Visualization analysis of rainfall-induced landslides hazards based on remote sensing and geographic information system-an overview

Figures & data

Table 1. Table for the Review of Representative Documents.

Characteristic	References	Approach	Contribution
Date: newest first(2021)	Khasanov et al. 2021	79 articles published during 2000–2020 on the causes and effects of landslides in Central Asia were reviewed via the Scopus database, and rainfall was studied as a major landslide hazard-causing factor.	It was found that GIS and RS technologies were not widely used in papers from 2000 to 2010 but gradually applied to the research of rainfall-induced landslides in Central Asia in the last ten years.
Citations: highest first(794)	Dai, Lee, and Ngai 2002b	The latest progress in landslide risk assessment and management (updated to 2002) was reviewed, and the applicability of landslide risk assessment models with rainfall data was discussed.	A landslide risk assessment and management framework that can reduce landslide risks was proposed, and the empirical and theoretical relationships between rainfall characteristics and landslide occurrence were summarized.
Usage (all time): most first	Van Westen, Castellanos, and Kuriakose 2008	The trend of collecting spatial information on landslide environmental factors (updated to 2008) was reviewed, spatial data were analyzed hierarchically, and a schematic diagram of landslide frequency associated with rainfall events was drawn	The methods of capturing and using spatial information such as triggers (rainfall included) and environmental factors and the importance of prioritizing methods for risk assessment that are appropriate for available data and analysis scales were discussed.
Usage (last 180 days): most first	Chae et al. 2017	The latest progress and development in the essential components of landslide hazard assessment (updated to 2017) was summarized, and different models for simulating possible landslides with rainfall data were compared and analyzed.	The probability method and physical method of susceptibility assessment were mainly analyzed, an early landslide warning was conducted based on the landslide remote sensing monitoring technology, and the remote sensing methods and primary reference materials of different rainfall monitoring were introduced in detail.

Figure 1. Number of documents published and cited quantity.

Table 2. Unified terms.

After the merger	Before the merger	After the merger	Before the merger	After the merger	Before the merger
ANN	Artificial Neural Network	AHP	Analytic Hierarchy Process	DEM	Digital Elevation Model
EBF	Evidential Belief Function	FR	Frequency Ratio	GIS	Geographic Information Systems
LSM	Landslide susceptibility mapping, LSM, Landslide Susceptibility Model, Landslide Susceptibility Modeling, Landslide Susceptibility Modelling	NDVI	Normalized Difference Vegetation Index	ROC	Receiver Operating Characteristic
RF	Random Forest	SVM	Support Vector Machine	WLC	Weighted Linear Combination

Figure 2. Number of documents published and cited quantity.

Figure 3. National Geographic visualization with more than 30 articles posted(by VOSviewer and Scimago).

Table 3. Distribution of journals(TOP 5).

NO.	Journal name	Post volume	A percentage of the total documents posted	The total number of citations	Start the year	Annual output	The annual number of citations
1	Natural Hazards	84	7.21	2462	2002	4	123
2	Environmental Earth Sciences	62	5.34	1942	2009	5	149
3	Landslides	60	5.17	2872	2005	4	169
4	Geomorphology	57	4.91	4334	1999	2	188
5	Geomatics Natural Hazards Risk	34	2.93	845	2011	3	77

Table 4. Highly cited literature information table(TOP 5).

Author	Cite Frequency	Year	Publication type	Main Contribution
Dai, Lee, and Ngai 2002b	794	2002	Review	A landslide risk assessment and management framework that can reduce landslide risks was proposed, and the research status of landslides was critically reviewed.
Dai and Lee 2002a	718	2002	Article	The GIS database was used to describe the statistical relationship among the physical characteristics of landslides, the landslide frequency, and the physical parameters that cause landslides.
Van Westen, Castellanos, and Kuriakose 2008	684	2008	Review	The spatial information was discussed and used for landslide susceptibility, hazard, and vulnerability assessment.
Yalcin 2008	493	2008	Article	Analytic hierarchy process (AHP), statistical index (WI), and weighting factor (WF) was used to generate susceptibility maps, and the performance of the three methods was compared.
Wu and Sidle 1995	439	1995	Article	A slope stability model (dSLAM) was proposed and integrated with the GIS for spatial data extraction and display.

Figure 4. The authors’ partnership is co-present (by CiteSpace).

Table 5. High Productivity Authors and Representative Works (Over 30).

Authors	Post volume	Cite Frequency	Masterpiece	Representative contribution
Pradhan and Lee 2010	57	6106	Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia	Modeling was made by the hybrid intelligent method via remote sensing data and GIS technology to realize the susceptibility mapping of landslide hazards.
Bui et al. 2012	40	4507	Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS	Modeling was made based on the GIS-integrated artificial intelligence method for the regional spatial prediction of landslide hazards.
Pham et al. 2017	34	3043	Landslide susceptibility assessment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods	The predicted performance of several technologies, such as function trees, multilayer perceptron neural networks, and Naive Bayes in landslide susceptibility assessment, was compared with the GIS.
Chen et al. 2017	31	2335	GIS-based landslide susceptibility modeling: a comparative assessment of kernel logistic regression, Naive-Bayes tree, and alternating decision tree models	Some potential applications of complex machine learning technology in landslide susceptibility mapping were explored based on GIS technology.

Figure 5. Network diagram of cooperation between countries(by VOSviewer ).

Figure 6. Cooperative analysis and mutation testing (by CiteSpace).

Figure 7. Keywords co-present theme map(by VOSviewer and Pajek).

Figure 8. Keyword cluster analysis map (by CiteSpace).

Figure 9. Keyword cluster analysis map (by CiteSpace).

Table 6. Typical Cases of Landslide Detection and Monitoring.

Reference	Question	Method	Conclusion
Kelevitz et al. 2022	How to incorporate Sentinel-1 remote sensing data into landslide hazard prediction and mitigation across the UK.	The Huolin Mountain Landslide Observation Station (HHLO) was used to show the landslide process clearly and experimented with for slope stability characterization and monitoring.	InSAR measurement results were consistent with other ground measurement results.
Kontoes et al. 2021	The contributions of interferometric synthetic aperture radar InSAR data to landslide detection needed to be figured out.	The rainfall-induced landslides hazards were evaluated by heuristic and statistical methods to compare the landslide models in areas with high and low occurrence frequency in Greece	The weights of evidence (WoE) were more accurate, relying on expert opinions and landslide inventory.
Han et al. 2022	Traditional measurement had problems such as low efficiency, low accuracy, prolonged time consumption, poor classification accuracy, etc.	The object-oriented classification and extraction were provided for the Sentinel-2 and GF-1 remote sensing images before and after the occurrence of landslides to learn about the forms, constituent elements, and damage degrees.	In selecting the optimal segmentation scale, the object-oriented landslide extraction process still needs to be improved in many aspects.
Guarascio et al. 2020	The large amount of computation of the dense sensor network made integrating different remote sensing measurement information difficult.	A method based on machine learning was proposed, which used a classifier to estimate rainfall by integrated approaches, and synthesized different remote sensing measurement information.	The accurate rainfall estimation was provided without rain gauges, which was more economical than the spatial interpolation method.

Table 7. Table of Typical Cases of Predictive Models.

Reference	Cite Frequency	Method	Contribute
Buisson et al. 1992	0	The application of AI in landslide prevention and soil erosion prediction was investigated, and enough attention was paid to the explicit expression of various knowledge, spatiotemporal reasoning, and uncertainty management capabilities embedded in artificial intelligence systems.	Artificial intelligence was first used in landslide research, and it was pointed out that the purpose of future work was to develop general-purpose methods and capabilities of the simulation system.
Qiao 2004	2	The primary environmental factors were quantitatively sampled based on RS and GIS technologies, and traditional floor plans were decoded into 3D maps.	Machine learning was first used for landslide research.
Marjanovic, Bajat, and Kovacevic 2009	33	AHP processed different model input parameters in the GIS environment to form a susceptibility mode as the training set of SVM and K-NN algorithms.	A deep learning algorithm was first used for the assessment of landslide susceptibility.

Figure 10. Sensitivity mapping to study the heat map(by VOSviewer).

Figure 11. Collinear plot of the risk assessment studies(by VOSviewer).

Khasanov, S., M. Juliev, U. Uzbekov, I. Aslanov, I. Agzamova, N. Normatova, S. Islamov, G. Goziev, S. Khodjaeva, and N. Holov. 2021. “Landslides in Central Asia: A Review of Papers Published in 2000–2020 with a Particular Focus on the Importance of GIS and Remote Sensing Techniques.” GeoScape 15 (2): 2. https://doi.org/10.2478/geosc-2021-0011.

 Web of Science ®Google Scholar

Dai, F. C., C. F. Lee, and Y. Y. Ngai. 2002. “Landslide Risk Assessment and Management: An Overview.” Engineering Geology 64 (1): 65–87. https://doi.org/10.1016/S0013-7952(01)00093-X.

 Web of Science ®Google Scholar

Van Westen, C. J., E. Castellanos, and S. L. Kuriakose. 2008. “Spatial Data for Landslide Susceptibility, Hazard, and Vulnerability Assessment: An Overview.” Engineering Geology 102 (3-4): 112–131. https://doi.org/10.1016/j.enggeo.2008.03.010.

 Web of Science ®Google Scholar

Chae, B. G., H. J. Park, F. Catani, A. Simoni, and M. Berti. 2017. “Landslide Prediction, Monitoring and Early Warning: A Concise Review Of State-of-the-Art.” Geosciences Journal 21 (6): 1033–1070. https://doi.org/10.1007/s12303-017-0034-4.

 Web of Science ®Google Scholar

Dai, F. C., and C. F. Lee. 2002. “Landslide Characteristics and Slope Instability Modeling Using GIS, Lantau Island, Hong Kong.” Geomorphology 42 (3-4): 213–228. https://doi.org/10.1016/S0169-555X(01)00087-3.

 Web of Science ®Google Scholar

Yalcin, A. 2008. “GIS-based Landslide Susceptibility Mapping Using Analytical Hierarchy Process and Bivariate Statistics in Ardesen,Turkey, Comparisons of Results and Confirmations.” Catena 72 (1): 1–12. https://doi.org/10.1016/j.catena.2007.01.003.

 Web of Science ®Google Scholar

Wu, W., and R. C. Sidle. 1995. “A Distributed Slope Stability Model for Steep Forested Basins.” Water Resources Research 31 (8): 2097–2110. https://doi.org/10.1029/95WR01136.

 Web of Science ®Google Scholar

Pradhan, B., and S. Lee. 2010. “Regional Landslide Susceptibility Analysis Using Back-Propagation Neural Network Model at Cameron Highland, Malaysia.” Landslides 7 (1): 13–30. https://doi.org/10.1007/s10346-009-0183-2.

 Web of Science ®Google Scholar

Bui, D. T., B. Pradhan, O. Lofman, I. Revhaug, and O. B. Dick. 2012. “Landslide Susceptibility Mapping at Hoa Binh Province, (Vietnam) Using an Adaptive Neuro-Fuzzy Inference System and GIS.” Computers & Geosciences 45: 199–211. https://doi.org/10.1016/j.cageo.2011.10.031.

 Web of Science ®Google Scholar

Pham, B. T., D. Tien Bui, H. R. Pourghasemi, P. Indra, and M. B. Dholakia. 2017. “Landslide Susceptibility Assesssment in the Uttarakhand Area, India, Using GIS: A Comparison Study of Prediction Capability of Naïve Bayes, Multilayer Perceptron Neural Networks, and Functional Trees Methods.” Theoretical and Applied Climatology 128 (1-2): 255–273. https://doi.org/10.1007/s00704-015-1702-9.

 Web of Science ®Google Scholar

Chen, W., X. S. Xie, J. B. Peng, J. L. Wang, Z. Duan, and H. Y. Hong. 2017. “GIS-based Landslide Susceptibility Modelling: A Comparative Assessment of Kernel Logistic Regression, Naïve-Bayes Tree, And Alternating Decision Tree Models.” Geomatics, Natural Hazards and Risk 8 (2): 950–973. https://doi.org/10.1080/19475705.2017.1289250.

 Web of Science ®Google Scholar

Kelevitz, K., A. Novellino, A. Watlet, J. Boyd, J. Whiteley, J. Chambers, C. Jordan, T. Wright, A. Hooper, and J. Biggs. 2022. “Ground and Satellite-Based Methods of Measuring Deformation at a UK Landslide Observatory: Comparison and Integration.” Remote Sensing 14 (12): 2836. https://doi.org/10.3390/rs14122836.

 Web of Science ®Google Scholar

Kontoes, C., C. Loupasakis, I. Papoutsis, S. Alatza, E. Poyiadji, A. Ganas, C. Psychogyiou, M. Kaskara, S. Antoniadi, and N. Spanou. 2021. “Landslide Susceptibility Mapping of Central and Western Greece, Combining NGI and WOE Methods, with Remote Sensing and Ground Truth Data.” Land 10 (4): 402. https://doi.org/10.3390/land10040402.

 Web of Science ®Google Scholar

Han, Y., P. Wang, Y. Zheng, M. Yasir, C. Xu, S. Nazir, M. S. Hossain, S. Ullah, and S. Khan. 2022. “Extraction of Landslide Information Based on Object-Oriented Approach and Cause Analysis in Shuicheng, China.” Remote Sensing 14 (3): 502. https://doi.org/10.3390/rs14030502.

 Web of Science ®Google Scholar

Guarascio, M., G. Folino, F. Chiaravalloti, S. Gabriele, A. Procopio, and P. Sabatino. 2020. “A Machine Learning Approach for Rainfall Estimation Integrating Heterogeneous Data Sources.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–11. https://doi.org/10.1109/TGRS.2020.3037776.

 Web of Science ®Google Scholar

Buisson, L., R. Martin Clouaire, L. Vieu, and J. L. Wybo. 1992. “Artificial Intelligence and Environmental Protection: A Survey of Selected Applications in France.” IFIP Transactions A-Computer Science and Technology 13: 635–644. https://doi.org/10.1109/71.113087.

 Google Scholar

Qiao, J. P. 2004. “A 3D Visible Evaluation of Landslide Risk Degree Under Integration of Gis and Artificial Intelligence.” Science in China Series E 46 (7): 142. https://doi.org/10.1360/03ez0017.

 Google Scholar

Marjanovic, M., B. Bajat, and M. Kovacevic. 2009. “Landslide Susceptibility Assessment with Machine Learning Algorithms//2009 International Conference on Intelligent Networking and Collaborative Systems.” IEEE, 273–278. https://doi.org/10.1109/INCOS.2009.25.

 Google Scholar

Data availability statement

The data used in this study are available by contacting the corresponding author.