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基于地理加权泊松模型的天然次生林进界株数空间分布与预测

张凌宇,刘兆刚*   

  1. (东北林业大学, 哈尔滨 150040)
  • 出版日期:2018-05-18

Prediction and spatial distribution of recruitment trees of natural secondary forest based on geographically weighted Poisson model.

ZHANG Ling-yu, LIU Zhao-gang*   

  1. (Northeast Forestry University, Harbin 150040, China).
  • Online:2018-05-18

摘要: 基于帽儿山实验林场2004—2016年森林资源二类调查固定样地(共108块)数据,采用全局Poisson模型和4种空间尺度(2.5、5、10、15 km)下的地理加权泊松模型(geographically weighted Poisson regression, GWPR)对天然次生林进界株数的空间分布进行了研究,并对5种模型的拟合效果以及影响林分进界株数的因子进行了分析,利用莫兰指数描述了模型残差在全局和局域两种水平上的空间自相关性.结果表明: 本文所选的林分及地形因子都显著影响天然次生林进界株数的空间分布,林分平均胸径是最主要的影响因子;在小尺度(2.5 km)下GWPR模型拥有很高的拟合精度,产生了最大范围的模型参数估计值,得到了较好的模型参数局域化空间分布效果;在较小尺度(2.5和5 km)下GWPR模型产生了较小范围的模型残差,模型的稳定性得到提升;在小尺度(2.5 km)下GWPR模型残差的全局空间自相关性达到最低,局域空间自相关性显著减小,并形成了不同观测值少量聚类这一理想的空间分布模式;在对进界株数空间分布的模拟效果上,小尺度(2.5 km)下的局域模型明显好于全局模型.

关键词: 进界株数, 空间分布, 空间尺度, 地理加权泊松模型, 莫兰指数

Abstract: Based on the data collected from 108 permanent  plots of the forest resources survey in Maoershan Experimental Forest Farm during 2004-2016, this study investigated the spatial distribution of recruitment trees in natural secondary forest by global Poisson regression and geographically weighted Poisson regression (GWPR) with four bandwidths of 2.5, 5, 10 and 15 km. The simulation effects of the 5 regressions and the factors influencing the recruitment trees in stands were analyzed, a description was given to the spatial autocorrelation of the regression residuals on global and local levels using Moran’sI. The results showed that the spatial distribution of the number of natural secondary forest recruitment was significantly influenced by stands and topographic factors, especially average DBH. The GWPR model with small scale (2.5 km) had high accuracy of model fitting, a large range of model parameter estimates was generated, and the localized spatial distribution effect of the model parameters was obtained. The GWPR model at small scale (2.5 and 5 km) had produced a small range of model residuals, and the stability of the model was improved. The global spatial autocorrelation of the GWPR model residual at the small scale (2.5 km) was the lowest, and the local spatial auto-correlation was significantly reduced, in which an ideal spatial distribution pattern of small clusters with different observations was formed. The local model at small scale (2.5 km) was much better than the global model in the simulation effect on the spatial distribution of recruitment tree number.

Key words: geographically weighted Poisson model (GWPR), recruitment trees, Moran’s I., spatial distribution, spatial scale