Comparison of design-based and model-based estimates for tropical forestry resource with post-stratification

被引:3
|
作者
Dessard, H [1 ]
机构
[1] Cirad Foret, F-34032 Montpellier 1, France
关键词
management planning; forest inventory; kriging; post-stratification;
D O I
10.1051/forest:19990803
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Forest resource management planning provides today the guide line for harvesting: the forest is cut in high yield units from which forest managers successively extract quantities of timber. Management mapping needs local assessment of the resource over the whole forest from a forest inventory. Prediction of the resource is made by two methods of kriging: one is ordinary kriging and the other one, named stratified kriging, takes into account an auxiliary qualitative variable. Results obtained from these technics are compared with those yielded by classical sampling. Thanks to an exhaustively sampled area, one can judge more objectively the suitability of each technique. If the total is similar for the different estimators, variances obtained by kriging are smaller. The difference between ordinary and stratified kriging concerns the distribution of the estimation. Taking into account the stratification was not very efficient and it might be better to use survey estimators. (C) 1999 Inra/Editions scientifiques et medicales Elsevier SAS.
引用
收藏
页码:651 / 665
页数:15
相关论文
共 50 条
  • [21] Analyzing individual growth with clustered longitudinal data: A comparison between model-based and design-based multilevel approaches
    Hsien-Yuan Hsu
    John J. H. Lin
    Susan T. Skidmore
    [J]. Behavior Research Methods, 2018, 50 : 786 - 803
  • [22] DESIGN-BASED ESTIMATION OF FOREST VOLUME WITHIN A MODEL-BASED SAMPLE SELECTION FRAMEWORK
    SCHREUDER, HT
    WILLIAMS, MS
    [J]. CANADIAN JOURNAL OF FOREST RESEARCH-REVUE CANADIENNE DE RECHERCHE FORESTIERE, 1995, 25 (01): : 121 - 127
  • [23] Spatially-restricted random sampling designs for design-based and model-based estimation
    Stevens, DL
    Olsen, AR
    [J]. ACCURACY 2000, PROCEEDINGS, 2000, : 609 - 616
  • [24] RECONCILING DESIGN-BASED AND MODEL-BASED CAUSAL INFERENCES FOR SPLIT-PLOT EXPERIMENTS
    Zhao, Anqi
    Ding, Peng
    [J]. ANNALS OF STATISTICS, 2022, 50 (02): : 1170 - 1192
  • [25] A NOTE ON MODEL-BASED STRATIFICATION
    KOTT, PS
    [J]. JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 1985, 3 (03) : 284 - 286
  • [26] Interpreting the uncertainty of model-based and design-based estimation in downscaling estimates from NFI data: a case-study in Extremadura (Spain)
    Guerra-Hernandez, Juan
    Botequim, Brigite
    Bujan, Sandra
    Jurado-Varela, Alfonso
    Alberto Molina-Valero, Juan
    Martinez-Calvo, Adela
    Perez-Cruzado, Cesar
    [J]. GISCIENCE & REMOTE SENSING, 2022, 59 (01) : 686 - 704
  • [27] Model-based support for authoring Design-based Learning and Maker Education materials in elementary education
    Veldhuis, Annemiek
    Xiao, Di
    Bekker, Tilde
    Markopoulos, Panos
    [J]. PROCEEDINGS OF 6TH FABLEARN EUROPE / MAKEED CONFERENCE 2022, 2022,
  • [28] Clustered data with small sample sizes: Comparing the performance of model-based and design-based approaches
    McNeish, Daniel M.
    Harring, Jeffery R.
    [J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2017, 46 (02) : 855 - 869
  • [29] The Precision of C Stock Estimation in the Ludhikola Watershed Using Model-Based and Design-Based Approaches
    Chinembiri T.S.
    Bronsveld M.C.
    Rossiter D.G.
    Dube T.
    [J]. Natural Resources Research, 2013, 22 (4) : 297 - 309
  • [30] A hybrid design-based and model-based sampling approach to estimate the temporal trend of spatial means
    Brus, D. J.
    de Gruijter, J. J.
    [J]. GEODERMA, 2012, 173 : 241 - 248