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Terrace extraction based on remote sensing images and digital elevation model in the loess plateau, China
Earth Science Informatics ( IF 2.7 ) Pub Date : 2020-02-10 , DOI: 10.1007/s12145-020-00444-x
Lanhua Luo , Fayuan Li , Ziyang Dai , Xue Yang , Wei Liu , Xuan Fang

Terraces are important objects of soil and water conservation and land use surveys in the Loess Plateau and considerably impact soil erosion and agricultural production. Obtaining the spatial distribution of terraces has great significance for soil and water conservation research and government policy-making. A new method of terrace extraction was proposed in this study based on remote sensing images and digital elevation model (DEM) used as experimental data. The extraction method combines Fourier transform and digital terrain analysis. First, by analyzing the spatial and frequency domain characteristics of the terraced image, we determined the analysis window to be 50 × 50, whereas the area having a direction maximum energy value greater than 0.25 and image standard deviation greater than 2000 was considered as the terraced candidate. Then, the positive terrain was automatically extracted from the DEM based on the regional growth method, and the terraces on the positive terrain were obtained. After a series of morphological image processing methods were conducted, the final terraces were extracted. The results showed that the accuracy of drawing and user accuracy of terrace extraction were 79.0% and 73.5%, respectively. Compared with the object-oriented method, it was found that the proposed method was more reliable and accurate. This method developed in this study has the characteristics of simple operation, strong universality, and high precision and can be applied to soil and water conservation monitoring and land use surveys in the Loess Plateau.

中文翻译:

基于遥感影像和数字高程模型的黄土高原梯田提取

梯田是黄土高原水土保持和土地利用调查的重要对象,对土壤侵蚀和农业生产产生重大影响。获得梯田的空间分布对水土保持研究和政府决策具有重要意义。提出了一种基于遥感影像和数字高程模型(DEM)作为实验数据的梯田提取新方法。提取方法结合了傅里叶变换和数字地形分析。首先,通过分析梯形图像的空间和频域特征,我们确定分析窗口为50×50,而方向最大能量值大于0.25且图像标准偏差大于2000的区域被视为梯形候选人。然后,根据区域增长方法,从DEM中自动提取正向地形,并获得正向地形的阶地。在进行了一系列形态图像处理方法之后,提取了最终的阶地。结果表明,绘制精度和梯田提取的用户精度分别为79.0%和73.5%。与面向对象的方法相比,发现该方法更加可靠,准确。本研究开发的该方法具有操作简单,通用性强,精度高等特点,可用于黄土高原水土保持监测和土地利用调查。并获得了正面地形上的阶地。在进行了一系列形态图像处理方法之后,提取了最终的阶地。结果表明,绘制精度和梯田提取的用户精度分别为79.0%和73.5%。与面向对象的方法相比,发现该方法更加可靠,准确。本研究开发的该方法具有操作简单,通用性强,精度高等特点,可用于黄土高原水土保持监测和土地利用调查。并获得了正面地形上的阶地。在进行了一系列形态图像处理方法之后,提取了最终的阶地。结果表明,绘制精度和梯田提取的用户精度分别为79.0%和73.5%。与面向对象的方法相比,发现该方法更加可靠,准确。本研究开发的这种方法具有操作简单,通用性强,精度高的特点,可用于黄土高原水土保持监测和土地利用调查。分别。与面向对象的方法相比,发现该方法更加可靠,准确。本研究开发的这种方法具有操作简单,通用性强,精度高的特点,可用于黄土高原水土保持监测和土地利用调查。分别。与面向对象的方法相比,发现该方法更加可靠,准确。本研究开发的该方法具有操作简单,通用性强,精度高等特点,可用于黄土高原水土保持监测和土地利用调查。
更新日期:2020-02-10
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