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The Application of Quadrat Method in Parameters of Forest Spatial Structure Investigation

Author: ZhouHongMin
Tutor: HuiGangYing
School: Chinese Academy of Forestry
Course: Forest cultivation
Keywords: Spatial structure Boundary correction 4th neighborhood distance determination method Sampling method Optimum plot size
CLC: S718.5
Type: Master's thesis
Year: 2009
Downloads: 153
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Analysis of spatial structure is based on spatial structure of an accurate description. Spatial structure analysis of the data processing and individual tree growth model Delete plot involves a reasonable boundary forest problem, and trees will be border border effect, therefore, it is necessary to stand forest boundary correction method is used. Currently, access to three spatial structure parameters (angle index, mingling and size than a few) is the main method used by the typical large plots (large standard ground) per timber positioning data analysis methods and non-sampling methods of analysis sample locations. There is no sampling method is given how to obtain the correct method of forest spatial structure parameters, the traditional sampling method can be used stand spatial structure research, conducting spatial structure parameters used when investigating how much plot size and quadrat The number has not been determined, therefore, the number of how many quadrats taken and the size of the study area spatial structure has become the most important technical issues facing investigation. In this paper, spatial structure analysis of plot boundaries wood processing method and spatial structure of the optimal parameter survey plot size and number two aspects of the study. In spatial structure analysis of plot boundaries wood processing method of the study, we propose a new method to eliminate border effects - 4th neighborhood distance determination method. On this basis, to a field survey plots and 10 100 m × 100 m analog sample material for the study, from the spatial structure parameter size than the number of mingling and angular scale and number of trees involved in the calculation of the proportion of other aspects of distance buffer, eight neighborhood symmetrical, eight neighborhood Translational and 4th neighborhood determination method from four kinds of boundary correction methods were compared. The results showed that: (1) from the buffer method, eight neighborhood Translational neighborhood distance and 4 determine the spatial structure analysis method is superior to eight neighborhood symmetrical; (2) 4th neighborhood distance determination method in the plot applicability of shape than eight neighborhood Translational; (3) 4th neighborhood distance determination method is superior in terms of use of information from the buffer method. In the spatial structure of the optimal parameter survey plot size and number of studies, using 30 100m × 100m analog samples for study materials from forest spatial distribution pattern consistent rate relationship subplots simulation study and an block 100m × 100m field survey plots, for example, verify that the new plots to determine the reasonableness and feasibility; based on actual survey plots to investigate time-consuming analysis and comparison between the survey quadrats consuming and cost, and to determine the optimum The plot size and number of plots. The results showed that: (1) spatial structure parameters extracted angular scale and plot size quadrat size and quantity. As the plot area increases and the increase in the number quadrat, the estimated spatial structure of trees were stabilized; (2) from the forest spatial pattern of view, plot size is 10m × 10m sampling number is 42, plot size is 15m × 15m sampling number is 23, plot size is 20m × 20m sampling number is 12, plot size is 25m × 25m sampling number is 9, plot size is 30m × 30m sampling number is 4 expression can accurately stand the spatial distribution pattern; (3) In considering the time-consuming and cost survey, the plot size is 30m × 30m, the number of samples to 4:00 for the spatial structure more appropriate survey quadrat survey area and the number of investigations.

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CLC: > Agricultural Sciences > Forestry > Forestry basic science > Forest Biology > Forest Ecology
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