On the potential of Kriging for forest management planning

Gunnarsson, Fredrik; Holm, Sören; Holmgren, Peter; Thuresson, Tomas

doi:10.1080/02827589809382981

Cited by 39 publications

(24 citation statements)

References 6 publications

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“…Gunnarson et al (1998) e Tuominen et al (2003 relataram que estimativas baseadas em correlação espacial não são eficientes em florestas manejadas ou exploradas, pois intervenções humanas causam mudanças abruptas, sendo a krigagem indicada para florestas naturais não manejadas e plantações florestais. Porém, isso está em desacordo com os preceitos do manejo florestal, pois as modificações na estrutura da floresta dele decorrentes não podem ser alterações de grande magnitude.…”

Section: Discussionunclassified

Influência da floresta alterada na distribuição espacial de três espécies da Floresta Ombrófila Mista avaliada pela geoestatística

et al. 2013

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e ElódioSebem 6 RESUMO -Os objetivos deste trabalho foram analisar e representar a distribuição espacial dos indivíduos de três espécies da Floresta Ombrófila Mista (FOM), sob distúrbio ocasionado por ações antrópicas, utilizando geoestatística e interpolações por krigagem, bem como verificar a influência das alterações da floresta no resultado obtido com essa metodologia. Os dados foram obtidos de levantamento fitossociológico realizado em 2007, contendo 45 unidades amostrais (ua) de 500 m 2 , em que foram mensurados indivíduos com DAP 10 cm. O fragmento, localizado em General Carneiro, PR, passou por ciclos de exploração de madeira, erva-mate, xaxim, pastoreio e coleta de sementes, que causaram abertura de grandes clareiras, contendo alta densidade de taquaras. Posteriormente, a área foi transformada em Reserva Particular do Patrimônio Natural. A condição de floresta alterada interferiu nos resultados para distribuição espacial dos indivíduos de D. sellowiana, M. scabrella e I. paraguariensis (xaxim, bracatinga e erva-mate) por ua. Apesar de ser detectada alta dependência espacial (>75%), foi também observada quebra da continuidade espacial para distribuição dessas espécies. Conclui-se que, nessa condição, geoestatística e krigagem não são indicadas devido aos usos inadequados dado à floresta, podendo ser utilizadas outras técnicas de interpolação para evitar as extrapolações de médias obtidas na amostragem da área total do fragmento. No entanto, por apresentar sensibilidade às alterações das florestas, análises geoestatísticas devem ser experimentadas quanto à sua utilização na avaliação da capacidade de resposta da floresta a eventos impactantes de grande magnitude.Palavras-chave: Floresta em distúrbio; Distribuição espacial de espécies; Interpolação. INFLUENCE OF FOREST DISTURBANCE IN THE SPATIAL DISTRIBUTION OF THE NUMBER OF INDIVIDUALS OF THREE SPECIES OF ARAUCARIA FOREST ASSESSED BY GEOSTATISTICAL

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Section: Discussionunclassified

Influência da floresta alterada na distribuição espacial de três espécies da Floresta Ombrófila Mista avaliada pela geoestatística

et al. 2013

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“…Spatial interpolation methods (especially kringing) have been previously argued as unsuitable for assessment purposes in managed forests (Akhavan et al 2010). On the other hand, several studies have reported successful application of such methods in homogenous natural forest areas (Gunnarsson et al 1998, Tuominen et al 2003.…”

Section: Discussionmentioning

confidence: 99%

“…In conventional management planning, stands are defined through the planning process. The type of of the management objectives is a component of this process (Gunnarsson et al 1998). However, the use of computers has changed this conventional approach, and spatially and temporally dynamic description and treatment units can readily be produced (Holmgren & Thuresson 1997).…”

Section: Introductionmentioning

confidence: 99%

Making objective forest stand maps of mixed managed forest with spatial interpolation and multi-criteria decision analysis

Destan¹,

Yilmaz²,

Şahin³

2013

iForest

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© iForest -Biogeosciences and Forestry IntroductionInformation about extant forest communities is of considerable importance in forest management planning. The stand is the smallest production and silvicultural unit, and the most crucial part of this information. The location of planning units, their size and silvicultural features are used to select management objectives and determine the working cycle, management regulations and silvicultural goals. Thus, the precision of decisions to be implemented and the planning to be realized are both closely related to the accuracy of the stand maps. Moreover, map accuracy depends on the type of forest inventory, the sample size, and the techniques used to evaluate the inventory results.Stand maps have previously been produced subjectively (Feng et al. 2006) in conjunction with current forest management practices. In conventional management planning, stands are defined through the planning process. The type of of the management objectives is a component of this process (Gunnarsson et al. 1998). However, the use of computers has changed this conventional approach, and spatially and temporally dynamic description and treatment units can readily be produced (Holmgren & Thuresson 1997). Thus, dynamic forest planning can be achieved with the use of geopositioned field plot data and computers (Wallerman et al. 2002).The sampling methods used in forest inventories vary according to different countries' forestry objectives and forest structures. Systematic sampling methods are recommended for large and homogeneous forest areas and have been implemented in all production and conservation areas of Turkey since 1964. However, if this sampling method is implemented with an insufficient number of sample plots and/or in heterogeneous forests, the results will be highly questionable because they may fail to reflect the true forest composition. Although Sherman (1996), Aurbi & Debouzie (2000), Flores et al. (2003) andD'Orazio (2003) used remote sensing data to improve the results of such inventories, the desired outcome was never achieved in practice.Spatial interpolation methods, which have been classified by Li & Heap (2008) as nongeostatistical, geostatistical and combined, came into use at the end of the 1960s and have been investigated for use in forest management. As described in Akhavan et al. (2010), Guibal (1973 was the first study to use kriging in the forest inventory process. Jost (1993) also used geostatistical methods to compare conventional inventory results based on systematic sampling with the results of the kriging method. Geostatistically based methods that utilize textural information are frequently used to analyze remote-sensing (RS) images (Zawadzki et al. 2005). The quality and quantity of these methods have increased through advances in the computer sciences and in the science of geographical information systems. These methods allow the use of a variety of ecological and technical parameters in the assessment of inventory results. Spatial interpolation and multi-...

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“…Therefore, their value for forest management planning has been considered low (e.g. Holmgren & Thuresson 1998). One reason suggested for the high stand-and plot-level estimation errors is the low spatial resolution of the satellite image material employed.…”

Section: Remote Sensing Imagerymentioning

confidence: 99%

“…Kriging has been applied in estimating forest variables in forest management planning, e.g. by Holmgren and Thuresson (1997) and Gunnarsson et al (1998). The use of geostatistical methods begins by studying the spatial variation of the variables to be estimated.…”

Section: Geostatistical Interpolation (V)mentioning

confidence: 99%

Estimation of local forest attributes by utilizing two-phase sampling and auxiliary data

Tuominen¹

2007

Diss. For.

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This thesis examines the feasibility of a forest inventory method based on two-phase sampling in estimating forest attributes at the stand or substand levels for forest management purposes. The method is based on multi-source forest inventory combining auxiliary data consisting of remote sensing imagery or other geographic information and field measurements. Auxiliary data are utilized as first-phase data for covering all inventory units. Various methods were examined for improving the accuracy of the forest estimates. Pre-processing of auxiliary data in the form of correcting the spectral properties of aerial imagery was examined (I), as was the selection of aerial image features for estimating forest attributes (II). Various spatial units were compared for extracting image features in a remote sensing aided forest inventory utilizing very high resolution imagery (III). A number of data sources were combined and different weighting procedures were tested in estimating forest attributes (IV, V).Correction of the spectral properties of aerial images proved to be a straightforward and advantageous method for improving the correlation between the image features and the measured forest attributes. Testing different image features that can be extracted from aerial photographs (and other very high resolution images) showed that the images contain a wealth of relevant information that can be extracted only by utilizing the spatial organization of the image pixel values. Furthermore, careful selection of image features for the inventory task generally gives better results than inputting all extractable features to the estimation procedure. When the spatial units for extracting very high resolution image features were examined, an approach based on image segmentation generally showed advantages compared with a traditional sample plot-based approach. Combining several data sources resulted in more accurate estimates than any of the individual data sources alone. The best combined estimate can be derived by weighting the estimates produced by the individual data sources by the inverse values of their mean square errors. Despite the fact that the plot-level estimation accuracy in two-phase sampling inventory can be improved in many ways, the accuracy of forest estimates based mainly on single-view satellite and aerial imagery is a relatively poor basis for making stand-level management decisions.

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On the potential of Kriging for forest management planning

Cited by 39 publications

References 6 publications

Influência da floresta alterada na distribuição espacial de três espécies da Floresta Ombrófila Mista avaliada pela geoestatística

Influência da floresta alterada na distribuição espacial de três espécies da Floresta Ombrófila Mista avaliada pela geoestatística

Making objective forest stand maps of mixed managed forest with spatial interpolation and multi-criteria decision analysis

Estimation of local forest attributes by utilizing two-phase sampling and auxiliary data

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