Impacts of Sloping Land Conversion Program on the vegetation in loess hilly and gully area of northern Shaanxi

Aiming at alleviating the serious soil erosion, the Chinese government initiated the Sloping Land Conversion Program （SLCP） in 1999. Now; after 8 years of project implementation, the ecological recovery effects of the SLCP have become the hot issue of academic circle. This paper; raking the loess hill and gully area of northern Shaanxi as an example, presents a methodology for assessing the vegetation restoration effect of SLCP with normalized difference vegetation index （NDVI）. The key components include calculation of the Growing Season NDVI （GSNDVI）, and estimation of the NVDI change induced by climate and SLCP, respectively. Based on the method, the NDVI change between 2000 and 2006 was obtained using the GSNDVI that excluded the noise from snow and ice. After the part of total NDVI change caused to： climate variation was estimated using empiric formulae, we obtained the part induced by human factors, i.e. the SLCP The human induced part of ND VI change was considered as an approximation indicating the effect of the SLCP on the vegetation. Finally, we analyzed the ND VI change characters of the whole study area, different slope lands and different land use types by spatial statistics method. Results show that the vegetation condition is significantly improved by the SLCP, particularly land types that directly involved in the SLCP, such as steeply slope farmlands, degraded grasslands, etc.

An applied research on remote sensing classification in the Loess Plateau

Dae to complex terrain of the Loess Plateau, the classification accuracy is unsatisfactory when a single supervised classification is used in die remote sensing investigation of the sloping field. Taking the loess hill and gully area of northern Shaanxi Province as a test area, a research was conducted to extract sloping field and other land use categories by applying an integrated classification. Based on an integration of supervised classification aad unsupervised classification, sampling method is remarkably unproved. The results show that the classification accuracy is satisfactory by the method and is of critical significance in obtaining up-to-date information of the sloping field, which should be helpful in the state key project of converting farmland to forest and grassland on slope land in this area. This research sought to improve the application accuracy of image classification in complex terrain areas.