CLASSIFICATION OF TRANSLATIONAL LANDSLIDE ACTIVITY USING VEGETATION ANOMALIES INDICATOR (VAI) IN KUNDASANG, SABAH

CLASSIFICATION OF TRANSLATIONAL LANDSLIDE ACTIVITY USING VEGETATION ANOMALIES INDICATOR (VAI) IN KUNDASANG, SABAH

This paper introduced a novel method of landslide activity mapping using vegetation anomalies indicators (VAIs) obtained from high resolution remotely sensed data. The study area was located in a tectonically active area of Kundasang, Sabah, Malaysia. High resolution remotely sensed data were used t...

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Bibliographic Details
Published in:International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLVI-4/W3-2021; pp. 247 - 256
Main Authors: Mohd Salleh, M. R., Norhairi, N. H. A., Ismail, Z., Abd Rahman, M. Z., Abdul Khanan, M. F., Asmadi, M. A., Razak, K. A., Tam, T. H., Osman, M. J.
Format: Journal Article Conference Proceeding
Language:English
Published: Gottingen Copernicus GmbH 01-01-2022
Copernicus Publications
Subjects:
Accuracy
Anomalies
Classification
Datasets
Distribution
Dormancy
Genetic algorithms
High resolution
Indicators
Labour
Landslides
Landslides & mudslides
Optimization
Plant cover
Remote sensing
Resolution
Spatial resolution
Support vector machines
Training
Vegetation
Malaysia
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Summary:This paper introduced a novel method of landslide activity mapping using vegetation anomalies indicators (VAIs) obtained from high resolution remotely sensed data. The study area was located in a tectonically active area of Kundasang, Sabah, Malaysia. High resolution remotely sensed data were used to assist manual landslide inventory process and production on VAIs. The inventory process identified 33, 139, and 31 of active, dormant, and relict landslides, respectively. Landslide inventory map were randomly divided into two groups for training (70%) and validation (30%) datasets. Overall, 7 group of VAIs were derived including (i) tree height irregularities; (ii) tree canopy gap; (iii) density of different layer of vegetation; (iv) vegetation type distribution; (v) vegetation indices (VIs); (vi) root strength index (RSI); and (vii) distribution of water-loving trees. The VAIs were used as the feature layer input of the classification process with landslide activity as the target results. The landslide activity of the study area was classified using support vector machine (SVM) approach. SVM parameter optimization was applied by using Grid Search (GS) and Genetic Algorithm (GA) techniques. The results showed that the overall accuracy of the validation dataset is between 61.4–86%, and kappa is between 0.335–0.769 for deep-seated translational landslide. SVM RBF-GS with 0.5m spatial resolution produced highest overall accuracy and kappa values. Also, the overall accuracy of the validation dataset for shallow translational is between 49.8–71.3%, and kappa is between 0.243–0.563 where SVM RBF-GS with 0.5m resolution recorded the best result. In conclusion, this study provides a novel framework in utilizing high resolution remote sensing to support labour intensive process of landslide inventory. The nature-based vegetation anomalies indicators have been proved to be reliable for landslide activity identification in Malaysia.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLVI-4-W3-2021-247-2022