Abstract:

Based on the GWR (geographically weighted regression) model supported by ArcGIS, the research explores the multi-scale relationship between vegetation change, climatic factors, and the sensitivity of vegetation to climate factors using AVHRR vegetation cover data combined with temperature and precipitation data in China from 1982–2010. Compared with the general linear regression (Ordinary Least Square, OLS) model, GWR gives a much better fitting result, with the goodness of fit increased from 0.3 to 0.6. The relationship between NDVI, annual rainfall, and average annual temperature has a significant spatial heterogeneity. Regression coefficients of climatic factors decrease from north to south and are higher in the northwest dry region of China. Temperature is more influential than rainfall on NDVI in most areas of China. Each ecological zone has different spatial scales when NDVI and the climatic factors maintain a stable relationship.

Key words: NDVI, climatic factors, geographically weighted regression (GWR), China

摘要：

在ArcGIS支撑下, 基于1982—2010年8 km分辨率的AVHRR NDVI及气温和降水数据, 应用最小二乘法和地理加权回归方法, 构建中国NDVI与气候因子的地理加权回归模型, 定量分析中国NDVI与气温和降水的相互关系, 获取各个回归参数的空间格局, 并将模拟结果与全局性回归结果进行对比。结果表明, 与线性回归模型相比, 地理加权回归模型的拟合效果显著提高, 拟合优度从0.3提高到0.6。气候因子对NDVI的影响具有空间异质性: 从北到南, 气候因子对NDVI的影响逐渐减小; 西北内陆等干旱荒漠地带, 气候因子对NDVI的影响较大。对中国大部分地区而言, 气温对NDVI的影响超过降水。各区NDVI与主导气候因子发生作用的特征尺度不同。

关键词: NDVI, 气候因子, GWR 模型, 中国