Landslide Hazard Assessment for Fayzabad District, Badakhshan Province, Afghanistan

Fayzabad District is one of those most impacted by landslide hazards in Afghanistan, accounting for 71% of all national landslide fatalities reported between 2012 and 2017. Necessary elevation data did not cover the very south of Fayzabad District; consequently, this study focuses on the northern two thirds of the district, where data were available. A landslide inventory was developed by mapping landslides using DEMs and high-resolution satellite imagery to aid in development and assessment of both Heuristic and bivariate statistical models of landslide susceptibility. Landslide statistics, including length, area, width, and pertinent relationships to geology, elevation, aspect, slope, and proximity to faults and streams were quantitatively calculated using geoprocessing tools. Hazard maps were produced using landslide susceptibility and proximity of villages to mapped landslides. Mapped susceptibility results indicate that in this part of Afghanistan landslides occur primarily on north to northwest aspects in loess or soil media over gneiss bedrock. Landslides are concentrated between 1500 m and 2000 m elevation and on 18° to 45° slopes within 60 m of a stream channel and or within 1 km of a fault. Landslide dimensions plot linearly on log-log scales, simplifying the development of predictive associations. Model results encapsulate a high proportion of landslide pixels within areas of high susceptibility, although there were significant variations between Heuristic and bivariate methods. Bivariate methods performed better universally, but may be over trained when the entire dataset is used to produce statistical weights. Use of subset of data to develop weights results in a more even distribution of landslides between low- to high-susceptibility zones. Findings in both the landslide inventory and susceptibility models are supported by prior studies of landslide behavior in Afghanistan. Programmatic workflows allowed for rapid production of many model components after initial reclassification and will facilitate further research in Afghanistan, and application of the methodology elsewhere. Map products potentially provide a new tool for hazard planners and aid groups in northeastern Afghanistan, and supplemental code will allow for rapid incorporation of new datasets as they are developed

Multi-criteria analysis of landslide susceptibility, Afghanistan

This presentation was given as part of the GIS Day@KU symposium on November 16, 2016. For more information about GIS Day@KU activities, please see http://gis.ku.edu/gisday/2016/.Landslides are among the most destructive forces of nature. Estimating susceptibility through modeling is an essential tool for planning and mitigation efforts. Some regions, however, are too dangerous or lack the capacity to develop extensive inventories for rigorous analyses. Remote sensing and GIS allow for initial risk assessment and hazard planning. Data derived primarily from remote sensing, or developed before and during war efforts of the last few decades were used for this study of landslide susceptibility in Afghanistan.Platinum Sponsors: KU Department of Geography and Atmospheric Science. Gold Sponsors: Enertech, KU Environmental Studies Program, KU Libraries. Silver Sponsors: Douglas County, Kansas, KansasView, State of Kansas Data Access & Support Center (DASC) and the KU Center for Global and International Studies

Landslide Hazard Assessment, Fayz Abad, Afghanistan

This presentation was given as part of the GIS Day@KU symposium on November 15, 2017. For more information about GIS Day@KU activities, please see http://gis.ku.edu/gisday/2017/PLATINUM SPONSORS: KU Department of Geography and Atmospheric Science KU Institute for Policy & Social Research GOLD SPONSORS: KU Libraries State of Kansas Data Access & Support Center (DASC) SILVER SPONSORS: Bartlett & West Kansas Applied Remote Sensing Program KU Center for Global and International Studies BRONZE SPONSORS: Boundles

Big-data approaches lead to an increased understanding of the ecology of animal movement

Nathan R, Monk CT, Arlinghaus R, et al. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science. 2022;375(6582): eabg1780.Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal "movement ecology" (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences