Combined ASTER and MODIS Emissivity database over Land (CAMEL) Coefficient Monthly Global 0.05Deg V003

Abstract Landslides can have significant and pervasive impacts to life and property around the world. Several attempts have been made to predict the geographic distribution of landslide activity at continental and global scales. These efforts shared common traits such as resolution, modeling approach, and explanatory variables. The lessons learned from prior research have been applied to build a new global susceptibility map from existing and previously unavailable data. Data on slope, faults, geology, forest loss, and road networks were combined using a heuristic fuzzy approach. The map was evaluated with a Global Landslide Catalog developed at the National Aeronautics and Space Administration, as well as several local landslide inventories. Comparisons to similar susceptibility maps suggest that the subjective methods commonly used at this scale are, for the most part, reproducible. However, comparisons of landslide susceptibility across spatial scales must take into account the susceptibility of the local subset relative to the larger study area. The new global landslide susceptibility map is intended for use in disaster planning, situational awareness, and for incorporation into global decision support systems. Data visualization Landslide susceptibility is shown using a 5-color Color Ramp from blue to red. Values represent Very Low (blue) to Very High (red) susceptibility to a landslide. Further reference Read more about the susceptibility map on the Global Precipitation Measurement (GPM) mission website. Stanley, T., and D. B. Kirschbaum (2017), A heuristic approach to global landslide susceptibility mapping, Nat. Hazards, 1–20, doi:10.1007/s11069-017-2757-y.

The open water layer represents the compilation of later winter open water observations including regionally focused helicopter-based open water surveys from historical exploration work and various open water observations throughout the Yukon. Distributed from GeoYukon by the Government of Yukon. Discover more digital map data and interactive maps from Yukon’s digital map data collection. For more information: geomatics.help@yukon.ca

Landslides are a widespread and often underestimated natural hazard, causing thousands of deaths and significant economic damage each year. They routinely block roads, destroy infrastructure, and disrupt communities worldwide. While intense or prolonged rainfall is the most common landslide trigger, earthquakes and human activities can also cause them. The global Landslide Hazard Assessment for Situational Awareness (LHASA) model provides users with a comprehensive view of landslide hazard in near real-time. Using machine learning, LHASA combines rainfall data from the Global Precipitation Measurement (GPM) satellite with antecedent soil moisture from the Soil Moisture Active Passive (SMAP) satellite to identify areas currently at risk from landslides. The model shows where and when landslides are most probable, then calculates the exposed population and road length within each administrative district, helping users understand both the hazard and potential impacts on communities and infrastructure. Daily global landslide exposure assessment from the LHASA (Landslide Hazard Assessment for Situational Awareness) model. Color-coded hazard levels range from red (very high exposure) to yellow (low exposure), helping users identify areas where landslides may occur. To learn more please visit: https://landslides.nasa.gov/

On 6 February 2023 at 4:17h, local time in Türkiye (01:17 GMT), an earthquake with a magnitude of 7.8 hit Türkiye and northwestern Syria. It mainly affected the cities of Adana, Adiyaman, Diyarbakir, Gaziantep, Hatay, Kahramanmaras, Kilis, Malatya, Osmaniye, and Sanliurfa in southeastern Türkiye and Aleppo, Hama, Idlib, Lattakie, and Tartous governorates in northwestern Syria. The earthquake’s epicentre was 23km east of the district of Nurdagi, in Türkiye’s Gaziantep city, at a depth of 24.1km. Türkiye is vulnerable to earthquakes because it lies on several fault lines, and the 7.8 quake seems to be the strongest since 1939 On the same day, at 13:45h, local time in Türkiye, another earthquake with a magnitude of 7.6 struck Türkiye. It hit four kilometres southeast of Ekinozu town in Kahraman Maras province, where the 7.8 earthquake hit hours earlier. As at 6 February, there had been at least 78 aftershocks.

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) emissivity database and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4 (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics. The integration steps include adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the UWBF 5 kilometer resolution, merging of the five ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). Provided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and the UWBF emissivity quality information. Known Issues * The following granules are missing from the product: January, February, and August of 2000; June and July of 2001; March of 2002; December of 2003; July of 2010; and February of 2016.

# EOS-RS Damage Proxy Map: Hualien County, Taiwan, Landslide, 24 June 2026, v1.2 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in Hualien County, Taiwan, due to the landslide that created a barrier lake on 22 June 2026. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite using a pre-event time series of (23 Feb 2026 to 11 June 2026) and a post-event acquisition (23 June 2026). The map covers the area indicated by the white polygon. Damage is shown by colored pixels of approximately 30m in size, where yellow to red indicates increasingly significant ground surface change before and after the event. Preliminary validation was conducted using news reports and pictures in selected areas. This map should be used as guidance to identify damaged areas and may be less reliable over vegetated or mountainous areas. Scattered pixels over such areas may be false positives, and a lack of colored pixels does not necessarily indicate an absence of damage. This product contains modified Copernicus Sentinel data (2026), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS). **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2026)