Understanding uncertainty in forest resources maps

被引:5
|
作者
Kangas, Annika [1 ]
Myllymaki, Mari [2 ]
Mehtatalo, Lauri [1 ]
机构
[1] Nat Resources Inst Finland Luke, Bioecon & Environm, Yliopistokatu 6, FI-80100 Joensuu, Finland
[2] Nat Resources Inst Finland Luke, Bioecon & Environm, Latokartanonkaari 9, FI-00790 Helsinki, Finland
基金
芬兰科学院;
关键词
autocorrelation; ensemble modelling; kriging; quantile; random forest; sequential Gaussian simulation; MAPPING ECOSYSTEM SERVICES; MODEL; INFERENCE; AREA; GEOSTATISTICS; ACCURACY; ERRORS;
D O I
10.14214/sf.22026
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Maps of forest resources and other ecosystem services are needed for decision making at different levels. However, such maps are typically presented without addressing the uncertainties. Thus, the users of the maps have vague or no understanding of the uncertainties and can easily make wrong conclusions. Attempts to visualize the uncertainties are also rare, even though the visualization would be highly likely to improve understanding. One complication is that it has been difficult to address the predictions and their uncertainties simultaneously. In this article, the methods for addressing the map uncertainty and visualize them are first reviewed. Then, the methods are tested using laser scanning data with simulated response variable values to illustrate their possibilities. Analytical kriging approach captured the uncertainty of predictions at pixel level in our test case, where the estimated models had similar log-linear shape than the true model. Ensemble modelling with random forest led to slight underestimation of the uncertainties. Simulation is needed when uncertainty estimates are required for landscape level features more complicated than small areas.
引用
收藏
页数:25
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