Stream Length-gradient Hotspot and Cluster Analysis (SL-HCA) to fine-tune the detection and interpretation of knickzones on longitudinal profiles

摘要: Abstract This work presents a new approach for fine-tuning the analysis of stream longitudinal profiles. We show that applying Hotspot and Cluster Analysis (HCA), based on Getis-Ord Gi* statistic, to length-gradient (SL) index improves visualization anomalous values, assisting in identification tectonic structures large landslides. High positive values indicate clustering SL anomalies (hotspots), mirror occurrence knickzones long-profiles. applied this methodology mountainous sector eastern Emilia-Romagna region, northern Italy. Remote sensing field surveys conducted hotspot sites landslides are main process associated over-steepened long-profile segments along streams connected valley slopes. Along-stream changes bedrock resistance accounts within sectors where hillslopes floors disconnected. demonstrate specific relationships between geometry intensity hotspots indicative responsible knickzone formation and, particular generally provide longest highest anomalies. The results suggest SL-HCA maps more advantageous detecting interpreting compared with traditional maps, since: i) they need less input data be computed, thus making them useful investigate regions poorly covered by detailed geological and/or difficult carried out ii) can help discriminate attributable gravitational mass movements from litho-structural ones.