Sampling Techniques for Forest InventoriesCRC Press, 26.10.2007 - 272 Seiten Sound forest management planning requires cost-efficient approaches to optimally utilize given resources. Emphasizing the mathematical and statistical features of forest sampling to assess classical dendrometrical quantities, Sampling Techniques for Forest Inventories presents the statistical concepts and tools needed to conduct a modern for |
Inhalt
| 1 | |
Sampling finite populations the essentials | 3 |
Sampling finite populations advanced topics | 31 |
Forest Inventory onephase sampling schemes | 53 |
Forest Inventory twophase sampling schemes | 79 |
Forest Inventory advanced topics | 97 |
Geostatistics | 135 |
Case Study | 147 |
The Swiss National Forest Inventory | 177 |
Estimating change and growth | 185 |
TransectSampling | 195 |
Simulations | 211 |
Conditional expectations and variances | 221 |
Solutions to selected exercises | 225 |
| 247 | |
| 253 | |
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ˆYpred AMSE angle count anticipated variance approximately assume asymptotically basal area boundary effects calculate chapter circle cluster cluster-sampling coefficient components consider covariance defined design-based variance error estimated variance expected value finite population forest area forest inventory function g-weights geostatistical given grid Hence Horvitz-Thompson HT estimator inclusion probabilities independent indicator variables Kriging least squares linear Mandallaz matrix model-assisted model-dependent approach needle notation nugget effect number of points obtain one-phase one-stage parameter plots point estimate Poisson predictions Problem procedure PSUs random variable ratio regression estimator residuals response variable sample mean sample points sampling scheme sampling Statistics Särndal second-stage simple random sampling statistical strata stratified surface area systematic sampling technique term Theorem threshold transect length two-phase sampling two-stage sampling unbiased estimate uniformly distributed usually variance estimate variance-covariance matrix variogram vector weighted least squares weights zero
Beliebte Passagen
Seite 247 - Cassel, CM, Sarndal, CE and Wretman, JH (1977). Foundations of Inference in Survey Sampling. Wiley, New York. Chaudhuri, A. and Adhikary, AK (1981).