Forest subcompartment classification method

Abstract

The invention relates to a forest subcompartment classification method. The method comprises the steps: enabling a clustering analysis idea and method to be applied in the subcompartment division research; directly comparing the qualities in dominant tree species (groups) with the consistent operation type and standing type or between samples; searching the growth dominant factors (standing conditions) of a forest subcompartment, and enabling the growth dominant factors (standing conditions) to serve as clustering factors, wherein the growth dominant factors (standing conditions) comprise the topographic conditions (slope, exposure and slope position) and soil conditions (soil thickness and humus thickness); employing the shortest distance clustering algorithm (shown in the description), wherein dkr, dpk and dqk are the distances between different clusters; taking the forest subcompartment as the classification object, and dividing all to-be-clustered forest subcompartment data in different classification modes or according to different classification numbers (two classes, three classes, four classes,..., or more classes); selecting the classification mode or classification number which enables the number of forest subcompartments in different classes are close to each other through combining with the factors that the internal difference in the same cluster is small and the difference between different clusters is remarkable; describing the features of forest subcompartments in each class, and guiding the actual production.

Description

[0001] 1. Technical field [0002] The invention relates to a method for classifying forest subcompartments, in particular to a method for classifying forest subcompartments of dominant tree species (groups) with the same management type and site type. [0003] 2. Technical background [0004] So far, the classification of forest sub-classes is mainly based on forest resources survey and monitoring work, and traditional modeling methods, and there is still no effective and mature method. The previous transmission method will cause a large error: [0005] ① Investigate errors caused by time. The survey process lasted for a long time, and the investigators made slight differences in soil thickness according to the sequence of surveys. [0006] ② Error caused by inaccurate observation position. Due to the influence of terrain and understory shrubs, the thickness of humus cannot be accurately measured, resulting in slight differences. [0007] ③ Error caused by different silvic...

AI Technical Summary

Problems solved by technology

[0010] In order to overcome the many troubles and errors that the existing classification methods inevitably bring in the forest small class classification process, the purpose of the present invention is to provide a reasonable idea of ​​cluster analysis and its application method

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