LS-TLS-WB

"RMSI team had carried out field investigations to understand the parameters, which were responsible for landslides in the study area. Since data availability for landslides in the study area was limited, a semi-qualitative landslide risk index using Spatial Multi Criteria Evaluation (SMCE) method in GIS format was used. Some historical events used in the study were available from NDMD, DesInventar (The Disaster Information Management System), Melbourne Energy Institute, and high resolution Satellite Imageries. To supplement this data, RMSI conducted a field survey in 20 Sucos and used high-resolution satellite imageries and 3D view of Google Earth imageries to identify an additional 773 landslide locations that helped develop the landslide susceptibility index of the study area. As part of developing the semi-qualitative landslide risk index, factors influencing landslide in the study area were selected based on best-practices derived from literature survey, expert opinion, and historical landslide data. Based on the outcome, six factors were considered for landslide susceptibility mapping, viz. slope angle, geology/ lithology, soil, land use-land cover, rainfall, and seismicity. These six classes of factors identified were assigned weights using the Analytical Hierarchy Process (AHP) - a theory of measurement through pair wise comparisons relying on the judgments of experts to derive priority scales. The analysis shows that approximately 76% of the total area of study area is susceptible to some level of landslide. Of this, 4%, 10%, 23%, and 38% areas lie in the very high, high, moderate, and low landslide susceptibility zones, respectively. Ainaro, Aitutu, Beboi Leten, Catrai Craic Cotolau, Edi, and Fatisi Sucos are most susceptible to landslide."

Data e Risorse

Campo Valore
access_constraints ["Not Specified: The original author did not specify a license."]
bbox-east-long 126.004306832
bbox-north-lat -8.5567885216
bbox-south-lat -9.2354383204
bbox-west-long 125.099440434
coupled-resource []
dataset-reference-date [{"type": "publication", "value": "2015-03-01T18:00:00Z"}]
frequency-of-update notPlanned
graphic-preview-description Thumbnail for 'LS-TLS-WB'
graphic-preview-file https://www.geonode-gfdrrlab.org/media/thumbs/layer-4af65ad4-e413-11e6-97d7-040146164b01-thumb.png
graphic-preview-type image/png
guid 4af65ad4-e413-11e6-97d7-040146164b01
licence []
metadata-date 2018-11-20T16:10:45Z
progress completed
resource-type dataset
responsible-party [{"name": "WorldBank", "roles": ["originator"]}]
spatial {"type": "Polygon", "coordinates": [[[125.099440434, -9.2354383204], [126.004306832, -9.2354383204], [126.004306832, -8.5567885216], [125.099440434, -8.5567885216], [125.099440434, -9.2354383204]]]}
spatial-reference-system 4326
spatial_harvester true
Tag
  • amerigeo
  • amerigeoss
  • disaster
  • drr
  • geo
  • geonode
  • geospatial
  • geoss
  • gfdrr
  • gfdrrlab
  • gis
  • global
  • landslide
  • timor-leste
  • world-bank
isopen False
metadata_created 2025-11-24T16:24:42.025776
metadata_modified 2025-11-24T16:24:42.025781
notes "RMSI team had carried out field investigations to understand the parameters, which were responsible for landslides in the study area. Since data availability for landslides in the study area was limited, a semi-qualitative landslide risk index using Spatial Multi Criteria Evaluation (SMCE) method in GIS format was used. Some historical events used in the study were available from NDMD, DesInventar (The Disaster Information Management System), Melbourne Energy Institute, and high resolution Satellite Imageries. To supplement this data, RMSI conducted a field survey in 20 Sucos and used high-resolution satellite imageries and 3D view of Google Earth imageries to identify an additional 773 landslide locations that helped develop the landslide susceptibility index of the study area. As part of developing the semi-qualitative landslide risk index, factors influencing landslide in the study area were selected based on best-practices derived from literature survey, expert opinion, and historical landslide data. Based on the outcome, six factors were considered for landslide susceptibility mapping, viz. slope angle, geology/ lithology, soil, land use-land cover, rainfall, and seismicity. These six classes of factors identified were assigned weights using the Analytical Hierarchy Process (AHP) - a theory of measurement through pair wise comparisons relying on the judgments of experts to derive priority scales. The analysis shows that approximately 76% of the total area of study area is susceptible to some level of landslide. Of this, 4%, 10%, 23%, and 38% areas lie in the very high, high, moderate, and low landslide susceptibility zones, respectively. Ainaro, Aitutu, Beboi Leten, Catrai Craic Cotolau, Edi, and Fatisi Sucos are most susceptible to landslide."
num_resources 30
num_tags 15
title LS-TLS-WB