Abstract
Landslide event inventories are one of the most critical datasets to increase knowledge on landslide occurrences. However, they are rarely available in various regions, especially in countries of the Global South. This study aims to generate rainfall-induced landslide event inventories and define the rainfall thresholds responsible for landslide occurrence at the national scale of Malawi, Africa. We mainly followed a three-step methodology to generate landslide inventories. First, we went through media reports to identify documented landslide events. Second, we used Sentinel-2 images to identify possible areas affected by landslides using automated change detection algorithms based on vegetation indices. Third, we manually went through optical images provided by Planet Lab and Google Earth and mapped landslides via visual image interpretation. Overall, we mapped 27 rainfall-induced landslide inventories between 2003 and 2022, with a total of 4709 individual landslides. We then analysed the Malawian terrain and identified two different landscape clusters (i.e. Cluster 1 and Cluster 2) showing similar morphometric and climatic conditions. Ultimately, we calculated the rainfall threshold for each landscape cluster. The minimum rainfall amounts responsible for landsliding correspond to 66 mm/two-day and 51 mm/day in Clusters 1 and 2, respectively. In this context, our paper not only presents and shares the first national-scale, digital rainfall-induced landslide event inventory database of Malawi but also suitable rainfall thresholds to be potentially exploited for a national scale landslide early warning system. A similar framework could be applied to generate landslide inventories for other data scarce regions.
This is a preview of subscription content, log in via an institution to check access.
Availability of data and material
The inventories we mapped for this study are available through the Supplementary Materials.
References
Abraham MT, Satyam N, Pradhan B (2021) Forecasting landslides using mobility functions: a case study from Idukki district, India. Indian Geotech J. https://doi.org/10.1007/s40098-020-00490-8
Aleotti P (2004) A warning system for rainfall-induced shallow failures. Eng Geol 73:247–265. https://doi.org/10.1016/j.enggeo.2004.01.007
Alvioli M, Marchesini I, Reichenbach P et al (2016) Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling. Geosci Model Dev 9:3975–3991. https://doi.org/10.5194/gmd-9-3975-2016
Amatya P, Kirschbaum D, Stanley T (2022) Rainfall-induced landslide inventories for Lower Mekong based on Planet imagery and a semi-automatic mapping method. Geosci Data J n/a. https://doi.org/10.1002/gdj3.145
Amatya P, Kirschbaum D, Stanley T, Tanyas H (2021) Landslide mapping using object-based image analysis and open source tools. Eng Geol 282:106000. https://doi.org/10.1016/j.enggeo.2021.106000
Bevacqua E, De Michele C, Manning C et al (2021) Guidelines for studying diverse types of compound weather and climate events. Earths Future 9:e2021EF002340. https://doi.org/10.1029/2021EF002340
Bevington A, Gleason H, Giroux-Bougard X, De Jong JT (2018) A review of free optical satellite imagery for watershed-scale landscape analysis. Conflu J Watershed Sci Manag 2:1–22. https://doi.org/10.22230/jwsm.2018v2n2a18
Bhuyan K, Tanyaş H, Nava L et al (2023) Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data. Sci Rep 13:162. https://doi.org/10.1038/s41598-022-27352-y
Borga M, Dalla Fontana G, Cazorzi F (2002) Analysis of topographic and climatic control on rainfall-triggered shallow landsliding using a quasi-dynamic wetness index. J Hydrol 268:56–71. https://doi.org/10.1016/S0022-1694(02)00118-X
Borga M, Stoffel M, Marchi L et al (2014) Hydrogeomorphic response to extreme rainfall in headwater systems: flash floods and debris flows. J Hydrol 518:194–205. https://doi.org/10.1016/j.jhydrol.2014.05.022
Broeckx J, Vanmaercke M, Duchateau R, Poesen J (2018) A data-based landslide susceptibility map of Africa. Earth Sci Rev 185:102–121. https://doi.org/10.1016/j.earscirev.2018.05.002
Camps-Valls G, Campos-Taberner M, Moreno-Martínez Á et al (2021) A unified vegetation index for quantifying the terrestrial biosphere. Sci Adv 7:1–11. https://doi.org/10.1126/sciadv.abc7447
Choi CE, Cui Y, Zhou GGD (2018) Utilizing crowdsourcing to enhance the mitigation and management of landslides. Landslides 15:1889–1899
Corbane C, Politis P, Kempeneers P et al (2020) A global cloud free pixel- based image composite from Sentinel-2 data. Data Br 31:105737. https://doi.org/10.1016/j.dib.2020.105737
de Oliveira NS, Rotunno Filho OC, Marton E, Silva C (2016) Correlation between rainfall and landslides in Nova Friburgo, Rio de Janeiro—Brazil: a case study. Environ Earth Sci 75:1358. https://doi.org/10.1007/s12665-016-6171-7
Deijns AAJ, Bevington AR, van Zadelhoff F et al (2020) Semi-automated detection of landslide timing using harmonic modelling of satellite imagery, Buckinghorse River, Canada. Int J Appl Earth Obs Geoinf 84:101943. https://doi.org/10.1016/j.jag.2019.101943
Deijns AAJ, Dewitte O, Thiery W et al (2022) Timing landslide and flash flood events from SAR satellite: a regionally applicable methodology illustrated in African cloud-covered tropical environments. Nat Hazards Earth Syst Sci 22:3679–3700. https://doi.org/10.5194/nhess-22-3679-2022
Depicker A, Jacobs L, Delvaux D et al (2020) The added value of a regional landslide susceptibility assessment: the western branch of the East African Rift. Geomorphology 353. https://doi.org/10.1016/j.geomorph.2019.106886
Dewitte O, Depicker A, Moeyersons J, Dille A (2022) Mass movements in tropical climates. In: Treatise on Geomorphology. Elsevier, pp 338–349
Dikshit A, Satyam DN (2018) Estimation of rainfall thresholds for landslide occurrences in Kalimpong, India. Innov Infrastruct Solut 3:1–10. https://doi.org/10.1007/S41062-018-0132-9/FIGURES/9
Dolozi M, Kaufulu Z (1992) The Manyani Hill landslide in North Eastern Kasungu, Malawi in National Papers of the Malawi Department of Antiquities 1
Dragović N, Vasiljević Ð, Stankov U, Vujičić M (2019) Go social for your own safety! Review of social networks use on natural disasters – case studies from worldwide. Open Geosci 11:352–366. https://doi.org/10.1515/geo-2019-0028
ECHO (2019) Malawi - Landslide (Media) (ECHO Daily Flash of 22 April 2019) - Malawi. In: ECHO Dly. | Reli
Emberson R, Kirschbaum D, Stanley T (2020) New global characterisation of landslide exposure. Nat Hazards Earth Syst Sci 20:3413–3424. https://doi.org/10.5194/nhess-20-3413-2020
ESA (2015) European Space Agency: Sentinel-2 user handbook - Google Scholar
Fang Z, Tanyas H, Gorum T et al (2023) Speech-recognition in landslide predictive modelling: a case for a next generation early warning system. Environ Model Softw 170:105833. https://doi.org/10.1016/j.envsoft.2023.105833
Franceschini R, Rosi A, Catani F, Casagli N (2022) Exploring a landslide inventory created by automated web data mining: the case of Italy. Landslides 19:841–853. https://doi.org/10.1007/s10346-021-01799-y
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
Funk C, Peterson P, Landsfeld M et al (2015) The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci Data 2:150066. https://doi.org/10.1038/sdata.2015.66
Gong P, Ruiliang Pu, Biging GS, Larrieu MR (2003) Estimation of forest leaf area index using vegetation indices derived from hyperion hyperspectral data. IEEE Trans Geosci Remote Sens 41:1355–1362. https://doi.org/10.1109/TGRS.2003.812910
Gorelick N, Hancher M, Dixon M et al (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27
Görüm T, Fidan S (2021) Spatiotemporal variations of fatal landslides in Turkey. Landslides. https://doi.org/10.1007/s10346-020-01580-7
Guzzetti F, Gariano SL, Peruccacci S et al (2020) Geographical landslide early warning systems. Earth Sci Rev 200. https://doi.org/10.1016/j.earscirev.2019.102973
Guzzetti F, Mondini AC, Cardinali M et al (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112:42–66. https://doi.org/10.1016/j.earscirev.2012.02.001
Handwerger AL, Huang M-H, Jones SY et al (2022) Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine. Nat Hazards Earth Syst Sci 22:753–773. https://doi.org/10.5194/nhess-22-753-2022
Hao L, van Westen C et al (2020) Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis. Earth Syst Sci Data 12:2899–2918. https://doi.org/10.5194/essd-12-2899-2020
Henrich V, Brüser K (2011) IDB - Imprint
Hidayat R, Sutanto SJ, Hidayah A et al (2019) Development of a landslide early warning system in Indonesia. Geosciences 9:451. https://doi.org/10.3390/geosciences9100451
Ho J-Y, Lee KT (2017) Performance evaluation of a physically based model for shallow landslide prediction. Landslides 14:961–980
Hochschild V, Märker M, Rodolfi G, Staudenrausch H (2003) Delineation of erosion classes in semi-arid southern African grasslands using vegetation indices from optical remote sensing data. Hydrol Process 17:917–928. https://doi.org/10.1002/hyp.1170
Hodge M, Biggs J, Goda K, Aspinall W (2015) Assessing infrequent large earthquakes using geomorphology and geodesy: the Malawi Rift. Nat Hazards 76:1781–1806. https://doi.org/10.1007/s11069-014-1572-y
Hong M, Kim J, Jeong S (2018) Rainfall intensity-duration thresholds for landslide prediction in South Korea by considering the effects of antecedent rainfall. Landslides 15:523–534. https://doi.org/10.1007/S10346-017-0892-X/TABLES/4
Huang F, Tao S, Chang Z et al (2021) Efficient and automatic extraction of slope units based on multi-scale segmentation method for landslide assessments. Landslides 18:3715–3731. https://doi.org/10.1007/S10346-021-01756-9/FIGURES/14
Huang Q, Wang C, Meng Y et al (2020) Landslide monitoring using change detection in multitemporal optical imagery. IEEE Geosci Remote Sens Lett 17:312–316. https://doi.org/10.1109/LGRS.2019.2918254
Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25:295–309. https://doi.org/10.1016/0034-4257(88)90106-X
Huffman GJ, Bolvin DT, Braithwaite D et al (2020) Integrated multi-satellite retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG). Adv Glob Chang Res 67:343–353. https://doi.org/10.1007/978-3-030-24568-9_19
Hughes AL, Palen L (2009) Twitter adoption and use in mass convergence and emergency events. Int J Emerg Manag 6:248–260
Hungr O, Leroueil S, Picarelli L (2014) The Varnes classification of landslide types, an update. Landslides 11:167–194. https://doi.org/10.1007/s10346-013-0436-y
IPCC IP on CC (2023) Summary for policymakers. In: Climate change 2022 – impacts, adaptation and vulnerability. Cambridge University Press, pp 3–34
Islam MR, Khan MNI, Khan MZ, Roy B (2021) A three decade assessment of forest cover changes in Nijhum dwip national park using remote sensing and GIS. Environ Challenges 4:100162. https://doi.org/10.1016/j.envc.2021.100162
Jacobs L, Dewitte O, Poesen J et al (2016) The Rwenzori Mountains, a landslide-prone region? Landslides 13:519–536. https://doi.org/10.1007/s10346-015-0582-5
Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recognit Lett 31:651–666. https://doi.org/10.1016/J.PATREC.2009.09.011
Juang CS, Stanley TA, Kirschbaum DB (2019) Using citizen science to expand the global map of landslides: Introducing the Cooperative Open Online Landslide Repository (COOLR). PLoS ONE 14:e0218657
Karger DN, Conrad O, Böhner J et al (2017) Climatologies at high resolution for the earth’s land surface areas. Sci Data 4. https://doi.org/10.1038/SDATA.2017.122
Keeley JE (2009) Fire intensity, fire severity and burn severity: a brief review and suggested usage. Int J Wildl Fire 18:116–126. https://doi.org/10.1071/WF07049
Kim SW, Chun KW, Kim M et al (2020) (2020) Effect of antecedent rainfall conditions and their variations on shallow landslide-triggering rainfall thresholds in South Korea. Landslides 182(18):569–582. https://doi.org/10.1007/S10346-020-01505-4
Kirschbaum DB, Adler R, Hong Y et al (2010) A global landslide catalog for hazard applications: method, results, and limitations. Nat Hazards 52:561–575. https://doi.org/10.1007/s11069-009-9401-4
Kocaman S, Gokceoglu C (2019) A CitSci app for landslide data collection. Landslides 16:611–615
Kohler MA, Linsley RK (1951) Predicting the runoff from storm rainfall - Google Books
Li L, Bensi M, Cui Q et al (2021) Social media crowdsourcing for rapid damage assessment following a sudden-onset natural hazard event. Int J Inf Manage 60:102378. https://doi.org/10.1016/j.ijinfomgt.2021.102378
Lin W-T, Lin C-Y, Chou W-C (2006) Assessment of vegetation recovery and soil erosion at landslides caused by a catastrophic earthquake: a case study in Central Taiwan. Ecol Eng 28:79–89. https://doi.org/10.1016/j.ecoleng.2006.04.005
Lombardo L, Tanyas H (2020) Chrono-validation of near-real-time landslide susceptibility models via plug-in statistical simulations. Eng Geol 278:105818. https://doi.org/10.1016/j.enggeo.2020.105818
Lu P, Qin Y, Li Z et al (2019) Landslide mapping from multi-sensor data through improved change detection-based Markov random field. Remote Sens Environ 231:111235. https://doi.org/10.1016/j.rse.2019.111235
Macheyeki AS, Mdala H, Chapola LS et al (2015) Active fault mapping in Karonga-Malawi after the December 19, 2009 Ms 6.2 seismic event. J African Earth Sci 102:233–246. https://doi.org/10.1016/J.JAFREARSCI.2014.10.010
Maki Mateso J-C, Bielders CL, Monsieurs E et al (2023) Characteristics and causes of natural and human-induced landslides in a tropical mountainous region: the rift flank west of Lake Kivu (Democratic Republic of the Congo). Nat Hazards Earth Syst Sci 23:643–666. https://doi.org/10.5194/nhess-23-643-2023
Malamud BD, Turcotte DL, Guzzetti F, Reichenbach P (2004) Landslide inventories and their statistical properties. Earth Surf Process Landforms 29:687–711. https://doi.org/10.1002/esp.1064
Martha TR, Kerle N, Jetten V et al (2010) Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology 116:24–36
Martha TR, Roy P, Jain N et al (2021) Geospatial landslide inventory of India—an insight into occurrence and exposure on a national scale. Landslides 18:2125–2141. https://doi.org/10.1007/s10346-021-01645-1
Melillo M, Brunetti MT, Peruccacci S et al (2018) A tool for the automatic calculation of rainfall thresholds for landslide occurrence. Environ Model Softw 105:230–243. https://doi.org/10.1016/j.envsoft.2018.03.024
Michel GP, Goerl RF, Kobiyama M (2015) Critical rainfall to trigger landslides in Cunha River basin, southern Brazil. Nat Hazards 75:2369–2384. https://doi.org/10.1007/s11069-014-1435-6
Mondini AC, Guzzetti F, Chang K-T et al (2021) Landslide failures detection and mapping using Synthetic Aperture Radar: Past, present and future. Earth Sci Rev 216:103574. https://doi.org/10.1016/j.earscirev.2021.103574
Monsieurs E, Dewitte O, Demoulin A (2019a) A susceptibility-based rainfall threshold approach for landslide occurrence. Nat Hazards Earth Syst Sci 19:775–789. https://doi.org/10.5194/nhess-19-775-2019
Monsieurs E, Jacobs L, Michellier C et al (2018a) Landslide inventory for hazard assessment in a data - poor context: a regional-scale approach in a tropical African environment. 2195–2209. https://doi.org/10.1007/s10346-018-1008-y
Monsieurs E, Kirschbaum DB, Tan J et al (2018b) Evaluating TMPA rainfall over the sparsely gauged East African rift. J Hydrometeorol 19:1507–1528. https://doi.org/10.1175/JHM-D-18-0103.1
Monsieurs E, Dewitte O, Depicker A, Demoulin A (2019b) Towards a transferable antecedent rainfall—susceptibility threshold approach for landsliding. Water 11(11):2202. https://doi.org/10.3390/w11112202
Msilimba G, Holmes P (2009) Landslides in northern and central Malawi; awareness, perceptions and coping strategies. South African Geogr J 91:38–45. https://doi.org/10.1080/03736245.2009.9725328
Msilimba GG, Holmes PJ (2005) A landslide hazard assessment and vulnerability appraisal procedure: Vunguvungu/Banga catchment, Northern Malawi. Nat Hazards 34:199–216. https://doi.org/10.1007/s11069-004-1513-2
Msilimba GG, Holmes PJ (2010) Landslides in the Rumphi District of Northern Malawi: characteristics and mechanisms of generation. Nat Hazards 54:657–677. https://doi.org/10.1007/s11069-009-9495-8
Mtilatila L, Bronstert A, Bürger G, Vormoor K (2020) Meteorological and hydrological drought assessment in Lake Malawi and Shire River basins (1970–2013). Hydrol Sci J 65:2750–2764. https://doi.org/10.1080/02626667.2020.1837384
Musaev A, Wang D, Pu C (2014) LITMUS: Landslide detection by integrating multiple sources. In: ISCRAM
Mwenelupembe J, Mylius HG (2002) Geological hazards and anthropogenic impacts on the environment in Malawi: lesson from a case study of debris flows in Zomba. pp 557–573
Nadim F, Kjekstad O, Peduzzi P et al (2006) Global landslide and avalanche hotspots. Landslides 3:159–173. https://doi.org/10.1007/s10346-006-0036-1
Neteler M, Mitasova H (2013) Open source GIS. Springer, US, Boston, MA
Nhamo G, Chikodzi D (2021) The catastrophic impact of tropical cyclone Idai in Southern Africa. Springer, pp 3–29
Nolasco-Javier D, Kumar L (2018) Deriving the rainfall threshold for shallow landslide early warning during tropical cyclones: a case study in northern Philippines. Nat Hazards 90:921–941. https://doi.org/10.1007/s11069-017-3081-2
Notti D, Cignetti M, Godone D, Giordan D (2023) Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine. Nat Hazards Earth Syst Sci 23:2625–2648. https://doi.org/10.5194/nhess-23-2625-2023
Ofli F, Imran M, Qazi U et al (2023) Landslide detection in real-time social media image streams. Neural Comput Appl 35:17809–17819. https://doi.org/10.1007/s00521-023-08648-0
Pennington C, Freeborough K, Dashwood C et al (2015) The National Landslide Database of Great Britain: Acquisition, communication and the role of social media. Geomorphology 249:44–51
Pepe M, Parente C (2018) Recognition of burned area change of detection analysis using images derived from satellite Sentinel-2: Case studio of Sorrento Penisola, Italy. J Appl Eng Sci 16:225–232. https://doi.org/10.5937/jaes16-17249
Planet (2022) Planet imagery product specifications. Planet Labs Inc 1–100
Planet Team (2017) Planet application program interface: In space for life on Earth. San Francisco, CA. https://api.planet.com
Polykretis C, Grillakis M, Alexakis D (2020) Exploring the impact of various spectral indices on land cover change detection using change vector analysis: a case study of Crete Island, Greece. Remote Sens 12:319. https://doi.org/10.3390/rs12020319
Powers DMW (2020) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. pp 37–63
Rahmati O, Kornejady A, Samadi M et al (2019) PMT: New analytical framework for automated evaluation of geo-environmental modelling approaches. Sci Total Environ 664:296–311. https://doi.org/10.1016/j.scitotenv.2019.02.017
Ramos-Bernal R, Vázquez-Jiménez R, Romero-Calcerrada R et al (2018) Evaluation of unsupervised change detection methods applied to landslide inventory mapping using ASTER imagery. Remote Sens 10:1987. https://doi.org/10.3390/rs10121987
Reder A, Rianna G, Pagano L (2018) Physically based approaches incorporating evaporation for early warning predictions of rainfall-induced landslides. Nat Hazards Earth Syst Sci 18:613–631. https://doi.org/10.5194/nhess-18-613-2018
Rhyma PP, Norizah K, Hamdan O et al (2020) Integration of normalised different vegetation index and soil-adjusted vegetation index for mangrove vegetation delineation. Remote Sens Appl Soc Environ 17:100280. https://doi.org/10.1016/j.rsase.2019.100280
Rodell M, Houser PR, Jambor U et al (2004) The global land data assimilation system. Bull Am Meteorol Soc 85:381–394. https://doi.org/10.1175/BAMS-85-3-381
Rondeaux G, Steven M, Baret F (1996) Optimization of soil-adjusted vegetation indices. Remote Sens Environ 55:95–107. https://doi.org/10.1016/0034-4257(95)00186-7
Rosi A, Tofani V, Tanteri L et al (2018) The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution. Landslides 15:5–19. https://doi.org/10.1007/s10346-017-0861-4
Rossi M, Luciani S, Valigi D et al (2017) Statistical approaches for the definition of landslide rainfall thresholds and their uncertainty using rain gauge and satellite data. Geomorphology 285:16–27. https://doi.org/10.1016/j.geomorph.2017.02.001
Salvatici T, Tofani V, Rossi G et al (2018) Application of a physically based model to forecast shallow landslides at a regional scale. Nat Hazards Earth Syst Sci 18:1919–1935
Saria E, Calais E, Stamps DS, et al (2014) Present‐day kinematics of the East African Rift. J Geophys Res Solid Earth 119:3584–3600. https://doi.org/10.1002/2013JB010901
Satyaningsih R, Jetten V, Ettema J et al (2023) Dynamic rainfall thresholds for landslide early warning in Progo Catchment, Java, Indonesia. Nat Hazards 119:2133–2158. https://doi.org/10.1007/s11069-023-06208-2
Scheip CM, Wegmann KW (2021) HazMapper: a global open-source natural hazard mapping application in Google Earth Engine. Nat Hazards Earth Syst Sci 21:1495–1511. https://doi.org/10.5194/nhess-21-1495-2021
Schlögel R, Marchesini I, Alvioli M et al (2018) Optimizing landslide susceptibility zonation: effects of DEM spatial resolution and slope unit delineation on logistic regression models. Geomorphology 301:10–20. https://doi.org/10.1016/j.geomorph.2017.10.018
Segoni S, Piciullo L, Gariano SL (2018a) A review of the recent literature on rainfall thresholds for landslide occurrence. Landslides 15:1483–1501. https://doi.org/10.1007/s10346-018-0966-4
Segoni S, Piciullo L, Gariano SL (2018b) Preface: landslide early warning systems: monitoring systems, rainfall thresholds, warning models, performance evaluation and risk perception. Nat Hazards Earth Syst Sci 18:3179–3186
Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45:427–437. https://doi.org/10.1016/j.ipm.2009.03.002
Stanley TA, Kirschbaum DB, Benz G et al (2021) Data-driven landslide nowcasting at the global scale. Front Earth Sci 9
Styron R, Pagani M (2020) The GEM global active faults database. Earthq Spectra 36:160–180. https://doi.org/10.1177/8755293020944182
Sun D, Shi S, Wen H et al (2021) A hybrid optimization method of factor screening predicated on GeoDetector and Random Forest for Landslide Susceptibility Mapping. Geomorphology 379:107623. https://doi.org/10.1016/J.GEOMORPH.2021.107623
Tanyaş H, Rossi M, Alvioli M et al (2019) A global slope unit-based method for the near real-time prediction of earthquake-induced landslides. Geomorphology 327:126–146. https://doi.org/10.1016/j.geomorph.2018.10.022
Tanyaş H, van Westen CJ, Allstadt KE et al (2017) Presentation and analysis of a worldwide database of earthquake-induced landslide inventories. J Geophys Res Earth Surf 122. https://doi.org/10.1002/2017JF004236
Thiery Y, Kaonga H, Mtumbuka H et al (2021) Landslide hazard assessment and mapping for Malawi (Southeastern Africa): from susceptibility to hazard by integration of temporal exceedance probabilities related to tropical meteorological events. EGUGA EGU21–6636
Tian Y-C, Gu K-J, Chu X et al (2014) Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice. Plant Soil 376:193–209. https://doi.org/10.1007/s11104-013-1937-0
Tibshirani R, Walther G, Hastie T (2001) Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc Ser B Stat Methodol 63:411–423. https://doi.org/10.1111/1467-9868.00293
Tordesillas A, Zhou Z, Batterham R (2018) A data-driven complex systems approach to early prediction of landslides. Mech Res Commun 92:137–141
Uehara TDT, Sehn Körting T, dos Soares AR, Quevedo RP (2022) Time-series metrics applied to land use and land cover mapping with focus on landslide detection. J Appl Remote Sens 16:1–30. https://doi.org/10.1117/1.JRS.16.034518
USGS (2021) Malawi - U.S. geological survey search results. In: United States Geol. surv. earthq. portal. Earthq. hazards program
USGS (2023) United States geological survey earthquake portal. Earthquake Hazards Program. https://www.usgs.gov/%0Anatural-hazards/earthquake-hazards/earthquakes. Accessed 1 Jul 2023
Uwihirwe J, Hrachowitz M, Bogaard TA (2020) Landslide precipitation thresholds in Rwanda. Landslides 17:2469–2481. https://doi.org/10.1007/s10346-020-01457-9
van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Eng Geol 102:112–131. https://doi.org/10.1016/j.enggeo.2008.03.010
Wang N, Lombardo L, Gariano SL et al (2021) Using satellite rainfall products to assess the triggering conditions for hydro-morphological processes in different geomorphological settings in China. Int J Appl Earth Obs Geoinf 102:102350. https://doi.org/10.1016/j.jag.2021.102350
Weier J, Herring D (2000) Measuring vegetation (NDVI & EVI). NASA Earth Observatory
Williams JN, Wedmore LNJ, Scholz CA et al (2022) The Malawi active fault database: an onshore-offshore database for regional assessment of seismic hazard and tectonic evolution. Geochem Geophys Geosyst 23:e2022GC010425. https://doi.org/10.1029/2022GC010425
Williams JN, Wedmore LNJ, Scholz CH et al (2021) Malawi Active Fault Database. https://doi.org/10.5281/ZENODO.5507190
Yuniawan RA, Rifa’i A, Faris F, et al (2022) Revised rainfall threshold in the Indonesian landslide early warning system. Geosciences 12:129. https://doi.org/10.3390/geosciences12030129
Zêzere JL, Trigo RM, Trigo IF (2005) Shallow and deep landslides induced by rainfall in the Lisbon region (Portugal): assessment of relationships with the North Atlantic Oscillation. Nat Hazards Earth Syst Sci 5:331–344. https://doi.org/10.5194/nhess-5-331-2005
Zêzere JL, Vaz T, Pereira S et al (2015) Rainfall thresholds for landslide activity in Portugal: a state of the art. Environ Earth Sci 73:2917–2936. https://doi.org/10.1007/s12665-014-3672-0
Zhao W, Li A, Nan X et al (2017) Postearthquake landslides mapping from Landsat-8 data for the 2015 Nepal earthquake using a pixel-based change detection method. IEEE J Sel Top Appl Earth Obs Remote Sens 10:1758–1768. https://doi.org/10.1109/JSTARS.2017.2661802
Zhao Y, Huang Y, Liu H et al (2018) Use of the normalized difference road landside index (NDRLI)-based method for the quick delineation of road-induced landslides. Sci Rep 8:17815. https://doi.org/10.1038/s41598-018-36202-9
Zhong C, Liu Y, Gao P et al (2020) Landslide mapping with remote sensing: challenges and opportunities. Int J Remote Sens 41:1555–1581. https://doi.org/10.1080/01431161.2019.1672904
Funding
This study was partly supported by the Belgian Science Policy Office (BELSPO) through the PAStECA Project (BR/165/A3/PASTECA) entitled “Historical Aerial Photographs and Archives to Assess Environmental Changes in Central Africa” (http://pasteca.africamuseum.be, last access: 7 August 2023) and the GEOTROP Project (B2/223/P1/GEOTROP) entitled “GEOmorphic hazards and compound events in a changing Tropical East Africa” (https://georiska.africamuseum.be/nl/activities/geotrop, last access: 07 August 2023) applicable.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Supplementary information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Niyokwiringirwa, P., Lombardo, L., Dewitte, O. et al. Event-based rainfall-induced landslide inventories and rainfall thresholds for Malawi. Landslides (2024). https://doi.org/10.1007/s10346-023-02203-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10346-023-02203-7