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| 基于NDVI和SIF的云南植被变化及预测研究 |
| Changes and Prediction of Vegetation in Yunnan Based on NDVI and SIF |
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| DOI： |
| 中文关键词: 归一化差异植被指数 日光诱导叶绿素荧光 植被变化 驱动机制 时空预测 云南省 |
| 英文关键词: Normalized difference vegetation index（NDVI） Solar induced chlorophyll fluorescence（SIF） Vegetation change Driving machanism 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: |
| 选取生物多样性热点地区云南省作为研究区，对归一化差异植被指数（NDVI）和日光诱导叶绿素荧光（SIF）数据进行监测和预测，揭示两者在监测植被变化中的差异性和互补性，通过相关性分析探讨云南省植被驱动机制，并采用BP神经网络模型和CA Markov模型对云南省植被变化进行时空预测。结果表明：时间上，NDVI和SIF均呈上升趋势；空间上，在较高植被覆盖地区SIF存在饱和现象。SIF在地形复杂区域对植被的响应更为准确。NDVI和SIF均与气象因子呈正相关，NDVI对温度更敏感，SIF对相对湿度更敏感。时序预测上，2020—2025年NDVI和SIF呈下降趋势；空间预测上，NDVI高和SIF较高类型区域减少。 |
| 英文摘要: |
| Taking Yunnan， a hotspot of biodiversity， as the research area， by monitoring and predicting normalized difference vegetation index（NDVI） and solar induced chlorophyll fluorescence（SIF）data， the differences and complementarities between the two in monitoring vegetation change were studied. The driving mechanism of vegetation was discussed by correlation analysis， and the temporal and spatial changes of vegetation were predicted by using BP neural network model and CA Markov model. The results indicated that both NDVI and SIF showed an upward trend with time. Spatially， SIF was saturated in areas with higher vegetation coverage and responded more accurately to vegetation in areas with complex terrain. Both NDVI and SIF were positively correlated with meteorological factors. NDVI was more sensitive to temperature and SIF was more sensitive to relative humidity. In term of temporal prediction， NDVI and SIF would decline from 2020 to 2025. In spatial prediction， the areas with high NDVI and higher SIF would decrease. |
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