Efficiency of local minima and GLM techniques in sinkhole extraction from a LiDAR-based terrain model  

作　　者：Péter Enyedi Melinda Pap Zoltán Kovács László Takács-Szilágyi Szilárd Szabó 

机构地区：[1]a Department of Physical Geography and Geoinformatics,University of Debrecen,Debrecen,Hungary [2]Internet of Things Research Institute,Eszterházy Károly University of Applied Sciences,Eger,Hungary

出　　处：《International Journal of Digital Earth》2019年第9期1067-1082,共16页国际数字地球学报（英文）

摘　　要：The aim of this paper was to study reliable automated delineationpossibilities of karst sinkholes using a LiDAR-based digital terrain model(DTM) with pixel-based classifications. We applied two approaches toextract sinkholes: (1) general linear modeling (GLM) with morphometricindices derived from DTM;(2) and a local minima-based delineationusing only LiDAR DTM as the input layer. The outcome of the localminima was significantly different from the reference ones but found allthe sinkholes without previous knowledge of the area. The GLM-basedoutcome did not differ statistically from the reference. Results showedthat these approaches were efficient in detecting sinkholes based onLIDAR derivatives, and can be used for risk assessment and hazardpreparedness in karst areas: GLM had an overall accuracy of 89.5% andlocal minima had an accuracy of 92.3%;both methods identifiedsinkholes but also had commission errors, identifying depressions assinkholes.

关 键 词：Karst mapping sinkhole identification general linear model statistical evaluation sink fill 

分 类 号：O62[理学—有机化学]