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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

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Forest Ecosystems 2020年第3期7卷 562-578页

作者： Svetlana Saarela AndréWästlund Emma Holmström Alex Appiah Mensah Sören Holm Mats Nilsson jonas.fridman Göran Ståhl Faculty of Forest Sciences Swedish University of Agricultural SciencesSLU Skogsmarksgränd 17UmeåSE-90183Sweden

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 ***: 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 ***: Through the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level bio

关键词： Aboveground biomass assessment Forest mapping Gauss-Newton Regression Hierarchical Model-Based inference LiDAR maps National Forest Inventory Uncertainty estimation Uncertainty map

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Species-specific,pan-European diameter increment models based on data of 2.3 million trees

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Forest Ecosystems 2018年第3期5卷 277-295页

作者： Mart-Jan Schelhaas Geerten M Hengeveld Nanny Heidema Esther Thurig Brigitte Rohner Giorgio Vacchiano Jordi Vayreda John Redmond Jaroslaw Socha jonas.fridman Stein Tomter Heino Polley Susana Barreiro Gert-Jan Nabuurs Wageningen University and Research Wageningen Environmental Research(WENR)Droevendaalsesteeg 36708PB WageningenThe Netherlands Wageningen University and Research BiometrisDroevendaalsesteeg 16708PB WageningenThe Netherlands Wageningen University and Research Forest and Nature Conservation Policy GroupDroevendaalsesteeg 36708PB WageningenThe Netherlands Swiss Federal Institute for Forest Snow and Landscape Research WSLResource AnalysisZuercherstrasse 111CH-8903 BirmensdorfSwitzerland European Commission Joint Research CentreDirectorate DSustainable Resources-Bio-Economy UnitIspraItaly CREAF 08193 Cerdanyola del VallesSpain Univ Autonoma Barcelona 08193 Cerdanyola del VallesSpain Forest Service Department of AgricultureFood and the MarineJohnstown Castle EstateCo.WexfordIreland Department of Biometry and Forest Productivity Institute of Forest Resources ManagementFaculty of ForestryUniversity of Agriculture in KrakowAI.29Listopada 4631-425CracowPoland Swedish University of Agricultural Sciences (SLU) 90183 UmeaSweden Norwegian Institute of Bioeconomy Research P.O.Box 115N-143 1AsNorway Thunen Institute Institute of Forest EcosystemsAlfred-Mo1ler-Strabe 1Haus41/4216225 EberswaldeGermany Forest Research Centre (CEF) Instituto Superior de AgronomiaUniversidade de LisboaTapada da Ajuda1349-017Lisbon.Portugal Wageningen University and Research Forest Ecology and Forest Management GroupDroevendaalsesteeg 36708PBWageningenThe Netherlands

Background: Over the last decades, many forest simulators have been developed for the forests of individual European countries. The underlying growth models are usually based on national datasets of varying size, obtained from National Forest Inventories or from long-term research plots. Many of these models include country-and location-specific predictors, such as site quality indices that may aggregate climate, soil properties and topography effects. Consequently, it is not sensible to compare such models among countries, and it is often impossible to apply models outside the region or country they were developed for. However, there is a clear need for more generically applicable but still locally accurate and climate sensitive simulators at the European scale, which requires the development of models that are applicable across the European continent. The purpose of this study is to develop tree diameter increment models that are applicable at the European scale, but still locally accurate. We compiled and used a dataset of diameter increment observations of over 2.3 million trees from 10 National Forest Inventories in Europe and a set of 99 potential explanatory variables covering forest structure, weather, climate, soil and nutrient ***: Diameter increment models are presented for 20 species/species groups. Selection of explanatory variables was done using a combination of forward and backward selection methods. The explained variance ranged from10% to 53% depending on the species. Variables related to forest structure(basal area of the stand and relative size of the tree) contributed most to the explained variance, but environmental variables were important to account for spatial patterns. The type of environmental variables included differed greatly among ***: The presented diameter increment models are the first of their kind that are applicable at the European scale. This is an important step towards the development of a new ge

关键词： European forests Diameter increment model Climate change Growth modelling National forest inventory

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