Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan
出版年份 2017 全文链接
标题
Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan
作者
关键词
-
出版物
Remote Sensing
Volume 9, Issue 9, Pages 943
出版商
MDPI AG
发表日期
2017-09-15
DOI
10.3390/rs9090943
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps
- (2016) Stefan Steger et al. GEOMORPHOLOGY
- Automated derivation and spatio-temporal analysis of landslide properties in southern Kyrgyzstan
- (2016) Darya Golovko et al. NATURAL HAZARDS
- Derivation of long-term spatiotemporal landslide activity—A multi-sensor time series approach
- (2016) Robert Behling et al. REMOTE SENSING OF ENVIRONMENT
- Tien Shan Geohazards Database: Landslide susceptibility analysis
- (2015) H.B. Havenith et al. GEOMORPHOLOGY
- Development of Multi-Temporal Landslide Inventory Information System for Southern Kyrgyzstan Using GIS and Satellite Remote Sensing
- (2015) Darya Golovko et al. Photogrammetrie Fernerkundung Geoinformation
- Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment
- (2015) Qianqian Wang et al. Sustainability
- Evaluating the quality of landslide inventory maps: comparison between archive and surveyed inventories for the Daunia region (Apulia, Southern Italy)
- (2014) Roberta Pellicani et al. Bulletin of Engineering Geology and the Environment
- Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale
- (2014) Mustafa Neamah Jebur et al. REMOTE SENSING OF ENVIRONMENT
- Robust Automated Image Co-Registration of Optical Multi-Sensor Time Series Data: Database Generation for Multi-Temporal Landslide Detection
- (2014) Robert Behling et al. Remote Sensing
- Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data
- (2014) Robert Behling et al. Remote Sensing
- Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances
- (2013) H. R. Pourghasemi et al. NATURAL HAZARDS
- A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS
- (2012) Biswajeet Pradhan COMPUTERS & GEOSCIENCES
- Landslide inventory maps: New tools for an old problem
- (2012) Fausto Guzzetti et al. EARTH-SCIENCE REVIEWS
- Landslide susceptibility assessment: what are the effects of mapping unit and mapping method?
- (2011) A. Erener et al. Environmental Earth Sciences
- Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand
- (2011) N. Intarawichian et al. Environmental Earth Sciences
- Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)
- (2008) Işık Yilmaz COMPUTERS & GEOSCIENCES
- A review of assessing landslide frequency for hazard zoning purposes
- (2008) Jordi Corominas et al. ENGINEERING GEOLOGY
- Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview
- (2008) Cees J. van Westen et al. ENGINEERING GEOLOGY
- Geophysical investigation and dynamic modelling of unstable slopes: case-study of Kainama (Kyrgyzstan)
- (2008) G. Danneels et al. GEOPHYSICAL JOURNAL INTERNATIONAL
- Comparing landslide inventory maps
- (2007) Mirco Galli et al. GEOMORPHOLOGY
- Predicting landslides for risk analysis — Spatial models tested by a cross-validation technique
- (2007) Chang-Jo Chung et al. GEOMORPHOLOGY
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started