应用最大熵绘制连续变化图的滑坡潜力图

IF 2.3 Q2 REMOTE SENSING
Rocío Ramos-Bernal, René Vázquez-Jiménez, Wendy Romero Rojas
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引用次数: 0

摘要

滑坡测绘清单对于预防灾害和降低风险至关重要。遥感技术利用遥感器记录来自地球表面的数据,这些数据以数字图像形式编码,分布在电磁波谱范围内,使我们能够获取各种类型的信息。结合适当的空间分析和建模技术,我们可以监测山体滑坡等使人与自然耦合系统面临风险的现象。最大熵模型(MaxEnt)是一种机器学习算法,以连续变化(CC)地图为输入,对潜在的模式进行预测。这些地图是通过对转换图像、归一化差异植被指数(NDVI)和主成分分析(PCA)采用线性回归和差分等无监督变化检测方法获得的。在选择补充输入数据时,使用了千斤顶检验法来评估边坡稳定性的主要决定因素:岩性(L)、角坡(AS)和地形方位(TO)。地面真实滑坡样本用于算法训练(2/3)和最终清单地图的精度评估(1/3)。通过结合 MaxEnt 模型、正割法阈值处理和坡度值小于 5° 的像素判别,得出的滑坡清单图显示出较高的准确性和与实际情况的直观一致性,误差和遗漏误差分别为 3.0% 和 3.5%,Kappa 一致性指数为 93.37%,AUC 为 0.75,这表明 MaxEnt 是一种实用高效的工具,能够快速准确地生成可靠的滑坡检测图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Landslide potential mapping applying maximum entropy to continuous change maps

Landslide potential mapping applying maximum entropy to continuous change maps

Landslide mapping inventories are crucial for disaster prevention and risk mitigation. Remote sensing uses remote sensors that record data from the Earth’s surface encoded in digital images distributed in electromagnetic spectrum ranges, allowing us access to various types of information. This, in conjunction with appropriate spatial analysis and modeling techniques, allows us to monitor the phenomena, such as landslides, that put man-nature coupled systems at risk. This paper presents a practical alternative for integrating landslide inventories in the central area of the state of Guerrero in Mexico by using the maximum entropy model (MaxEnt), a machine learning algorithm oriented to the potential prediction of patterns using continuous change (CC) maps as input. These maps were obtained using the unsupervised change detection methods linear regression and difference applied to transformed images, the normalized difference vegetation index (NDVI), and principal component analysis (PCA). The selection of supplementary input data was made by using the jackknife test to assess the contribution of the main determinant factors of slope stability: lithology (L), angular slopes (AS), and terrain orientation (TO). Ground truth landslide samples were used for the algorithm training (2/3) and the accuracy assessment of the final inventory map (1/3). The landslide inventory map derived by combining the MaxEnt model, the thresholding by the secant method, and the discrimination of pixels with slope values less than 5° reveals a high accuracy and visual concordance with reality, reaching 3.0% and 3.5% in commission and omission errors, a Kappa concordance index of 93.37%, and an AUC of 0.75, indicating MaxEnt is a practical and efficient tool that allows for the rapid and accurate generation of reliable maps for the detection of landslides.

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来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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