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Lidar-Derived Rockfall Inventory—An Analysis of the Geomorphic Evolution of Rock Slopes and Modifying the Rockfall Activity Index (RAI).
- Source :
- Remote Sensing; Sep2023, Vol. 15 Issue 17, p4223, 23p
- Publication Year :
- 2023
-
Abstract
- Rockfall presents a significant risk to the safety and economy of communities and infrastructure in mountainous regions. The recently-developed Rockfall Activity Index (RAI) utilizes high-resolution terrestrial lidar-derived digital elevation models (DEMs) of rock slopes to categorize a slope face into seven distinct morphological units, or "RAI classes". This paper focuses on a comprehensive study conducted at four sites in Alaska, USA, where a robust lidar-based five-year inventory of 4381 rockfall events was analyzed. The primary objective was to investigate variations in failure characteristics, such as cumulative magnitude–frequency distributions, non-cumulative power–law parameters, average annual failure rates, and average failure depths, among the different RAI classes. The findings demonstrate that the seven RAI classes effectively differentiate the rock slope based on unique mass-wasting characteristics. Furthermore, the research explores spatial and temporal variations in these failure characteristics. Based on the study's outcomes, recommendations are provided for modifying the RAI parameters for each RAI class, specifically the annual failure rate and average failure depth. These modifications aim to enhance the accuracy and effectiveness of rockfall hazard assessments. [ABSTRACT FROM AUTHOR]
- Subjects :
- ROCK slopes
ROCKFALL
DIGITAL elevation models
INVENTORIES
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 15
- Issue :
- 17
- Database :
- Complementary Index
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 171859139
- Full Text :
- https://doi.org/10.3390/rs15174223