[Spatio-temporal Variation in Monthly Vegetation NPP and Its Responses to Climate and Terrain Factors in Chang-Zhu-Tan Green Heart Area].

The Chang-Zhu-Tan ecological Green Heart area is the largest urban agglomeration Green Heart area in China. To clarify the spatiotemporal changes and driving factors of net primary productivity （NPP） of vegetation in the Chang-Zhu-Tan Green Heart area, an improved Carnegie Ames Stanford Approach （CASA） model was used to estimate the monthly vegetation NPP from 2011 to 2020 based on measured and remote sensing data. With the help of ArcGIS 10.2 software, linear regression and spatial partial correlation analysis were used to explore the spatiotemporal variations in monthly vegetation NPP and its responses to climate and terrain factors. The results showed that the monthly vegetation NPP in the Chang-Zhu-Tan Green Heart area showed an overall trend of first increasing and then decreasing, with an average vegetation NPP growth rate （measured in C, the same below） of 5.75 g·（m²·month）^-1 from January to July and a monthly decrease rate of 2.64 g·（m²·month）^-1 from August to December. The spatial distribution of monthly vegetation NPP in the Green Heart area showed a significant difference, with a coefficient of variation ranging from 34.52% to 48.76%. The high values of vegetation NPP in each month were concentrated in the ecological core areas of the central and eastern parts in the Green Heart area, whereas the low values were mainly distributed along the Xiangjiang River in the western part and in the narrow and elongated areas in the southern part of the Green Heart Area. The impact of climate and terrain factors on the monthly NPP of vegetation in the Green Heart area had both positive and negative inhibitory effects. Among climate factors, solar radiation was the major controlling factor for monthly vegetation NPP changes. The impact of terrain factors on vegetation NPP was relatively weak, with slope orientation having a greater positive impact on vegetation NPP than altitude and slope.