Comparison of spatial prediction techniques for developing Pinus radiata productivity surfaces across New Zealand

Palmer, David J.; Höck, Barbara; Kimberley, Mark O.; Watt, Michael S.; Lowe, David J.; Payn, T. W.

doi:10.1016/j.foreco.2009.07.057

Cited by 47 publications

(40 citation statements)

References 22 publications

(28 reference statements)

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“…Mapping variables involves estimating values at unsampled areas with interpolation methods. However, the effectiveness of the method used depends on the accuracy of the spatial interpolation, as mentioned in several studies which also discuss the most appropriate methods for the interpolation of variables (Erdogan, 2009;Palmer et al, 2009;Simbahan et al, 2006;Sun et al, 2009;Xie et al, 2011). Independently of the scale of analysis, accurate spatial predictions are fundamental in the evaluation of the effects of fire on the landscape and strategies to mitigate their impacts.…”

Section: P Pereira Et Al: a Case Study Of A Burnt Grassland In Lithmentioning

confidence: 99%

Spatial models for monitoring the spatio-temporal evolution of ashes after fire – a case study of a burnt grassland in Lithuania

et al. 2013

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Abstract. Ash thickness is a key variable in the protection of soil against erosion agents after planned and unplanned fires. Ash thickness measurements were conducted along two transects (flat and sloping areas) following a grided experimental design. In order to interpolate data with accuracy and identify the techniques with the least bias, several interpolation methods were tested in the grided plot. Overall, the fire had a low severity. However, the fire significantly reduced the ground cover, especially on sloping areas, owing to the higher fire severity and/or less biomass previous to the fire. Ash thickness depended on fire severity and was thin where fire severity was higher and thicker in lower fire severity sites. The ash thickness decreased with time after the fire. Between 4 and 16 days after the fire, ash was transported by wind. The greatest reduction took place between 16 and 34 days after the fire as a result of rainfall, and was more efficient where fire severity was higher. Between 34 and 45 days after the fire, no significant differences in ash thickness were identified among ash colours and only traces of the ash layer remained. The omni-directional experimental variograms showed that variable structure did not change significantly with time. The ash spatial variability increased with time, particularly on the slope, as a result of water erosion.

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Section: P Pereira Et Al: a Case Study Of A Burnt Grassland In Lithmentioning

confidence: 99%

Spatial models for monitoring the spatio-temporal evolution of ashes after fire – a case study of a burnt grassland in Lithuania

et al. 2013

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“…In order to test the accuracy of volume predictions, cross-validation and resampling methods have been applied (Nanos et al 2004, Zawadzki et al 2005, Palmer et al 2009, Tang & Bian 2009, Kovacs et al 2011, Tattoni et al 2011, Viana et al 2012, aimed at comparing the observed value at a given point with its prediction obtained from the interpolated continuous surface. However, in areas characterized by highly heterogeneous forest structures, the above approach may not be adequate, and additional control points in the field may be required.…”

Section: Discussionmentioning

confidence: 99%

“…These methods allow the use of a variety of ecological and technical parameters in the assessment of inventory results. Palmer et al (2009) compared the spatial predictions derived from Inverse Distance Weighting (IDW), Partial Least Squares (PLS), Regression Kriging (RK), and Ordinary Kriging (OK) for Pinus radiata mean volume, annual increment and mean top height. Viana et al (2012) made estimations of the crown biomass of Pinus pinaster stands and aboveground shrubland biomass using forest inventory data, remotely sensed imagery for auxiliary variables and spatial prediction models.…”

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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“…Contudo, essas informações não levam em consideração a distribuição espacial dos diferentes estratos formados e possíveis relações entre estratos vizinhos. Assim, a utilização de interpoladores geoestatísticos, como a krigagem ordinária e a cokrigagem ordinária, oferece uma alternativa potencialmente eficiente para a busca de informações complementares que melhor caracterizem os povoamentos florestais, já que consideram explicitamente a informação espacial (Meng et al, 2009;Palmer et al, 2009;Akhavan & Kia-Daliri, 2010;Viana et al, 2012;Singh & Das, 2014), o que auxilia o processo de estratificação.…”

Section: Introductionunclassified

Estratificação de um povoamento de eucalipto por interpoladores geoestatísticos e sensoriamento remoto

Reis

Mello

Raimundo

et al. 2016

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi avaliar a utilização de interpoladores geoestatísticos e imagens de sensoriamento remoto, para a estratificação de povoamentos de Eucalyptus sp. em idade de corte. As estimativas da amostragem casual estratificada foram comparadas às estimativas da amostragem sistemática quanto à variável volume de madeira. A área de estudo correspondeu a um povoamento clonal de Eucalyptus sp. no Município de Lagoa Grande, MG. O inventário florestal foi realizado em 2011, em quatro situações de amostragem e diferentes números de parcelas. A estratificação da área foi feita por meio de krigagem da área basal (variável principal), da reflectância na banda TM5 (variável auxiliar) obtida em uma imagem Landsat 5 TM e com base na cokrigagem dessas variáveis. A área basal e a reflectância na banda TM5 apresentaram estrutura de dependência espacial. A estratificação reduziu o erro de amostragem em até 40%, em comparação ao erro da amostragem sistemática. As imagens de sensoriamento remoto e os interpoladores geoestatísticos krigagem e cokrigagem são eficientes na definição dos estratos em povoamentos de Eucalyptus sp., em decorrência da maior precisão dos estimadores da amostragem casual estratificada, que permitem estimativas volumétricas mais precisas do inventário florestal do que os da amostragem sistemática.Termos para indexação: Eucalyptus, cokrigagem, imagens, inventário florestal, krigagem. Stratification of an eucalyptus plantation through geostatistical interpolators and remote sensingAbstract -The objective of this work was to evaluate the use of geostatistical interpolators and remote sensing images for the stratification of Eucalyptus sp. stands, at harvesting age. Estimates by stratified random sampling and systematic sampling were compared for the timber volume variable. The study area was a clonal plantation of Eucalyptus sp. in the municipality of Lagoa Grande, in the state of Minas Gerais, Brazil. The forest inventory was performed in 2011, considering four sampling situations and different plot numbers. The area stratification was carried out based on kriging of the basal area (target variable), on the reflectance values in the TM5 band (auxiliary variable) extracted from a Landsat 5 TM image, and on the cokriging of these variables. Basal area and reflectance in the TM5 band showed a spatial dependence structure. Stratification reduced the sampling error up to 40%, in comparison with that generated from systematic sampling. Remote sensing images and kriging and cokriging interpolators are efficient to define the strata in Eucalyptus sp. stands, as a consequence of the higher precision of the stratified random sampling estimators, which allow more precise volume estimates of the forest inventory than those of systematic sampling.Index terms: Eucalyptus, cokriging, images, forest inventory, kriging. IntroduçãoO inventário florestal contínuo (IFC) é o sistema de amostragem mais utilizado no Brasil para acompanhar o crescimento da floresta. O IFC consiste de uma rede de parcelas perm...

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Comparison of spatial prediction techniques for developing Pinus radiata productivity surfaces across New Zealand

Cited by 47 publications

References 22 publications

Spatial models for monitoring the spatio-temporal evolution of ashes after fire – a case study of a burnt grassland in Lithuania

Spatial models for monitoring the spatio-temporal evolution of ashes after fire – a case study of a burnt grassland in Lithuania

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

Estratificação de um povoamento de eucalipto por interpoladores geoestatísticos e sensoriamento remoto

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