中蒙俄经济走廊沿海与内陆NDVI分布特征

为了研究中蒙俄经济走廊沿海与内陆地区归一化植被指数（NDVI）的分布特征，利用MOD13Q1 NDVI数据集、MERRA2气象再分析数据和AVHRR土地覆盖数据，以中蒙俄经济走廊研究区内2种代表性植被（林地和树木茂盛的草地）为例，采用回归分析法和相关性分析法研究了近年来研究区NDVI在不同纬度、距海岸线不同距离及不同时间的变化特征，并分析了气温与降水对NDVI的影响。结果表明：2015—2019年内5月下旬—9月，沿海地区植被生长状态基本优于内陆地区，从距离海岸线500 km左右开始，NDVI明显下降；沿海地区（0~500 km）与内陆地区NDVI在时间变化上也略有不同，沿海地区2种代表性植被的NDVI时序变化模型拟合效果较好。受沿海与内陆地区降水、气温差异影响，在植被生长顶峰期（0719—0723），随着纬度上升，沿海与内陆地区NDVI差异逐渐减小：沿海地区NDVI在上升期比内陆地区约提前1个月进入饱和，下降期滞后约1个月；低纬度地区2种植被对降水量更为敏感，相关系数最高为0.393（显著性水平P<0.01），而高纬度地区两种植被对气温更为敏感，相关系数最高为0.534（P<0.01）。

Distribution Characteristics of NDVI in Coastal and Inland Areas for China-Mongolia-Russia Economic Corrido

In order to obtain the distribution characteristics of NDVI (normalized difference vegetation index) in coastal and inland areas of China-Mongolia-Russia economic corridor, by using MOD13Q1 NDVI dataset, MERRA2 reanalysis data and AVHRR land cover data, the variation characteristics of NDVI in different distances from the coastline, different latitudes and different time in the eastern part of the China-Mongolia-Russia Economic Corrido in recent years were studied, and the effects of temperature and precipitation on NDVI were analyzed. The results showed that from late May to September in 2015-2019, the growth state of vegetation in coastal areas was basically better than that in inland areas, and NDVI decreased significantly at about 500 km away from the coastline. The coastal (0-500 km) NDVI changed slightly over time. The NDVI time series change model of two representative vegetations (woodland and lush grassland) in coastal area has been established, and the fitting effect is better. Affected by the difference in precipitation and temperature between coastal and inland areas, with the increase of latitude, the difference of NDVI between coastal and inland areas gradually decreases during the peak period of vegetation growth (July 19-23), the NDVI in coastal areas enters saturation one month earlier than that in inland areas, and the decline period lags about 1 month; In low latitude area, these two vegetations are more sensitive to precipitation, and the correlation coefficient is up to 0.393(P<0.01), but in high latitude area, the two vegetations are more sensitive to temperature, and the correlation coefficient is up to 0.534(P<0.01).