors become regular volunteer donors显示文摘 | Kwame A M Achina G Appiah R | 2014 | Transfusion2014，54，1： | 1 |
| 4 | Improved production of adenovirus vectors expressing apoptotic transgenes显示文摘 | Bruder J T Appiah A Kirkman W M | 2000 | Hum Gene Ther2000，，11： | 1 |
| 5 | Resource leveraging via networks by high - technology entrepreneurial firms 显示文摘 | Wilson H I M Appiah - Kubi K | 2002 | Journal of High Technology Management Re- search2002，13，1： | 1 |
| 6 | Economic cost of deforestation in semi-deciduous forests -- A case of two forest districts in Ghana显示文摘 | Damnyag L Tyynel T Appiah M | 2011 | Ecological Economics2011，70，12： | 1 |
| 7 | A new constitutive model for prediction of springback in sheet metal forming 显示文摘 | Appiah E Jain M Ghosh S | 2004 | Fatigue & Fracture of Engineering Materials & Structures2004，712，1： | 1 |
| 8 | Substance use du ring pregnancy: time for policy to catch up with research 显示文摘 | Lester B M Andreozzi L Appiah L | 2004 | Harm Reduct J2004，1，1： | 1 |
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