Automatic Extraction and Size Distribution of Landslides in Kurdistan Region, NE Iraq

Abstract

This study aims to assess the localization and size distribution of landslides using automatic remote sensing techniques in (semi-) arid, non-vegetated, mountainous environments. The study area is located in the Kurdistan region (NE Iraq), within the Zagros orogenic belt, which is characterized by the High Folded Zone (HFZ), the Imbricated Zone and the Zagros Suture Zone (ZSZ). The available reference inventory includes 3,190 landslides mapped from sixty QuickBird scenes using manual delineation. The landslide types involve rock falls, translational slides and slumps, which occurred in different lithological units. Two hundred and ninety of these landslides lie within the ZSZ, representing a cumulated surface of 32 km 2 . The HFZ implicates 2,900 landslides with an overall coverage of about 26 km 2 . We first analyzed cumulative landslide number-size distributions using the inventory map. We then proposed a very simple and robust algorithm for automatic landslide extraction using specific band ratios selected upon the spectral signatures of bare surfaces as well as posteriori slope and the normalized difference vegetation index (NDVI) thresholds. The index is based on the contrast between landslides and their background, whereas the landslides have high reflections in the green and red bands. We applied the slope threshold map to remove low slope areas, which have high reflectance in red and green bands. The algorithm was able to detect ~96% of the recent landslides known from the reference inventory on a test site. The cumulative landslide number-size distribution of automatically extracted landslide is very similar to

Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data

“…Most of the existing automated methods using optical remote sensing data have been developed for one-time landslide mapping after a single major triggering event. These methods are based on either a single post-event classification (e.g., [15,[20][21][22][23]) or a bi-temporal change detection between an image pair acquired before and after the triggering event (e.g., [24][25][26][27][28]). Single post-event classification approaches assume that all of the mapped landslides have been caused by the analyzed triggering event without further specifying the time period of landslide occurrence.…”

“…In this context, the use of multi-temporal satellite remote sensing data opens up the opportunity for the development of efficient methods for systematic spatiotemporal mapping of landslides over large areas. For the purpose of post-failure mapping, mainly optical remote sensing data have been used, as most of the landslide processes lead to disturbance of the Earth's surface resulting in significant changes in the reflectance characteristics of these surfaces [6,[13][14][15].…”

Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)

“…This belt is around 2000 km long, extending from SE Turkey through Iraq to southern Iran [35][36][37][38]. The Iraqi part of the Zagros orogenic belt consists of three main tectonic zones: (1) the Inner Platform (stable shelf); (2) the Outer Platform (unstable shelf), which comprises the Mesopotamia Foredeep, the Foothill Zone, the High Folded Zone, and the Imbricated Zone (IZ); and (3) the Zagros Suture Zone (ZSZ) ( [9,30,36,39,40]; Figure 1).…”

“…First, we extracted the Hypsometric Integral (HI; Equation (6)) [78], an index appropriate to identify the evolutionary stage of a landscape development [37,79,80]. The HI values range between 0 to 1, which refer to erosion progression where the high values represent mountainous relief and low values flattened plain landscape [79].…”

Landslide Mapping in Vegetated Areas Using Change Detection Based on Optical and Polarimetric SAR Data

“…A common way for landslide detection is the mapping of rapid changes of the vegetation layer derived from vegetation indices calculated for pre-and post-event optical EO imagery, e.g., [9][10][11][12][13][14][15].…”