基于景观尺度的黄土丘陵区植被覆盖时空变化——以山西省晋城市为例

doi:10.13686/j.cnki.dzyzy.2022.04.010

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摘要植被覆盖是评价区域生态环境状态的主要影响因子.基于景观生态学的分析方法，以2006-2015年的MOD13Q1遥感数据为基础，获取植被覆盖指数，对山西省晋城市植被覆盖时空变化进行研究.结果表明:研究区植被覆盖呈现增加趋势，主要增加的类型为高植被覆盖.植被覆盖指数分布与高程和坡度正相关，即高程越高、坡度越大的地区植被覆盖指数越高.在景观尺度上，植被覆盖的类型呈现出一种集中趋势，即景观内部异质性和多样性降低，高植被覆盖斑块对景观的支配程度加强且逐渐处于优势地位.在斑块类型尺度上，以高植被覆盖为主，且斑块所占比例呈增加趋势.此外高植被覆盖景观破碎程度减小，复杂度降低，形状逐渐规则.

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关键词 ：遥感, MOD13Q1数据, 植被覆盖, 景观格局, 黄土丘陵区, 山西省

Abstract：Vegetation coverage is the main factor affecting the evaluation of regional eco-environment. Through the landscape ecological analysis method, the vegetation coverage index (VCI) is acquired to study the spatiotemporal variation of vegetation coverage in Jincheng City of Shanxi Province based on the MOD13Q1 remote sensing data during 2006-2015. The results indicate that the vegetation coverage show an increasing trend, dominated by high coverage vegetation. The distribution of VCI is positively correlated with elevation and slope, i.e., the higher elevation and the steeper slope, the greater VCI. The vegetation coverage types show a concentrated tendency at landscape scale, while the internal heterogeneity and diversity of landscape are reduced, the high vegetation coverage patch becomes more dominant and gradually superior. At patch scale, high coverage vegetation is dominant, with increasing proportion of patch. Furthermore, the high coverage vegetation is characterized by reduced landscape fragmentation and complexity, and gradually regular shape.

Key words： remote sensing MOD13Q1 data vegetation coverage landscape pattern hilly loess region Shanxi Province

收稿日期: 2021-02-23

PACS:

X171.1

基金资助:中国地质调查局项目"东北边境珲春-虎头地区综合地质遥感调查"(DD20208015).

作者简介: 曹会(1987-),女,硕士,工程师,主要从事遥感解译工作,通信地址黑龙江省牡丹江市东安区卧龙街45号,E-mail//22234540@qq.com

引用本文:

曹会, 刘立文, 李雨珂, 温秋园. 基于景观尺度的黄土丘陵区植被覆盖时空变化——以山西省晋城市为例[J]. 地质与资源, 2022, 31(4): 530-538,529.
CAO Hui, LIU Li-wen, LI Yu-ke, WEN Qiu-yuan. SPATIOTEMPORAL VARIATION OF VEGETATION COVERAGE ON LANDSCAPE SCALE IN HILLY LOESS REGION: A Case Study of Jincheng City in Shanxi Province. GEOLOGY AND RESOURCES, 2022, 31(4): 530-538,529.

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