| 汪智泳,袁伟韬,曾程瑶,任 畑,蒙 颖,邹离离.基于GEE的喀斯特地区植被覆盖时空变化及影响因素分析[J].林业调查规划,2026,51(2):106-113 |
| 基于GEE的喀斯特地区植被覆盖时空变化及影响因素分析 |
| Spatiotemporal Variations of Vegetation and Influencing Factors in Karst Regions Based on Google Earth Engine |
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| DOI： |
| 中文关键词: 植被覆盖度 时空变化 GEE平台 地理探测器 影响因素 喀斯特地区 |
| 英文关键词: vegetation coverage spatiotemporal variation Google Earth Engine geographic detector influencing factors karst region |
| 基金项目:贵州省科技计划项目(黔科合支撑[2023]一般176). |
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| 中文摘要: |
| 植被覆盖度变化可以揭示喀斯特地区生态系统的脆弱性和抗干扰能力。基于谷歌地球引擎
云平台,获取2003—2022年30 m分辨率Landsat地表反射率数据,运用像元二分模型估算植被覆盖度,采用趋势分析法、MK检验等方法对思南县2003—2022年植被覆盖度时空变化进行定量分析,同时利用地理探测器分析其空间分异性的地理因子。结果表明,2003—2022年间,思南县植被覆盖度持续改善,总体呈现增长趋势,该地区的多年平均植被覆盖度较高,空间分布特点为“中心低、四周高”;植被覆盖度变化趋势主要以显著改善区域为主,植被显著退化的区域多集中在人口密集区;植被覆盖度影响因素按重要性排序为:年均温>高程>土地利用>土壤类型>年均降水量>人口密度>GDP>植被类型>坡向>坡度。植被覆盖度的空间分异性受年均温与土壤类型的交互影响最为显著。 |
| 英文摘要: |
| Variations of vegetation coverage can reveal the vulnerability and resilience of ecosystems in karst regions. Based on the Google Earth Engine cloud platform, Landsat surface reflectance data with a 30 m resolution from 2003 to 2022 were obtained. The pixel dichotomy model was employed to estimate vegetation coverage, and the trend analysis and the Mann-Kendall test were applied to quantitatively analyze the spatiotemporal changes of vegetation coverage in Sinan County from 2003 to 2022. Additionally, the geographic detector method was used to analyze geographical factors contributing to its spatial heterogeneity. The results showed that from 2003 to 2022, vegetation coverage in Sinan County continuously
improved, showing an overall increasing trend. The multi-year average vegetation coverage was relatively high, with a spatial distribution characteristic of “low in the center and high in the periphery”. The main change trend of vegetation coverage was significant improvement, while areas with significant vegetation
degradation were mostly concentrated in densely populated regions. The importance of factors influencing vegetation coverage, in descending order, was: annual average temperature>elevation>land use>soil type>annual average precipitation>population density>GDP>vegetation type>aspect>slope. The spatial heterogeneity of vegetation coverage was most significantly affected by the interaction between annual average temperature and soil type. |
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