Abstract：
The development of ecological restoration projects is unsatisfactory, and soil erosion is still a problem in ecologically restored areas. Traditional soil erosion studies are mostly based on satellite remote sensing data and traditional soil erosion models, which cannot accurately characterize the soil erosion conditions in ecological restoration areas (mainly plantation forests). This paper uses high-resolution unmanned aerial vehicle (UAV) images as the base data, which could improve the accuracy of the study. Considering that traditional soil erosion models cannot accurately express the complex relationships between erosion factors, this paper applies convolutional neural network (CNN) models to identify the soil erosion intensity in ecological restoration areas, which can solve the problem of nonlinear mapping of soil erosion. In this study area, compared with the traditional method, the accuracy of soil erosion identification by applying the CNN model improved by 25.57%, which is better than baseline methods. In addition, based on research results, this paper analyses the relationship between land use type, vegetation cover, and slope and soil erosion. This study makes five recommendations for the prevention and control of soil erosion in the ecological restoration area, which provides a scientific basis and decision reference for subsequent ecological restoration decisions.
摘要：
生态修复工程的发展不尽如人意,在生态恢复地区，水土流失仍然是一个问题。传统的土壤侵蚀研究大多基于卫星遥感数据和传统的土壤侵蚀模型,这不能准确描述生态恢复区(主要是人工林)的土壤侵蚀状况。本文以高分辨率无人机影像为基础数据,这可以提高研究的准确性。考虑到传统的土壤侵蚀模型不能准确表达侵蚀因子之间的复杂关系，本文应用卷积神经网络(CNN)模型对生态修复区的土壤侵蚀强度进行识别,可以解决土壤侵蚀的非线性映射问题。在这个研究领域，与传统方法相比，应用CNN模型的土壤侵蚀识别精度提高了25.57%，比基线方法更好。此外,根据研究成果，本文分析了土地利用类型之间的关系，植被覆盖,坡度和土壤侵蚀。本研究对生态修复区水土流失防治提出了五点建议,为后续生态修复决策提供科学依据和决策参考。
