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Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors

Saarela, Svetlana and Wästlund, André and Holmström, Emma and Appiah Mensah, Alex and Holm, Sören and Nilsson, Mats and Fridman, Jonas and Ståhl, Göran (2020). Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors. Forest Ecosystems. 7 , 43 , 1-17
[Journal article]

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Abstract

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 km(2)large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m x18 m map units was found to range between 9 and 447 Mg center dot ha(-1). The corresponding root mean square errors ranged between 10 and 162 Mg center dot ha(-1). For the entire study region, the mean aboveground biomass was 55 Mg center dot 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.

Authors/Creators:Saarela, Svetlana and Wästlund, André and Holmström, Emma and Appiah Mensah, Alex and Holm, Sören and Nilsson, Mats and Fridman, Jonas and Ståhl, Göran
Title:Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors
Year of publishing :2020
Volume:7
Article number:43
Number of Pages:17
Publisher:Springer
ISSN:2095-6355
Language:English
Publication Type:Journal article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science
Keywords:Aboveground biomass assessment, Forest mapping, Gauss-Newton Regression, Hierarchical Model-Based inference, LiDAR maps, National Forest Inventory, Uncertainty estimation, Uncertainty map
URN:NBN:urn:nbn:se:slu:epsilon-p-106907
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-106907
Additional ID:
Type of IDID
DOI10.1186/s40663-020-00245-0
Web of Science (WoS)000545120400001
ID Code:17328
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management

(S) > Dept. of Forest Ecology and Management
(S) > Southern Swedish Forest Research Centre
Deposited By: SLUpub Connector
Deposited On:25 Aug 2020 11:52
Metadata Last Modified:25 Aug 2020 11:52

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