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| 基于kNDVI的云南省植被变化特征及预测研究 |
| Study on Vegetation Change Characteristics and Prediction in Yunnan Based on kNDVI |
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| DOI： |
| 中文关键词: 核归一化差异植被指数 植被变化特征 驱动机制 时空预测 云南省 |
| 英文关键词: Kernel normalized difference vegetation index（kNDVI） Vegetation change characteristic Driving mechanism Spatiotemporal prediction Yunnan |
| 基金项目:国家自然科学基金资助项目（41961053）；云南省重大科技专项计划基金资助项目（202202AD080010） |
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| 中文摘要: |
| 以云南省为研究区，基于归一化差异植被指数（NDVI）构建核归一化差异植被指数（kNDVI），通过相关性分析揭示kNDVI驱动机制，并利用CA-Markov模型预测2020—2025年植被变化趋势。结果表明：kNDVI整体呈上升趋势，增长率为0005，空间上表现为由东北向西南递增，且对低植被覆盖地区的变化更敏感；相关性分析显示，相对湿度对植被影响最大，温度和降水量对植被影响较小。kNDVI与人口密度、生产总值呈现较强的相关性，农业总值、牧场面积和农田面积与kNDVI呈负相关，其中牧场面积负相关性最强。未来云南省低、较低和较高植被类型覆盖区面积均减少，高植被类型区域呈增加趋势。 |
| 英文摘要: |
| Taking Yunnan as the research area， this research established kernel normalized difference vegetation index（kNDVI） based on normalized difference vegetation index（NDVI）， revealed the driving mechanism of kNDVI by correlation analysis， and predicted the vegetation change trend from 2020 to 2025 by CA Markov model. Results showed that kNDVI presented an overall upward trend with a growth rate of 0.005， spatially increasing from northeast to southwest， and was more sensitive to changes in areas with low vegetation coverage. Correlation analysis indicated that relative humidity had the greatest impact on vegetation， while temperature and precipitation had relatively minor effects. kNDVI had a strong correlation with population density and GDP， and a negative correlation with total agriculture value， pasture area and farmland area， among which pasture area had the strongest negative correlation. In the future， the coverage area of low， relatively low and relatively high level vegetation types in Yunnan would all decrease， while the areas with high level vegetation types would showed an increasing trend. |
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