Vegetation disturbances can fundamentally alter ecosystem structure and function, with profound implications for carbon emissions and sequestration. While disturbance attribution has been extensively studied in forest ecosystems, its application to other land cover types, particularly pasture-dominated systems, remains underexplored. Here, we introduce an approach-Shapeletbased Vegetation Change Detection with Random Forest (SVCD-RF)- that integrates time-series analysis with a Random Forest (RF) machine learning algorithm to attribute causal disturbance agents in mixed pasture and forest ecosystems. We apply the SVCD algorithm to Landsat timeseries data in the arid and semi-arid Qilian Mountains of China, detecting vegetation disturbances and categorizing them into three phases: pre-disturbance, onset, and post-disturbance. For each disturbed pixel, we derive phase-specific spectral and temporal metrics, along with topographic variables, to construct feature sets. These are linked to 1800 purposively sampled and manually interpreted training labels, used to train separate RF classifiers for forest and pasture areas. Disturbance agents are classified into Development or Degradation (DD), Agricultural Expansion (AE), and Greenup. Classification accuracy is evaluated via stratified random sampling, generating 1137 and 1849 validation labels for pasture and forest, respectively. The resulting map achieves high accuracy, with overall accuracies of 89.1 % f 1.8 % for pasture and 85.6 % f 1.5 % for forest at the 95 % confidence level. User's and producer's accuracies for most classes exceed 80 %, except for AE in the forest, yielding lower values (56.3 % and 61.2 %, respectively). Stratified area estimates indicate that AE was the dominant disturbance agent before 2012, especially in pasture ecosystems, with an estimated area of 1107.90 f 51.14 km2, significantly surpassing DD (818.58 f 24.71 km2) and Greenup (713.36 f 32.10 km2). In contrast, Greenup emerges as the dominant disturbance process after 2012, particularly within forest ecosystems. We underscore the potential of the SVCD-RF approach for mapping vegetation disturbance agents in complex, mixed-use landscapes.