基于南方结构化植被指数的植被覆盖管理因子遥感反演

Remote sensing inversion of vegetation cover management factor based on the southern structured vegetation cover index

为探索不同尺度植被覆盖与管理措施因子C的最佳估算模型,以南京市为研究区域,利用南方结构化植被指数(The southern structured vegetation cover index,Vs)对C因子进行估算。在坡面尺度,将不同植被类型Vs与C因子进行拟合;在区域尺度,借助Vs筛选最佳遥感指数,进而遥感反演C因子。结果表明:1)基于Vs估算C因子有效提高模型精度。2)C因子对黄度指数更加敏感,因此衰老植被和枯枝落叶覆盖对于地表土壤的保护作用是不容忽视的。3)基于最佳遥感指数反演C因子,模型R2=0.598,ME>0.5,因此该模型被推荐用于大尺度下的C因子估算。Vs有效地筛选遥感指数,且黄度指数和绿度指数相结合估计C因子更加精准。

[Background]The C-factor estimation models cannot be used universally used in northern and southern China due to the influence of spatial heterogeneity.A review of current literature suggests that there is a lack of research on the vertical structure of forest water and soil conservation in southern China.Therefore,the objective of this research was to explore the best estimation model of C factor at different scales based on the Southern Structured Vegetation Index(Vs).Nanjing is a typical subtropical hilly area with abundant vegetation.[Methods]This study investigated a total of 87 typical quadrats(six land use types)from 16 mountains in 8 districts of Nanjing.The quadrats were arranged according to the principle of uniform spatial distribution.The data including the longitude and latitude,elevation,slope,slope position,land use,litter layer thickness,tree species,shrub,grassland and tree height of the sample plot recorded in detail.The remote sensing index was extracted from the corresponding remote sensing images.The Vs of different vegetation types was fitted with the C value to obtain the C factor prediction model.At the regional scale,the best remote sensing index was selected by Vs,then the C factor was retrieved by remote sensing.[Results]1)C-factor estimation based on Vs effectively improved the accuracy of the model.2)Factor C showed an increased sensitivity to yellowness index and as a result the protective effect of senile vegetation and dead leaf cover on surface soil cannot be ignored.3)Inversion of C factor based on the optimal remote sensing index,model R2=0.598,ME>0.5,and thus this model is recommended for estimation of C factor in large scale.[Conclusions]In the southern region,the C-factor of slope scale estimated by Vs is more accurate.At the regional scale,the remote sensing index based on Vs screening can effectively invert the vertical information of vegetation to the remote sensing index.This is the first reported study focusing on the structured vegetation index in southern China.