Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine

Handwerger, Alexander L.; Huang, M. H.; Jones, Shannan Y.; Amatya, Pukar; Kerner, Hannah; Kirschbaum, Dalia

doi:10.5194/nhess-22-753-2022

Cited by 31 publications

(48 citation statements)

References 57 publications

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“…We removed all pixels with rdNDVI > −10. Lastly, we searched for debris flows (and other ground surface deformation) by examining SAR backscatter change with data acquired by the 10 m Copernicus Sentinel-1 (S1) satellites (see full description in Handwerger et al, 2022). We measured the change in SAR backscatter by using the log ratio approach, defined as…”

Section: Debris Flow Identification From Remote Sensing and Field Workmentioning

confidence: 99%

“…We used a 3-month pre-and post-event window and 0 • slope threshold to identify ground surface changes associated with the debris flows. We then created a binary map to highlight debris flows by removing all pixels with I ratio < 99th percentile value (i.e., threshold suggested by Handwerger et al, 2022).…”

Section: Debris Flow Identification From Remote Sensing and Field Workmentioning

confidence: 99%

“…HazMapper v1.0 is available at https://doi.org/10.5281/zenodo.4103348 (Scheip and Wegmann, 2020). The SAR backscatter change method code is available at https://github.com/alhandwerger/GEE_scripts_ for_Handwerger_et_al_2022_NHESS (Huang and Handwerger, 2022).…”

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confidence: 99%

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Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars

Handwerger

Wang

et al. 2022

Nat. Hazards Earth Syst. Sci.

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Abstract. In steep wildfire-burned terrains, intense rainfall can produce large runoff that can trigger highly destructive debris flows. However, the ability to accurately characterize and forecast debris flow susceptibility in burned terrains using physics-based tools remains limited. Here, we augment the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) to simulate both overland and channelized flows and assess postfire debris flow susceptibility over a regional domain. We perform hindcast simulations using high-resolution weather-radar-derived precipitation and reanalysis data to drive non-burned baseline and burn scar sensitivity experiments. Our simulations focus on January 2021 when an atmospheric river triggered numerous debris flows within a wildfire burn scar in Big Sur – one of which destroyed California's famous Highway 1. Compared to the baseline, our burn scar simulation yields dramatic increases in total and peak discharge and shorter lags between rainfall onset and peak discharge, consistent with streamflow observations at nearby US Geological Survey (USGS) streamflow gage sites. For the 404 catchments located in the simulated burn scar area, median catchment-area-normalized peak discharge increases by ∼ 450 % compared to the baseline. Catchments with anomalously high catchment-area-normalized peak discharge correspond well with post-event field-based and remotely sensed debris flow observations. We suggest that our regional postfire debris flow susceptibility analysis demonstrates WRF-Hydro as a compelling new physics-based tool whose utility could be further extended via coupling to sediment erosion and transport models and/or ensemble-based operational weather forecasts. Given the high-fidelity performance of our augmented version of WRF-Hydro, as well as its potential usage in probabilistic hazard forecasts, we argue for its continued development and application in postfire hydrologic and natural hazard assessments.

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Section: Debris Flow Identification From Remote Sensing and Field Workmentioning

confidence: 99%

Section: Debris Flow Identification From Remote Sensing and Field Workmentioning

confidence: 99%

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Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars

Handwerger

Wang

et al. 2022

Nat. Hazards Earth Syst. Sci.

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“…changes in surface backscattering intensity of SAR signal between two images) (e.g. Mondini et al, 2017;, Esposito et al, 2020, DeVries et al, 2020, Handwerger et al, 2022 for which amplitude correlation is a common method used in amplitude change detection (Mondini et al, 2017, Konishi &Suga, 2018, Jung andYun et al, https://doi.org/10.5194/nhess-2022-172 Preprint. Discussion started: 23 June 2022 c Author(s) 2022.…”

Section: Introductionmentioning

confidence: 99%

Timing landslide and flash flood events from SAR satellite: a new method illustrated in African cloud-covered tropical environments

Deijns

Dewitte²,

Thiery³

et al. 2022

Preprint

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Abstract. Landslides and flash floods are geomorphic hazards (GH) that often co-occur and interact. They generally occur very quickly, leading to catastrophic socioeconomic impacts. Understanding the temporal patterns of occurrence of GH events is essential for hazard assessment, early warning and disaster risk reduction strategies. However, temporal information is often poorly constrained, especially in frequently cloud-covered tropical regions, where optical-based satellite data is insufficient. Here we present a new method to accurately estimate GH event timing which requires no prior knowledge of the GH event timing, using Synthetic Aperture Radar (SAR) remote sensing. SAR can penetrate through clouds and therefore provides an ideal tool for constraining GH event timing. We use the open-access Copernicus Sentinel-1 (S1) SAR satellite that provides global coverage, high spatial resolution (~10–15 m) and a high repeat time (6–12 days) from 2016 to 2020. We investigate the amplitude, detrended amplitude, spatial amplitude correlation, coherence and detrended coherence time series in their suitability to constrain GH event timing. We apply the method on four recent large GH events located in Uganda, Rwanda, Burundi and DRC containing a total of about 2500 manually mapped landslides and flash flood features located in several contrasting landscape types. The GH event timing estimation accuracies vary among the GH events and the data products. Coherence and detrended coherence estimated timing accuracies range from a 1 day to a 47 day difference. The spatial amplitude correlation estimated timing accuracy ranges from a 1 day to an 85 day difference. The amplitude and detrended amplitude estimated timing accuracies range from a 13 to a 1000 day difference. The amplitude time series reflects the influence of seasonal dynamics, which causes the timing estimations to be further away from the actual GH event occurrence compared to the other data products. Timing estimations are generally closer to the actual GH event occurrence for GH events within homogenous densely vegetated landscape, and further for GH events within complex cultivated heterogenous landscapes. We believe that the complexity of the different contrasting landscapes we study is an added value for the transferability of the method and together with the open access and global coverage of S1 data it has the potential to be widely applicable.

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“…Google Earth Engine (GEE) cloud processing (Gorelick et al, 2017) could also be used to create time series of several satellite data( Optical, SAR), which are useful to detect the change and the recovery of vegetation and to map landslides and their effect on vegetated areas (Scheip and Wegmann, 2021;Yu et al, 2018;Handwerger et al, 2022).…”

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confidence: 99%

Semi-automatic mapping of shallow landslides using free Sentinel-2 and Google Earth Engine

Notti

Cignetti

Godone

et al. 2022

Preprint

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Abstract. The global availability of Sentinel-2 data and the widespread coverage of free-cost and high-resolution images nowadays give opportunities to map, at low-cost, shallow landslides triggered by extreme events (e.g. rainfall, earthquake). A rapid and low-cost shallow landslides mapping could improve damages estimations, susceptibility models or land management. This work presents a semi-automatic methodology to map potential landslides (PL) using Sentinel-2 images, and it is the first step toward more detailed mapping. We create a GIS-based and user-friendly methodology to extract PL based on pre- post- event NDVI variation and geomorphological filtering. The semi-automatic inventory was compared with benchmark landslides inventory drawn on high-resolution images. We also used the Google Earth Engine scripts to extract the NDVI time series and make a multi-temporal analysis. We apply this to two study areas in NW Italy hatted in 2016 and 2019 by extreme rainfall events. The results show that the semi-automatic mapping based on Sentinel-2 allows detecting the majority of shallow landslides larger than satellite ground pixel (100 m2). PL density and distribution well match with the benchmark. However, the false positives (30 % to 50 % of cases) are challenging to filter, especially when they correspond to river bank erosions or cultivated land.

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Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine

Cited by 31 publications

References 57 publications

Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars

Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars

Timing landslide and flash flood events from SAR satellite: a new method illustrated in African cloud-covered tropical environments

Semi-automatic mapping of shallow landslides using free Sentinel-2 and Google Earth Engine

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