Predicting the site index of sugi plantations from GIS-derived environmental factors in Miyazaki Prefecture

Mitsuda, Yasushi; Ito, Satoshi; Sakamoto, So

doi:10.1007/s10310-007-0004-1

Cited by 39 publications

(35 citation statements)

References 29 publications

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“…In addition to soil properties and chemistry, Berrill et al [14] tested DEM-produced topographic indices, including a topographic relative moisture index and topographic exposure scores, and found them useful in predicting coast redwood SI. Cryptomeria japonica D.Don plantation SI was predicted using a topographic wetness index and a solar radiation index with the models explaining 65% of the variation in SI [15]. Laamrani et al [16] found that topographic variables derived from a DEM accounted for between 25% and 31% of the variation in black spruce SI in Canadian boreal forests.…”

Section: Introductionmentioning

confidence: 99%

High Resolution Site Index Prediction in Boreal Forests Using Topographic and Wet Areas Mapping Attributes

Bjelanovic

Comeau

White

2018

Forests

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Abstract:The purpose of this study was to evaluate the relationships between environmental factors and the site index (SI) of trembling aspen, lodgepole pine, and white spruce based on the sampling of temporary sample plots. LiDAR generated digital elevation models (DEM) and wet areas mapping (WAM) provided data at a 1 m resolution for the study area in Alberta. Six different catchment areas (CA), ranging from 0.5 ha to 10 ha, were tested to reveal optimal CA for calculation of the depth-to-water (DTW) index from WAM. Using different modeling methods, species-specific SI models were developed for three datasets: (1) topographic and wet area variables derived from DEM and WAM, (2) only WAM variables, and (3) field measurements of soil and topography. DTW was selected by each statistical method for each species and, in most cases, DTW was the strongest predictor in the model. In addition, differences in strength of relationships were found between species. Models based on remotely-sensed information predicted SI with a root mean squared error (RMSE) of 1.6 m for aspen and lodgepole pine, and 2 m for white spruce. This approach appears to adequately portray the variation in productivity at a fine scale and is potentially applicable to forest growth and yield modeling and silviculture planning.

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Section: Introductionmentioning

confidence: 99%

High Resolution Site Index Prediction in Boreal Forests Using Topographic and Wet Areas Mapping Attributes

Bjelanovic

Comeau

White

2018

Forests

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“…The quality of the final map was evaluated according to the type of bedrock and at different scales using the same validation dataset. Finally, on the basis of previous studies showing an effect of SWHC on forest productivity (Bravo-Oviedo and Montero, 2005;Mitsuda et al, 2007), we compared the ability of SWHC' and SWHC" to predict potential growth for three common tree species of European forests. The suitability of using basic soil information available in forested areas to map SWHC" and to improve potential production modelling is then discussed.…”

Section: Introductionmentioning

confidence: 99%

Mapping soil water holding capacity over large areas to predict potential production of forest stands

et al. 2011

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Ingrid Seynave. Mapping soil water holding capacity over large areas to predict the potential production of forest stands. Geoderma, Elsevier, 2011, 160, pp.355-366. 10 AbstractEcological studies need environmental descriptors to establish the response of species or communities to ecological conditions. Soil water resource is an important factor but is poorly used by plant ecologists because of the lack of accessible data. We explore whether a large number of plots with basic soil information collected within the framework of forest inventories allows the soil water holding capacity (SWHC) to be mapped with enough accuracy to predict tree species growth over large areas. We first compared the performance of available pedotransfer functions (PTFs) and showed significant differences in the prediction quality of SWHC between the PTFs selected.We also showed that the most efficient class PTFs and continuous PTFs compared had similar performance, but there was a significant reduction in efficiency when they were applied to soils different from those used to calibrate them. With a root mean squared error (RMSE) of 0.046 cm 3 cm -3 (n = 227 horizons), we selected the Al Majou class PTFs to predict the SWHC in the soil horizons described in every plot, thus allowing 84% of SWHC variance to be explained in soils free of stone (n = 63 plots). Then, we estimated the soil water holding capacity by integrating the stone content collected at the soil pit scale (SWHC') and both the stone content at the soil pit scale and rock outcrop at the plot scale (SWHC") for the 100.307 forest plots recorded in France within the framework of forest inventories. The SWHC" values were interpolated by kriging to produce a map with 1 km² cell size, with a wider resolution leading to a decrease in map accuracy. The SWHC" given by the map ranged from 0 to 148 mm for a soil down to 1 m depth. The RMSE between map values and plot estimates was 33.9 mm, the best predictions being recorded for soils developed on marl, clay, and hollow silicate rocks, and in flat areas. Finally, the ability of SWHC' and SWHC" to predict 3 height growth for Fagus sylvatica, Picea abies and Quercus petraea is discussed. We show a much better predictive ability for SWHC" compared to SWHC'. The values of SWHC" extracted from the map were significantly related to tree height growth. They explained 10.7% of the height growth index variance for Fagus sylvatica (n = 866), 14.1% for Quercus petraea (n = 877) and 10.3% for Picea abies (n = 2067). The proportions of variance accounted by SWHC" were close to those recorded with SWHC" values estimated from the plots (11.5, 11.7, and 18.6% for Fagus sylvatica, Quercus petraea and Picea abies, respectively). We conclude that SWHC" can be mapped using basic soil parameters collected from plots, the predictive ability of the map and of data derived from the plot being close. Thus, the map could be used just as well for small areas as for large areas, directly or indirectly through water balance indices, to predict forest growth an...

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“…When selecting suitable species, site productivity is the most important information; it is affected by environmental factors, such as topography, geology (e.g. Kayahara et al,1998;Mitsuda et al, 2007;Mitsuda and Ito, 2015;Wang and Klinka,1996). Climatic conditions are also a critical factor determining site productivity at the macro scale; therefore, the relationships between site productivity and climatic factors have been studied (e.g.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Growth Responses to Climatic Conditions of Sugi (<i>Cryptomeria japonica</i>) and Hinoki (<i>Chamaecyparis obtusa</i>) Using a Carbon Balance-Based Growth Model

Mitsuda

Hosoda

Iehara

2017

FORMATH

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Abstract:This paper aimed to reveal differences in growth response to climatic condition between sugi (Cryptomeria japonica) and hinoki (Chamaecyparis obtusa) by comparing parameters of a carbon balancebased growth model. Seven carbon balance-based growth model parameters related to the rate of photosynthesis, temperature and humidity controls were parameterized by a Bayesian calibration using growth data derived from permanent plots. A hierarchical Bayes model was applied to this parameterization, where the common parameter was estimated first and the species-specific parameters were derived from the common parameter thereafter. Simulated light-response curves using estimated parameters were similar and the photosynthetic rate of sugi was higher than that of hinoki. The patterns of response to temperature represented by estimated parameters were different between sugi and hinoki; sugi responded rapidly to changes in temperature while hinoki responded relatively slowly, resulting in a higher tolerance to low temperature than sugi. Estimated parameters indicated that the photosynthetic rate controlling function for humidity could not be implemented for either species in the model. The parameterization of a carbon balance-based growth model revealed differences in the potential photosynthetic ability and response to temperature of sugi and hinoki. However, we could not represent the control effect of humidity on the photosynthetic rate.

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Predicting the site index of sugi plantations from GIS-derived environmental factors in Miyazaki Prefecture

Cited by 39 publications

References 29 publications

High Resolution Site Index Prediction in Boreal Forests Using Topographic and Wet Areas Mapping Attributes

High Resolution Site Index Prediction in Boreal Forests Using Topographic and Wet Areas Mapping Attributes

Mapping soil water holding capacity over large areas to predict potential production of forest stands

Comparison of Growth Responses to Climatic Conditions of Sugi (<i>Cryptomeria japonica</i>) and Hinoki (<i>Chamaecyparis obtusa</i>) Using a Carbon Balance-Based Growth Model

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