滑坡敏感性图(LSM)经常被政府部门用于进行土地利用管理和规划,为城市和基础设施规划的决策者提供支持。常规滑坡磁化率图的精度往往受到分类误差的影响。因此,他们变得不那么可靠,这使得它很难满足决策者的需求。因此,本文提出了通过将小基线子集-干涉合成孔径雷达(SBAS-InSAR)技术和LSM相结合来减少分类误差并提高LSM的可靠性。通过使用逻辑回归模型(LR)和支持向量机模型(SVM),在东川区进行了LSM生成实验。它被分为五类:非常高的易感性,高磁化率,中等磁化率,低磁化率,和非常低的敏感性。然后,通过2018年1月至2021年1月的升、降轨道哨兵-1A数据获得了东川地区的地表变形速率。要更正分类错误,通过构造权变矩阵,将SBAS-InSAR技术集成到最优模型下的LSM中。最后,比较校正前后获得的LSM。此外,结合遥感图像对校正结果进行了验证和分析,InSAR变形结果,和实地调查。根据研究结果,在SBAS-InSAR校正整合后,LSM中66,094个分类错误细胞(59.48km2)的敏感性等级显着提高。增强的磁化率类别和遥感图像的光谱特征与InSAR累积变形的趋势和现场调查结果高度一致。建议将SBAS-InSAR和LSM集成在一起可以有效地纠正分类误差,并进一步提高LSM在滑坡预测中的可靠性。利用该方法得到的LSM对指导地方政府部门防灾减灾具有重要作用,有利于消除山体滑坡的风险。
Landslide susceptibility maps (LSM) are often used by government departments to carry out land use management and planning, which supports decision makers in urban and infrastructure planning. The accuracy of conventional landslide susceptibility maps is often affected by classification errors. Consequently, they become less reliable, which makes it difficult to meet the needs of decision-makers. Therefore, it is proposed in this paper to reduce classification errors and improve LSM reliability by integrating the Small Baseline Subsets-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique and LSM. By using the logistic regression model (LR) and the support vector machine model (SVM), experiments were conducted to generate LSM in the Dongchuan district. It was classified into five classes: very high susceptibility, high susceptibility, medium susceptibility, low susceptibility, and very low susceptibility. Then, the surface deformation rate of the Dongchuan area was obtained through the ascending and descending orbit sentinel-1A data from January 2018 to January 2021. To correct the classification errors, the SBAS-InSAR technique was integrated into LSM under the optimal model by constructing the contingency matrix. Finally, the LSMs obtained before and after
correction were compared. Moreover, the
correction results were validated and analyzed by combining remote sensing images, InSAR deformation results, and field surveys. According to the research results, the susceptibility class of 66,094 classification error cells (59.48 km2) was significantly improved in the LSM after the integration of the SBAS-InSAR
correction. The enhanced susceptibility classes and the spectral characteristics of remote sensing images are highly consistent with the trends of InSAR cumulative deformation and the results of field investigation. It is suggested that integrating SBAS-InSAR and LSM is effective in correcting classification errors and further improving the reliability of LSM for landslide prediction. The LSM obtained by using this method plays an important role in guiding local government departments on disaster prevention and mitigation, which is conducive to eliminating the risk of landslides.