Regional estimation of Japanese cedar (Cryptomeria japonica D. Don) productivity by use of digital terrain analysis

Abstract

Digital terrain modeling and spatial climatic data have been used to estimate the spatial distribution of Japanese cedar (Cryptomeria japonica D. Don) forest productivity on a regional-scale. The study was conducted on Japanese cedar forests in Himi city, Oyabe city, Takaoka city, and Imizu city (a total area of 683 km 2 ) in northwestern Toyama Prefecture. On the basis of data from 146 sample stands, above-ground net primary productivity (ANPP) was calculated from tree height, age, and density using existing ANPP conversion equations for Japanese cedar stands. Six topographic factors (slope, profile curvature, plan curvature, openness, wetness index, and topographic radiation index) were calculated from a 10-m cell size digital elevation model. Three climatic factors (annual mean temperature, annual total precipitation, and annual maximum snow depth) were obtained from an existing spatial data set. Relationships between ANPP and environmental factors were analyzed by regression tree models. For the tree model with ANPP as a dependent variable, four environmental factors (annual mean temperature, wetness index, openness, topographic radiation index) were adopted as independent variables. Annual mean temperature was the first split variable in this model and explained 25.5% of the total deviance in ANPP. Wetness index, which represents soil moisture variation caused by lateral flow, explained 11.5% of the total deviance in ANPP. The resulting tree model explained approximately half of the total deviation in ANPP and indicated that the spatial distribution of Japanese cedar productivity was controlled by regional-scale interactions between climatic and topographic processes. A high-resolution map of productivity was prepared by use of the ANPP prediction model and vegetation information obtained from satellite data.

Preliminary Analysis on Site Index of Sugi (Cryptomeria japonica) Planted Forests Using the National Forest Inventory Data in Kyushu Island

“…Mitsuda et al (2007) reported relatively high values for the correlation coefficient with SRI, logUCA, and VTEX as −0.615, 0.449, and −0.409, respectively. Other studies have shown that topographic factors were good predictors of site index of sugi planted forests (e.g., Takeshita,1964;Chen and Abe,1999;Minowa et al, 2005;Zushi, 2007). In addition, topographic factors were considered as important explanatory variables of site index prediction models for other species (e.g., McNab,1989;Iverson et al, 1997;Mitsuda et al, 2001).…”

“…In addition, topographic factors were considered as important explanatory variables of site index prediction models for other species (e.g., McNab,1989;Iverson et al, 1997;Mitsuda et al, 2001). Annual mean temperature was the most representative variable to predict site productivity of sugi planted forest in the northwestern Toyama Prefecture (Zushi, 2007). Takeshita et al (1967) earlier reported that annual precipitation was an important factor that could be used in predicting site indices of sugi planted forests with wide areas.…”

“…The topographic factors of each plot were derived from the 50-m resolution digital elevation model published by the Geographic Survey Institute, Japan. Annual mean temperature and annual precipitation at a 1-km resolution were used as climatic factors, because past studies listed these two factors as important climatic factors to predict sugi site productivity (e.g., Zushi, 2007). The climatic factors of each plot were acquired from the average climatic data of a 1-km grid published by the Japan Meteorological Agency.…”

“…Several papers have previously described the relationships between the site indices of various species and topographic factors derived from digital terrain analysis using digital elevation models(e.g., McNab,1989;Iverson et al, 1997;Mitsuda et al, 2001;2007;Minowa et al, 2005). Topographic factors can serve as good predictors of a site index model for a relatively smaller area, whereas climatic factors should be considered in modeling a site index for a relatively wider area.…”

Relationship between the Parameter of Photosynthetic Rate of a Process-Based Growth Model for Sugi (<i>Cryptomeria japonica</i>) Planted Forests and Topographic Factors

“…There are two main approaches to developing a model for predicting site productivity: the empirical regression model-based approach (e.g., Iverson et al, 1997;McNab, 1989;Minowa et al, 2005;Mitsuda et al, 2001;2007;Monserud et al, 1990;Zushi, 2007) and the process-based model based approach (e.g., Coops and Waring, 2001;Coops et al, 1998;Tickle et al, 2001). In the empirical regression model approach, a site index of target species (an index representing site productivity defined by the height of the dominant trees at a specific reference age; Davis and Johnson, 1987) is generated using a linear regression model with environmental factors as explanatory variables.…”