Banca de QUALIFICAÇÃO: MATHEUS DA COSTA GONDIM

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
STUDENT : MATHEUS DA COSTA GONDIM
DATE: 18/03/2021
TIME: 08:30
LOCAL: Sala virtual no google meet
TITLE:

OPTIMUM CLUSTER SIZE FOR ESTIMATING VOLUME DE MADEIRA IN AMAZONIAN FOREST USING THE BRAZILIAN NATIONAL FOREST INVENTORY CLUSTER

KEY WORDS:

Uncertainty analysis. Monte Carlo method. Bootstrap. Sampling theory. Accuracy and precision.

PAGES: 40
BIG AREA: Ciências Agrárias
AREA: Recursos Florestais e Engenharia Florestal
SUMMARY:

Smaller and less sample units is generally wanted in forest inventories. This because the number of individuals measured in the plots is reduced, turning the fieldwork into a less costly activity. This research explores sampling techniques through a set of clusters standardized by the Brazilian National Forest Inventory. The aim was twofold: to identify (1) the smallest cluster size that provides the same accuracy and precision as the original cluster, and (2) the smallest sample size that provides the same accuracy and precision as the original size. Data from 22 8,000-m² clusters were installed in the National Forest of Bom Futuro, Brazil. Three products were considered: (1) merchantable volume of the trees with DBH ≥ 20 cm, (2) merchantable volume of the trees with DBH ≥ 50 cm, and (3) merchantable volume of the trees with DBH ≥ 50 cm, with stem quality 1 and 2. For Chapter 1, consecutive reductions of 800 m² were performed in two directions (distal and proximal), thus originating 20 scenarios. For Chapter 2, 21 reductions were accomplished. Monte Carlo and Bootstrap methods were used as resampling techniques necessary to compute the accuracy and precision. In Chapter 1, for Product 1 and 2, the smallest cluster that presented the same precision and accuracy as the entire cluster had 6 subplots per subunit, reduced in the proximal direction. For Product 3, a cluster with 14 subplots per subunit presented precision and accuracy similar to the entire cluster reduced in the same direction.

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