Sloping farmland identification using hierarchical classification in the Xi-He region of China

Abstract

The Loess Plateau suffers the most serious soil erosion in China. Sloping cultivated land is one of the most common land types in the region, and it leads to severe soil erosion. Analyses based on fine resolution satellite imagery can play a key role in the surveying of sloping farmland. In this article, a combination of remote-sensing (RS) and geographical information system (GIS) techniques under the hierarchical classification framework is used to investigate the sloping cultivated land in the Xi–He ecological engineering demonstration region of the Loess Plateau. This article synthetically adopts Système Pour l'Observation de la Terre 5 (SPOT-5) high-resolution images, Thematic Mapper (TM) multi-spectral images, and a terrain map at a scale of 1:10,000 to calculate the spectral, textural, and slope attributes of the sloping land type. A Bayesian classification method is employed to distinguish the crop and non-crop areas with a priori knowledge provided by local experts. The Gabor filter and the grey-level co-occurrence matrices (GLCM) method are used in combination under a man–machine interactive interpretation to extract the differences of texture trends and density distributions between sloping cultivated land and terraces, and a window-based method of texture analysis operations is adopted. A classification accuracy assessment by field survey shows that the total interpretation accuracy exceeds 80%. The sloping cultivated land in the research region covers an area of 233.06 km2, and the sub-watersheds, such as the Yangliu watershed, need key management in the future.