The majority of continental earthquakes occur in mountainous regions, where they can trigger thousands of landslides over areas of several tens of thousands of km2
]. These landslides are responsible for more deaths globally than any other secondary earthquake hazard [3
]. Earthquake-triggered landslides cause damage to power, transportation and communication infrastructure, isolating remote communities and disrupting emergency response efforts, and may cause further hazards such as dam-outburst floods [4
Information on where landslides have occurred is therefore essential for emergency response coordination and for directing site-specific investigations on the ground, e.g., [6
]. This information must be rapidly generated and communicated in order to limit delays to resource allocation and therefore be of practical value [8
]. The information may take several forms, from detailed maps of landslide locations, with individual events recorded either as points, polylines or polygons, to landslide density maps that identify regions that have experienced high numbers or large areas of landslides [9
Following past earthquakes that triggered extensive landsliding, landslide information products have been generated too slowly for use in emergency response. The most common method, which is to identify triggered landslides through comparison of pre-event and post-event optical satellite imagery, is labour-intensive and reliant on the acquisition of cloud-free imagery. In some cases, automation can alleviate the labour-intensive nature of this process, e.g., [10
], but cloud-cover often presents an insurmountable barrier to rapid production of landside maps using optical imagery. This delays the supply of information to emergency response coordinators, as was the case in the aftermath of the 2015 Nepal and 2016 Ecuador earthquakes [9
When cloud-free optical satellite imagery is unavailable, emergency response coordinators must rely on ground-based observations, which may not have wide or homogeneous areal coverage, and on the outputs from predictive models. Such models estimate where landslides are likely to have occurred based on factors such as peak ground acceleration, topographic slope and proximity to rivers or active faults, e.g., [12
]. However, these models are generally static in time, empirical in nature, and are strongly dependent on input data quality. Peak ground acceleration, for example, may be poorly constrained immediately following the earthquake [15
]. Additionally, the models may fail to capture differences in susceptibility for different regions, as illustrated by the significant differences in triggered landsliding between the 2008 Mw
7.9 Wenchuan earthquake and the 2015 Mw 7.8 Gorkha earthquake [1
]. The inclusion of observed landslide data in these models improves their predictive skill but the improvement is limited if these data are clustered, as they necessarily must be if mapped using optical satellite imagery through small gaps in cloud-cover [14
Synthetic Aperture Radar (SAR) satellite imagery, which uses active emission and sensing of electromagnetic radiation in the microwave rather than the visible light spectrum, can acquire useable imagery in cloudy conditions as radar is able to penetrate cloud cover. SAR may therefore provide a solution to the problem of mapping landslides when cloud obstructs optical imagery. In recent years the number of satellite-based SAR systems has vastly increased, leading to a corresponding increase in the frequency and regularity of image acquisition everywhere on Earth [16
]. For example, the European Space Agency’s (ESA’s) Sentinel-1 satellite constellation (comprising the Sentinel-1a and Sentinel-1b satellites), imagery from which is used in this study, comprises two satellites and acquires imagery on ascending and descending tracks every 12 days for tectonic regions globally and every 6 days in Europe [17
]. These data are freely available to download.
SAR products are routinely used in other rapid response situations, for example in flood mapping or in the production of interferograms to map ground deformation after an earthquake or during an episode of volcanic unrest [18
]. NASA’s Advanced Rapid Imaging and Analysis (ARIA) project uses SAR to produce damage proxy maps in urban areas following earthquakes, cyclones or wildfires [20
]. SAR methods such as offset tracking, e.g., [22
], persistant scatterer interferometry, e.g., [23
] and traditional differential InSAR, e.g., ref. [25
] are also used in monitoring the movements of slow-moving landslides. Persistent scatterer interferometry and traditional differential InSAR have been used in several cases to supplement pre-existing inventories with ground surface deformation information, which can be used to evaluate the state of activity of the landslides [26
]. However, the potential use of SAR in rapid production of landslide maps for emergency response has only been demonstrated on individual landslides or catchments, and with limited success [21
A clear example of the limitations of landslide mapping using optical imagery, and the potential that SAR has to overcome these limitations was the Gorkha earthquake on 25 April 2015, which triggered over 25,000 landslides in the surrounding mountains (Figure 1
]. Figure 2
shows a timeline of mapping efforts carried out by an international team of researchers using optical satellite imagery and intended for use by emergency response coordinators [9
]. Although the earthquake occurred during Nepal’s dry season, cloud cover caused severe delays to landslide mapping, with almost no cloud-free imagery available in the first week following the earthquake and some areas remaining unmapped up until the onset of the monsoon on 9 June 2015, roughly one-and-half months later. The emergency response process evolves quickly in comparison. For example, the United Nations response framework following a disaster mandates an initial assessment after 72 h and a second after 2 weeks [9
]. In the case of the 2015 Gorkha earthquake, the impending monsoon season applied additional time pressure, because the arrival of the monsoon would make cloud-free optical image acquisition unlikely and because it was anticipated that the earthquake would increase the severity of rainfall triggered landsliding [7
]. The acquisition of useable SAR imagery and generation of associated products occurred comparatively quickly (Figure 2
). Five days following the earthquake, NASA’s ARIA team released an initial damage proxy map for building damage in Kathmandu based on SAR data acquired by the Italian Space Agency’s COSMO-SkyMed satellite system. The first post-event imagery acquired on each satellite acquisition track by ESA’s Sentinel-1a satellite is shown on Figure 1
, with the first of these being acquired 4 days after the Gorkha mainshock. Sentinel-1 coverage has since improved with the launch of a second satellite, Sentinel-1b, in 2016. Had it been possible to use SAR products in mapping landslides following the Gorkha earthquake, critical information on landslide distribution could have been delivered to first responders and government agencies with greater areal coverage and better timeliness than was possible from optical satellite data.
In this paper, we investigate automatic methods to detect landslides using SAR and present a new method based on SAR coherence. We tested this method on the landslides triggered by the Gorkha earthquake, using a comprehensive independent inventory of triggered landslides produced from manual analysis of optical satellite imagery [2
]. Additionally, multiple reports have been published discussing the emergency response effort following the earthquake, allowing identification of how SAR landslide products could have been used if they had been available [5
Both maps of individual landslides and of landslide density are useful in the emergency response process [9
]. We therefore assessed each classification surface in terms of their ability to: (1) identify individual landslides at a pixel-by-pixel scale; and (2) identify areas that had experienced extensive landsliding at a series of increasingly coarse spatial scales. To do this we produced aggregate classification surfaces, for which the original surface was divided into N
pixel squares and the mean pixel value within each square was taken as the aggregate classifier value. These were then normalised as before to produce a surface of values between 0 and 1 for each classifier. A landslide density surface was calculated as the percentage mapped landslide area of each aggregate pixel. For the purpose of ROC analysis, which requires a binary validation dataset, we assigned aggregate pixels with over 50% landslide density as ‘landslide’ and those with under 50% ‘non-landslide’, although we also test the sensitivity of all methods to this choice.
shows a map of landslides from Roback et al. [2
] and each normalised coherence-based classification surface. Two areas are shown, selected to contain different sizes of landslides. The first is around the Village Development Committee (an administrative region) of Jharlang, located in the Himalayan foothills within Dhading District. The second area covers the Langtang Valley in Rasuwa District, where an exceptionally large landslide with an area of 1.7 km2
led to hundreds of fatalities [6
]. As was found by Yun et al. [21
], the large landslide in the Langtang Valley is visible in the ARIA classification surface. However, in the Jharlang area, where landslides were smaller, the ARIA method was less successful, and the surface is noisy. The new method, Bx-S struggles to differentiate between landslide and non-landslide pixels in both locations, as does absolute coherence, which suffers from false positives.
ROC analysis confirms that all three methods perform poorly as landslide classifiers on a pixel-by-pixel scale, with AUC <0.6 (Figure 9
). However, Bx-S and to a lesser extent ARIA and absolute coherence are more successful at identifying areas of intense landsliding. ROC analysis shows that increasing pixel size through aggregation results in improved performance for all methods. In particular, Bx-S outperforms the two existing methods at all aggregations and does better with increasing aggregation. ROC AUC for Bx-S increases from 0.56 to 0.77 when aggregated from 20 m × 22 m pixels to 300 m × 330 m. For the same aggregation, absolute coherence ROC AUC increases from 0.55 to 0.72 and the ARIA method ROC AUC increases from 0.57 to 0.68. Figure 10
shows classification surfaces for the whole area at an aggregated pixel size of 200 m × 220 m (10 × 10 pixels), along with a smaller region within Gorkha district. In this smaller region, Bx-S appears relatively successful in recreating the spatial pattern of landslide density, while ARIA and absolute coherence have many false positives, making it difficult to identify the correlation with landslide density. To allow direct comparison, the inset region in Figure 10
is shown in Figure A2
) prior to aggregation.
Several factors exerted a relatively strong influence on classification ability for the different classifiers: one related to spatial scales, a second to the time window of SAR acquisition, and a third to the definition of ‘landslide’ pixels. First, increasing the size of the boxcar window worsens performance for all three classifiers. We have presented all results in this study using a 3 × 3-pixel window but we also tested 5 × 5 and 20 × 20 windows. In most cases increasing the size of the boxcar window reduced ROC AUC, although not for the Bx-S classification surface (Table 1
). We carried out the comparison on aggregates of 10 × 10 individual pixels. This was in order to lessen the effect of coarsening resolution on classification ability discussed above.
Second, for all three classifiers, classification ability worsened when the time window between SAR image acquisition was increased. This was expected as a longer time window will have increased temporal decorrelation unrelated to landsliding, particularly in vegetated areas. Third, results were affected by how aggregate ‘landslide’ pixels were defined. For the purposes of ROC analysis, an aggregate landslide pixel was defined as one comprising at least 50% individual landslide pixels. In varying this threshold, we found that ROC AUC was higher when landslide pixels were more strictly defined by a higher threshold (Table 2
). The classifiers are therefore better able to identify a region that has experienced more severe landsliding, which may affect how they can be applied. We also tested the effect of altering the size of the RapidSAR search window from 41 × 41, which is used throughout this study, to 21 × 21, 61 × 61 and 81 × 81. This had little effect on ROC AUC on both individual and aggregated pixel surfaces (No more than 0.01 difference) but computation time was noticeably different. The time taken for an 81 × 81 window was around double that of the 21 × 21 window.