Land use change is extremely sensitive to natural factors and human influence in active debris flow. It is therefore necessary to determine the factors that influence land use change. This paper took Wudu District, Gansu Province, China as a study area, and a systemic analysis of the transformational extent and rate of debris flow waste-shoal land (DFWSL) was carried out from 2005 to 2015. The results show that from 2005 to 2015, cultivated land resources transformed to other types of land; cultivated lands mainly transformed to grassland from 2005 to 2010 and construction land from 2010 to 2015. Moreover, the growth rate of construction land from 2005 to 2010 was only 0.11%, but increased to 6.87% between 2010 and 2015. The latter is more than 60 times the former. This increase was brought about by natural disasters (debris flow, earthquakes, and landslides) and anthropogenic factors (national policies or strategies), which acted as driving forces in debris flow area. The former determines the initial use type of the DFWSL while the latter only affects the direction of land use and transformation.

Major earthquakes can cause serious vegetation destruction in affected areas. However, little is known about the spatial patterns of damaged vegetation and its influencing factors. Elucidating the main influencing factors and finding out the key vegetation type to reflect spatial patterns of damaged vegetation are of great interest in order to improve the assessment of vegetation loss and the prediction of the spatial distribution of damaged vegetation caused by earthquakes. In this study, we used Moran’s I correlograms to study the spatial autocorrelation of damaged vegetation and its potential driving factors in the nine worst-hit Wenchuan earthquake-affected cities and counties. Both dependent and independent variables showed a positive spatial autocorrelation but with great differences at four aggregation levels (625 × 625 m, 1250 × 1250 m, 2500 × 2500 m, and 5000 × 5000 m). Shrubs can represent the characteristics of all damaged vegetation due to the significant linear relationship between their Moran’s I at the four aggregation levels. Clustering of similar high coverage of damaged vegetation occurred in the study area. The residuals of the standard linear regression model also show a significantly positive autocorrelation, indicating that the standard linear regression model cannot explain all the spatial patterns in damaged vegetation. Spatial autoregressive models without spatially autocorrelated residuals had the better goodness-of-fit to deal with damaged vegetation. The aggregation level 8 × 8 is a scale threshold for spatial autocorrelation. There are other environmental factors affecting vegetation destruction. Our study provides useful information for the countermeasures of vegetation protection and conservation, as well as the prediction of the spatial distribution of damaged vegetation, to improve vegetation restoration in earthquake-affected areas.