| Popis: |
The stands stratification for successive forest inventory is usually based on stands cadastral information, such as theage, the species, the spacing, and the management regime, among others. The size of the sample is usually conditioned by thevariability of the forest and by the required precision. Thus, the control of the variation through the efficient stratification has stronginfluence on sample precision and size. This study evaluated: the stratification propitiated by two spatial interpolators, the statisticianone represented by the krigage and the deterministic one represented by the inverse of the square of the distance; evaluated theinterpolators in relation to simple random sampling and the traditional stratification based on cadastral data, in the reduction of thevariance of the average and sampling error; and defined the optimal number of strata when spatial interpolators are used. For thegeneration of the strata, it was studied 4 different dendrometric variables: volume, basal area, dominant height and site index in 2different ages: 2.5 years and 3.5 years. It was concluded that the krigage of the volume per hectare obtained at 3.5 years of age reducedin 47% the stand average variance and in 32% the inventory sampling error, when compared to the simple random sampling. Thevolume interpolator IDW, at 3.5 years of age, reduced in 74% the stand average variance and in 48% the inventory sampling error.The less efficient stratificator was the one based on age, species and spacing. In spite of the IDW method having presented highefficiency, it doesn t guarantee that the efficiency be maintained, if a new sampling is accomplished in the same projects, contrarily tothe geostatistic krigage. In forest stands that don t present spatial dependence, the IDW method can be used with great efficiency in thetraditional stratification. The less efficient stratification method is the one based on the control of age, species and spacing (STR),contributing with 17% of the average variability reduction and with 13% of the sampling error reduction. The optimal number ofstrata that minimizes the variance is 6 for both interpolators. |
