半方差函数模型在滑坡、泥石流沟分布识别中的应用——以四川洪溪河流域为例
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摘要
快速、精确地识别地震后滑坡、泥石流沟的空间分布与覆盖范围,对于认识滑坡、泥石流灾害机理和震后灾区治理至关重要。目前提取滑坡、泥石流沟分布的方法主要是基于光谱信息与纹理信息,人为因素影响大,训练过程繁琐。该文提出一种基于半方差函数(semi-variance)模型与高空间分辨率影像实现少光谱信息、无训练样本条件下自动提取滑坡、泥石流沟的方法。以汶川重灾区四川省平武县洪溪河流域为例进行实验研究,结果表明:在滑坡以裸土、岩石出露为主,且具有数字高程模型(DEM)地形信息的情况下,该方法可以很好地识别典型滑坡与泥石流沟,并能勾画其边界范围;研究区内48.21%的滑坡与泥石流沟覆盖面积得以正确识别,特大型滑坡与大型滑坡识别数量比例分别为100%与80%,泥石流沟识别数量比例为70%。
After Wenchuan Ms 8.0 earthquake,the landslides and debris flows triggered by the rainfall occur frequently.Quickly and accurately identify the distribution and coverage of the landslides and debris flows in space is very important in geo-hazards research.Currently,the most common methods to identify the landslides and debris flows are often based on the multi-band and texture information,with heavy human factor impacts and complex training process.In this paper,an automatic method was provided by using the semi-variance model to realize the identification of landslides and debris flows with less spectrum information and none trained samples.Hongxi Basin in Sichuan was chosen as the study area,the results showed that:under the condition that the landslides were covered mostly by bare soil and rocks,with the terrain information provided by the digital elevation model(DEM),the method were used to identify the landslides and debris flows.Compared with the visual interpretation results,this method can successfully identify the distribution of landslides and debris flows with characteristic features,and can delineate the bounds.And 48.21% of the landslides and debris flows were accurately identified in the study area.And 100% of the huge landslides,80% of the big landslides,and 70% of the debris flows were identified in number.This study can provide a new method to identify landslides and debris flows.
After Wenchuan Ms 8.0 earthquake,the landslides and debris flows triggered by the rainfall occur frequently.Quickly and accurately identify the distribution and coverage of the landslides and debris flows in space is very important in geo-hazards research.Currently,the most common methods to identify the landslides and debris flows are often based on the multi-band and texture information,with heavy human factor impacts and complex training process.In this paper,an automatic method was provided by using the semi-variance model to realize the identification of landslides and debris flows with less spectrum information and none trained samples.Hongxi Basin in Sichuan was chosen as the study area,the results showed that:under the condition that the landslides were covered mostly by bare soil and rocks,with the terrain information provided by the digital elevation model(DEM),the method were used to identify the landslides and debris flows.Compared with the visual interpretation results,this method can successfully identify the distribution of landslides and debris flows with characteristic features,and can delineate the bounds.And 48.21% of the landslides and debris flows were accurately identified in the study area.And 100% of the huge landslides,80% of the big landslides,and 70% of the debris flows were identified in number.This study can provide a new method to identify landslides and debris flows.
引文
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[14] YANG W T,WANG M,KERLE N,et al.Analysis of changes in post-seismic landslide distribution and its effect on building reconstruction[J].Natural Hazards & Earth System Science,2015,15(4):817-825.
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[16] SONG C H,DICKINSON M B,SU L,et al.Estimating average tree crown size using spatial information from IKONOS and QuickBird images:Across-sensor and across-site comparisons[J].Remote Sensing of Environment,2010,114(5):1099-1107.
[2] MANTOVANI F,SOETERS R,VAN WESTEN C J.Remote sensing techniques for landslide studies and hazard zonation in Europe[J].Geomorphology,1996,15(3):213-225.
[3] METTERNICHT G,HURNI L,GOGU R.Remote sensing of landslides:An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments[J].Remote Sensing of Environment,2005,98(2):284-303.
[4] H?LBLING D,FüREDER P,ANTOLINI F,et al.A semi-automated object-based approach for landslide detection validated by persistent scatterer interferometry measures and landslide inventories[J].Remote Sensing,2012,4(5):1310-1336.
[5] 杨鑫.浅谈遥感图像监督分类与非监督分类[J].四川地质学报,2008,28(3):251-254.
[6] 曹宝,秦其明,马海建,等.面向对象方法在SPOT5遥感图像分类中的应用——以北京市海淀区为例[J].地理与地理信息科学,2006,22(2):46-49.
[7] BHASKARAN S,PARAMANANDA S,RAMNARAYAN M.Per-pixel and object-oriented classification methods for mapping urban features using IKONOS satellite data[J].Applied Geography,2010,30(4):650-665.
[8] 李玲,王红,刘庆生,等.基于纹理特征和支持向量机ALOS图像土地覆被分类[J].国土资源遥感,2011(4):58-63.
[9] 陈波,张友静,陈亮.结合纹理的SVM遥感影像分类研究[J].测绘工程,2007,16(5):23-27.
[10] COBURN C A,ROBERTS A C B.A multiscale texture analysis procedure for improved forest stand classification[J].International Journal of Remote Sensing,2004,25(20):4287-4308.
[11] JUPP D L B,STRAHLER A H,WOODCOCK C E.Autocorrelation and regularization in digital images.II.Simple image models[J].IEEE Transactions on Geoscience and Remote Sensing,1989,27(3):247-258.
[12] JUPP D L B,STRAHLER A H,WOODCOCK C E.Autocorrelation and regularization in digital images.I.Basic theory[J].IEEE Transactions on Geoscience & Remote Sensing,1988,26(4):463-473.
[13] WOODCOCK C E,STRAHLER A H,JUPP D L B.The use of variograms in remote sensing:I.Scene models and simulated images[J].Remote Sensing of Environment,1988,25(3):323-348.
[14] YANG W T,WANG M,KERLE N,et al.Analysis of changes in post-seismic landslide distribution and its effect on building reconstruction[J].Natural Hazards & Earth System Science,2015,15(4):817-825.
[15] SONG C H,B.DICKINSON M,SU L H,et al.Estimating tree crown size with spatial information of high resolution optical remotely sensed imagery[J].International Journal of Remote Sensing,2007,28(15):3305-3322.
[16] SONG C H,DICKINSON M B,SU L,et al.Estimating average tree crown size using spatial information from IKONOS and QuickBird images:Across-sensor and across-site comparisons[J].Remote Sensing of Environment,2010,114(5):1099-1107.