Abstract
The Lower Mekong River Basin in Southeast Asia experiences frequent rainfall-triggered landslides especially during the monsoon season. In this study, the influence of land use and land cover change and other causative factors on landslide susceptibility is evaluated in the Lower Mekong Basin. Frequency ratio analysis is performed to quantify the relationship between LULC change and susceptibility. Detailed landslide inventory maps are used for analysis with yearly LULC maps. The LULC change is used as a contributing variable in a logistic regression-based susceptibility model with other variables including distance to roads, slope, aspect, forest loss, and soil properties. The Receiver Operating Characteristic (ROC) curve and Area Under the Curve are estimated for the model trained by each landslide inventory. The models show good performance, with AUC values ranging from 0.697 to 0.958 and an average AUC equal to 0.820. Both the Frequency Ratio analysis and the Logistic Regression models indicate LULC change from agricultural land to forest has a positive correlation with landslide occurrence. The most significant factors in the models are found to be distance to roads, slope, and aspect. A better understanding of the effects of LULC on landslide susceptibility can be useful for local land and disaster management and for the implementation of LULC as a factor in future susceptibility models. Using datasets that are unique to the Lower Mekong region, this study provides additional insights into the relationship between causative factors and landslide activity to better inform regional and global landslide susceptibility modeling.








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References
Amatya P, Kirschbaum D, Stanley T, Tanyas H (2021a) Landslide mapping using object-based image analysis and open source tools. Eng Geol. https://doi.org/10.1016/j.enggeo.2021.106000
Amatya P, Kirschbaum D, Stanley T (2021b) Rainfall-induced landslide inventories for Lower Mekong based on Planet imagery and a semi-automatic mapping method. Geosci Data J. https://doi.org/10.1002/gdj3.145
Bai SB, Wang J, Lü GN et al (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology 115(23):31. https://doi.org/10.1016/j.geomorph.2009.09.025
Batjes NH, Ribeiro E, Van Oostrum A (2020) Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019). Earth Syst Sci Data 12:299–320. https://doi.org/10.5194/essd-12-299-2020
Bornaetxea T, Rossi M, Marchesini I, Alvioli M (2018) Effective surveyed area and its role in statistical landslide susceptibility assessments. Nat Hazards Earth Syst Sci Discuss. https://doi.org/10.5194/nhess-2018-88
Bruschi VM, Bonachea J, Remondo J et al (2013) Land management versus natural factors in land instability: some examples in northern Spain. Environ Manage 52:398–416. https://doi.org/10.1007/s00267-013-0108-7
Camilo DC, Lombardo L, Mai PM et al (2017) Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized generalized linear model. Environ Model Softw 97:145–156. https://doi.org/10.1016/j.envsoft.2017.08.003
Chen CY, Huang WL (2013) Land use change and landslide characteristics analysis for community-based disaster mitigation. Environ Monit Assess 185:4125–4139. https://doi.org/10.1007/s10661-012-2855-y
Chen L, Guo Z, Yin K et al (2019) The influence of land use and land cover change on landslide susceptibility: a case study in Zhushan Town, Xuan’en County (Hubei, China). Nat Hazards Earth Syst Sci 19:2207–2228. https://doi.org/10.5194/nhess-19-2207-2019
Dai FC, Lee CF, Ngai YY (2002) Landslide risk assessment and management: an overview. Eng Geol 64:65–87. https://doi.org/10.1016/S0013-7952(01)00093-X
Dandridge C, Lakshmi V, Bolten J, Srinivasan R (2019) Evaluation of satellite-based rainfall estimates in the lower mekong river basin (southeast asia). Remote Sens. https://doi.org/10.3390/rs11222709
Dandridge C, Fang B, Lakshmi V (2020) Downscaling of SMAP soil moisture in the lower mekong river basin. Water (switzerland). https://doi.org/10.3390/w12010056
Das G, Lepcha K (2019) Application of logistic regression (LR) and frequency ratio (FR) models for landslide susceptibility mapping in relli khola river basin of Darjeeling Himalaya, India. SN Appl Sci 1:1–22. https://doi.org/10.1007/s42452-019-1499-8
Degraff JV, Cannon SH, Gartner JE (2015) The timing of susceptibility to post-fire debris flows in the western United States. Environ Eng Geosci 21:277–292. https://doi.org/10.2113/gseegeosci.21.4.277
Deng X, Xu D, Zeng M, Qi Y (2018) Landslides and cropland abandonment in China’s mountainous areas: spatial distribution, empirical analysis and policy implications. Sustain. https://doi.org/10.3390/su10113909
Fayne JV, Bolten JD, Doyle CS et al (2017) Flood mapping in the lower Mekong river basin using daily MODIS observations. Int J Remote Sens 38:1737–1757. https://doi.org/10.1080/01431161.2017.1285503
Felicísimo ÁM, Cuartero A, Remondo J, Quirós E (2013) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10:175–189. https://doi.org/10.1007/s10346-012-0320-1
Forbes K, Broadhead J, Bischetti GB, et al (2012) The role of trees and forests in the prevention of landslides and rehabilitation of landslide-affected areas in Asia, 2nd edn. In collaboration with for landslides, pp 12–21
Froude MJ, Petley DN (2018) Global fatal landslide occurrence from 2004 to 2016. Nat Hazards Earth Syst Sci 18:2161–2181. https://doi.org/10.5194/nhess-18-2161-2018
Gariano SL, Petrucci O, Rianna G et al (2018) Impacts of past and future land changes on landslides in southern Italy. Reg Environ Chang 18:437–449. https://doi.org/10.1007/s10113-017-1210-9
Glade T (2003) Landslide occurrence as a response to land use change: a review of evidence from New Zealand. CATENA 51:297–314. https://doi.org/10.1016/S0341-8162(02)00170-4
Gorsevski PV, Gessler PE, Foltz RB, Elliot WJ (2006) Spatial prediction of landslide hazard using logistic regression and ROC analysis. Trans GIS 10:395–415. https://doi.org/10.1111/j.1467-9671.2006.01004.x
Hansen MC, Potapov PV, Moore R et al (2013) High-resolution global maps of 21st-century forest cover change. Science 80(342):850–853. https://doi.org/10.1126/science.1244693
Hemasinghe H, Rangali RSS, Deshapriya NL, Samarakoon L (2018) Landslide susceptibility mapping using logistic regression model (a case study in Badulla District, Sri Lanka). Proc Eng 212:1046–1053. https://doi.org/10.1016/j.proeng.2018.01.135
Hengl T, De Jesus JM, Heuvelink GBM et al (2017) SoilGrids250m Global gridded soil information based on machine learning. PLoS ONE 12(2):e0169748
Hewawasam T (2010) Effect of land use in the upper mahaweli catchment area on erosion landslides and siltation in hydropower reservoirs of Sri Lanka. J Natl Sci Found Sri Lanka 38:3–14. https://doi.org/10.4038/jnsfsr.v38i1.1721
Highland L, Bobrowsky P (2008) The landslide handbook-a guide to understanding landslides, pp 4–42
Horafas D, Gkeki T (2017) Applying logistic regression for landslide susceptibility mapping. The case study of Krathis Watershed, North Peloponnese, Greece. Am J Geograph Inf Syst 6:23–28. https://doi.org/10.5923/s.ajgis.201701.03
Indhanu T, Chub-Uppakarn T, Chalermyanont T (2020) Geotechnical analysis of a landslide in Nakorn Si Thammarat Province, Southern Thailand. Lect Notes Civ Eng 62:923–927. https://doi.org/10.1007/978-981-15-2184-3_120
Jaboyedoff M, Michoud C, Derron MH et al (2016) Human-induced landslides: toward the analysis of anthropogenic changes of the slope environment. Landslides Eng Slopes Exp Theory Pract 1:217–232. https://doi.org/10.1201/b21520-20
Kafy AA, Shahinoor Rahman M, Ferdous L (2017) Exploring the association of land cover change and Landslides in the chittagong hill tracts (Cht): a remote sensing perspective. In: Proceedings of the international conference on disaster risk mitigation
Karsli F, Atasoy M, Yalcin A et al (2009) Effects of land-use changes on landslides in a landslide-prone area (Ardesen, Rize, NE Turkey). Environ Monit Assess 156:241–255. https://doi.org/10.1007/s10661-008-0481-5
Kean JW, Staley DM, Cannon SH (2011) In situ measurements of post-fire debris flows in southern california: comparisons of the timing and magnitude of 24 debris-flow events with rainfall and soil moisture conditions. J Geophys Res Earth Surf 116:1–21. https://doi.org/10.1029/2011JF002005
Khan H, Shafique M, Khan MA et al (2019) Landslide susceptibility assessment using frequency ratio, a case study of northern Pakistan. Egypt J Remote Sens Sp Sci 22:11–24. https://doi.org/10.1016/j.ejrs.2018.03.004
Larsen MC, Parks JE (1997) How wide is a road? The association of roads and mass-wasting in a forested montane environment. Earth Surf Process Landforms 22:835–848. https://doi.org/10.1002/(SICI)1096-9837(199709)22:9%3c835::AID-ESP782%3e3.0.CO;2-C
Le MH, Sutton JRP, Du BD et al (2018) Comparison and bias correction of TMPA precipitation products over the lower part of Red-Thai Binh river Basin of Vietnam. Remote Sens. https://doi.org/10.3390/rs10101582
Le MH, Lakshmi V, Bolten J, Du BD (2020) Adequacy of Satellite-derived precipitation estimate for hydrological modeling in vietnam Basins. J Hydrol. https://doi.org/10.1016/j.jhydrol.2020.124820
Le MH, Nguyen BQ, Pham HT et al (2022) Assimilation of SMAP products for improving streamflow simulations over tropical climate region—Is spatial information more important than temporal information? Remote Sens. https://doi.org/10.3390/rs14071607
Lee S, Sambath T (2006) Landslide susceptibility mapping in the damrei romel area, cambodia using frequency ratio and logistic regression models. Environ Geol 50:847–855. https://doi.org/10.1007/s00254-006-0256-7
Liu J, Wu Z, Zhang H (2021) Analysis of changes in landslide susceptibility according to land use over 38 years in Lixian county, China. Sustain. https://doi.org/10.3390/su131910858
Lombardo L, Mai PM (2018) Presenting logistic regression-based landslide susceptibility results. Eng Geol 244:14–24. https://doi.org/10.1016/j.enggeo.2018.07.019
McAdoo BG, Quak M, Gnyawali KR et al (2018) Roads and landslides in nepal: how development affects environmental risk. Nat Hazards Earth Syst Sci 18:3203–3210. https://doi.org/10.5194/nhess-18-3203-2018
Meijer JR, Huijbregts MAJ, Schotten KCGJ, Schipper AM (2018) Global patterns of current and future road infrastructure. Environ Res Lett. https://doi.org/10.1088/1748-9326/aabd42
Mohammed IN, Bolten JD, Srinivasan R et al (2018a) Ground and satellite based observation datasets for the lower mekong river basin. Data Br 21:2020–2027. https://doi.org/10.1016/j.dib.2018.11.038
Mohammed IN, Bolten JD, Srinivasan R, Lakshmi V (2018b) Improved hydrological decision support system for the Lower Mekong River Basin using satellite-based earth observations. Remote Sens. https://doi.org/10.3390/rs10060885
Mohammed IN, Bolten JD, Srinivasan R, Lakshmi V (2018c) Satellite observations and modeling to understand the Lower mekong river basin streamflow variability. J Hydrol 564:559–573. https://doi.org/10.1016/j.jhydrol.2018.07.030
Mondal A, Le MH, Lakshmi V (2022) Land use, climate, and water change in the vietnamese mekong delta (VMD) using earth observation and hydrological modeling. J Hydrol Reg Stud. https://doi.org/10.1016/j.ejrh.2022.101132
Mugagga F, Kakembo V, Buyinza M (2012) Land use changes on the slopes of mount elgon and the implications for the occurrence of landslides. CATENA 90:39–46. https://doi.org/10.1016/j.catena.2011.11.004
NASA JPL (2020) NASADEM Merged DEM Global 1 arc second V001. NASA EOSDIS land process DAAC. Accessed from https://doi.org/10.5067/MEaSUREs/NASADEM/NASADEM_HGT.001
Penna D, Borga M, Aronica GT et al (2014) The influence of grid resolution on the prediction of natural and road-related shallow landslides. Hydrol Earth Syst Sci 18:2127–2139. https://doi.org/10.5194/hess-18-2127-2014
Persichillo MG, Bordoni M, Meisina C (2017) The role of land use changes in the distribution of shallow landslides. Sci Total Environ 574:924–937. https://doi.org/10.1016/j.scitotenv.2016.09.125
Pisano L, Zumpano V, Malek, et al (2017) Variations in the susceptibility to landslides, as a consequence of land cover changes: a look to the past, and another towards the future. Sci Total Environ 601–602:1147–1159. https://doi.org/10.1016/j.scitotenv.2017.05.231
Pourghasemi HR, Moradi HR, Fatemi Aghda SM (2013) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 69:749–779. https://doi.org/10.1007/s11069-013-0728-5
Prastica RMS, Apriatresnayanto R, Marthanty DR (2019) Structural and green infrastructure mitigation alternatives prevent ciliwung river from water-related landslide. Int J Adv Sci Eng Inf Technol 9(6):1825–1832. https://doi.org/10.18517/ijaseit.9.6.8413
Reichenbach P, Busca C, Mondini AC, Rossi M (2014) The Influence of Land Use Change on Landslide susceptibility zonation: The Briga catchment test site (Messina, Italy). Environ Manage 54:1372–1384. https://doi.org/10.1007/s00267-014-0357-0
Reichenbach P, Rossi M, Malamud BD et al (2018) A review of statistically-based landslide susceptibility models. Earth-Sci Rev 180:60–91. https://doi.org/10.1016/j.earscirev.2018.03.001
Remondo J, González A, Díaz de Terán JR et al (2003) Validation of landslide susceptibility maps; examples and applications from a case study in northern Spain. Nat Hazards 30:437–449. https://doi.org/10.1023/B:NHAZ.0000007201.80743.fc
Saah D, Tenneson K, Poortinga A et al (2020) Primitives as building blocks for constructing land cover maps. Int J Appl Earth Obs Geoinf 85:101979. https://doi.org/10.1016/j.jag.2019.101979
Shahabi H, Khezri S, Bin AB, Hashim M (2014) Landslide susceptibility mapping at central Zab Basin, Iran: A comparison between analytical hierarchy process, frequency ratio and logistic regression models. CATENA 115:55–70. https://doi.org/10.1016/j.catena.2013.11.014
Shu H, Hürlimann M, Molowny-Horas R et al (2019) Relation between land cover and landslide susceptibility in Val d’Aran, Pyrenees (Spain): historical aspects, present situation and forward prediction. Sci Total Environ 693:1–14. https://doi.org/10.1016/j.scitotenv.2019.07.363
Silalahi FES, Pamela AY, Hidayat F (2019) Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geosci Lett. https://doi.org/10.1186/s40562-019-0140-4
Spruce J, Bolten J, Srinivasan R, Lakshmi V (2018) Developing land use land cover maps for the lower mekong basin to aid hydrologic modeling and basin planning. Remote Sens. https://doi.org/10.3390/rs10121910
Spruce J, Bolten J, Mohammed IN et al (2020) Mapping land use land cover change in the Lower Mekong Basin from 1997 to 2010. Front Environ Sci. https://doi.org/10.3389/fenvs.2020.00021
Van Zyl JJ (2001) The shuttle radar topography mission (SRTM): a breakthrough in remote sensing of topography. Acta Astronaut 48:559–565. https://doi.org/10.1016/S0094-5765(01)00020-0
Winter MG, Dixon N, Wasowski J, Dijkstra TA (2010) Introduction to land-use and climate change impacts on landslides. Q J Eng Geol Hydrogeol 43:367–370. https://doi.org/10.1144/1470-9236/10-035
Yan L, Xu W, Wang H et al (2019) Drainage controls on the Donglingxing landslide (China) induced by rainfall and fluctuation in reservoir water levels. Landslides 16:1583–1593
Zhou C, Yin K, Cao Y et al (2018) Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the three Gorges reservoir area, China. Comput Geosci 112:23–37. https://doi.org/10.1016/j.cageo.2017.11.019
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Chelsea Dandridge. The first draft of the manuscript was written by Chelsea Dandridge and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Dandridge, C., Stanley, T., Kirschbaum, D. et al. The influence of land use and land cover change on landslide susceptibility in the Lower Mekong River Basin. Nat Hazards 115, 1499–1523 (2023). https://doi.org/10.1007/s11069-022-05604-4
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DOI: https://doi.org/10.1007/s11069-022-05604-4