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林业科学 ›› 2000, Vol. 36 ›› Issue (2): 26-32.doi: 10.11707/j.1001-7488.20000205

• 论文及研究报告 • 上一篇    下一篇

天然林区小班森林资源数据的更新模型

杜纪山 唐守正 王洪良   

  1. 中国林业科学研究院资源信息研究所,北京100091;吉林省林业调查规划设计院,延吉133000
  • 收稿日期:1999-05-31 修回日期:1900-01-01 出版日期:2000-03-25 发布日期:2000-03-25

UPDATE MODELS OF FOREST RESOURCE DATA FOR SUBCOMPARTMENTS IN NATURAL FOREST

Du Jishan,Tang Shouzheng,Wang Hongliang   

  1. The research Institute of Forest Resource Information Techniques, CAF Beijing100091;Academy of Forest Inventory and Planning of Jilin Province Yanji 133000
  • Received:1999-05-31 Revised:1900-01-01 Online:2000-03-25 Published:2000-03-25

摘要:

以吉林省汪清林业局为例,根据1 997年森林经理调查的848块固定样地数据,与全林整体模型方法相结合,建立了适合于天然林区林业局(场)无人为干预小班森林资源数据更新的林分级生长模型组。该组模型包括林分密度指数、平均高、断面积、形高、郁闭度等林分测算因子的生长或变化模型。分别组成树种,以地位级指数、林分密度指数、预估间隔期作为自变量,更适用于天然混交林和异龄林小班的资源数据更新。当小班调查得到各组成树种的平均高、平均直径以及小班的年龄、每公顷株数、郁闭度、散生木蓄积等因子后,应用本文提出的林分级生长模型组可以实现无人为干预小班森林资源数据的全面更新和连续预测。

关键词: 生长模型, 小班, 更新, 天然林区

Abstract:

In order to update the forest resource data of subcompartment in forest bureau and forest farm in natural forest region without human disturbance, combined with integrated stand model, stand-level growth model group is developed based on 848 permanent plots of forest management inventory in 1997, taking Wangqing Forest Bureau in Jilin province as an example. The model group is made of many growth or change models used in stand measurement and calculating factors, such as stand density index, mean height, basal area, form-height, canopy density. By composing tree species of the subcompartment, taking site class index, stand density index, and prediction interval as independent variables for updating the data of natural growth subcompartment, the model group is more suitable for updating the data in the natural mixed-species and uneven-aged subcompartment. Given the stand factors of subcompartment, such as mean height and mean diameter of stand composed tree species, age, number of stems per hectare, canopy density, volume of open grown tree, the forest resource data of subcompartment without human disturbance can be updated completely and predicted continually by using the proposed model group.

Key words: Growth model, Subcompartment, Update, Natural forest