基于厘米级高分辨率无人机影像,应用面向对象方法(Object-Based Image Analysis,OBIA)对吉林省伊通县椽子沟流域的横坡改垄、地埂植物带、生态恢复乔木林、生态恢复草地等水土保持措施进行自动精准识别。应用超绿指数(Excess Green Inde...基于厘米级高分辨率无人机影像,应用面向对象方法(Object-Based Image Analysis,OBIA)对吉林省伊通县椽子沟流域的横坡改垄、地埂植物带、生态恢复乔木林、生态恢复草地等水土保持措施进行自动精准识别。应用超绿指数(Excess Green Index,ExG)、超红指数(Excess Red Index,ExR)、归一化差异指数(Normalized Difference Index,NDI)等光谱指数,形状的主方向、形状指数等形状特征,均值(Mean)、方差(Variance)、对比度(Contrast)等纹理特征进行措施的特征提取。结果表明:研究区水土保持措施识别的总体精度可达91.24%,Kappa系数为0.87;对垄台、垄沟等线性水土保持措施总体精度可达72.33%,Kappa系数为0.63。基于厘米级无人机影像,应用面向对象方法基本可实现对黑土区水土保持措施的精准识别,也可对垄台垄沟等线性措施进行自动识别,研究结果可为水土保持措施实施范围及完好程度的动态监测提供参考依据。展开更多
The objective of this paper is to investigate a simple and practical method for soil productivity assessment in the black soil region of Northeast China. Firstly, eight kinds of physicochemical properties for each of ...The objective of this paper is to investigate a simple and practical method for soil productivity assessment in the black soil region of Northeast China. Firstly, eight kinds of physicochemical properties for each of 120 soil samples collected from 25 black soil profiles were analyzed using cluster and correlation analysis. Subsequently, parameter indices were calculated using physicochemical properties. Finally, a modified productivity index （MPI） model were developed and validated. The results showed that the suitable parameters for soil productivity assessment in black soil region of Northeast China were soil available water, soil pH, clay content, and organic matter content. Compared with original productivity index （PI） model, MPI model added clay content and organic matter content in parameters while omitted bulk density. Simulation results of original PI model and MPI model were compared using crop yield of land block where investigated soil profiles were located. MPI model was proven to perform better with a higher significant correlation with maize yield. The correlation equation between MPI and yield was： Y= 3.2002Ln（MP/）＋ 10.056, R^2 = 0.7564. The results showed that MPI model was an effective and practical method to assess soil productivity in the research area.展开更多
文摘基于厘米级高分辨率无人机影像,应用面向对象方法(Object-Based Image Analysis,OBIA)对吉林省伊通县椽子沟流域的横坡改垄、地埂植物带、生态恢复乔木林、生态恢复草地等水土保持措施进行自动精准识别。应用超绿指数(Excess Green Index,ExG)、超红指数(Excess Red Index,ExR)、归一化差异指数(Normalized Difference Index,NDI)等光谱指数,形状的主方向、形状指数等形状特征,均值(Mean)、方差(Variance)、对比度(Contrast)等纹理特征进行措施的特征提取。结果表明:研究区水土保持措施识别的总体精度可达91.24%,Kappa系数为0.87;对垄台、垄沟等线性水土保持措施总体精度可达72.33%,Kappa系数为0.63。基于厘米级无人机影像,应用面向对象方法基本可实现对黑土区水土保持措施的精准识别,也可对垄台垄沟等线性措施进行自动识别,研究结果可为水土保持措施实施范围及完好程度的动态监测提供参考依据。
基金supported by the National Natural Science Foundation of China (40671111)
文摘The objective of this paper is to investigate a simple and practical method for soil productivity assessment in the black soil region of Northeast China. Firstly, eight kinds of physicochemical properties for each of 120 soil samples collected from 25 black soil profiles were analyzed using cluster and correlation analysis. Subsequently, parameter indices were calculated using physicochemical properties. Finally, a modified productivity index （MPI） model were developed and validated. The results showed that the suitable parameters for soil productivity assessment in black soil region of Northeast China were soil available water, soil pH, clay content, and organic matter content. Compared with original productivity index （PI） model, MPI model added clay content and organic matter content in parameters while omitted bulk density. Simulation results of original PI model and MPI model were compared using crop yield of land block where investigated soil profiles were located. MPI model was proven to perform better with a higher significant correlation with maize yield. The correlation equation between MPI and yield was： Y= 3.2002Ln（MP/）＋ 10.056, R^2 = 0.7564. The results showed that MPI model was an effective and practical method to assess soil productivity in the research area.
