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哈尔滨市景观生态风险时空变化驱动力及分布预测 被引量:2

Spatial-temporal change driving forces and distribution prediction of landscape ecological risk in Harbin
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摘要 本文以哈尔滨市为研究对象,首先构建了景观生态风险及预测模型;然后对1998—2018年景观生态风险时空变化驱动力进行分析;最后对研究区域2023年景观生态风险空间分布进行预测。结果表明:(1)研究区域近20年景观生态风险先降低后升高,空间格局变化较为显著。(2)道路距离、第二产业、农业、高程为景观生态风险变化主要驱动力。1998—2008年,主城区周围道路距离系数逐年减小,南部地区逐年增高,其他时间内未见明显变化;农业和高程对区域景观生态风险的总体影响显著,存在空间差异;第二产业对区域景观生态风险影响程度呈现先增后降的变化。(3)预测所得的2023年景观生态风险空间差别不明显,降低区域比升高区域面积大。 This paper takes Harbin as the research object, constructs a landscape ecological risk and prediction model, analyzes the driving force of temporal and spatial change of landscape ecological risk from 1998 to 2018, and predicts the spatial distribution of regional landscape ecological risk in 2023. The results show that:(1)The regional landscape ecological risk decreased firstly and then increased in recent 20 years, and the spatial pattern changed significantly.(2)Road distance, secondary industry, agriculture and elevation are the main driving forces for the change of landscape ecological risk. From 1998 to 2008, the distance coefficient of roads decreased around the main urban area and increased in the south. No significant change was observed at other times.The overall impact of agriculture and elevation on regional landscape ecological risk was significant, and there were spatial differences. The degree of impact of secondary sector of the economy on regional landscape ecological risk increased first and then decreased.(3)The predicted spatial difference of landscape ecological risk in 2023 is not obvious, and the reduced area is larger than the increased area.
作者 张玉娟 曲建光 侯建国 ZHANG Yujuan;QU Jianguang;HOU Jianguo(College of Surveying and Mapping Engineering,Heilongjiang Institute of Technology,Harbin 150050,China)
出处 《测绘通报》 CSCD 北大核心 2022年第2期83-89,94,共8页 Bulletin of Surveying and Mapping
基金 黑龙江省博士后专项经费(LBH-Q20171) 黑龙江省省属本科高校基本科研业务费(2020CX01) 黑龙江工程学院省级领军人才梯队培育计划(2020LJ01) 国家自然科学基金(31800538)。
关键词 景观生态风险 生态服务价值 地理加权回归 灰色预测 哈尔滨市 landscape ecological risk ecological service value geographically weighted regression grey prediction Harbin city
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