耦合恢复力的林区土地生态适宜性评价——以吉林省汪清县为例

doi:10.11821/dlxb201503010

摘要/Abstract

摘要：

本研究试图将植被变化与恢复能力纳入林区土地生态适宜性评价中,从空间动态的角度对典型林业县土地生境进行生态适宜性评价;并综合应用人工神经网络、趋势分析与偏相关分析、R/S分析等手段,分别进行生境质量评价、植被变化与驱动分析、趋势可持续性判断。基于流域单元的评价结果良好展示了研究区土地生态适宜性分异,即自然生境本底条件较差、人类活动强度较大的低适宜区域,和有较好生态本底条件、可持续生态恢复能力的高适宜区域。在研究整体概念框架上,本研究提出将判定持续性的Hurst指数作为恢复力的空间表达手段,可以有效对具体区域林业管理进行实际应用;在具体方法上,研究所采用的SOFM神经网络聚类、Theil-Sen Median 趋势分析、Mann-Kendall 检验等数理计量手段可以有效降低主观评价导致的不确定性。

关键词: 土地生态适宜性, 恢复力, 生境质量, 偏相关, Hurst指数

Abstract:

This article tries to examine the changing trend and sustainability of vegetation according to the resilience concept. Then it analyzes the wood land habitats in a typical forest county from a temporal-spatial perspective. Moreover, artificial neural network, trend analysis, partial correlation analysis and R/S analysis were adopted to respectively evaluate the habitat quality, the change of vegetation, the driving force and the judgment of trend dependence. The result shows that there exists good ecological suitability on watershed level. The low ecological suitable region is in the habitat with a poor natural background and intense human activity, while the high ecological suitable region has good ecological conditions in the habitats and the sustainable ability in ecological resilience. This study uses Hurst index to judge the persistence as the spatial expression of resilience. In this concept system, the resilience perspective can be applied in specific regional forestry management. Compared with the subjective evaluations, the mathematical measurement methods used in this article, such as neural network clustering of SOFM, the trend analysis of Theil-Sen Median, and the significance test of Mann-Kendall, can effectively reduce the uncertainty.

Key words: forest land, resilience, habitat quality, partial correlation, Hurst index

刘焱序, 王仰麟, 彭建, 魏海, 宋治清, 张小飞. 耦合恢复力的林区土地生态适宜性评价——以吉林省汪清县为例[J]. 地理学报, 2015, 70(3): 476-487.

Yanxu LIU, Yanglin WANG, Jian PENG, Hai WEI, Zhiqing SONG, Xiaofei ZHANG. Land ecological suitability assessment for forest coupled with the resilience perspective: A case study in Wangqing county, Jilin province, China[J]. Acta Geographica Sinica, 2015, 70(3): 476-487.

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指标因子	敏感因素	数据来源	归一化方法
高程	生物多样性	地理空间数据云,30m分辨率GDEM	根据分布规律将平均值加减2倍的标准差作为上下限,则1073以上为1,336以下为0,在此区间内正向归一化
坡度	土壤侵蚀	同上,ArcGIS中生成坡度	25°以上为1,2°以下为0,区间内正向归一化
坡向	生物多样性	同上,ArcGIS中生成坡向	南为0,东南、西南0.25,东、西、平地0.5,东北、西北0.75,北为1
覆被类型	土壤侵蚀,生物多样性	寒区旱区科学数据中心,中国植被功能型数据^[22],合并部分地类	建设用地为1,耕地0.75,草地0.5,针叶林0.2,针阔混交林0.15,阔叶林0.1、湿地、水体0
土壤有机质	土壤侵蚀,生物多样性	世界土壤数据库（HWSD）的中国土壤数据集(v1.1)^[23],提取土壤有机碳含量代表有机质	按分布规律将表土有机碳0.74%以下赋为1,1.95%以上赋为0,区间内反向归一化
降雨侵蚀力	土壤侵蚀	中国气象科学数据共享服务网,插值时取全国671个站点,再将结果裁切	由分布规律知1300~2100 MJ·mm/(ha·h·a)间的像元占95.39%,其余较零散。因而设定2100以上为1,1300以下为0,区间内正向归一化

因子	不滞后	滞后一个月	因子	不滞后	滞后一个月
极端最低气温	0.91^**	0.91^**	平均气温	0.91^**	0.89^**
平均最低气温	0.92^**	0.89^**	降水量	0.64^**	0.65^**
极端最高气温	0.85^**	0.87^**	平均水汽压	0.93^**	0.84^**
平均最高气温	0.91^**	0.88^**	平均相对湿度	0.72^**	0.46^**

分区	组合关系
高适宜区	高质量生境—高度持续恢复,较高质量生境—高度持续恢复,高质量生境—持续恢复
较高适宜区	高质量生境—可能恢复,较高质量生境—持续恢复,中等质量生境—高度持续恢复, 中等质量生境—持续恢复
中等适宜区	较高质量生境—可能恢复,中等质量生境—可能恢复,低质量生境—高度持续恢复, 低质量生境—持续恢复
较低适宜区	高质量生境—退化与可能退化,较高质量生境—退化与可能退化,低质量生境—可能恢复
低适宜区	中等质量生境—退化与可能退化,低质量生境—退化与可能退化