Spatial Prediction Models for Landslide Activity Mapping Using Vegetation Anomalies

Spatial Prediction Models for Landslide Activity Mapping Using Vegetation Anomalies

Abstract:

An area that located in Kundasang which in Ranau district in Sabah, Malaysia that lies along the bank of Kundasang valley was chosen for comparing the reliability of frequency ratio (FR) and weight of evidence (WoE) methods for landslide activity probability mapping by using related vegetation anomalies indicator. The locations of 47 and 189 of active and dormant landslides respectively were identified using 4 raster layers (topographic openness, hillshade, colour composite and high resolution orthophoto). Each landslide activites were randomly divided into two groups as training (70%) and testing (30%) datasets. Tree height irregularities, DVI, NDVI, SAVI, and OSAVI were considered as landslide bio-indicator. The landslide activity probability maps were prepared using the FR and WoE method. The generated maps were validated by calculating the success and prediction rates from area under receiver operating characteristics (ROC) curve. The results of WoE method were relatively reliable (AUC > 0.8) for dormant landslide while only about 40% of active landslide have been predicted accurately. Similar trend yielded for FR method where least accuracy for active landslide prediction.

 

Article by:

Mohd Radhie Mohd Salleh (I Net Spatial Sdn Bhd), Zamri Ismail, Shakirah Amirah Binti Mohd Ariff, Muhammad Zulkarnain Abd Rahman*, Mohd Faisal Abdul Khanan, Mohd Asraff Asmadi, and Khamarrul Azahari Razak 

 

How to cite: 

Mohd Salleh, M. R., Ismail, Z., Mohd Ariff, S. A., Abd Rahman, M. Z., Abdul Khanan, M. F., Asmadi, M. A., and Razak, K. A.: SPATIAL PREDICTION MODELS FOR LANDSLIDE ACTIVITY MAPPING USING VEGETATION ANOMALIES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W16, 441–449, https://doi.org/10.5194/isprs-archives-XLII-4-W16-441-2019, 2019.

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