USLE／RUSLE中植被覆盖与管理因子研究进展

The study on cover-management factor in USLE and RUSLE: a review

通用土壤流失方程(USLE)及修正通用土壤流失方程(RUSLE)是世界范围内应用最广泛的土壤侵蚀预报模型,模型中C因子表示植被覆盖和管理措施对土壤侵蚀的作用,是人为控制土壤侵蚀的重要因子。回溯了C因子发展演变历程,依据国内外最新研究成果,系统阐述了不同尺度C因子估算方法。在小区、坡面、小流域尺度上,C因子确定主要依赖于野外实验观测,研究条件的一致性尤其是标准小区的统一是C因子值可比性的前提。流域、区域尺度C因子确定通常需要利用遥感影像,遥感技术的发展促进了流域、区域尺度C因子估算方法的进步,使提取的C因子图更加精细、准确,但是使用遥感数据全面刻画C因子含义仍然是一大挑战,因此仍需加强C因子相关研究。共归纳了10种确定C因子的方法,介绍了不同方法的优缺点及适用条件,提出了我国C因子研究应加强的工作,希望为相关领域研究者提供参考。

The Universal Soil Loss Equation （USLE） and the Revised Universal Soil Loss Equation （USLE） are two most widely soil erosion prediction models worldwide. In both of these models, C factor, i.e., the Cover-Management Factor, quantifies the effects of vegetation cover and management practices on soil erosion, which is the most important factor in USLE/RUSLE models that can be easily controlled to alleviate soil erosion. The authors reviewed the evolvement of C factor and concluded various estimation methods for C factor at different scales by integrating recent domestic and international developments in this field. Therefore, five methods to quantify C factor were summarized at plot, hillslope and small watershed scales： 1 ）Obtaining C factor values from USLE/RUSLE handbooks; 2） Estimating C factor values based on the definition of C factor;3） Computing C factor values by its sub-factors; 4） Calculating C factor values using inverse method based on the USLE/RUSLE （C=A/（R·K·L·S·P）;5） Assessing C factor with linear and nonlinear models between C factor and vegetation coverage. Currently, estimation of C factor mainly depended on field experiments and observations at plot, hillslope and small watershed scales. Thus, the consistency of experimental conditions among different experiments, especially the consistency of standard runoff plots, is important and a prerequisite for comparability of C factor values. Once again, five methods to quantify C factor at watershed, region scales were summarized as well ： 1） Assigning C-factors values from reported values in literature according to land-cover/land-use categories; 2） Estimating C factor values at larger scales by equations based on relationships between C factor values and vegetation coverage acquired at smaller scales; 3 ） Performing direct regression between image bands ratios / vegetation indices and C factor values determined in the field; 4）Estimating C factor values by linear Spectral Mixture Analysis; 5） Improving in mapping of C factor values by geostatistical methods with remote sensing images. In general, at watershed and region scales, C factor values were usually estimated by using remote sensing images, The development of remote sensing technology promoted the evolvement of C factor estimation methods, which makes C factor mapping more precise. However, full interpretation of C factor values by using remote sensing date remains a major challenge, requiring further research on C factor. In conclusion, the authors summarized 10 ways to estimate C factor values, their advantages and disadvantages and also their applicable conditions in detail. Furthermore, the authors emphasized the important aspects of further research on C factor in future and wished extensive involvement of more researchers in this field.