Combined use of socio economic analysis, remote sensing and GIS data for landslide hazard mapping using ANN

2009 ◽  
Vol 37 (3) ◽  
pp. 409-421 ◽  
Author(s):  
S. Prabu ◽  
S. S. Ramakrishnan
Keyword(s):  
Remote Sensing ◽  
Economic Analysis ◽  
Remote Sensing And Gis ◽  
Landslide Hazard ◽  
Hazard Mapping ◽  
Combined Use ◽  
Gis Data ◽  
Landslide Hazard Mapping

scholarly journals Remote sensing and GIS technology in landslide hazard mapping in the Colombian Andes

Episodes ◽  
1992 ◽  
Vol 15 (1) ◽  
pp. 32-35 ◽  
Author(s):  
E. Leroi ◽  
O. Rouzeau ◽  
J. -Y. Scanvic ◽  
C.C. Weber ◽  
G. Vargas C.
Keyword(s):  
Remote Sensing ◽  
Remote Sensing And Gis ◽  
Landslide Hazard ◽  
Hazard Mapping ◽  
Gis Technology ◽  
Colombian Andes ◽  
Landslide Hazard Mapping

scholarly journals APPLICATION OF PALSAR-2 REMOTE SENSING DATA FOR LANDSLIDE HAZARD MAPPING IN KELANTAN RIVER BASIN, PENINSULAR MALAYSIA

2016 ◽  
Vol XLI-B8 ◽  
pp. 413-416 ◽  
Author(s):  
Amin Beiranvand Pour ◽  
Mazlan Hashim
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Remote Sensing Data ◽  
Peninsular Malaysia ◽  
Landslide Hazard ◽  
Hazard Mapping ◽  
Geological Structures ◽  
Sensing Data ◽  
Kelantan River Basin ◽  
Landslide Hazard Mapping

Yearly, several landslides ensued during heavy monsoons rainfall in Kelantan river basin, peninsular Malaysia, which are obviously connected to geological structures and topographical features of the region. In this study, the recently launched Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) onboard the Advanced Land Observing Satellite-2 (ALOS-2), remote sensing data were used to map geological structural and topographical features in the Kelantan river basin for identification of high potential risk and susceptible zones for landslides. Adaptive Local Sigma filter was selected and applied to accomplish speckle reduction and preserving both edges and features in PALSAR-2 fine mode observation images. Different polarization images were integrated to enhance geological structures. Additionally, directional filters were applied to the PALSAR-2 Local Sigma resultant image for edge enhancement and detailed identification of linear features. Several faults, drainage patterns and lithological contact layers were identified at regional scale. In order to assess the results, fieldwork and GPS survey were conducted in the landslide affected zones in the Kelantan river basin. Results demonstrate the most of the landslides were associated with N-S, NNW-SSE and NE-SW trending faults, angulated drainage pattern and metamorphic and Quaternary units. Consequently, structural and topographical geology maps were produced for Kelantan river basin using PALSAR-2 data, which could be broadly applicable for landslide hazard mapping.

scholarly journals APPLICATION OF PALSAR-2 REMOTE SENSING DATA FOR LANDSLIDE HAZARD MAPPING IN KELANTAN RIVER BASIN, PENINSULAR MALAYSIA

2016 ◽  
Vol XLI-B8 ◽  
pp. 413-416
Author(s):  
Amin Beiranvand Pour ◽  
Mazlan Hashim
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Remote Sensing Data ◽  
Peninsular Malaysia ◽  
Landslide Hazard ◽  
Hazard Mapping ◽  
Geological Structures ◽  
Sensing Data ◽  
Kelantan River Basin ◽  
Landslide Hazard Mapping

Yearly, several landslides ensued during heavy monsoons rainfall in Kelantan river basin, peninsular Malaysia, which are obviously connected to geological structures and topographical features of the region. In this study, the recently launched Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) onboard the Advanced Land Observing Satellite-2 (ALOS-2), remote sensing data were used to map geological structural and topographical features in the Kelantan river basin for identification of high potential risk and susceptible zones for landslides. Adaptive Local Sigma filter was selected and applied to accomplish speckle reduction and preserving both edges and features in PALSAR-2 fine mode observation images. Different polarization images were integrated to enhance geological structures. Additionally, directional filters were applied to the PALSAR-2 Local Sigma resultant image for edge enhancement and detailed identification of linear features. Several faults, drainage patterns and lithological contact layers were identified at regional scale. In order to assess the results, fieldwork and GPS survey were conducted in the landslide affected zones in the Kelantan river basin. Results demonstrate the most of the landslides were associated with N-S, NNW-SSE and NE-SW trending faults, angulated drainage pattern and metamorphic and Quaternary units. Consequently, structural and topographical geology maps were produced for Kelantan river basin using PALSAR-2 data, which could be broadly applicable for landslide hazard mapping.

Research on Non-Landslide Selection Method for Landslide Hazard Mapping

2021 ◽  
Author(s):  
Xia Li ◽  
Jiulong Cheng ◽  
Dehao Yu ◽  
Yangchun Han
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Selection Method ◽  
Landslide Hazard ◽  
River Channel ◽  
Information Value ◽  
Hazard Mapping ◽  
Landslide Prediction ◽  
Conditioning Factors ◽  
Landslide Hazard Mapping

Abstract Most landslide prediction models need to select non-landslides. At present, non-landslides mainly use subjective inference or random selection method, which makes it easy to select non-landslides in high-risk areas. To solve this problem and improve the accuracy of landslide prediction, the method of selecting non-landslide by Information value (IV) is proposed in this study. Firstly, 230 historical landslides and 10 landslide conditioning factors are extracted and interpreted by using Remote Sensing (RS) image, Geographic Information System (GIS) and field survey. Secondly, random, buffer, river channel or slope, and IV methods are used to obtain non-landslides, and the obtained non-landslides are applied to the popular SVM model for landslide hazard mapping (LHM) in western area of Tumen City. The landslide hazard map based on the river channel or slope method is seriously inconsistent with the actual situation of study area, Therefore, the three methods of random, buffer, and IV are verified and compared by accuracy, receiver operating characteristic (ROC) curve and the area under curves (AUC). The results show that the landslide prediction accuracy of the three methods is more than 80%, and the prediction accuracy is high, but the IV is higher. In addition, IV can identify the very high hazard regions with smaller area. Therefore, it is more reasonable to use IV to select non-landslides, and IV method is more practical in landslide prevention and engineering construction. The research results may be useful to provide basic information of landslide hazard for decision makers and planners.

Estimation of forest Leaf Area Index using remote sensing and GIS data for modelling net primary production

1997 ◽  
Vol 18 (16) ◽  
pp. 3459-3471 ◽  
Author(s):  
S. E. Franklin ◽  
M. B. Lavigne ◽  
M. J. Deuling ◽  
M. A. Wulder ◽  
E. R. Hunt
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Net Primary Production ◽  
Remote Sensing And Gis ◽  
Area Index ◽  
Gis Data
Sign in / Sign up

Export Citation Format

Share Document