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      Determinación de la intensidad de muestreo en inventario forestal continuo en un bosque tropical lluvioso denso, Amazonia Oriental, Brasil Translated title: Sampling intensity determination for continuous forest inventory, dense ombrophilous forest, Eastern Amazon, Brazil

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          Abstract

          Resumen El muestreo optimizado en inventarios forestales permite perfeccionar las tecnologías de manejo forestal sostenible. Se desarrolló una metodología para determinar la intensidad optima de muestreo en inventarios forestales continuos, ajustándolos al principio n° 08 del FSC (Forest Stewardship Council). Datos de un censo con mapeo de árboles con DAP ≥ 35 cm fueron utilizados. Un simulador de plan de corte que combina un algoritmo genético y un Sistema de Información Geográfica (GIS) fue desarrollado. Diez planes de corte fueron simulados, cinco con intensidad de 22 m3 ha-1 y cinco con 30 m3 ha-1. El área efectiva de explotación forestal fue dividida en 4 690 parcelas (100 x 100 m), en las cuales fueron asignados los árboles mapeados en el censo. En la preparación del algoritmo genético dos enfoques (A y B) fueron considerados. El enfoque A buscó maximizar el número de especies muestreadas con base en las intensidades de muestreo predefinidas 1:1 000, 1:750, 1:500, 1:250 y 1:200. El enfoque B buscó minimizar el número de parcelas permanentes para muestrear todas las especies cosechadas. No fue posible muestrear todas las especies cosechadas utilizando intensidades de muestreo predefinidas. Para cumplir con el Principio n° 08 del FSC, la metodología determinó una intensidad óptima de muestreo de 1:180 y 1:165 para las intensidades de corte de 22 m3 ha-1 y 30 m3 ha-1 respectivamente. No hubo diferencia significativa entre el número de especies cosechadas en las diferentes intensidades de corte.

          Translated abstract

          Abstract The optimized sampling in forest inventories allows improving technologies of sustainable forest management. It was developed a methodology to determine the optimum sampling intensity for continuous forest inventories in order to meet the requirement of the Principle 08 of the Forest Stewardship Council (FSC). We used a census data, where trees equal or greater than 35 cm of dbh were measured and mapped. A cutting plan simulator that combines a genetic algorithm and a Geographic Information System (GIS) was developed. Ten plans were simulated, five with a cutting intensity of 22 m3 ha-1 and other five with cutting intensity of 30 m3 ha-1. The effective area of logging was divided into 4 690 plots. During the genetic algorithm implementation, two approaches (A and B) were cosidered. Approach A aimed to maximize the number of sampled species, based on pre-defined sampling intensities of 1:1 000, 1:750, 1:500, 1:250 and 1:200. Approach B, in turn, aimed to minimize the number of permanent plots to sample all harvested species. It was not possible to sample all harvested species using pre-defined sampling intensities. To meet the requirements of FSC 8th Principle, the methodology determined an optimum sampling intensity of 1:180 and 1:165 for cutting intensities of 22 m3 ha-1 and 30 m3 ha-1, respectively. There were no significant differences between the numbers of harvested species in the both cutting intensities.

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          Most cited references 22

          • Record: found
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          The roles of nearest neighbor methods in imputing missing data in forest inventory and monitoring databases

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            Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

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              • Record: found
              • Abstract: not found
              • Article: not found

              Multicriteria inventory classification using a genetic algorithm

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                Author and article information

                Contributors
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Journal
                kuru
                Revista Forestal Mesoamericana Kurú
                Kurú
                Instituto Tecnológico de Costa Rica (Cartago, Costa Rica, Cartago, Costa Rica )
                2215-2504
                December 2018
                : 15
                : 37
                : 47-56
                Affiliations
                São Paulo orgnameOrsa Florestal S.A Brazil pacorrea@ 123456orsaflorestal.com.br
                Minas Gerais orgnameUniversidade Federal de Viçosa orgdiv1Departamento de Solos Brazil elpidiofilho@ 123456gmail.com
                Minas Gerais Minas Gerais orgnameUniversidade Federal de Viçosa orgdiv1Departamento de Engenharia Florestal Brazil aepifania16@ 123456gmail.com
                Minas Gerais Minas Gerais orgnameUniversidade Federal de Viçosa orgdiv1Departamento de Engenharia Florestal Brazil alsouzaal@ 123456gmail.com
                orgnameInstituto de Qualificação Profissional orgdiv1Faculdade de Engenharia de Agrimensura da Bahia Brazil luizmarcos.matos@ 123456gmail.com
                Article
                S2215-25042018000200047
                10.18845/rfmk.v15i37.3601

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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                Figures: 0, Tables: 0, Equations: 0, References: 42, Pages: 10
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                Product Information: SciELO Costa Rica
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