{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T07:38:21Z","timestamp":1781941101821,"version":"3.54.5"},"reference-count":93,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,17]],"date-time":"2020-04-17T00:00:00Z","timestamp":1587081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Thematic Excellence Programme of the Ministry for Innovation and Technology in Hungary","award":["ED_18-1-2019-0028"],"award-info":[{"award-number":["ED_18-1-2019-0028"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Gullies reduce both the quality and quantity of productive land, posing a serious threat to sustainable agriculture, hence, food security. Machine Learning (ML) algorithms are essential tools in the identification of gullies and can assist in strategic decision-making relevant to soil conservation. Nevertheless, accurate identification of gullies is a function of the selected ML algorithms, the image and number of classes used, i.e., binary (two classes) and multiclass. We applied Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Random Forest (RF) on a Systeme Pour l\u2019Observation de la Terre (SPOT-7) image to extract gullies and investigated whether the multiclass (m) approach can offer better classification accuracy than the binary (b) approach. Using repeated k-fold cross-validation, we generated 36 models. Our findings revealed that, of these models, both RFb (98.70%) and SVMm (98.01%) outperformed the LDA in terms of overall accuracy (OA). However, the LDAb (99.51%) recorded the highest producer\u2019s accuracy (PA) but had low corresponding user\u2019s accuracy (UA) with 18.5%. The binary approach was generally better than the multiclass approach; however, on class level, the multiclass approach outperformed the binary approach in gully identification. Despite low spectral resolution, the pan-sharpened SPOT-7 product successfully identified gullies. The proposed methodology is relatively simple, but practically sound, and can be used to monitor gullies within and beyond the study region.<\/jats:p>","DOI":"10.3390\/ijgi9040252","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T03:23:06Z","timestamp":1587439386000},"page":"252","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Machine Learning for Gully Feature Extraction Based on a Pan-Sharpened Multispectral Image: Multiclass vs. Binary Approach"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1865-7011","authenticated-orcid":false,"given":"Kwanele","family":"Phinzi","sequence":"first","affiliation":[{"name":"Doctoral School of Earth Sciences, Department of Physical Geography and Geoinformatics, University of Debrecen, Egyetem t\u00e9r 1, 4032 Debrecen, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D\u00e1vid","family":"Abriha","sequence":"additional","affiliation":[{"name":"Doctoral School of Earth Sciences, Department of Physical Geography and Geoinformatics, University of Debrecen, Egyetem t\u00e9r 1, 4032 Debrecen, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5963-2710","authenticated-orcid":false,"given":"L\u00e1szl\u00f3","family":"Bertalan","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Geoinformatics, University of Debrecen, Egyetem t\u00e9r 1, 4032 Debrecen, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6368-4660","authenticated-orcid":false,"given":"Imre","family":"Holb","sequence":"additional","affiliation":[{"name":"Institute of Horticulture, University of Debrecen, B\u00f6sz\u00f6rm\u00e9nyi \u00fat 138, 4032 Debrecen, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2670-7384","authenticated-orcid":false,"given":"Szil\u00e1rd","family":"Szab\u00f3","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Geoinformatics, University of Debrecen, Egyetem t\u00e9r 1, 4032 Debrecen, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,17]]},"reference":[{"key":"ref_1","unstructured":"(2020, January 13). FAO Soil Erosion: The Gratest Challenge for Sustainable Soil Management. Available online: http:\/\/www.fao.org\/3\/ca4395en\/ca4395en.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.catena.2005.06.001","article-title":"Gully erosion: Impacts, factors and control","volume":"63","author":"Valentin","year":"2005","journal-title":"Catena"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1088\/1748-9326\/aaea8b","article-title":"Soil erosion in East Africa: An interdisciplinary approach to realising pastoral land management change","volume":"13","author":"Blake","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_4","first-page":"319","article-title":"Assessing environmental changes in abandoned german vineyards. Understanding key issues for restoration management plans","volume":"67","author":"Neumann","year":"2018","journal-title":"Hung. Geogr. Bull."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1002\/ldr.509","article-title":"The relationship between land use and soil erosion in the communal lands near Peddie town, Eastern Cape, South Africa","volume":"14","author":"Kakembo","year":"2003","journal-title":"L. Degrad. Dev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"272","DOI":"10.17221\/20\/2013-JFS","article-title":"The influence of deforestation on runoff generation and soil erosion (Case study: Kasilian Watershed)","volume":"59","author":"Gholami","year":"2013","journal-title":"J. For. Sci."},{"key":"ref_7","first-page":"201","article-title":"Landscape degradation in the world and in Hungary","volume":"68","year":"2019","journal-title":"Hung. Geogr. Bull."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1080\/0035919X.2019.1634652","article-title":"Land use\/land cover dynamics and soil erosion in the Umzintlava catchment (T32E), Eastern Cape, South Africa","volume":"74","author":"Phinzi","year":"2019","journal-title":"Trans. R. Soc. S. Afr."},{"key":"ref_9","first-page":"89","article-title":"A review on sheet erosion measurements in Hungary","volume":"13","author":"Jakab","year":"2015","journal-title":"J. Landsc. Ecol."},{"key":"ref_10","unstructured":"Arabameri, A., Chen, W., Loche, M., Zhao, X., Li, Y., Lombardo, L., Cerda, A., Pradhan, B., and Bui, D.T. (2019). Comparison of machine learning models for gully erosion susceptibility mapping. Geosci. Front., 1\u201312."},{"key":"ref_11","unstructured":"Bull, L.J., and Kirkby, M.J. (2002). Gully erosion in dryland environments. Dryland Rivers: Hydrology and Geomorphology of Semi-Arid, John Wiley & Sons Ltd."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.catena.2008.07.001","article-title":"Thresholds for channel initiation at road drain outlets","volume":"75","author":"Takken","year":"2008","journal-title":"Catena"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.catena.2014.10.022","article-title":"The impact of permanent gullies on present-day land use and agriculture in loess areas (E. Poland)","volume":"126","author":"Gawrysiak","year":"2015","journal-title":"Catena"},{"key":"ref_14","unstructured":"Garland, G.G., Hoffman, M.T., and Todd, S. (2000). Soil Degradation: A National Review of Land Degradation in South Africa, South African National Biodiversity Institute."},{"key":"ref_15","unstructured":"Hoffman, T., and Ashwell, A. (2001). Nature Divided: Land Degradation in South Africa, University of Cape Town Press."},{"key":"ref_16","unstructured":"De Villiers, M.C., Nell, J.P., Barnard, R.O., and Henning, A. (2020, February 15). Salt-Affected Soils: South Africa. Available online: https:\/\/www.researchgate.net\/profile\/Anoop_Srivastava7\/post\/I_am_looking_for_a_recent_soil_salinity_map_of_Africa\/attachment\/59d654de79197b80779ac3f2\/AS:523166767423489@1501744085582\/download\/faosodicrza+%283%29.doc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"11","DOI":"10.4314\/sajg.v6i1.2","article-title":"Mapping soil erosion in a quaternary catchment in Eastern Cape using geographic information system and remote sensing","volume":"6","author":"Phinzi","year":"2017","journal-title":"S. Afr. J. Geomatics"},{"key":"ref_18","unstructured":"Campbell, J.B., and Wynne, R.H. (2011). Introduction to Remote Sensing, Guilford Press."},{"key":"ref_19","unstructured":"Lillesand, T., Kiefer, R.W., and Chipman, J. (2015). Remote Sensing and Image Interpretation, John Wiley & Sons. [7th ed.]."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Richards, J.A., and Xiuping, J. (2006). Remote Sensing Digital Image Analysis: An Introduction, Springer. [4th ed.].","DOI":"10.1007\/3-540-29711-1"},{"key":"ref_21","unstructured":"Fulajt\u00e1r, E. (1999, January 24\u201329). Identification of severely eroded soils from remote sensing data tested in ri\u0161\u0148ovce, Slovakia. Proceedings of the 10th International Soil Conservation Meeting, Indianapolis, IN, USA."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2723","DOI":"10.1080\/01431160600857469","article-title":"Automatic identification of erosion gullies with ASTER imagery in the Brazilian Cerrados","volume":"28","author":"Vrieling","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8287","DOI":"10.3390\/rs6098287","article-title":"Detection of gully-affected areas by applying object-based image analysis (OBIA) in the region of Taroudannt, Morocco","volume":"6","author":"Marzolff","year":"2014","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"169","DOI":"10.21120\/LE\/10\/3-4\/10","article-title":"UAS photogrammetry and object-based image analysis (GEOBIA): Erosion monitoring at the Kaz\u00e1r badland, Hungary","volume":"10","author":"Bertalan","year":"2016","journal-title":"Landsc. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.scitotenv.2017.07.198","article-title":"Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling","volume":"609","author":"Pourghasemi","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1080\/22797254.2018.1482524","article-title":"Mapping soil degradation using remote sensing data and ancillary data: South-East Moravia, Czech Republic","volume":"52","year":"2019","journal-title":"Eur. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1080\/03736245.2012.742786","article-title":"Gully location mapping at a national scale for South Africa","volume":"94","author":"Mararakanye","year":"2012","journal-title":"S. Afr. Geogr. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep learning classification of land cover and crop types using remote sensing data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.isprsjprs.2017.03.001","article-title":"Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning","volume":"140","author":"Vetrivel","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JRS.11.042609","article-title":"Comprehensive survey of deep learning in remote sensing: Theories, tools, and challenges for the community","volume":"11","author":"Ball","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_32","first-page":"22","article-title":"Deep Learning for Remote Sensing Image Understanding","volume":"4","author":"Zhang","year":"2016","journal-title":"J. Sensors"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/JSTARS.2018.2810320","article-title":"Symmetrical dense-shortcut deep fully convolutional networks for semantic segmentation of very-high-resolution remote sensing images","volume":"11","author":"Chen","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5192","DOI":"10.1080\/01431161.2019.1579383","article-title":"Assessing the efficiency of multispectral satellite and airborne hyperspectral images for land cover mapping in an aquatic environment with emphasis on the water caltrop (Trapa natans)","volume":"40","author":"Burai","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"9323","DOI":"10.1109\/TGRS.2019.2926110","article-title":"A Data-Driven Approach for Accurate Rainfall Prediction","volume":"57","author":"Manandhar","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kai, W., Jinyi, G., and Nan, Z. (2019, January 10\u201311). Evaluation on Water Source Conservation Capacity of West Liaohe River Basin Based on Invest Model. Proceedings of the 2019 International Conference on Smart Grid and Electrical Automation (ICSGEA), IEEE, Xiangtan, China.","DOI":"10.1109\/ICSGEA.2019.00106"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1111\/j.1365-2664.2006.01141.x","article-title":"Modelling ecological niches with support vector machines","volume":"43","author":"Drake","year":"2006","journal-title":"J. Appl. Ecol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Goldblatt, R., You, W., Hanson, G., and Khandelwal, A.K. (2016). Detecting the boundaries of urban areas in India: A dataset for pixel-based image classification in google earth engine. Remote Sens., 8.","DOI":"10.3390\/rs8080634"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.biosystemseng.2011.06.002","article-title":"Residual soil nitrate prediction from imagery and non-imagery information using neural network technique","volume":"110","author":"Gautam","year":"2011","journal-title":"Biosyst. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2444","DOI":"10.3390\/s19112444","article-title":"A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran)","volume":"19","author":"Bui","year":"2019","journal-title":"Sensors"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, L., Ren, C., Li, L., Wang, Y., Zhang, B., Wang, Z., and Li, L. (2019). A comparative assessment of geostatistical, machine learning, and hybrid approaches for mapping topsoil organic carbon content. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8040174"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1833","DOI":"10.1007\/s11629-019-5409-8","article-title":"Assessing the performance of decision tree and neural network models in mapping soil properties","volume":"16","author":"Hateffard","year":"2019","journal-title":"J. Mt. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.1080\/01431161.2014.903435","article-title":"Land-use\/cover classification in a heterogeneous coastal landscape using RapidEye imagery: Evaluating the performance of random forest and support vector machines classifiers","volume":"35","author":"Adam","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.iswcr.2018.12.002","article-title":"The assessment of water-borne erosion at catchment level using GIS-based RUSLE and remote sensing: A review","volume":"7","author":"Phinzi","year":"2019","journal-title":"Int. Soil Water Conserv. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: An applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1080\/17538947.2018.1501107","article-title":"Efficiency of local minima and GLM techniques in sinkhole extraction from a LiDAR-based terrain model","volume":"12","author":"Enyedi","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.geomorph.2011.07.003","article-title":"Object-based gully feature extraction using high spatial resolution imagery","volume":"134","author":"Shruthi","year":"2011","journal-title":"Geomorphology"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, F., Zhen, Z., Wang, B., and Mi, Z. (2018). Comparative study on KNN and SVM based weather classification models for day ahead short term solar PV power forecasting. Appl. Sci., 8.","DOI":"10.3390\/app8010028"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Maurer, T. (2013, January 21\u201324). How to pan-sharpen images using the gram-schmidt pan-sharpen method\u2014A recipe. Proceedings of the ISPRS Hannover Workshop, Hannover, Germany.","DOI":"10.5194\/isprsarchives-XL-1-W1-239-2013"},{"key":"ref_51","first-page":"375","article-title":"Identification of roofing materials with discriminant function analyand random forest classifiers on pan-sharpened worldview-2 imagery\u2014A comparison","volume":"67","author":"Abriha","year":"2018","journal-title":"Hung. Geogr. Bull."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Grochala, A., and Kedzierski, M. (2017). A method of panchromatic image modification for satellite imagery data fusion. Remote Sens., 9.","DOI":"10.3390\/rs9060639"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ge, W., Cheng, Q., Tang, Y., Jing, L., and Gao, C. (2018). Lithological classification using Sentinel-2A data in the Shibanjing ophiolite complex in Inner Mongolia, China. Remote Sens., 10.","DOI":"10.3390\/rs10040638"},{"key":"ref_54","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_55","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","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_57","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.3390\/rs70202046","article-title":"Classification of herbaceous vegetation using airborne hyperspectral imagery","volume":"7","author":"Burai","year":"2015","journal-title":"Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Sabat-tomala, A., and Raczko, E. (2020). Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data. Remote Sens., 12.","DOI":"10.3390\/rs12030516"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.geomorph.2014.04.006","article-title":"Object-based gully system prediction from medium resolution imagery using Random Forests","volume":"216","author":"Shruthi","year":"2014","journal-title":"Geomorphology"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Phinzi, K., Ngetar, N.S., and Ebhuoma, O. (2020). Soil erosion risk assessment in the Umzintlava catchment (T32E), Eastern Cape, South Africa, using RUSLE and random forest algorithm. S. Afr. Geogr. J., 1\u201324.","DOI":"10.1080\/03736245.2020.1716838"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1016\/j.geoderma.2018.09.008","article-title":"Comparison of various uncertainty modelling approaches based on geostatistics and machine learning algorithms","volume":"337","year":"2019","journal-title":"Geoderma"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Deng, S., Katoh, M., Yu, X., Hyypp\u00e4, J., and Gao, T. (2016). Comparison of tree species classifications at the individual tree level by combining ALS data and RGB images using different algorithms. Remote Sens., 8.","DOI":"10.3390\/rs8121034"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1080\/02626667.2018.1425802","article-title":"Extracting water-related features using reflectance data and principal component analysis of Landsat images","volume":"63","author":"Dyke","year":"2018","journal-title":"Hydrol. Sci. J."},{"key":"ref_64","unstructured":"Vapnik, V. (2013). The Nature of Statistical Learning Theory, Springer. [2nd ed.]."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"853","DOI":"10.5194\/nhess-5-853-2005","article-title":"Spatial prediction models for landslide hazards: Review, comparison and evaluation","volume":"5","author":"Brenning","year":"2005","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_66","first-page":"27","article-title":"Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms","volume":"12","author":"Otukei","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_67","first-page":"377","article-title":"Regolith-geology mapping with support vector machine: A case study over weathered Ni-bearing peridotites, New Caledonia","volume":"64","author":"Sevin","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Wu, Y., and Zhang, X. (2020). Object-Based tree species classification using airborne hyperspectral images and LiDAR data. Forests, 11.","DOI":"10.3390\/f11010032"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Gholizadeh, A., Bor\u016fvka, L., Saberioon, M., and Va\u0161\u00e1t, R. (2016). A memory-based learning approach as compared to other data mining algorithms for the prediction of soil texture using diffuse reflectance spectra. Remote Sens., 8.","DOI":"10.3390\/rs8040341"},{"key":"ref_70","unstructured":"Lantz, B. (2015). Machine Learning with R: Expert Techniques for Predictive Modeling to Solve All your Data Analysis Problems, Packt Publishing Ltd. [3rd ed.]."},{"key":"ref_71","first-page":"333","article-title":"A comparison of the discrimination of discriminant analysis and logistic regression under multivariate normality","volume":"1985","author":"Harrell","year":"1985","journal-title":"Biostat. Stat. Biomed. Public Heal. Environ. Sci."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1504\/IJAPR.2016.079050","article-title":"Linear vs. quadratic discriminant analysis classifier: A tutorial","volume":"3","author":"Tharwat","year":"2016","journal-title":"Int. J. Appl. Pattern Recognit."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.pce.2017.01.023","article-title":"Use of Landsat series data to analyse the spatial and temporal variations of land degradation in a dispersive soil environment: A case of King Sabata Dalindyebo local municipality in the Eastern Cape Province, South Africa","volume":"100","author":"Dube","year":"2017","journal-title":"Phys. Chem. Earth"},{"key":"ref_74","unstructured":"(2020, February 21). Machine Learning Mastery. Available online: https:\/\/machinelearningmastery.com\/."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Heckel, K., Urban, M., Schratz, P., Mahecha, M.D., and Schmullius, C. (2020). Predicting Forest Cover in Distinct Ecosystems: The Potential of Multi-Source Sentinel-1 and -2 Data Fusion. Remote Sens., 12.","DOI":"10.3390\/rs12020302"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_77","first-page":"113","article-title":"Landscape transform and spatial metrics for mapping spatiotemporal land cover dynamics using Earth Observation data-sets","volume":"32","author":"Singh","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1080\/10106049.2017.1307460","article-title":"Quantifying land use\/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS\u00ae","volume":"33","author":"Lamine","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press. [3rd ed.].","DOI":"10.1201\/9780429052729"},{"key":"ref_80","first-page":"21","article-title":"Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests","volume":"2","year":"2011","journal-title":"J. Stat. Model. Anal."},{"key":"ref_81","unstructured":"Field, A., Miles, J., and Field, Z. (2012). Discovering Statistics Using R, SAGE Publications."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Tallarida, R.J., and Murray, R.B. (1987). Dunnett\u2019s Test (Comparison with a Control). Manual of Pharmacologic Calculations, Springer. [2nd ed.].","DOI":"10.1007\/978-1-4612-4974-0_45"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1111\/j.1468-2958.2002.tb00828.x","article-title":"Eta Squared, Partial Eta Squared, and Misreporting of Effect Size in Communication Research","volume":"28","author":"Levine","year":"2002","journal-title":"Hum. Commun. Res."},{"key":"ref_84","unstructured":"(2020, February 21). The R Project for Statistical Computing. Available online: https:\/\/www.r-project.org."},{"key":"ref_85","first-page":"1","article-title":"Robust statistical methods in R using the WRS2 package","volume":"52","author":"Mair","year":"2019","journal-title":"Behav. Res. Methods"},{"key":"ref_86","unstructured":"(2020, February 21). Jamovi. Available online: https:\/\/www.jamovi.org\/about.html."},{"key":"ref_87","unstructured":"Gallucci, M. (2020, February 21). GMLj: General Analysis for Linear Models. Available online: https:\/\/gamlj.github.io."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1080\/13658816.2015.1092546","article-title":"Possibilities of land use change analysis in a mountainous rural area: A methodological approach","volume":"30","author":"Bertalan","year":"2016","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/rs12020295","article-title":"Comparison of Different Machine Learning Methods for Debris Flow Susceptibility Mapping: A Case Study in the Sichuan Province, China","volume":"12","author":"Xiong","year":"2020","journal-title":"Remote Sens."},{"key":"ref_91","first-page":"720","article-title":"Weighted One-Against-All","volume":"2","author":"Beygelzimer","year":"2004","journal-title":"Am. Assoc. Artif. Intell."},{"key":"ref_92","first-page":"113","article-title":"Reducing multiclass to binary: A unifying approach for margin classifiers","volume":"1","author":"Allwein","year":"2000","journal-title":"J. Mach. Learn. Res."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Karydas, C., and Panagos, P. (2020). Towards an Assessment of the Ephemeral Gully Erosion Potential in Greece Using Google Earth. Water, 12.","DOI":"10.3390\/w12020603"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/4\/252\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:31:27Z","timestamp":1760362287000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/4\/252"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,17]]},"references-count":93,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["ijgi9040252"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9040252","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,17]]}}}