{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:29:45Z","timestamp":1764174585692,"version":"build-2065373602"},"reference-count":81,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,8]],"date-time":"2018-12-08T00:00:00Z","timestamp":1544227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Natural hazards include a wide range of high-impact phenomena that affect socioeconomic and natural systems. Landslides are a natural hazard whose destructive power has caused a significant number of victims and substantial damage around the world. Remote sensing provides many data types and techniques that can be applied to monitor their effects through landslides inventory maps. Three unsupervised change detection methods were applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster)-derived images from an area prone to landslides in the south of Mexico. Linear Regression (LR), Chi-Square Transformation, and Change Vector Analysis were applied to the principal component and the Normalized Difference Vegetation Index (NDVI) data to obtain the difference image of change. The thresholding was performed on the change histogram using two approaches: the statistical parameters and the secant method. According to previous works, a slope mask was used to classify the pixels as landslide\/No-landslide; a cloud mask was used to eliminate false positives; and finally, those landslides less than 450 m2 (two Aster pixels) were discriminated. To assess the landslide detection accuracy, 617 polygons (35,017 pixels) were sampled, classified as real landslide\/No-landslide, and defined as ground-truth according to the interpretation of color aerial photo slides to obtain omission\/commission errors and Kappa coefficient of agreement. The results showed that the LR using NDVI data performs the best results in landslide detection. Change detection is a suitable technique that can be applied for the landslides mapping and we think that it can be replicated in other parts of the world with results similar to those obtained in the present work.<\/jats:p>","DOI":"10.3390\/rs10121987","type":"journal-article","created":{"date-parts":[[2018,12,10]],"date-time":"2018-12-10T03:36:41Z","timestamp":1544413001000},"page":"1987","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Evaluation of Unsupervised Change Detection Methods Applied to Landslide Inventory Mapping Using ASTER Imagery"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6284-3263","authenticated-orcid":false,"given":"Roc\u00edo N.","family":"Ramos-Bernal","sequence":"first","affiliation":[{"name":"Departamento de Tecnolog\u00eda Qu\u00edmica y Ambiental, ESCET, Universidad Rey Juan Carlos, C\/Tulip\u00e1n s\/n, M\u00f3stoles, 28933 Madrid, Spain"},{"name":"Cuerpo Acad\u00e9mico UAGro CA-93 Riesgos Naturales y Geotecnolog\u00eda, FI, Universidad Aut\u00f3noma de Guerrero, Av\/L\u00e1zaro C\u00e1rdenas s\/n, CU, Chilpancingo, 39070 Guerrero, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1693-8303","authenticated-orcid":false,"given":"Ren\u00e9","family":"V\u00e1zquez-Jim\u00e9nez","sequence":"additional","affiliation":[{"name":"Cuerpo Acad\u00e9mico UAGro CA-93 Riesgos Naturales y Geotecnolog\u00eda, FI, Universidad Aut\u00f3noma de Guerrero, Av\/L\u00e1zaro C\u00e1rdenas s\/n, CU, Chilpancingo, 39070 Guerrero, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5700-2184","authenticated-orcid":false,"given":"Ra\u00fal","family":"Romero-Calcerrada","sequence":"additional","affiliation":[{"name":"Geography Group, Departamento de Ciencias de la Educaci\u00f3n, Lenguaje, Cultura y Artes, Ciencias Hist\u00f3rica-Jur\u00eddicas y Human\u00edsticas y Lenguas Modernas, Facultad de Ciencias Jur\u00eddicas y Sociales. Universidad Rey Juan Carlos, Paseo de los Artilleros s\/n, 28032 Vic\u00e1lvaro, Madrid, Spain"}]},{"given":"Patricia","family":"Arrogante-Funes","sequence":"additional","affiliation":[{"name":"Departamento de Tecnolog\u00eda Qu\u00edmica y Ambiental, ESCET, Universidad Rey Juan Carlos, C\/Tulip\u00e1n s\/n, M\u00f3stoles, 28933 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3501-7051","authenticated-orcid":false,"given":"Carlos J.","family":"Novillo","sequence":"additional","affiliation":[{"name":"Departamento de Tecnolog\u00eda Qu\u00edmica y Ambiental, ESCET, Universidad Rey Juan Carlos, C\/Tulip\u00e1n s\/n, M\u00f3stoles, 28933 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s10064-005-0023-0","article-title":"Landslide hazard and risk zonation\u2014Why is it still so difficult?","volume":"65","author":"Soeters","year":"2006","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s11069-012-0312-4","article-title":"The need for data: Natural disasters and the challenges of database management","volume":"70","author":"Wirtz","year":"2014","journal-title":"Nat. Hazards"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5571","DOI":"10.1007\/s12665-014-3811-7","article-title":"Landslide spatial susceptibility mapping by using GIS and remote sensing techniques: A case study in Zigui County, the Three Georges reservoir, China","volume":"73","author":"Chen","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_4","unstructured":"Ramos-Bernal, R.N., V\u00e1zquez-Jim\u00e9nez, R., and Romero-Rojas, W. (2018). Modeling the Susceptibility to Landslides by Remote Sensing Techniques. Case Study: Central Area of the State of Guerrero in M\u00e9xico, Havana, Cuba, 22-04-2018, Ministerio de Comunicaciones."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s002679910020","article-title":"Comparing landslide maps: A case study in the upper Tiber River Basin, central Italy","volume":"25","author":"Guzzetti","year":"2000","journal-title":"Environ. Manag."},{"key":"ref_6","unstructured":"IPCC (2012). Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, Cambridge University Press."},{"key":"ref_7","first-page":"25","article-title":"Remote Sensing, Natural Hazards and the contribution of ESA Sentinels missions","volume":"6","author":"Poursanidis","year":"2017","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_8","unstructured":"CRED-UNISDR (2016). 2015 Disasters in Numbers, United Nations Office for Disaster Risk Reduction."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.geomorph.2014.02.032","article-title":"Landslide incidence in the North of Portugal: Analysis of a historical landslide database based on press releases and technical reports","volume":"214","author":"Pereira","year":"2014","journal-title":"Geomorphology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.geomorph.2015.01.029","article-title":"Remote sensing as tool for development of landslide databases: The case of the Messina Province (Italy) geodatabase","volume":"249","author":"Ciampalini","year":"2015","journal-title":"Geomorphology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s10346-017-0861-4","article-title":"The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: Geomorphological features and landslide distribution","volume":"15","author":"Rosi","year":"2018","journal-title":"Landslides"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"S319","DOI":"10.1785\/BSSA08601BS319","article-title":"Landslides triggered by the 1994 Northridge, California, earthquake","volume":"86","author":"Harp","year":"1996","journal-title":"Bull. Seismol. Soc. Am."},{"key":"ref_13","unstructured":"Cardinali, M., Ardizzone, F., Galli, M., Guzzetti, F., and Reichenbach, P. (1999, January 14\u201316). Landslides triggered by rapid snow melting: The December 1996\u2013January 1997 event in Central Italy. Proceedings of the 1st Plinius Conference on Mediterranean Storms, Maratea, Italy."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bucknam, R.C., Coe, J.A., Chavarr\u00eda, M.M., Godt, J.W., Tarr, A.C., Bradley, L., Rafferty, S., Hancock, D., Dart, R.L., and Johnson, M.L. (2001). Landslides Triggered by Hurricane Mitch in Guatemala\u2013Inventory and Discussion, USGS. US Geological Survey Open File Report 01-443.","DOI":"10.3133\/ofr01443"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1002\/esp.1064","article-title":"Landslide inventories and their statistical properties","volume":"29","author":"Malamud","year":"2004","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Glade, T., Anderson, M., and Crozier, M.J. (2005). Systematic procedures of landslide hazard mapping for risk assessment using spatial prediction models. Landslide Risk Assessment, John Wiley.","DOI":"10.1002\/9780470012659"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"115","DOI":"10.5194\/nhess-6-115-2006","article-title":"Landslide hazard assessment in the Collazzone area, Umbria, Central Italy","volume":"6","author":"Guzzetti","year":"2006","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhao, C., Zhang, Q., Peng, J., Zhu, W., and Lu, Z. (2018). Multi-Temporal Loess Landslide Inventory Mapping with C-, X-and L-Band SAR Datasets\u2014A Case Study of Heifangtai Loess Landslides, China. Remote Sens., 10.","DOI":"10.3390\/rs10111756"},{"key":"ref_20","unstructured":"Xiaodong, Z., Ya, G., and Deren, L. (2006, January 8\u201311). A strategy of change detection based on remotely sensed imagery and GIS data. Proceedings of the ISPRS Commission. VII Symposium \u201cRemote Sensing: From Pixels to Proceses\u201d, Enschede, The Netherlands."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s11069-016-2342-9","article-title":"Monitoring desertification by remote sensing using the Tasselled Cap transform for long-term change detection","volume":"83","author":"Zanchetta","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.02.013","article-title":"Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images","volume":"116","author":"Zhang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.earscirev.2012.02.001","article-title":"Landslide inventory maps: New tools for an old problem","volume":"112","author":"Guzzetti","year":"2012","journal-title":"Earth-Sci. Rev."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Herv\u00e1s, J., and Bobrowsky, P. (2009). Mapping: Inventories, susceptibility, hazard and risk. Landslides\u2013Disaster Risk Reduction, Springer.","DOI":"10.1007\/978-3-540-69970-5_19"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.geomorph.2004.11.001","article-title":"The effectiveness of hillshade maps and expert knowledge in mapping old deep-seated landslides","volume":"67","author":"Poesen","year":"2005","journal-title":"Geomorphology"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/0169-555X(95)00071-C","article-title":"Remote sensing techniques for landslide studies and hazard zonation in Europe","volume":"15","author":"Mantovani","year":"1996","journal-title":"Geomorphology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.rse.2005.08.004","article-title":"Remote sensing of landslides: An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments","volume":"98","author":"Metternicht","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/S0273-1177(03)00470-8","article-title":"Characterizing and monitoring rockslides from SAR techniques","volume":"33","author":"Singhroy","year":"2004","journal-title":"Adv. Space Res."},{"key":"ref_29","unstructured":"Singhroy, V. (2002). Landslide Hazards: CEOS, the Use of Earth Observing Satellites for Hazard Support: Assessments and Scenarios, NOAA. Final Report of the CEOS Disaster Management Support Group."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/S0273-1177(97)00882-X","article-title":"Landslide characterisation in Canada using interferometric SAR and combined SAR and TM images","volume":"21","author":"Singhroy","year":"1998","journal-title":"Adv. Space Res."},{"key":"ref_31","unstructured":"Gupta, R., and Saha, A. (2018, November 26). Mapping Debris Flows in the Himalayas, Natural Resource Management. GEOSPATIALWORLD 2001, 4. Available online: https:\/\/www.geospatialworld.net\/article\/mapping-debris-flows-in-the-himalayas\/."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/S0273-1177(03)00471-X","article-title":"Locating landslides using multi-temporal satellite images","volume":"33","author":"Cheng","year":"2004","journal-title":"Adv. Space Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cageo.2011.05.010","article-title":"Landslide identification and classification by object-based image analysis and fuzzy logic: An example from the Azdavay region (Kastamonu, Turkey)","volume":"38","author":"Aksoy","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1109\/TGRS.2013.2250293","article-title":"Semiautomatic object-oriented landslide recognition scheme from multisensor optical imagery and DEM","volume":"52","author":"Rau","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.5194\/nhess-17-1285-2017","article-title":"Application of Landsat-8 and ALOS-2 data for structural and landslide hazard mapping in Kelantan, Malaysia","volume":"17","author":"Pour","year":"2017","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s12524-017-0675-9","article-title":"Landslide mapping and assessment by integrating Landsat-8, PALSAR-2 and GIS techniques: A case study from kelantan state, peninsular Malaysia","volume":"46","author":"Hashim","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Si, A., Zhang, J., Tong, S., Lai, Q., Wang, R., Li, N., and Bao, Y. (2018). Regional Landslide Identification Based on Susceptibility Analysis and Change Detection. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7100394"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Review article digital change detection techniques using remotely-sensed data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1109\/TGRS.2014.2346535","article-title":"Fast subpixel mapping algorithms for subpixel resolution change detection","volume":"53","author":"Wang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","first-page":"1","article-title":"Applying the Chi-square transformation and automatic secant thresholding to Landsat imagery as unsupervised change detection methods","volume":"11","author":"Novillo","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1080\/014311699213659","article-title":"Monitoring land-cover changes: A comparison of change detection techniques","volume":"20","author":"Mas","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","first-page":"1595","article-title":"The comparative study of three methods of remote sensing image change detection","volume":"37","author":"Xu","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_43","unstructured":"Chuvieco, E. (2010). Teledetecci\u00f3n Ambiental: La Observaci\u00f3n de la Tierra Desde el Espacio, Ariel. [1st ed.]."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2013.03.006","article-title":"Change detection from remotely sensed images: From pixel-based to object-based approaches","volume":"80","author":"Hussain","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_45","unstructured":"Travieso-Gonz\u00e1lez, C.M. (2018). Thresholding Algorithm Optimization for Change Detection to Satellite Imagery. Colorimetry and Image Processing, InTech."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1109\/36.843009","article-title":"Automatic analysis of the difference image for unsupervised change detection","volume":"38","author":"Bruzzone","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","unstructured":"D\u2019Addabbo, A., Satalino, G., Pasquariello, G., and Blonda, P. (2004, January 20\u201324). Three different unsupervised methods for change detection: An application. Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2004), Anchorage, AK, USA."},{"key":"ref_48","first-page":"221","article-title":"Review of Change Detection Techniques from Remotely Sensed Images","volume":"10","author":"Deilami","year":"2015","journal-title":"Res. J. Appl. Sci. Eng. Technol."},{"key":"ref_49","unstructured":"Herv\u00e1s, J., and Rosin, P.L. (2001, January 27\u201330). Tratamiento digital de im\u00e1genes de teledetecci\u00f3n en el espectro \u00f3ptico para el reconocimiento y control de deslizamientos. Proceedings of the Simposio Nacional Sobre Taludes y Laderas Inestables, Madrid, Spain."},{"key":"ref_50","unstructured":"INEGI (2012). Gu\u00eda Para la Interpretaci\u00f3n de Cartograf\u00eda. Uso del Suelo y Vegetaci\u00f3n. Escala 1:250,000, INEGI."},{"key":"ref_51","unstructured":"INEGI (2018, October 20). Censo de Poblaci\u00f3n y Vivienda 2010. Resultados Definitivos, M\u00e9xico 2011. Available online: http:\/\/cuentame.inegi.org.mx\/monografias\/informacion\/gro\/territorio\/div_municipal.aspx?tema=me&e=12."},{"key":"ref_52","unstructured":"Raisz, E. (1964). US Navy Geographical Branch, Mapa, Escala 1:3,000,000, Landforms of M\u00e9xico."},{"key":"ref_53","unstructured":"Cerca-Mart\u00ednez, M. (2004). Deformaci\u00f3n y Magmatismo Cret\u00e1cico Tard\u00edo-Terciario Temprano en la Zona de la Plataforma Guerrero-Morelos. [Ph.D. Thesis, Universidad Nacional Aut\u00f3noma de M\u00e9xico]."},{"key":"ref_54","first-page":"201","article-title":"Mesozoic geologic evolution of the Xolapa migmatitic complex north of Acapulco, southern Mexico: Implications for paleogeographic reconstructions","volume":"26","author":"Solari","year":"2009","journal-title":"Revista Mexicana de Ciencias Geol\u00f3gicas"},{"key":"ref_55","unstructured":"Figueroa, H. (2018, November 26). Hurac\u00e1n Ingrid y tormenta Manuel inundan al pa\u00eds. Available online: https:\/\/bit.ly\/2PniA2L."},{"key":"ref_56","unstructured":"Qihao, W. (2011). Land-use and land-cover change detection. Advances in Environmental Remote Sensing Sensors, Algorithms, and Applications, CRC Press Taylor & Francis Group."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"125","DOI":"10.5194\/isprs-annals-III-7-125-2016","article-title":"Change detection with multi-source defective remote sensing images based on evidential fusion","volume":"3","author":"Chen","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2148","DOI":"10.1109\/TGRS.2005.852480","article-title":"A Modified Sun-Canopy-Sensor Topographic Correction in Forested Terrain","volume":"43","author":"Soenen","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/S0034-4257(97)00177-6","article-title":"Topographic normalization of Landsat TM images of forest based on subpixel sun\u2013canopy\u2013sensor geometry","volume":"64","author":"Gu","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1080\/07038992.1982.10855028","article-title":"On the slope-aspect correction of multispectral scanner data","volume":"8","author":"Teillet","year":"1982","journal-title":"Can. J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"287","DOI":"10.3390\/ijgi6090287","article-title":"Topographic Correction to Landsat Imagery through Slope Classification by Applying the SCS C Method in Mountainous Forest Areas","volume":"6","author":"Novillo","year":"2017","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1037\/h0071325","article-title":"Analysis of a complex of statistical variables into principal components","volume":"24","author":"Hotelling","year":"1933","journal-title":"J. Educ. Psychol."},{"key":"ref_63","first-page":"43","article-title":"An\u00e1lisis de componentes principales en teledetecci\u00f3n. Consideraciones estad\u00edsticas para optimizar su interpretaci\u00f3n","volume":"17","author":"Ferrero","year":"2002","journal-title":"Teledetecci\u00f3n"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/0304-3800(95)00192-1","article-title":"The use of satellite NDVI data for the validation of global vegetation phenology models: Application to the Frankfurt Biosphere Model","volume":"91","author":"Ramage","year":"1996","journal-title":"Ecol. Model."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1080\/01431168608948944","article-title":"Satellite remote sensing of primary production","volume":"7","author":"Tucker","year":"1986","journal-title":"Int. J. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_67","first-page":"49","article-title":"On the generalized distance in statistics","volume":"12","author":"Mahalanobis","year":"1936","journal-title":"Proc. Natl. Inst. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1007\/BF02834632","article-title":"Mahalanobis distance","volume":"4","author":"McLachlan","year":"1999","journal-title":"Resonance"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/S0034-4257(97)00112-0","article-title":"A comparison of four algorithms for change detection in an urban environment","volume":"63","author":"Ridd","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_70","unstructured":"Wicklin, R., and What Is Mahalanobis Distance? The DO Loop (2018, May 25). Statistical Programming in SAS with an Emphasis on SAS\/IML Programs 2012. Available online: http:\/\/blogs.sas.com\/content\/iml\/2012\/02\/15\/what-is-mahalanobis-distance.html."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Reimann, C., Dutter, R., Filzmoser, P., and Garrett, R. (2008). Statistical Data Analysis Explained, John Wiley & Sons Ltd.","DOI":"10.1002\/9780470987605"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change detection techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_73","unstructured":"Malila, W.A. (1980, January 3\u20136). Change vector analysis: An approach for detecting forest changes with Landsat. Proceedings of the LARS Symposia."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"369","DOI":"10.14358\/PERS.69.4.369","article-title":"Land-use\/land-cover change detection using improved change-vector analysis","volume":"69","author":"Chen","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.ins.2014.01.037","article-title":"A novel approach for change detection of remotely sensed images using semi-supervised multiple classifier system","volume":"269","author":"Roy","year":"2014","journal-title":"Inf. Sci."},{"key":"ref_76","first-page":"66","article-title":"\u00cdndice de susceptibilidad a movimientos del terreno y su aplicaci\u00f3n en una regi\u00f3n semi\u00e1rida","volume":"17","year":"2000","journal-title":"Revista Mexicana de Ciencias Geol\u00f3gicas"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0304-3800(03)00176-5","article-title":"Assessing the accuracy of spatial simulation models","volume":"167","author":"Couto","year":"2003","journal-title":"Ecol. Model."},{"key":"ref_79","first-page":"1541","article-title":"Distinguishing vegetation from soil background information. [by gray mapping of Landsat MSS data]","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.rse.2005.01.002","article-title":"Spectral mixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability?","volume":"95","author":"Song","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"2261","DOI":"10.1016\/j.asr.2006.03.036","article-title":"Detecting landslide location using KOMPSAT 1 and its application to landslide-susceptibility mapping at the Gangneung area, Korea","volume":"38","author":"Lee","year":"2006","journal-title":"Adv. Space Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/12\/1987\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:32:10Z","timestamp":1760196730000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/12\/1987"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,8]]},"references-count":81,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["rs10121987"],"URL":"https:\/\/doi.org\/10.3390\/rs10121987","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,12,8]]}}}