Evaluation of forest inventory processes in a forest under concession in the southwestern Brazilian Amazon

KAUAI, Felipe; CORTE, Ana Paula Dalla; CYSNEIROS, Vinicius; PELISSARI, Allan Libanio; SANQUETTA, Carlos Roberto

doi:10.1590/1809-4392201801331

Felipe KAUAI ^** Corresponding author: kauaifex@gmail.com

Universidade Federal do Paraná − UFPR, Departamento de Ciências Florestais, Av. Lothário Meissner, 632, CEP 80210-170, Curitiba, Brazil.

Ana Paula Dalla CORTE

Universidade Federal do Paraná − UFPR, Departamento de Ciências Florestais, Av. Lothário Meissner, 632, CEP 80210-170, Curitiba, Brazil.

Vinicius CYSNEIROS

Universidade Federal do Paraná − UFPR, Departamento de Ciências Florestais, Av. Lothário Meissner, 632, CEP 80210-170, Curitiba, Brazil.

Allan Libanio PELISSARI

Universidade Federal do Paraná − UFPR, Departamento de Ciências Florestais, Av. Lothário Meissner, 632, CEP 80210-170, Curitiba, Brazil.

Carlos Roberto SANQUETTA

Universidade Federal do Paraná − UFPR, Departamento de Ciências Florestais, Av. Lothário Meissner, 632, CEP 80210-170, Curitiba, Brazil.

* Corresponding author: kauaifex@gmail.com

Associate Editor:

Francesco Ripullone

Figures (4)
Tables (3)

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Figure 1.
Sampling plot distribution design of sampling processes used in the simulations performed on census data from a sustainable management forest area in Jamari National Forest, southwestern Brazilian Amazon. A - simple random sampling; B - two-stage cluster sampling; C - two-stage cluster sampling with a Maltese cross unit.

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Figure 2
Weighted relative sampling errors as a function of sampling plot size used in inventory simulations for different sampling methods on data from a sustainable management forest area in Jamari National Forest, southwestern Brazilian Amazon. A - Simple Random Sampling. B - Two Stage Cluster Sampling.

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Figure 3
Linear regression models of the relative sampling error as a function of stand density for simulated samplings on data from a sustainable management forest area in Jamari National Forest, southwestern Brazilian Amazon. A - simple random sampling [sampling plot sizes of 2000 m² (R² = 0.98, p = 1.51 x 10^-9), 4000 m² (R² = 0.96, p = 1.42 x 10^-8), 6000 m² (R² = 0.94, p = 1.42 x 10^-7), and 8000 m² (R² = 0.95, p = 4.7 x 10^-3)]. B - two-stage cluster sampling [sampling plot sizes of 2,000 m² (R² = 0.94, p = 8.77 x 10^-8), 4000 m² (R² = 0.95, p = 7.73 x 10^-8), 6000 m² (R² = 0.89, p = 2.98 x 10^-6), and 8000 m² (R² = 0.95, p = 4.12 x 10^-8)].

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Figure 4
Linear regression models of the relative sampling error as a function of basal area for simulated samplings on data from a sustainable management forest area in Jamari National Forest, southwestern Brazilian Amazon. A - simple random sampling [sampling plot sizes of 2000 m² (R² = 0.77, p = 1.1 x 10^-4), 4000 m² (R² = 0.76, p = 1.5 x 10^-4), 6000 m² (R² = 0.61, p = 1.6 x 10^-3), and 8000 m² (R² = 0.68, p = 4.6 x 10^-3)]. B - two-stage cluster sampling [sampling plot sizes of 2000 m² (R² = 0.65, p = 9.70 x 10^-4), 4000 m² (R² = 0.64, p = 1.17 x 10^-3), 6000 m² (R² = 0.54, p = 3.77 x 10^-3), and 8000 m² (R² = 0.79, p = 7.54 x 10^-5)].

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Table 1
Main timber species exploited in the concession area of Jamari National Forest (Rondônia state, southwestern Brazilian Amazon), indicating their respective basal area and stand density in the inventory area used to run sampling simulations.

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Table 2
Summary of sampling parameters used in simulations of three inventory processes using data from a sustainable management forest area in Jamari National Forest, southwestern Brazilian Amazon. N plots = number of plots for a 10% sampling intensity.

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Table 3
Mean weighted relative sampling error, mean relative sampling error and relative sampling error for all species combined for sampling simulations with three forest inventory methods (simple random sampling, SRS; two-stage cluster sampling, TSCS; Maltese cross unit, IFN) and different sampling plot sizes (2000, 4000, 6000 and 8000 m²) using data from a sustainable management forest area in Jamari National Forest, southwestern Brazilian Amazon. Values are the mean ± standard deviation.

Species	Basal area (m² ha^-1)	Stand density (100 trees ha^-1 )
Peltogyne paniculata Benth.	0.2654	83.1
Caryocar glabrum Pers.	0.0884	18.7
Brosimum rubescens Taub.	0.0600	14.9
Astronium lecointei Ducke.	0.2861	67.4
Clarisia racemosa Ruíz & Pav.	0.1082	33.1
Apuleia leiocarpa (Vogel) J.F.Macbr.	0.0293	4.6
Couratari stellata A. C. Sm.	0.1761	30.2
Goupia glabra Aubl.	0.0951	17.8
Dipteryx odorata (Aubl.) Willd.	0.0915	21.1
Qualea paraensis Ducke.	0.0624	18.4
Hymenolobium heterocarpum Ducke.	0.1413	23.8
Dinizia excelsa Ducke.	0.5903	51.3

Sampling process	Sampling plot size (m²)	N plots
Simple random sampling (SRS)	2,000	964
4,000	482
6,000	321
8,000	241
Two-stage cluster sampling (TSCS)	2,000	964
4,000	482
6,000	321
8,000	241
Maltese cross unit (IFN)	8,000	241

Method/plot size	Mean weighted relative SE (%)	Mean relative SE (%)	Relative SE all species (%)
SRS 2,000	2.14 ± 0.88	32.40 ± 13.07	9.04
SRS 4,000	2.81 ± 1.10	43.24 ± 19.29	8.25
SRS 6,000	2.56 ± 0.90	34.68 ± 15.43	7.74
SRS 8,000	2.23 ± 0.95	33.84 ± 15.04	10.38
TSCS 2,000	17.29 ± 7.24	26.91 ± 0.88	74.74
TSCS 4,000	8.57 ± 3.06	133.94 ± 121.11	25.68
TSCS 6,000	5.15 ± 2.31	75.58 ± 61.18	15.06
TSCS 8,000	4.38 ± 1.82	65.31 ± 27.19	21.74
IFN 8,000	2.77 ± 1.29	41.05 ± 16.88	21.74

[1] * Corresponding author: kauaifex@gmail.com

Brasil

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Evaluation of forest inventory processes in a forest under concession in the southwestern Brazilian Amazon

Avaliação de processos de inventário florestal em uma floresta sob concessão na Amazônia brasileira

ABSTRACT