Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>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. </SPAN></P><P><SPAN>For more information about the fields, see the How-to-Guide documentation under the “Trainings” tab at https://landslides.nasa.gov and the publications below. </SPAN></P><P><SPAN>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. </SPAN></P><P><SPAN STYLE="font-weight:bold;">Methods </SPAN></P><P><SPAN>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. </SPAN></P><P><SPAN>Manually digitized landslides (method = Manual): </SPAN></P><P><SPAN>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. </SPAN></P><P><SPAN>Automatically digitized landslides (method = Automatic): </SPAN></P><P><SPAN>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. </SPAN></P><P><SPAN STYLE="font-weight:bold;">For Further Reference </SPAN></P><P><SPAN>Landslides.nasa.gov </SPAN></P><P><SPAN>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. </SPAN></P><P><SPAN>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. </SPAN></P><P><SPAN>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. </SPAN></P><P><SPAN>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 </SPAN></P><P><SPAN>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 </SPAN></P></DIV></DIV></DIV>
Service Item Id: a442900ab36549ffb50cd722307ffa43
Copyright Text: NASA Goddard Space Flight Center, landslides.nasa.gov
Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>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. </SPAN></P><P><SPAN>For more information about the fields, see the How-to-Guide documentation under the “Trainings” tab at https://landslides.nasa.gov and the publications below. </SPAN></P><P><SPAN>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. </SPAN></P><P><SPAN STYLE="font-weight:bold;">Methods </SPAN></P><P><SPAN>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. </SPAN></P><P><SPAN>Manually digitized landslides (method = Manual): </SPAN></P><P><SPAN>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. </SPAN></P><P><SPAN>Automatically digitized landslides (method = Automatic): </SPAN></P><P><SPAN>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. </SPAN></P><P><SPAN STYLE="font-weight:bold;">For Further Reference </SPAN></P><P><SPAN>Landslides.nasa.gov </SPAN></P><P><SPAN>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. </SPAN></P><P><SPAN>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. </SPAN></P><P><SPAN>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. </SPAN></P><P><SPAN>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 </SPAN></P><P><SPAN>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 </SPAN></P></DIV></DIV></DIV>
Service Item Id: a442900ab36549ffb50cd722307ffa43
Copyright Text: NASA Goddard Space Flight Center, landslides.nasa.gov