{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T23:26:31Z","timestamp":1781565991041,"version":"3.54.5"},"reference-count":67,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,30]],"date-time":"2022-01-30T00:00:00Z","timestamp":1643500800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"IARAI","award":["VAT number (UID): ATU74131236"],"award-info":[{"award-number":["VAT number (UID): ATU74131236"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The effects of the spatial resolution of remote sensing (RS) data on wildfire susceptibility prediction are not fully understood. In this study, we evaluate the effects of coarse (Landsat 8 and SRTM) and medium (Sentinel-2 and ALOS) spatial resolution data on wildfire susceptibility prediction using random forest (RF) and support vector machine (SVM) models. In addition, we investigate the fusion of the predictions from the different spatial resolutions using the Dempster\u2013Shafer theory (DST) and 14 wildfire conditioning factors. Seven factors are derived separately from the coarse and medium spatial resolution datasets for the whole forest area of the Guilan Province, Iran. All conditional factors are used to train and test the SVM and RF models in the Google Earth Engine (GEE) software environment, along with an inventory dataset from comprehensive global positioning system (GPS)-based field survey points of wildfire locations. These locations are evaluated and combined with coarse resolution satellite data, namely the thermal anomalies product of the moderate resolution imaging spectroradiometer (MODIS) for the period 2009 to 2019. We assess the performance of the models using four-fold cross-validation by the receiver operating characteristic (ROC) curve method. The area under the curve (AUC) achieved from the ROC curve yields 92.15% and 91.98% accuracy for the respective SVM and RF models for the coarse RS data. In comparison, the AUC for the medium RS data is 92.5% and 93.37%, respectively. Remarkably, the highest AUC value of 94.71% is achieved for the RF model where coarse and medium resolution datasets are combined through DST.<\/jats:p>","DOI":"10.3390\/rs14030672","type":"journal-article","created":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T01:46:21Z","timestamp":1643593581000},"page":"672","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["A Google Earth Engine Approach for Wildfire Susceptibility Prediction Fusion with Remote Sensing Data of Different Spatial Resolutions"],"prefix":"10.3390","volume":"14","author":[{"given":"Sepideh","family":"Tavakkoli Piralilou","sequence":"first","affiliation":[{"name":"Department of Geoinformatics\u2014Z_GIS, University of Salzburg, 5020 Salzburg, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Golzar","family":"Einali","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omid","family":"Ghorbanzadeh","sequence":"additional","affiliation":[{"name":"Institute of Advanced Research in Artificial Intelligence (IARAI), Landstra\u00dfer Hauptstra\u00dfe 5, 1030 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1341-3264","authenticated-orcid":false,"given":"Thimmaiah Gudiyangada","family":"Nachappa","sequence":"additional","affiliation":[{"name":"Group Digital Transformation\u2014New Propositions Swiss Re Europe S.A., Niederlassung Deutschland, Arabellastrasse 30, 81925 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3860-8674","authenticated-orcid":false,"given":"Khalil","family":"Gholamnia","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1860-8458","authenticated-orcid":false,"given":"Thomas","family":"Blaschke","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics\u2014Z_GIS, University of Salzburg, 5020 Salzburg, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1203-741X","authenticated-orcid":false,"given":"Pedram","family":"Ghamisi","sequence":"additional","affiliation":[{"name":"Institute of Advanced Research in Artificial Intelligence (IARAI), Landstra\u00dfer Hauptstra\u00dfe 5, 1030 Vienna, Austria"},{"name":"Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, 09599 Freiberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., and Aryal, J. (2019). Forest fire susceptibility and risk mapping using social\/infrastructural vulnerability and environmental variables. Fire, 2.","DOI":"10.3390\/fire2030050"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"109867","DOI":"10.1016\/j.jenvman.2019.109867","article-title":"Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility","volume":"260","author":"Moayedi","year":"2020","journal-title":"J. Environ. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.foreco.2015.02.006","article-title":"Global Forest Resources Assessment 2015: What, why and how?","volume":"352","author":"MacDicken","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.firesaf.2019.01.006","article-title":"Predictive modeling of wildfires: A new dataset and machine learning approach","volume":"104","author":"Sayad","year":"2019","journal-title":"Fire Saf. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1111\/geb.12246","article-title":"Global fire size distribution is driven by human impact and climate","volume":"24","author":"Hantson","year":"2015","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100045","DOI":"10.1016\/j.pdisas.2019.100045","article-title":"Wildfire management in Canada: Review, challenges and opportunities","volume":"5","author":"Tymstra","year":"2020","journal-title":"Prog. Disaster Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.geomorph.2017.10.018","article-title":"Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models","volume":"301","author":"Marchesini","year":"2018","journal-title":"Geomorphology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1007\/s11431-008-6009-y","article-title":"Effects of raster resolution on landslide susceptibility mapping: A case study of Shenzhen","volume":"51","author":"Tian","year":"2008","journal-title":"Sci. China Ser. E Technol. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jher.2021.10.002","article-title":"DEM resolution effects on machine learning performance for flood probability mapping","volume":"40","author":"Avand","year":"2022","journal-title":"J. Hydro-Environ. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1080\/19475705.2017.1289249","article-title":"Comparison of the fuzzy AHP method, the spatial correlation method, and the Dong model to predict the fire high-risk areas in Hyrcanian forests of Iran","volume":"8","author":"Eskandari","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s13762-017-1371-6","article-title":"Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS","volume":"15","author":"Nami","year":"2018","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"47395","DOI":"10.1007\/s11356-021-13881-y","article-title":"Fire-susceptibility mapping in the natural areas of Iran using new and ensemble data-mining models","volume":"28","author":"Eskandari","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.agrformet.2018.12.015","article-title":"Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability","volume":"266","author":"Jaafari","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kalantar, B., Ueda, N., Idrees, M.O., Janizadeh, S., Ahmadi, K., and Shabani, F. (2020). Forest fire susceptibility prediction based on machine learning models with resampling algorithms on remote sensing data. Remote Sens., 12.","DOI":"10.3390\/rs12223682"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Naderpour, M., Rizeei, H.M., and Ramezani, F. (2021). Forest Fire Risk Prediction: A Spatial Deep Neural Network-Based Framework. Remote Sens., 13.","DOI":"10.3390\/rs13132513"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kim, S., Lim, C.-H., Kim, G., Lee, J., Geiger, T., Rahmati, O., Son, Y., and Lee, W.-K. (2019). Multi-temporal analysis of forest fire probability using socio-economic and environmental variables. Remote Sens., 11.","DOI":"10.3390\/rs11010086"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gholamnia, K., Gudiyangada Nachappa, T., Ghorbanzadeh, O., and Blaschke, T. (2020). Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping. Symmetry, 12.","DOI":"10.3390\/sym12040604"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4771","DOI":"10.5194\/hess-22-4771-2018","article-title":"Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization","volume":"22","author":"Khosravi","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mohammadi, A., Karimzadeh, S., Jalal, S.J., Kamran, K.V., Shahabi, H., Homayouni, S., and Al-Ansari, N. (2020). A Multi-Sensor Comparative Analysis on the Suitability of Generated DEM from Sentinel-1 SAR Interferometry Using Statistical and Hydrological Models. Sensors, 20.","DOI":"10.3390\/s20247214"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1007\/s11069-020-03899-9","article-title":"The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin, NW China","volume":"101","author":"Chen","year":"2020","journal-title":"Nat. Hazards"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Meena, S.R., and Gudiyangada Nachappa, T. (2019). Impact of Spatial Resolution of Digital Elevation Model on Landslide Susceptibility Mapping: A case Study in Kullu Valley, Himalayas. Geosciences, 9.","DOI":"10.3390\/geosciences9080360"},{"key":"ref_22","first-page":"1024","article-title":"Dempster-Shafer theory of evidence: A new approach to spatially model wildfire risk potential in central Chile","volume":"613","author":"Castillo","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1080\/19475705.2017.1413012","article-title":"Multi-criteria risk evaluation by integrating an analytical network process approach into GIS-based sensitivity and uncertainty analyses","volume":"9","author":"Ghorbanzadeh","year":"2018","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mezaal, M., Pradhan, B., and Rizeei, H. (2018). Improving Landslide Detection from Airborne Laser Scanning Data Using Optimized Dempster\u2013Shafer. Remote Sens., 10.","DOI":"10.3390\/rs10071029"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"125275","DOI":"10.1016\/j.jhydrol.2020.125275","article-title":"Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory","volume":"590","author":"Nachappa","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1007\/s12665-018-7667-0","article-title":"The application of a Dempster\u2013Shafer-based evidential belief function in flood susceptibility mapping and comparison with frequency ratio and logistic regression methods","volume":"77","author":"Tehrany","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_27","first-page":"218","article-title":"Mapping spatial distribution of forest fire using MCDM and GIS (case study: Three forest zones in Guilan Province)","volume":"21","author":"Zarekar","year":"2013","journal-title":"Iran. J. For. Poplar Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.envsoft.2016.07.016","article-title":"Effect of raster resolution and polygon-conversion algorithm on landslide susceptibility mapping","volume":"84","author":"Arnone","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1080\/10106049.2015.1041559","article-title":"Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran","volume":"31","author":"Rahmati","year":"2015","journal-title":"Geocarto Int."},{"key":"ref_30","first-page":"219","article-title":"Evaluating and mapping the fire risk in the forests and rangelands of Sirachal using fuzzy analytic hierarchy process and GIS","volume":"6","author":"Eskandari","year":"2020","journal-title":"J. For. Res. Dev."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ljubomir, G., Pamu\u010dar, D., Drobnjak, S., and Pourghasemi, H.R. (2019). Modeling the Spatial Variability of Forest Fire Susceptibility Using Geographical Information Systems and the Analytical Hierarchy Process. Spatial Modeling in GIS and R for Earth and Environmental Sciences, Elsevier.","DOI":"10.1016\/B978-0-12-815226-3.00015-6"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Jaafari, A., and Pourghasemi, H.R. (2019). Factors Influencing Regional-Scale Wildfire Probability in Iran. Spatial Modeling in GIS and R for Earth and Environmental Sciences, Elsevier.","DOI":"10.1016\/B978-0-12-815226-3.00028-4"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.enggeo.2015.12.009","article-title":"A physically-based multi-hazard risk assessment platform for regional rainfall-induced slope failures and debris flows","volume":"203","author":"Chen","year":"2016","journal-title":"Eng. Geol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1007\/s12665-014-3502-4","article-title":"Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran","volume":"73","author":"Pourtaghi","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.jenvman.2019.05.131","article-title":"Wildfires impact on the economic susceptibility of recreation activities: Application in a Mediterranean protected area","volume":"245","author":"Molina","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tien Bui, D., Le, K.-T., Nguyen, V., Le, H., and Revhaug, I. (2016). Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression. Remote Sens., 8.","DOI":"10.3390\/rs8040347"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Valizadeh Kamran, K., Blaschke, T., Aryal, J., Naboureh, A., Einali, J., and Bian, J. (2019). Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches. Fire, 2.","DOI":"10.3390\/fire2030043"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gudiyangada Nachappa, T., Tavakkoli Piralilou, S., Ghorbanzadeh, O., Shahabi, H., and Blaschke, T. (2019). Landslide Susceptibility Mapping for Austria Using Geons and Optimization with the Dempster-Shafer Theory. Appl. Sci., 9.","DOI":"10.3390\/app9245393"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"485","DOI":"10.5194\/nhess-10-485-2010","article-title":"Assessment and validation of wildfire susceptibility and hazard in Portugal","volume":"10","author":"Verde","year":"2010","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.earscirev.2011.01.001","article-title":"Post-wildfire soil erosion in the Mediterranean: Review and future research directions","volume":"105","author":"Shakesby","year":"2011","journal-title":"Earth-Sci. Rev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1007\/s11069-018-3449-y","article-title":"A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping","volume":"94","author":"Ghorbanzadeh","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.1080\/19475705.2021.1920480","article-title":"Detection of areas prone to flood risk using state-of-the-art machine learning models","volume":"12","author":"Costache","year":"2021","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1997","DOI":"10.1080\/19475705.2017.1403974","article-title":"Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China)","volume":"8","author":"Hong","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s12145-010-0045-4","article-title":"A roadmap for a dedicated Earth Science Grid platform","volume":"3","author":"Cossu","year":"2010","journal-title":"Earth Sci. Inform."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.rse.2011.09.022","article-title":"Landsat: Building a strong future","volume":"122","author":"Loveland","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1080\/17538947.2019.1585976","article-title":"Big Earth data: Disruptive changes in Earth observation data management and analysis?","volume":"13","author":"Sudmanns","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_48","unstructured":"(2020, May 17). GoogleEarthEngine. Available online: https:\/\/earthengine.google.com\/#intro."},{"key":"ref_49","unstructured":"Vapnik, V. (2013). The Nature of Statistical Learning Theory, Springer Science & Business Media."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1007\/s10346-013-0391-7","article-title":"Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression","volume":"11","author":"Kavzoglu","year":"2013","journal-title":"Landslides"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2016.2616418","article-title":"Advanced spectral classifiers for hyperspectral images: A review","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_52","first-page":"145","article-title":"Land-Cover Change Detection in a Part of Cameron Highlands, Malaysia Using ETM+ Satellite Imagery and Support Vector Machine (SVM) Algorithm","volume":"12","author":"Mohammadi","year":"2019","journal-title":"EnvironmentAsia"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.catena.2014.10.017","article-title":"Flood susceptibility assessment using GIS-based support vector machine model with different kernel types","volume":"125","author":"Tehrany","year":"2015","journal-title":"Catena"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10346-015-0557-6","article-title":"Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree","volume":"13","author":"Bui","year":"2016","journal-title":"Landslides"},{"key":"ref_55","unstructured":"Ho, T.K. (1995, January 14\u201316). Random decision forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.catena.2017.11.022","article-title":"Prediction of the landslide susceptibility: Which algorithm, which precision?","volume":"162","author":"Pourghasemi","year":"2018","journal-title":"Catena"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Xu, R., Lin, H., L\u00fc, Y., Luo, Y., Ren, Y., and Comber, A. (2018). A Modified Change Vector Approach for Quantifying Land Cover Change. Remote Sens., 10.","DOI":"10.3390\/rs10101578"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., Tiede, D., and Aryal, J. (2019). Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sens., 11.","DOI":"10.3390\/rs11020196"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1080\/19475705.2016.1278404","article-title":"Modelling the spatial variability of wildfire susceptibility in Honduras using remote sensing and geographical information systems","volume":"8","author":"Valdez","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/LGRS.2017.2763979","article-title":"A Novel Approach of Fuzzy Dempster\u2013Shafer Theory for Spatial Uncertainty Analysis and Accuracy Assessment of Object-Based Image Classification","volume":"15","author":"Feizizadeh","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1550147719865876","DOI":"10.1177\/1550147719865876","article-title":"A novel method to determine basic probability assignment in Dempster\u2013Shafer theory and its application in multi-sensor information fusion","volume":"15","author":"Fei","year":"2019","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1109\/JSTARS.2020.3043836","article-title":"Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network (CNN) Streams Combined by the Dempster\u2014Shafer (DS) model","volume":"14","author":"Ghorbanzadeh","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Shahabi, H., Jarihani, B., Tavakkoli Piralilou, S., Chittleborough, D., Avand, M., and Ghorbanzadeh, O. (2019). A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia. Sensors, 19.","DOI":"10.3390\/s19224893"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Shafer, G. (1976). A mathematical theory of evidence, Princeton University Press.","DOI":"10.1515\/9780691214696"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1016\/j.scitotenv.2016.06.176","article-title":"Application of Dempster\u2013Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, Iran","volume":"568","author":"Rahmati","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/S0167-8809(01)00189-X","article-title":"Modeling land-use change in the Ipswich watershed, Massachusetts, USA","volume":"85","author":"Schneider","year":"2001","journal-title":"Agric. Ecosyst. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/672\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:11:34Z","timestamp":1760134294000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/672"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,30]]},"references-count":67,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030672"],"URL":"https:\/\/doi.org\/10.3390\/rs14030672","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,30]]}}}