Geographical variation in age-height relationships for dominant trees in Japanese cedar (<i>Cryptomeria japonica</i> D. Don) forests in Japan

Nishizono, Tomohiro; Kitahara, Fumiaki; Iehara, Toshiro; Mitsuda, Yasushi

doi:10.1007/s10310-013-0416-z

Cited by 26 publications

(24 citation statements)

References 32 publications

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“…The size of trees to be measured was different for each circle; trees >1 cm, 5 cm and >18 cm in diameter at breast height (DBH) were measured in the S, M and L circles, respectively. In each plot, the tree height was measured for at least 20 trees, which were expected to be selected from a wide range of tree sizes to construct a DBH-height curve for the plot [23,25,31,38,39]. We used the information of species, DBH and tree height from the 40 measurement variables to calculate the plot-level stand volume.…”

Section: In Situ Datamentioning

confidence: 99%

“…Because we did not have any accurate wall-to-wall digital maps of the forest types for this study area, we used forest type information (FTYPE; categorical variable), which was determined by the FRMS plot data itself. The FRMS plots were grouped into two classes (evergreen coniferous forest, ECF; broad-leaved forest, BF) by the dominant analysis [31,43] and the following procedures: (1) To obtain the dominant tree species group in all plots, first, all species of trees within each FRMS plot were classified into five tree species groups (evergreen coniferous, deciduous coniferous, evergreen broad-leaved, deciduous broad-leaved and bamboo); then, the dominant tree species group was determined based on the maximum basal area (m 2 ) within each plot. (2) Plots that were dominated by bamboo trees were excluded from the analysis.…”

Section: Additional Feature Variablesmentioning

confidence: 99%

“…Deciduous coniferous trees were not found in this study area, that is to say, the ECF class was only found for coniferous trees. (3) Two broad-leaved forest types (i.e., evergreen broad-leaved forest and deciduous broad-leaved forest) were aggregated and defined as BF, because of insufficient FRMS plots in these classes (for more detailed information on this procedure, see [31]). The sample sizes for the ECF and BF classes were 501 and 390, respectively.…”

Section: Additional Feature Variablesmentioning

confidence: 99%

“…Therefore, there is still the potential to improve their method by using additional feature variables in the same region. The topographic and climatic characteristics within the study area varied; hence, we assumed that adding environmental factors, derived from a digital elevation model (DEM), and climatic data to satellite image data could improve estimation accuracy, because these environmental factors affect stand structure and tree growth [26][27][28][29][30][31][32]. We also considered the utility of forest type information, because the reflective properties of light and the ranges of stand volume differ among various forest types.…”

Section: Introductionmentioning

confidence: 99%

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Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables

et al. 2014

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Abstract:The main objective of this study was to evaluate the effectiveness of adding feature variables, such as forest type information and topographic-and climatic-environmental factors to satellite image data, on the accuracy of stand volume estimates made with the k-nearest neighbor (k-NN) technique in southwestern Japan. Data from the Forest Resources Monitoring Survey-a national plot sampling survey in Japan-was used as in situ data in this study. The estimates obtained from three Landsat Enhanced Thematic Mapper Plus (ETM+) datasets acquired in different seasons with various combinations of additional feature variables were compared. The results showed that although the addition of environmental factors to satellite image data did not always help improve estimation OPEN ACCESSRemote Sens. 2015, 7 379 accuracy, the use of summer rainfall (SRF) data had a consistent positive effect on accuracy improvement. Therefore, SRF may be a useful feature variable to consider in stand volume estimation in this study area. Moreover, the use of forest type information is very effective at reducing k-NN estimation errors when using an optimum combination of satellite image data and environmental factors. All of the results indicated that the k-NN technique combined with appropriate feature variables is applicable to nationwide stand volume estimation in Japan.

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Section: In Situ Datamentioning

confidence: 99%

Section: Additional Feature Variablesmentioning

confidence: 99%

Section: Additional Feature Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables

et al. 2014

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“…Currently, many foresters have paid increased attention to the estimation of site productivity through site indices based on the national forest inventory (NFI) data [12][13][14][15][16][17][18]. However, NFI data always lack stand top height in some countries, such as China [19,20] and Japan [18].…”

Section: Introductionmentioning

confidence: 99%

Spatial Autoregressive Models for Stand Top and Stand Mean Height Relationship in Mixed Quercus mongolica Broadleaved Natural Stands of Northeast China

Lou

Zhang

Lei

et al. 2016

Forests

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Abstract:The relationship of stand top and stand mean height is important for forest growth and yield modeling, but it has not been explored for natural mixed forests. Observations of stand top and stand mean height can present spatial dependence or autocorrelation, which should be considered in modeling. Simultaneous autoregressive (SAR) models, including spatial lag model (SLM), spatial Durbin model (SDM) and spatial error model (SEM), within nine spatial weight matrices were utilized to model the stand top and stand mean height relationship in the mixed Quercus mongolica Fisch. ex Ledeb. broadleaved natural stands of Northeast China, using ordinary least squares (OLS) as a benchmark model. The results showed that there was a high linear relationship between stand top and stand mean height and that there was a positive spatial autocorrelation pattern in model residuals of OLS. Moreover, SEM and SDM performed better than OLS in terms of reducing the spatial dependence of model residuals and model fitting, regardless of which spatial weight matrix was used. SEM was better than SDM. SLM scarcely reduced the spatial autocorrelation of model residuals. Among nine spatial matrices in SEM, rook contiguous matrix performed best in model fitting, followed by inverse distances raised to the second power (1/d 2 ) and local statistics model matrix (LSM).

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Estimating polymorphic growth curve sets with nonchronological data

Moriguchi

2020

Ecology and Evolution

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When we collect the growth curves of many individuals, orderly variation in the curves is often observed rather than a completely random mixture of various curves. Small individuals may exhibit similar growth curves, but the curves differ from those of large individuals, whereby the curves gradually vary from small to large individuals. It has been recognized that after standardization with the asymptotes, if all the growth curves are the same (anamorphic growth curve set), the growth curve sets can be estimated using nonchronological data; otherwise, that is, if the growth curves are not identical after standardization with the asymptotes (polymorphic growth curve set), this estimation is not feasible. However, because a given set of growth curves determines the variation in the observed data, it may be possible to estimate polymorphic growth curve sets using nonchronological data. In this study, we developed an estimation method by deriving the likelihood function for polymorphic growth curve sets. The method involves simple maximum likelihood estimation. The weighted nonlinear regression and least‐squares method after the log‐transform of the anamorphic growth curve sets were included as special cases. The growth curve sets of the height of cypress (Chamaecyparis obtusa) and larch (Larix kaempferi) trees were estimated. With the model selection process using the AIC and likelihood ratio test, the growth curve set for cypress was found to be polymorphic, whereas that for larch was found to be anamorphic. Improved fitting using the polymorphic model for cypress is due to resolving underdispersion (less dispersion in real data than model prediction). The likelihood function for model estimation depends not only on the distribution type of asymptotes, but the definition of the growth curve set as well. Consideration of these factors may be necessary, even if environmental explanatory variables and random effects are introduced.

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Geographical variation in age-height relationships for dominant trees in Japanese cedar (Cryptomeria japonica D. Don) forests in Japan

Cited by 26 publications

References 32 publications

Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables

Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables

Spatial Autoregressive Models for Stand Top and Stand Mean Height Relationship in Mixed Quercus mongolica Broadleaved Natural Stands of Northeast China

Estimating polymorphic growth curve sets with nonchronological data

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