Transferability of object-based image analysis approaches for landslide detection in the Himalaya Mountains of northern Pakistan

Alam Sher Bacha*, H.M.A. Van Der Werff, Muhammad Shafique, Hawas Khan

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

14 Citations (Scopus)
125 Downloads (Pure)


A landslide inventory is indispensable for determination of landslide susceptibility, hazard, risk assessment and disaster mitigation strategies. These inventories were traditionally developed using manual digitization of remote sensing images and aerial photographs, and pixel-based image classification. Recently, Object-Based Image Analysis (OBIA) supersedes visual interpretation and pixel-based methods. OBIA utilizes spectral, textural, contextual, morphological and topographical information in remote sensing images. However, OBIA-based landslide detection methods are often designed for specific areas and remote sensing dataset. The aim of this study is to evaluate the transferability of three published OBIA landslide detection methods for semi-automated landslide detection in the Himalaya mountainous region of northern Pakistan. A SPOT-6 multi-spectral image with Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) Digital Elevation Model (DEM) derivatives, i.e. slope, aspect, hill-shade, relief, elevation and stream network are used for landslide detection using eCognition developer software. The three published methods scale parameters for image segmentation and parameter thresholds are evaluated first. It is observed that the aforementioned methods are not directly applicable to our study area and remote sensing datasets. Therefore, an alternate (proposed) method is developed for semi-automated landslide detection. Accuracy assessment of the selected methods and proposed method is assessed by Precision, Recall and F1 measures. Using the proposed method, a total of 357 landslides are detected with 91.46% Precision, 93.31% Recall and 92.38% F1 measure accuracy.

Original languageEnglish
Pages (from-to)3390-3410
Number of pages21
JournalInternational journal of remote sensing
Issue number9
Early online date2 Jan 2020
Publication statusPublished - 2 May 2020


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