Landslide impact analysis in eThekwini, Metropolitan Municipality, KwaZulu-NatalProvince, South Africa as of 20 April 20

**UNOSAT code: FL20220418ZAF** This map illustrates satellite-detected landslides/mudflow in eThekwini, Metropolitan Municipality, eThekwini District, KwaZulu-Natal Province, South Africa as observed from a Kompsat-3 image acquired on 21 April 2022. Within the analyzed area, 270 ha of landslide scars were observed. Based on Worldpop population data, about 283,000 people live inside the analyzed area. Within the analysis extent, UNITAR-UNOSAT identified 506 damaged structures and 197 potentially damaged structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20220418ZAF** This map illustrates satellite-detected landslides/mudflow-related impact in eThekwini, Metropolitan Municipality, eThekwini District, KwaZulu-Natal Province, South Africa as observed from a Kompsat-3 image acquired on 21 April 2022. Within the analyzed area, 21 ha of landslide scars were observed. Based on Worldpop population data, about 55,000 people live inside the analyzed area. Within the analysis extent, UNITAR-UNOSAT identified 121 damaged structures and 247 potentially damaged structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20220418ZAF** This map illustrates satellite-detected landslides/mudflow in eThekwini, Metropolitan Municipality, eThekwini District, KwaZulu-Natal Province, South Africa as observed from a Sentinel-2 image acquired on 28 April 2022.Within the analyzed area, 11 ha of landslide scars were observed. Based on Worldpop population data, about 27,000 people live inside the analyzed area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20220418ZAF** This map illustrates satellite-detected landslides/mudflow in Umzumbe & Ray Nkonyeni Local Municipalities, Ugu District, KwaZulu-Natal Province, South Africa as observed from a Kompsat-3 image acquired on 21 April 2022. Within the analyzed area, 75 ha of landslide scars were observed. Based on Worldpop population data, about 67,000 people live inside the analyzed area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

The Cooperative Open Online Landslide Repository (COOLR) contains citizen science data from Landslide Reporter Catalog (LRC), data from NASA’s Global Landslide Catalog (GLC), NASA manually or automatically digitized landslides, and other inventories contributed by the research community. These are sorted into two main layers, "coolr_reports" and "coolr_events". The layers are further differentiated into a point or polygon layer, named accordingly. Within these layers, the user can further query out the manual or automated landslide inventories, events of a certain date, event triggers, and more. Each feature has a “title” in the attribute table with the digitization method, location, and event date (written as YYYY-MM-DD). The citation for each event inventory is located within the attribute table field “citation”. See the methods section for further detail about manual vs automatic landslide detection. The citation for each event inventory is located within the attribute table field “citation”. If you utilize this inventory, please reference each citation of the event(s) used. For example, if you use the entire inventory layer, please reference all the event citations contained within the layer. If you use just the events past a certain date, or another subset of the layer, please query those citations out and reference those. In addition, a CSV containing all citations can be found on the download page: https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521. Methods We use 'landslide' as a general term to represent all event types. The catalog differentiates between landslide types when the information is available in the ls_type field. Manually digitized landslides (method = Manual): These are hand-digitized landslide points or polygons, created by NASA scientists or contributed by other institutions. They are created on a case-by-case basis, depending on time and resources, or if there is an event where a partner requests it. The ability to hand-digitize depends on the availability of high-resolution cloud-free imagery, ideally before and after the event. Depending on the event size, location, and the satellite overpass, imagery from multiple dates may be used. For points, the scientist tries to identify where the initiation zone of each landslide is using an elevation layer. For polygons, the initiation zone is not differentiated. Automatically digitized landslides (method = Automatic): The open-source Semi-Automatic Landslide Detection (SaLaD) system was created to rapidly map landslides. SALaD uses object-based image analysis and machine learning to detect landslides from optical imagery. To successfully detect landslides, cloud-free imagery from before and after an event is needed, in addition to training data from that region or a region nearby. The raw polygon output of this system can be useful for situational awareness, but it will require manual corrections to weed out false positives for other use cases. Once that is done, initiation points can be created using a DEM. For Further Reference Landslides.nasa.gov Amatya, P., Kirschbaum, D., Stanley, T., and Tanyas, H. (2021). Landslide mapping using object-based image analysis and open source tools. Engineering Geology. 282. doi: 10.1016/j.enggeo.2021.106000. Amatya, P., Kirschbaum, D., and Stanley, T. (2021). Rainfall-induced landslide inventories for Lower Mekong based on Planet imagery and a semi-automatic mapping method. Geoscience Data Journal. doi: 10.1002/gdj3.145. Juang CS, Stanley TA, and Kirschbaum DB (2019) Using citizen science to expand the global map of landslides: Introducing the Cooperative Open Online Landslide Repository (COOLR). PLOS ONE. 14(7): e0218657. doi: 1371/journal.pone.0218657. Kirschbaum, D.B., Stanley, T., & Zhou, Y. (2015). Spatial and temporal analysis of a global landslide catalog. Geomorphology, 249, 4-15. doi:10.1016/j.geomorph.2015.03.016 Kirschbaum, D.B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52, 561-575. doi:10.1007/s11069-009-9401-4

Date of Images: Post-Event: 6/26/2026, 6/27/2026 Pre-Event: May 2026 Summary: These Black Marble Day-Night Band (BRDF-Corrected) images were created by the NASA Black Marble Science team. The images are corrected for atmospheric, terrain, lunar BRDF, and straylight effects, and directly measures light intensity on the ground in units of nanowatts/(steradian centimeter squared). The images are scaled from 0 - 30. The baseline image is from September 2025, generated from all daily atmospheric and lunar-corrected data from that month. There is a layer to display where clouds are present. This comparison between the images is meant as a visual assessment of outage impacts to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NASA's Black Marble Nighttime Light product suite is a state-of-the-art daily global collection of standard products for monitoring nighttime lights (NTL). Utilizing the Visible Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) aboard the Suomi-NPP, NOAA-20, and NOAA-21 satellites. The image is in inferno color scale. Yellow represents presence of more light; dark blue less lights. Observations may be obscured by total or partial cloud cover. Other factors such as snow reflectance, moon phase, wildfires, and wildfire smoke may distort light radiance and require further analysis when assessing power outages. Satellite/Sensor: Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP), NOAA-20, and NOAA-21 Resolution: 15 arc-seconds (~500 meters) Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. Service URL: See REST Endpoint on the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202606_EARTHQUAKE_VENEZUELA/202611_blackmarble_brdf_cloud/ImageServer/WMSServer