Automated Satellite-Based Landslide Identification Product for Nepal

Landslide event inventories are a vital resource for landslide susceptibility and forecasting applications. However, landslide inventories can vary in accuracy, availability, and timeliness as a result of varying detection methods, reporting, and data availability. This study presents an approach to use publicly available satellite data and open source software to automate a landslide detection process called the Sudden Landslide Identification Product (SLIP). SLIP utilizes optical data from the Landsat 8 OLI sensor, elevation data from the Shuttle Radar Topography Mission (SRTM), and precipitation data from the Global Precipitation Measurement (GPM) mission to create a reproducible and spatially customizable landslide identification product. The SLIP software applies change detection algorithms to identify areas of new bare-earth exposures that may be landslide events. The study also presents a precipitation monitoring tool that runs alongside SLIP called the Detecting Real-time Increased Precipitation (DRIP) model that helps identify the timing of potential landslide events detected by SLIP. Using SLIP and DRIP together, landslide detection is improved by reducing problems related to accuracy, availability, and timeliness that are prevalent in the state-of-the-art of landslide detection. A case study and validation exercise was performed in Nepal for images acquired between 2014 and 2015. Preliminary validation results suggest 56% model accuracy, with errors of commission often resulting from newly cleared agricultural areas. These results suggest that SLIP is an important first attempt in an automated framework that can be used for medium resolution regional landslide detection, although it requires refinement before being fully realized as an operational tool

Unsupervised classification for landslide detection from airborne laser scanning

Landslides are natural disasters that cause extensive environmental, infrastructure and socioeconomic damage worldwide. Since they are difficult to identify, it is imperative to evaluate innovative approaches to detect early-warning signs and assess their susceptibility, hazard and risk. The increasing availability of airborne laser-scanning data provides an opportunity for modern landslide mapping techniques to analyze topographic signature patterns of landslide, landslide-prone and landslide scarred areas over large swaths of terrain. In this study, a methodology based on several feature extractors and unsupervised classification, specifically k-means clustering and the Gaussian mixture model (GMM) were tested at the Carlyon Beach Peninsula in the state of Washington to map slide and non-slide terrain. When compared with the detailed, independently compiled landslide inventory map, the unsupervised methods correctly classify up to 87% of the terrain in the study area. These results suggest that (1) landslide scars associated with past deep-seated landslides may be identified using digital elevation models (DEMs) with unsupervised classification models; (2) feature extractors allow for individual analysis of specific topographic signatures; (3) unsupervised classification can be performed on each topographic signature using multiple number of clusters; (4) comparison of documented landslide prone regions to algorithm mapped regions show that algorithmic classification can accurately identify areas where deep-seated landslides have occurred. The conclusions of this study can be summarized by stating that unsupervised classification mapping methods and airborne light detection and ranging (LiDAR)-derived DEMs can offer important surface information that can be used as effective tools for digital terrain analysis to support landslide detection.Fil: Tran, Caitlin J.. California State Polytechnic University; Estados UnidosFil: Mora, Omar E.. California State Polytechnic University; Estados UnidosFil: Fayne, Jessica V.. University of California at Los Angeles; Estados UnidosFil: Lenzano, María Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentin

A High-Resolution Airborne Color-Infrared Camera Water Mask for the NASA ABoVE Campaign

The airborne AirSWOT instrument suite, consisting of an interferometric Ka-band synthetic aperture radar and color-infrared (CIR) camera, was deployed to northern North America in July and August 2017 as part of the NASA Arctic-Boreal Vulnerability Experiment (ABoVE). We present validated, open (i.e., vegetation-free) surface water masks produced from high-resolution (1 m), co-registered AirSWOT CIR imagery using a semi-automated, object-based water classification. The imagery and resulting high-resolution water masks are available as open-access datasets and support interpretation of AirSWOT radar and other coincident ABoVE image products, including LVIS, UAVSAR, AIRMOSS, AVIRIS-NG, and CFIS. These synergies offer promising potential for multi-sensor analysis of Arctic-Boreal surface water bodies. In total, 3167 km2 of open surface water were mapped from 23,380 km2 of flight lines spanning 23 degrees of latitude and broad environmental gradients. Detected water body sizes range from 0.00004 km2 (40 m2) to 15 km2. Power-law extrapolations are commonly used to estimate the abundance of small lakes from coarser resolution imagery, and our mapped water bodies followed power-law distributions, but only for water bodies greater than 0.34 (±0.13) km2 in area. For water bodies exceeding this size threshold, the coefficients of power-law fits vary for different Arctic-Boreal physiographic terrains (wetland, prairie pothole, lowland river valley, thermokarst, and Canadian Shield). Thus, direct mapping using high-resolution imagery remains the most accurate way to estimate the abundance of small surface water bodies. We conclude that empirical scaling relationships, useful for estimating total trace gas exchange and aquatic habitats on Arctic-Boreal landscapes, are uniquely enabled by high-resolution AirSWOT-like mappings and automated detection methods such as those developed here

Tracking transient boreal wetland inundation with Sentinel-1 SAR: Peace-Athabasca Delta, Alberta and Yukon Flats, Alaska

Accurate and frequent mapping of transient wetland inundation in the boreal region is critical for monitoring the ecological and societal functions of wetlands. Satellite Synthetic Aperture Radar (SAR) has long been used to map wetlands due to its sensitivity to surface inundation and ability to penetrate clouds, darkness, and certain vegetation canopies. Here, we track boreal wetland inundation by developing a two-step modified decision-tree algorithm implemented in Google Earth Engine using Sentinel-1 C-band SAR and Sentinel-2 Multispectral Instrument (MSI) time-series data as inputs. This approach incorporates temporal as well as spatial characteristics of SAR backscatter and is evaluated for the Peace-Athabasca Delta, Alberta (PAD), and Yukon Flats, Alaska (YF) from May 2017 to October 2019. Within these two boreal study areas, we map spatiotemporal patterns in wetland inundation classes of Open Water (OW), Floating Plants (FP), Emergent Plants (EP), and Flooded Vegetation (FV). Temporal variability, frequency, and maximum extents of transient wetland inundation are quantified. Retrieved inundation estimates are compared with in-situ field mapping obtained during the NASA Arctic-Boreal Vulnerability Experiment (ABoVE), and a multi-temporal Landsat-derived surface water map. Over the 2017–2019 study period, we find that fractional inundation area ranged from 18.0% to 19.0% in the PAD, and from 10.7% to 12.1% in the YF. Transient wetland inundation covered ~595 km2 of the PAD, comprising ~9.1% of its landscape, and ~102 km2 of the YF, comprising ~3.6%. The implications of these findings for wetland function monitoring, and estimating landscape-scale methane emissions are discussed, together with limitations and uncertainties of our approach. We conclude that time series of Sentinel-1 C-band SAR backscatter, screened with Sentinel-2 MSI optical imagery and validated by field measurements, offer a valuable tool for tracking transient boreal wetland inundation

Landslide Change Detection Based on Multi-Temporal Airborne LiDAR-Derived DEMs

Remote sensing technologies have seen extraordinary improvements in both spatial resolution and accuracy recently. In particular, airborne laser scanning systems can now provide data for surface modeling with unprecedented resolution and accuracy, which can effectively support the detection of sub-meter surface features, vital for landslide mapping. Also, the easy repeatability of data acquisition offers the opportunity to monitor temporal surface changes, which are essential to identifying developing or active slides. Specific methods are needed to detect and map surface changes due to landslide activities. In this paper, we present a methodology that is based on fusing probabilistic change detection and landslide surface feature extraction utilizing multi-temporal Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) to map surface changes demonstrating landslide activity. The proposed method was tested in an area with numerous slides ranging from 200 m2 to 27,000 m2 in area under low vegetation and tree cover, Zanesville, Ohio, USA. The surface changes observed are probabilistically evaluated to determine the likelihood of the changes being landslide activity related. Next, based on surface features, a Support Vector Machine (SVM) quantifies and maps the topographic signatures of landslides in the entire area. Finally, these two processes are fused to detect landslide prone changes. The results demonstrate that 53 out of 80 inventory mapped landslides were identified using this method. Additionally, some areas that were not mapped in the inventory map displayed changes that are likely to be developing landslides

Flood Mapping in the Lower Mekong River Basin Using Daily MODIS Observations

In flat homogenous terrain such as in Cambodia and Vietnam, the monsoon season brings significant and consistent flooding between May and November. To monitor flooding in the Lower Mekong region, the near real-time NASA Flood Extent Product (NASA-FEP) was developed using seasonal normalized difference vegetation index (NDVI) differences from the 250 m resolution Moderate Resolution Imaging Spectroradiometer (MODIS) sensor compared to daily observations. The use of a percentage change interval classification relating to various stages of flooding reduces might be confusing to viewers or potential users, and therefore reducing the product usage. To increase the product usability through simplification, the classification intervals were compared with other commonly used change detection schemes to identify the change classification scheme that best delineates flooded areas. The percentage change method used in the NASA-FEP proved to be helpful in delineating flood boundaries compared to other change detection methods. The results of the accuracy assessments indicate that the 75% NDVI change interval can be reclassified to a descriptive 'flood' classification. A binary system was used to simplify the interpretation of the NASA-FEP by removing extraneous information from lower interval change classes

(Table A1) Optical attenuation coefficients of glacier ice layer B (53-124 cm ice depth) from 350-600 nm (West Greenland)

Formal estimates of attenuation coefficient for Layer A, interpolated to 1 nm resolution using a convolution (Savitsky-Golay) filter, and reported for the useable range of values, as described in the article

(Table A2) Optical attenuation coefficients of glacier ice layer A (12-77 cm ice depth) from 350-700 nm (West Greenland)

Formal estimates of attenuation coefficient for Layer A, interpolated to 1 nm resolution using a convolution (Savitsky-Golay) filter, and reported for the useable range of values, as described in the article

Optical attenuation coefficients of glacier ice from 350-700 nm and raw irradiance values from 350-900 nm

Optical attenuation coefficients of glacier ice from 350-700 nm were estimated from in-ice solar irradiance measured over the spectral range 350-900 nm and 12-124 cm depth collected at a site in the western Greenland ablation zone (67.15 oN, 50.02 oW). The acquired spectral irradiance measurements are used to calculate irradiance (flux) attenuation coefficients using an exponential decay Bouguer law model. Spectral absorption coefficients are estimated using the method of Warren et. al. (2006), which relates the attenuation coefficient to the absorption coefficient in the visible spectrum. The attenuation coefficients are calculated with linear regression between ice thickness in units of solid ice equivalent referenced to 917 kg/m3 and co-located transmittance. Solid ice equivalent thickness is calculated from in-situ ice density measured in the field on an ice core extracted from the measurement location. The ice density was 699 kg/m3 from 0-8 cm depth, 801 kg/m3 from 4-45 cm , 883 kg/m3 from 45-74 cm, and 888 kg/m3 from 74-122 cm. The depth-weighted ice density in the regions where attenuation was measured was 835 kg/m3 (12-77 cm) and 855 kg/m3 (53-124 cm). The field measurements were completed between 13:45 and 14:35 local time (UTC -3), at solar zenith angles of ~48–51o. Solar noon at this time and location is ~13:26

(Table A3) Raw irradiance values of glacier ice layer B (53-124 cm depth) from 350-900 nm (West Greenland)

This dataset contains the raw irradiance values that can be used to compute transmittance and attenuation coefficient, and are not interpolated or filtered, so the user can decide how to use the data