Long-term trend analysis (LTA) is a typical approach to understand the growth and degradation of vegetation in the area. However, the dynamic of the spatio-temporal characteristics of the vegetation is often difficult to be captured by the LTA. This study combines LTA with a time-series mutation detection of normalized vegetation index (NDVI) for identifying the spatial variation of vegetation, where northern mountainous region of Haihe River Basin is used as a case. The results show that 78.1% of the NDVI in the area changed abruptly from 2000 to 2018, among which 1.7% was degraded. An inflection point of NDVI, which occurred in 2011, was found by time-series mutation detection. A significant degradation of 1.6% in the northwest steppe and southeast forest-farmland region, and 1.2% of degradation in the east forest-farmland region are also observed before the inflection point (i.e. 2000-2011) and after (i.e. 2011-2018). The results also show that the overall improvement of vegetation in the mountainous area of the Northern Haihe River has minor correlation with the meteorological factors, meanwhile local deterioration results from meteorological factors and human activities. Compared with the conventional LTA, the proposed method gains insight of the dynamic trend of vegetation spatial changes in the study area and therefore providing useful information for vegetation recovery strategies.