<p>Exploring the intensity of cyclic changes of ecosystems in regional space helps to analyze their variability patterns, which is of great significance for ecological factor monitoring and ecological management. This study proposes a spatial trend analysis method based on spatial spectral data. By extracting pixel gradient and spatial trend in the time domain from multi-temporal spectral data, it reduces feature redundancy while preserving pixel spatial continuity. Using the Yellow River Basin as a case study, monthly vegetation cover data from 2022 were analyzed to examine the spatial distribution characteristics of vegetation cover intensity changes. The results of the study are as follows: (1) Compared to the original gradient distribution, the SIDM method increases Moran’s <i>I</i> by 0.106, decreases Geary’s <i>C</i> by 0.097, and enhances spatial aggregation. This facilitates clearer depiction of the spatial continuity structure of vegetation changes, providing spatial support for identifying key change areas and implementing zoned management in ecological monitoring. (2) The third quarter of 2022 is the most luxuriant period of vegetation in the watershed, with the highest vegetation cover of 0.772 in August, and the overall increase of the year is 0.033. (3) The gradient of vegetation cover shows an overall northwestern to southeastern upward trend, with a maximum increase of 4.6% and a minimum decrease of −2.4%. The positive change area dominated by Sichuan, Henan, Shanxi and Shandong is 553,750 km², and the negative change area dominated by Ningxia and Inner Mongolia is 171,250 km².</p>