Area-level analysis of forest inventory variables

被引:18
作者
Magnussen, Steen [1 ]
Mauro, Fransisco [2 ]
Breidenbach, Johannes [3 ]
Lanz, Adrian [4 ]
Kaendler, Gerald [5 ]
机构
[1] Nat Resources Canada, Canadian Forest Serv, 506 West Burnside Rd, Victoria, BC V8Z 1M5, Canada
[2] Oregon State Univ, Forest Engn Resources & Management Dept, Coll Forestry, 053 Peavy Hall, Corvallis, OR 97331 USA
[3] Norwegian Inst Bioecon Res, POB 115, NO-1431 As, Norway
[4] WSL, Swiss Fed Res Inst, Zurcherstr 111, CH-8903 Birmensdorf Zh, Switzerland
[5] Forest Res Inst, Wonnhaldestr 4, D-79100 Freiburg, Germany
关键词
Direct estimators; Empirical best linear unbiased prediction; Hierarchical Bayes empirical best linear unbiased prediction; Non-stationary spatial effects; Shrinkage factor; Variance smoothing; MEAN SQUARED ERROR; LASER SCANNER DATA; ASSISTED ESTIMATION; SPLINE REGRESSION; MIXED-MODEL; STEM VOLUME; BASAL AREA; PREDICTION; ESTIMATORS; BIOMASS;
D O I
10.1007/s10342-017-1074-z
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Small-area estimation is a subject area of growing importance in forest inventories. Modelling the link between a study variable Y and auxiliary variables X-in pursuit of an improved accuracy in estimators-is typically done at the level of a sampling unit. However, for various reasons, it may only be possible to formulate a linking model at the level of an area of interest (AOI). Area-level models and their potential have rarely been explored in forestry. This study demonstrates, with data (Y = stem volume per ha) from four actual inventories aided by aerial laser scanner data (3 cases) or photogrammetric point clouds (1 case), application of three distinct models representing the currency of area-level modelling. The studied AOIs varied in size from forest management units to forest districts, and municipalities. The variance explained by X declined sharply with the average size of an AOI. In comparison with a direct estimate mean of Y in an AOI, all three models achieved practically important reduction in the relative root-mean-squared error of an AOI mean. In terms of the reduction in mean-squared errors, a model with a spatial location effect was overall most attractive. We recommend the pursuit of a spatial model component in area-level modelling as promising within the context of a forest inventory.
引用
收藏
页码:839 / 855
页数:17
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