Albuquerque, J. P. D., Herfort, B., and Eckle, M.: The Tasks of the Crowd: A Typology of Tasks in Geographic Information Crowdsourcing and a Case Study in Humanitarian Mapping, Remote Sens., 8, 1–22, https://doi.org/10.3390/rs8100859, 2016.
Ansari, H., Zan, F. D., and Bamler, R.: Efficient Phase Estimation for Interferogram Stacks, IEEE T. Geosci. Remote, 56, 4109–4125, 10.1109/TGRS.2018.2826045. 2018.
Avelar, A. S., Netto, A. L. C., Lacerda, W. A, Becker, L. B., and Mendonça, M. B.: Mechanisms of the recent catastrophic landslides in the mountainous range of Rio de Janeiro, Brazil, in: Landslide science and practice. Springer, Berlin Heidelberg, 265–270, ISBN978-3-642-31337-0-34, 2013.
Ávila, F. F., Alvalá, R. C., Mendes, R. M., and Amore, D. J.: The influence of land use/land cover variability and rainfall intensity in triggering landslides: a back-analysis study via physically based models, Nat. Hazards, 105, 1139–1161, https://doi.org/10.1007/s11069-020-04324-x, 2021.
Brasil: Lei no 12.651, de 25 de maio de 2012, https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2012/lei/l12651.htm (last access: 1May 2022), 2012.
Carvalho-Filho, A., Lumbreras, J. F., and Santos, R. D.: Os Solos do Estado do Rio de Janeiro, CPRM, Brasília, Brazil, 36pp., https://www.embrapa.br/busca-de-publicacoes/-/publicacao/1090208/os-solos-do-estado-do-rio-de-janeiro (last access: 6May 2022), 2000.
Catani, F., Lagomarsino, D., Segoni, S., and Tofani, V.: Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues, Nat. Hazards Earth Syst. Sci., 13, 2815–2831, https://doi.org/10.5194/nhess-13-2815-2013, 2013.
Chau, K. T. and Chan, J. E.: Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island, Landslides, 2, 280–290, https://doi.org/10.1007/s10346-005-0024-x, 2005.
Chen, C. W. and Zebker, H. A.: Two-dimensional phase unwrapping with use of statistical models for cost functions in nonlinear optimization, J. Opt. Soc. Am. A, 18, 338–351, https://doi.org/10.1364/JOSAA.18.000338, 2001.
Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D. T., Duan, Z., and Ma, J.: A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility, Catena, 151, 147–160, https://doi.org/10.1016/j.catena.2016.11.032, 2017.
Costanzo, D., Rotigliano, E., Irigaray, C., Jiménez-Perálvarez, J. D., and Chacón, J.: Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain), Nat. Hazards Earth Syst. Sci., 12, 327–340, https://doi.org/10.5194/nhess-12-327-2012, 2012.
CPRM (Geological Survey of Brazil): Avaliação técnica pós-desastre, Petropolis, RJ 2022, Ministry of Mines and Energy MME, 9pp., https://rigeo.cprm.gov.br, last access: 1May 2022.
Dai, F. C. and Lee, C. F.: Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong, Geomorphology, 42, 213–228, https://doi.org/10.1016/s0169-555x(01)00087-3, 2002.
Dean, W.: A ferro e fogo: a história e a devastação da Mata Atlântica brasileira, Companhia das Letras, São Paulo, ISBN9788571645905, 1996.
Dembélé, M. and Zwart, S. J.: Evaluation and Comparison of Satellite-Based Rainfall Products in Burkina Faso, West Africa, Int. J. Remote Sens., 37, 3995–4014, https://doi.org/10.1080/01431161.2016.1207258, 2016.
Dias, H. C., Hölbling, D., and Grohmann, C. H.: Landslide Susceptibility Mapping in Brazil: A Review, Geosciences, 11, 1–15, https://doi.org/10.3390/geosciences11100425, 2021.
Dias, M. C. A., Saito, S. M., Alvalá, R. C., Stenner, C., Pinho, G., Nobre, C. A., Fonseca, M. R. S., Santos, C., Amadeu, P., Silva, D., Lima, C. O., Ribeiro, J., Nascimento, F., and Corrêa, C. O.: Estimation of exposed population to landslides and floods risk areas in Brazil, on intra-urban scale, Int. J. Disast. Risk Re., 31, 449–459, https://doi.org/10.1016/j.ijdrr.2018.06.002, 2018.
Dourado, F., Arraes, T. C., and Silva, M. F.: The “Megadesastre” in the Mountain Region of Rio de Janeiro State – Causes, Mechanisms of Mass Movements and Spatial Allocation of Investments for Reconstruction Post Disaster, Anuário do Instituto de Geeociências, 35, 43–54, https://doi.org/10.11137/2012_2_43_54, 2012.
Fattahi, H., Agram, P. S., Tymofyeyeva, E., and Bekaert, D. P.: FRInGE; Full-Resolution InSAR time series using Generalized Eigenvectors, American Geophysical Union, Fall Meeting 2019, 9–13December 2019, San Francisco, CA, USA, abstract #G11B-0514, https://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/497561 (last access: 6May 2022), 2019.
FIRJAN: Firjan estima perda de R$ 665 milhões no PIB de Petrópolis após a forte chuva da última semana, FIRJAN, https://firjan.com.br/noticias/firjan-estima-perda-de-r-665-milhoes-no-pib-de-petropolis-apos-a-forte-chuva-da-ultima-semana-1.htm, last access: 20March 2022.
Fonseca, M. J. G., Derze, G. R., Barreto, A. M., and Williams, G. H.: Mapa Geológico do Estado do Rio de Janeiro, Technical report, Departamento Nacional de Produção Mineral (DPMN), Rio de Janeiro, Brazil, 141pp., http://www.cprm.gov.br/publique/Gestao-Territorial/Geologia%2C-Meio-Ambiente-e-Saude/Projeto-Rio-de-Janeiro-3498.html (last access: 6May 2022), 1998.
Fundação SOS Mata Atlântica: Atlas dos Remanescentes Florestais da Mata Atlântica período 2016–2017, Technical report, Fundação SOS Mata Atlântica, Instituto Nacional de Pesquisas Espaciais, São Paulo, https://cms.sosma.org.br/wp-content/uploads/2021/05/SOSMA_Atlas-da-Mata-Atlantica_2019-2020.pdf(last access: 6May 2022), 2018.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: 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, 2015.
Galarneau, T. J. and Bosart, L. F.: Ridge Rollers: Mesoscale disturbances on the periphery of cutoff anticyclones, 21st Conf. on Weather Analysis and Forecasting/17th Conf. on Numerical Weather Prediction, 28July–5August 2005, Atlanta, GA, USA, Amer. Meteor. Soc., 3.2, https://ams.confex.com/ams/WAFNWP34BC/techprogram/paper_94414.htm (last access: 2May 2022), 2006.
Goldblatt, R., Jones, N., and Mannix, J.: Assessing OpenStreetMap Completeness for Management of Natural Disaster by Means of Remote Sensing: A Case Study of Three Small Island States (Haiti, Dominica and St.Lucia), Remote Sens., 12, 1–25, https://doi.org/10.3390/rs12010118, 2020.
Gonçalves, L. F. H.: Avaliação e Diagnóstico da Distribuição Espacial e Temporal dos Movimentos de Massa com a Expansão da Área Urbana em Petrópolis-RJ, PhD Thesis, Universidade Federal do Rio de Janeiro-UFRJ, Rio de Janeiro, Brazil, 1998.
Guerra, A.: Catastrophic events in Petrópolis city (Rio de Janeiro state), between 1940 and 1990, GeoJournal, 37, 349–354, https://doi.org/10.1007/BF00814015, 1995.
Guzzetti, F., Carrara, A., Cardinali, M., and Reichenbach, P.: Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy, Geomorphology, 31, 181–216, https://doi.org/10.1016/s0169-555x(99)00078-1, 1999.
Hermance, J. F. and Sulieman, H. M.: Adequacy of the daily TMPA 3B42 high-resolution satellite precipitation product for monitoring hydrometeorological hazards in the Southeast Sahel of Africa, Int. J. Remote Sens., 39, 2579–2596, https://doi.org/10.1080/01431161.2018.1430405, 2018.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 2020.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K.-L., Joyce, R. J., Kidd, C., Nelkin, E. J., Sorooshian, S., Stocker, E. F., Tan, J., Wolff, D. B., and Xie, P.: Satellite Precipitation Measurement: Volume 1, edited by: Levizzani, V., Kidd, C., Kirschbaum, D. B., Kummerow, C. D., Nakamura, K., and Turk, F. J., 343–353, Springer Cham, ISBN978-3030245672, 2020.
IBGE: Cidades, Instituto Brasileiro de Geografia e Estatística, https://cidades.ibge.gov.br/brasil/retropolisis/panorama, last access: 25September 2018.
Janowiak, J., Joyce, B., and Xie, P.: NCEP/CPC L3 Half Hourly 4km Global (60S – 60N) Merged IR V1, edited by: Savtchenko, A., Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/P4HZB9N27EKU, 2017.
Jiang, L. and Bauer-Gottwein, P.: How do GPM IMERG precipitation estimates perform as hydrological model forcing? Evaluation for 300 catchments across Mainland China, J. Hydrol., 572, 486–500, https://doi.org/10.1016/j.jhydrol.2019.03.042, 2019.
Jolivet, R., Agram, Piyush, S., Lin, Y. N., Simons, M., Doin, M.-P., Peltzer, G., and Li, Z.: Improving InSAR geodesy using Global Atmospheric Models, J. Geophys. Res.-Sol. Ea., 119, 2324–2341, https://doi.org/10.1002/2013JB010588, 2014.
Kanwal, S., Atif, S., and Shafiq, M.: GIS based landslide susceptibility mapping of northern areas of Pakistan, a case study of Shigar and Shyok Basins, Geomat. Nat. Hazards Risk, 8, 348–366, https://doi.org/10.1080/19475705.2016.1220023, 2017.
Lambin, E. F. and Ehrlich, D.: The surface temperature-vegetation index space for land cover and land-cover change analysis, Int. J. Remote Sens., 17, 463–487, https://doi.org/10.1080/01431169608949021, 1996.
Li, H., Herfort, B., Huang, W., Zia, M., and Zipf, A.: Exploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique, ISPRS J. Photogramm., 166, 41–51, https://doi.org/10.1016/j.isprsjprs.2020.05.007, 2020.
Maddox, R. A., Hoxit, L. R., Chappell, C. F., and Caracena, F.: Comparison of meteorological aspects of the Big Thompson and Rapid City flash floods, Mon. Weather Rev., 106, 375–389, 1978.
Marchezini, V. and Wisner, B.: Challenges for vulnerability reduction in Brazil: Insights from the PAR framework, in: Reduction of vulnerability to disasters: From knowledge to action, edited by: Marchzini, V., Wisner, B., Londe, L., RIMA, ISBN978-85-7656-050-0, 2017.
Marengo, J. A. and Alves, L. M.: The 2011 intense rainfall and floods in Rio de Janeiro, B. Am. Meteorol. Soc., 93, S176, https://doi.org/10.1175/2012BAMSStateoftheClimate.1, 2012.
Martinotti, M. E., Pisano, L., Marchesini, I., Rossi, M., Peruccacci, S., Brunetti, M. T., Melillo, M., Amoruso, G., Loiacono, P., Vennari, C., Vessia, G., Trabace, M., Parise, M., and Guzzetti, F.: Landslides, floods and sinkholes in a karst environment: the 1–6 September 2014 Gargano event, southern Italy, Nat. Hazards Earth Syst. Sci., 17, 467–480, https://doi.org/10.5194/nhess-17-467-2017, 2017.
Mendes, R. M., Andrade, M. R. M., Graminha, C. A., Prieto, C., Ávila, F. F., Camarinha, P. I.: Stability Analysis on Urban Slopes: Case Study of an Anthropogenic-Induced Landslide in São José dos Campos, Brazil, Geotech. Geol. Eng., 36, 599–610, https://doi.org/10.1007/s10706-017-0303-z, 2018a.
Mendes, R. M., de Andrade, M. R. M., Tomasella, J., de Moraes, M. A. E., and Scofield, G. B.: Understanding shallow landslides in Campos do Jordão municipality – Brazil: disentangling the anthropic effects from natural causes in the disaster of 2000, Nat. Hazards Earth Syst. Sci., 18, 15–30, https://doi.org/10.5194/nhess-18-15-2018, 2018b.
Minghini, M. and Frassinelli, F.: OpenStreetMap history for intrinsic quality assessment: Is OSM up-to-date?, Open Geospatial Data Softw. Stand., 4, 1–17, https://doi.org/10.1186/s40965-019-0067-x, 2019.
Mitchell, M. J., Arritt, R. W., and Labas, K.: A climatology of the warm season Great Plains low-level jet using wind profiler observations, Weather Forecast., 10, 576–591, https://doi.org/10.1175/1520-0434(1995)010<0576:ACOTWS>2.0.CO;2, 1995.
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Fonseca, G. A. B., and Kent, J.: Biodiversity hotspots for conservation priorities, Nature, 403, 853–858, https://doi.org/10.1038/35002501, 2000.
Nehren, U., Schlüter, S., Raeding, C., Sattler, D., and Hissa, H. (Eds.): Strategies and Tools for a Sustainable Rural Rio de Janeiro, Springer Series on Environmental Management, https://doi.org/10.1007/978-3-319-89644-1_20, 2018.
Ohlmacher, G. C.: Plan curvature and landslide probability in regions dominated by Earth flows and Earth Slides, Eng. Geol., 91, 117–134, https://doi.org/10.1016/j.enggeo.2007.01.005, 2007.
Parida, B. R., Collado, W. B., Borah, R., Hazarika, M. K., and Samarakook, L.: Detecting drought-prone areas of rice agriculture using a MODIS-derivd soil moisture index, GIScience and Remote Sensing, 45, 109–129, 2008.
Penha, M. M., Ferrari, A. L., Junho, M. C. B., Souza, S. L. A., and Brennes, T. L.: Projeto Carta Geológica do Estado do Rio de Janeiro: Folha Itaipava, Technical report, vol. 1, Convênio DRM/IG-UFRJ, Rio de Janeiro, Brazil, http://www.cprm.gov.br/publique/Gestao-Territorial/Geologia%2C-Meio-Ambiente-e-Saude/Projeto-Rio-de-Janeiro-3498.html (last access: 3May 2022), 1981.
Pisano, L., Zumpano, V., Malek, Z., Rosskopf, C. M., and Parise, M.: 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, 2017.
Reichenbach, P., Rossi, M., Malamud, B., Mihir, M., and Guzzetti, F.: A review of statistically-based landslide susceptibility models, Earth-Sci. Rev., 180, 60–91, https://doi.org/10.1016/j.earscirev.2018.03.001, 2018.
Reis, A. A., Fernandes, W. S., and Ramos, M.-H.: Assessing two precipitation data sources at basins of special interest to hydropower production in Brazil, Brazilian Journal of Water Resources, 25, 1–16, https://doi.org/10.1590/2318-0331.252020190068, 2020.
Rosi, A., Canavesi, V., Segoni, S., Nery, T. D., Catani, F., and Casagli, N.: Landslides in the Mountain Region of Rio de Janeiro: A Proposal for the Semi-Automated Definition of Multiple Rainfall Thresholds, Geosciences, 9, 1–15, https://doi.org/10.3390/geosciences9050203, 2019.
Schlossmacher, E. J.: An Iterative Technique for Absolute Deviations Curve Fitting, J. Am. Stat. Assoc., 68, 857–859, https://doi.org/10.2307/2284512, 1973.
Silva, R. F. B., Batistella, M., Moran, E. F., and Liu, D.: Land changes fostering Atlantic Forest transition in Brazil: Evidence from the Paraíba Valley, Prof. Geogr., 69, 80–93, https://doi.org/10.1080/00330124.2016.1178151, 2017.
Souza Jr., C. M., Shimbo, J. Z., Rosa, M. R., Parente, L. L., Alencar, A. A., Rudorff, B. F. T., Hasenack, H., Matsumoto, M., Ferreira, L. G., Souza-Filho, P. W. M., de Oliveira, S. W., Rocha, W. F., Fonseca, A. V., Marques, C. B., Diniz, C.,G., Costa, D., Monteiro, D., Rosa, E. R., Vélez-Martin, E., Weber, E. J., Lenti, F. E. B., Paternost, F. F., Pareyn, F.,G.,C., Siqueira, J. V., Viera, J. L., Neto, L. C. F., Saraiva, M. M., Sales, M. H., Salgado, M. P. G., Vasconcelos, R., Galano, S., Mesquita, V. V., and Azevedo, T.: Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine, Remote Sens., 12, 1–17, https://doi.org/10.3390/rs12172735, 2020.
Wang, J., Dong, X., Kennedy, A., Hagenhoff, B., and Xi, B.: A regime-based evaluation of southern and northern great plains warm-season precipitation events in WRF, Weather Forecast., 34, 805–831, 2019.
Zhan, Z., Qin, Q., and Wang, X.: The Application of LST/NDVI Index for Monitoring Land Surface Moisture in Semiarid Area, Geoscience and Remote Sensing, IGARSS 2004, 2004 IEEE International Geoscience and Remote Sensing Symposium, 20–24September 2004, Anchorage, AK, USA, IEEE, 3, 1551–1555, https://doi.org/10.1109/IGARSS.2004.1370609, 2004.
