Sampling Techniques for Forest Inventories

preface Introduction and terminology Sampling finite populations: the essentials Sampling schemes and inclusion probabilities The Horvitz-Thompson estimator Simple random sampling without replacement Poisson sampling Unequal probability sampling with replacement Estimation of ratios Stratification and post-stratification Two-stage sampling Single-stage cluster-sampling Systematic sampling Exercises Sampling finite populations: advanced topics Three-stage element sampling Abstract nonsense and elephants Model-assisted estimation procedures Exercises Forest Inventory: one-phase sampling schemes Generalities One-phase one-stage simple random sampling scheme One-phase one-stage cluster random sampling scheme One-phase two-stage simple random sampling One-phase two-stage cluster random sampling Exercises Forest Inventory: two-phase sampling schemes Two-phase one-stage simple random sampling Two-phase two-stage simple random sampling Two-phase one-stage cluster random sampling Two-phase two-stage cluster random sampling Internal linear models in two-phase sampling Remarks on systematic sampling Exercises Forest Inventory: advanced topics The model-dependent approach Model-assisted approach Small-area estimation Modeling relationships Exercises Geostatistics Variograms Ordinary Kriging Kriging with sampling error Double Kriging for two-phase sampling schemes Exercises CASE STUDY Optimal sampling schemes for forest inventory Preliminaries Anticipated variance under the local Poisson model Optimal one-phase one-stage sampling schemes Discrete approximations of PPS Optimal one-phase two-stage sampling schemes Optimal two-phase sampling schemes Exercises The Swiss National Forest Inventory Estimating change and growth Exercises Transect Sampling Generalities IUR transect sampling PPL transect sampling Transects with fixed length Buffon's needle problem Exercises APPENDIX A: Simulations Preliminaries Simple random sampling Systematic cluster sampling Two-phase simple systematic sampling Figures APPENDIX B: Conditional expectations and variances APPENDIX C: Solutions to selected exercises Bibliography INDEX