Python辅助下的坡度提取不确定性分析

Uncertainty Analysis for Slope Extraction Based on Python

以30 m空间分辨率的DEM为试验数据源,借助Python地理建模技术深入研究坡度提取的不确定性。以平均坡度来代表坡度的一般水平,将研究区划分为7种地貌类型,将30 m分辨率的DEM数据重采样成30~120 m分辨率的10组DEM数据,使用以Arc GIS平台和Python开发语言为基础的地理建模技术,定量分析平均坡度与DEM空间分辨率、区域地貌特征的关系,研究坡度提取的不确定性。结果表明:研究区内不同地貌区域提取的平均坡度都随DEM分辨率的减小而减小,衰减速率基本不变;其回归方程的常数项与所在地貌单元的沟壑密度呈二次函数变化特征;坡度提取的精度与DEM的分辨率呈正相关的关系;基于Python的地理建模技术有效地整合了坡度提取分析方法,极大地提高了分析效率;研究结果进一步验证了现有坡度提取方法的不确定性。

Taking DEM with spatial resolution of 30 meters as experimental data source,the uncertainty of slope extraction is deeply studied by using Python geographic modeling technology.The general level of slope is to be represented by the average slope,and the study area is divided into 7 landscape types,the 30 m resolution of DEM data is resampled into 10 groups DEM data of 30 ~ 120 m resolution.Using Arc GIS platform and Python development language based geographic modeling technology,quantitative analysize DEM spatial resolution,mean slope and area geomorphic features,the uncertainty of slope extraction is studied.The research results show that the average slope region in different geomorphic regions are extracted with decreasing resolution of DEM decreases,the decay rate is basically unchanged.The regression equation of the constant term and the geomorphic unit is characteristic of the two gully density curve.