Application of spatio-temporal data mining and knowledge discovery for detection of vegetation degradation

Increasing time-series remote sensing images provide the information about the evolution processes of ecosystems on multi-spatial scales. Vegetation plays an important role in sustaining the natural environment and supporting human being with goods and ecosystem services. Detection of vegetation degradation has become a hot spot of multi-disciplinary researches recently. In this paper, a case study of spatio-temporal data mining and knowledge discovery for detection of vegetation degradation has been conducted. The special issues focused on the quantitative determination of historical evolutionary trend and furthermore, the sustainability of different trends in the future. Taking the Circum-Bohai-Sea region as the case study area, the Unary Linear Regression Model (ULRM) has been established based on the time-series SPOT-VGT images from 1998 to 2008, and then the Hurst index has been calculated by R/S method on the spatial scales of cell (1km²) and the whole study area. It turned out that, the combined analysis between Slope of ULRM and Hurst index could effectively reveal the characteristics of vegetation changes, which included the degraded areas in the past as well as the risk level of degradation in the future. Overall, the areas of vegetation degradation in the future amount to 38.87 thousand square kilometers, which accounts for 7.55% of the whole study area. In addition, these degraded areas mainly distributed around the metropolitan regions, coastal zone, and so on. The findings will help us with more intelligent strategies of degradation prevention.