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Wang D.-Z.,Agricultural University of Hebei | Zhang D.-Y.,Agricultural University of Hebei | Jiang F.-L.,Agricultural University of Hebei | Bai Y.,Chengde Bureau of Environmental Protection of Hebei | And 2 more authors.
Chinese Journal of Applied Ecology | Year: 2015

It is often difficult to estimate site indices for different types of plantation by using an ordinary site index model. The objective of this paper was to establish a site index model for plantations in varied site conditions, and assess the site qualities. In this study, a nonlinear mixed site index model was constructed based on data from the second class forest resources inventory and 173 temporary sample plots. The results showed that the main limiting factors for height growth of Larix principis-rupprechtii were elevation, slope, soil thickness and soil type. A linear regression model was constructed for the main constraining site factors and dominant tree height, with the coefficient of determination being 0.912, and the baseline age of Larix principis-rupprechtii determined as 20 years. The nonlinear mixed site index model parameters for the main site types were estimated (R2> 0.85, the error between the predicted value and the actual value was in the range of -0.43 to 0.45, with an average root mean squared error (RMSE) in the range of 0.907 to 1.148). The estimation error between the predicted value and the actual value of dominant tree height for the main site types was in the confidence interval of [-0.95, 0.95]. The site quality of the high altitude-shady-sandy loam-medium soil layer was the highest and that of low altitude-sunny-sandy loam-medium soil layer was the lowest, while the other two sites were moderate. © 2015, Editorial Board of Chinese Journal of Applied Ecology. All right reserved. Source


Jing X.,Chengde Bureau of Environmental Protection of Hebei | Jing X.,Chinese Academy of Forestry | Cao L.,Chengde Bureau of Environmental Protection of Hebei | Guo Z.,Xinjiang Academy of Forestry | And 4 more authors.
Chinese Journal of Applied and Environmental Biology | Year: 2015

The distribution of vegetation often changes along habitat gradient. To a large extent, the patch mosaic pattern of vegetations in a landscape can reflect the spatial heterogeneity of habitats. In the heterogeneous landscapes of mountainous areas, topography is regarded as the most important factor of restricting vegetation distributions. In this paper, our aim was to explore the general distribution of major vegetation types with the variation of topography, and to select the optimum combination of topographic factors for each vegetation type, so that rational conservation management plan can be made based on the optimum mosaics of vegetation types in the landscape. Filed sample plot investigation was carried out to get vegetation data. Topographic factors were calculated by digital elevation model (DEM). Vegetation types were classified by ground investigation and remote sensing imagery interpretation. The relationship between vegetation distribution and topographic factors was analyzed by overlaying the remote sensing classification pixel map and maps of slope, aspect, altitude, and profile curvature. The vegetation in Xiaodonggou of Altai Mountains could be classified into five types including coniferous forest, broadleaved forest, conifer-broadleaf mixed forest, shrubs, and grassland. The topographic features in the study region are as follows: the slope mainly ranges from 15° to 35°, accounting for 64.32% of the study area. West-south aspect is the main aspect, followed by east-north aspect, accounting for 15.02% and 14.78% respectively of the study area. There are lesser areas in east-south and south aspect, accounting for 9.30% and 10.20% of the study area respectively. In the Xiaodonggou of Altai Mountains, the main altitude gradient ranges from 1 200 m to 2 000 m, accounting for 87.19% of the study area. The value of profile curvature ranges from 5° to 10°, accounting for 33.31% of the study area, while the ranges from 3° to 5° and from 0° to 3°, accounting for 22.84% and 22.44% of the study area respectively. The optimal (high frequency of distribution) combination of topographic factors for different vegetation types are as follows: coniferous forest mainly distributed in slopes from 15° to 35° on west-north aspects with altitude of 1 800 m to 2 000 m, and profile curvature from 0° to 3°. Broadleaved forest is mainly distributed in slopes from 15° to 35° on west-north aspects, with altitude of 1 400 m to 1 600 m, and profile curvature of 5° to 10°. Conifer-broadleaf mixed forest is mainly distributed in slopes from 15° to 35° on north aspects, with altitude of 1 600 m to 1 800 m, and profile curvature from 5° to 10°. The optimal combination of topographic habitat for shrubs is in slopes from 15°to 35° on west aspects, with altitude of 1 400 m to 1 600 m, and profile curvature from 5° to 10°. The optimal combination of topographic habitat for grassland distribution is in slopes from 15° to 35° on south aspects, with altitude from 1 200 m to 1 400 m, and profile curvature from 5° to 10°. The results of this paper can be an important reference for biodiversity conservation, resources utilization and sustainable management planning at landscape scale. © 2015, Science Press. All rights reserved. Source


Jing X.,Chengde Bureau of Environmental Protection of Hebei | Jing X.,Chinese Academy of Forestry | Cao L.,Chengde Bureau of Environmental Protection of Hebei | Zang R.,Chinese Academy of Forestry
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2013

Biodiversity is the basis of ecosystem functioning. Species richness has been widely used in biodiversity studies. Understanding the spatial distribution of species richness at landscape scale is vitally important in biodiversity conservation and natural resources management. Predicting species richness on large scale could help managers to rationally conserve and utilize natural resources. With the availability of remotely sensed data and the development of geographical information system (GIS) techniques spatial analysis on large scale has been possible. Integrating field sample plot investigation, remote sensing (RS), and geographic information system is a novel way to explore the distribution of species richness at macro spatial scales. The Altai Mountains is one of the magnificent Mountains of Asia, which distributes across Mongolia, China, Kazakhstan, and Russia. In this study, we adopted the above mentioned approach to predict the spatial distribution of tree species richness in Xiaodonggou forest region of the Altai Mountains in Xinjiang, Northwest China. In the south and north slope of the Xiaodonggou forest region, a investigation transect was selected respectively. In each of the transect, we set investigation plots (each was 20 m×20 m in size) at intervals of 50 m along the altitude. All woody plants in the plots with diameter at breast height (DBH) ≥1cm were identified and measured. The species richness in each plot was calculated. Normalized difference vegetation index (NDVI) was obtained from ETM+ image. In order to overlay ETM+ image and topographic factor maps, we selected ETM + image in size of 30 m×30m. The predictor variables include climate, topography, and NDVI. Principle component analysis (PCA) and multiple linear regression were firstly utilized for obtaining the environmental factors and developing the predictive model of species richness distribution. Annual minimum temperature, annual average relative humidity, aspect, slope and NDVI were selected into the predictive model. Tree species richness distribution map was produced by GIS. The residual map was produced by the inverse distance weighted interpolation (IDW) method. The residual map was used to evaluate the validity of the model. In order to analyze variation of species richness with the topographic factors, the spatial distribution map of species richness was overlaid with the slope, aspect and elevation maps, respectively. The results showed that the areas with 3-4 tree species occupied 70.08% of the total study region. In slopes of 0- 5°, the areas with tree species richness of 3 had the highest presence frequency, while in slopes of other ranges, the areas with tree species richness of 4 had the highest presence frequency. In west and northwest aspects, the areas with three tree species had the highest presence frequency, in the other aspects, however, the areas contained four tree species had the highest presence frequency. Along with the altitudinal gradient, the tree species richness showed a unimodal distribution pattern, which is consistent with the hypothesis of mid-domain effect. The statistic results of residual types showed that strongly predicting area and moderately predicting area together reached 94. 62% of the total study area, which implied that our predictive model was robust and could be successfully implemented in this forest region. Source

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