Identificación de áreas erosionadas y en riesgo de erosión utilizando imágenes Landsat 8 OLI y Sentinel-2, procesamiento digital y SIG
Contenido principal del artículo
El objetivo de esta investigación fue identificar y comparar Áreas Erosionadas y en Riesgo De Erosión (EAER, por sus siglas en inglés) como indicadores de degradación de suelos por erosión hídrica en una cuenca hidrográfica empleando imágenes Landsat 8 OLI y Sentinel-2. Para ello, se emplearon técnicas de procesamiento digital y Sistemas de Información Geográfica (SIG), enfocándose en los datos espectrales de reflectancia de imágenes satelitales. El estudio implicó estimaciones del Riesgo Potencial de Erosión Hídrica (RPEH), y generación de cartografías EAER a partir del cálculo de distancia espectral euclidiana a suelos desnudos y de una técnica de percepción remota seleccionada mediante regresión lineal. Se determinaron curvas ROC (Características Operativas del Receptor) para definir umbrales de clasificación, los cuales fueron validados mediante clasificaciones supervisadas y asociados a valores de RPEH. Los resultados indican que los EAER1 identificaron más áreas erosionadas que los EAER2. De igual modo, se evidenció que los resultados derivados de Sentinel-2 tuvieron mayores aciertos que los de Landsat 8. El análisis de RPEH, además de las cartografías EAER desarrolladas y otros datos y criterios, podrían ayudar a considerar medidas necesarias de conservación de suelos.
Mohammed S. Alsafadi K. Talukdar S. Kiwan S., Hennawi S., Alshihabi O., Sharaf M. y Harsanyie E. Estimation of soil erosion risk in southern part of Syria by using RUSLE integrating geo informatics approach. Remote Sensing Applications: Society and Environment. 2020 Nov; 20: 100375. https://doi.org/10.1016/j.rsase.2020.100375
Duguma TA. Soil erosion risk assessment and treatment priority classification: A Case Study on Guder Watersheds, Abay River Basin, Oromia, Ethiopia. HELIYON. 2022 Ago; 8(8): e10183. https://doi.org/10.1016/j.heliyon.2022.e10183
Ávila BD. y Ávila HF. Spatial and temporal estimation of the erosivity factor r based on daily rainfall data for the department of Atlántico, Colombia. Ingeniería e Investigación. 2015 May; 35 (2), 23-29. https://revistas.unal.edu.co/index.php/ingeinv/article/view/47773
Omuto CT. y Vargas R. Soil loss atlas of Malawi. Food & Agriculture Organization. 2019. https://openknowledge.fao.org/server/api/core/bitstreams/5fd68b91-a221-40fb-95cd-fe3d31f97394/content
FAO y GTIS. Estado mundial del recurso suelo (EMRS). Resumen técnico. Roma. 2015. https://openknowledge.fao.org/server/api/core/bitstreams/07a444e7-97a3-4e1f-b5d9-ddd84ad129c6/content
Morales-Pavón J. Valdés-Rodríguez O. Servín-Martínez A. Hernández-Zárate J. Tejero-Andrade J. y Domínguez-Sánchez G. Plantas tropicales para contener suelo y evitar deslizamientos superficiales: estudio de caso Ricinus communis [Paper presentation]. II Reunión Internacional, Científica y Tecnológica; XXIX Reunión Científica y Tecnológica Forestal y Agropecuaria, Veracruz, México. 2016. https://www.researchgate.net/publication/309548349_Plantas_tropicales_para_contener_suelo_y_evitar_ deslizamientos_superficiales_estudio_de_caso_Ricinus_communis
Chaudhary B. y Kumar S. Soil erosion estimation and prioritization of Koshalya-Jhajhara watershed in North India. Indian Journal of Soil Conservation. 2018; 46 (3): 305-311. https://www.researchgate.net/publication/330855428_Soil_erosion_estimation_ and_prioritization_of_Koshalya-Jhajhara_watershed_in_North_India
Food and Agriculture Organization of the United Nations (FAO) (Ed.). Soil erosion: the greatest challenge to sustainable soil management. Food & Agriculture Organization. 2019.https://openknowledge.fao.org/server/api/core/bitstreams/a268e967-a373-4a56-adac-ae1d7ab3567f/content
Sestras P. Mircea S. Roșca S. Bilașco Ș. Sălăgean T. Dragomir L. Herbei M. Bruma S. Sabou C. Marković R. y Kader S. GIS based soil erosion assessment using the USLE model for efficient land management: a case study in an area with diverse pedo-geomorphological and bioclimatic characteristics. Notulae Botanicae Horti Agrobotanici Cluj-Napoca. 2023; 51(3): 13263. https://doi.org/10.15835/nbha51313263
Camargo-Roa CE. Pacheco-Angulo CE. Monjardin-Armenta SA. y López-Falcón R. Gómez- Orgulloso T. Identification of Eroded and Erosion Risk Areas Using Remote Sensing and GIS in the Quebrada Seca watershed. Ingeniería e Investigación. 2023 Ago; 43(3): e105003-e105003. https://revistas.unal.edu.co/index.php/ingeinv/article/view/105003
Chuvieco E. Fundamentals of Satellite Remote Sensing an environmental Approach (2nd ed.). CRC Press Taylor and Francis Group. 2016 Mar. https://doi.org/10.1111/phor.12184
Beguería S. Identifying erosion areas at basin scale using remote sensing data and GIS: a case study in a geologically complex mountain basin in the Spanish Pyrenees. International Journal of Remote Sensing. 2006 Mar; 27(20): 4585-4598. https://doi.org/10.1080/01431160600735640
Rosales - Rodríguez CA. Hazard maps of shallow landslides associated with infiltration processes in the Sapuyes river basin. Ingeniería e Investigación. 2021 Abr; 41(1): e84611. https://doi.org/10.15446/ing.investig.v41n1.84611
Lillesand T. Kiefer RW. y Chipman J. Remote sensing and image interpretation (7th Ed). John Wiley & Sons. 2015. https://www.wiley.com/en-sg/Remote+Sensing+and+Image+Interpretation%2C+7th+Edition-p-9781118343289
Ngandam AH. Etouna J. Nongsi BK. Mvogo FA. y Noulaquape FG. Assessment of land degradation status and its impact in arid and semi-arid areas by correlating spectral and principal component analysis neo-bands. International Journal of Advanced Remote Sensing and GIS. 2016 Feb; 5(2): 1539 – 1560. https://doi.org/10.23953/CLOUD. IJARSG.77
Sartori A. Cano J. Montaner D. Mattar C. Moraga J. Alfaro W. Soto G. Morales L. Quintanilla O. Andrés E. Gavilán C. y Trujillo G. Reporte de Neutralidad en la Degradación de las Tierras (NDT) ante la Convención de las Naciones Unidas de Lucha Contra la Desertificación (CNULD) Estrategia Nacional de Cambio Climático y Recursos Vegetacionales (2017-2025) de Chile. Unidad de Cambio Climático y Servicios Ambientales (UCCSA), Gerencia de Desarrollo y Fomento Forestal (GEDEFF), Corporación Nacional Forestal (CONAF). 2018. https://redd.unfccc.int/uploads/4833_6_reporte_ldn__282ene2018_29_-_vfpc.pdf
Orr BJ. Cowie AL. Castillo VM. Chasek P. Crossman ND. Erlewein A. Louwagie G. Maron M. Metternicht GI. Minelli S. Tengberg AE. Walter S. y Welton S. Scientific Conceptual Framework for Land Degradation Neutrality. A Report of the Science-Policy Interface. United Nations Convention to Combat Desertification (UNCCD). 2017. https://www.unccd.int/sites/default/files/documents/2019-06/LDN_CF_report_web-english.pdf
Najafi M. Fakhireh A. Pahlavan A. Moradzadeh M. y Noori S. Determining the suitable indices for assessment of cover change in west of Karkheh river using satellite data. Journal of Applied Science and Environmental Studies. 2020 Ene; 3(1): 1-14. https://doi.org/10.48393/IMIST.PRSM/jases-v3i1.18928
Meinen BU. y Robinson DT. From hillslopes to watersheds: Variability in model outcomes with the USLE. Environmental Modelling and Software. 2021 Dic; 146: 105229. https://doi. org/10.1016/j.envsoft.2021.105229
Pal SC. y Chakrabortty R. Modeling of water induced surface soil erosion and the potential risk zone prediction in a sub-tropical watershed of Eastern India. Modeling Earth Systems and Environment. 2019 Nov; 5: 369-393. https://doi.org/10.1007/s40808-018-0540-z
Li S. y Chen X. New bare-soil index for rapid mapping developing areas using landsat 8 data. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences. 2014 Abr; 40(4): 139. https://doi.org/10.5194/isprsarchives-
XL- 4-139-2014
Romero W. Ramos R. Vázquez R. Arrogante P. y Arroyo R. Detección de deslizamientos de laderas por el método de regresión lineal utilizando imágenes Áster en la zona centro del estado de Guerrero, México [Paper presentation]. XXV Congreso de la Asociación de Geógrafos Españoles. Universidad Autónoma de Madrid, Madrid, Espain. 2017. https://www.age-geo- grafia.es/downloads/Naturaleza_Territorio_y_Ciudad_AGE2017.pdf
Basu T. Das A. y Pal S. Application of geographically weighted principal component analysis and fuzzy approach for unsupervised landslide susceptibility mapping on Gish River Basin, India. Geocarto International. 2020 May; 37(5): 1294-1317. https://doi.org/10.1080/10106 049.2020.1778105
Demaría MR. y Aguado I. Dinámica espacio-temporal del porcentaje de suelo desnudo en pastizales semi-áridos de Argentina. GeoFocus. 2013; 13(2), 133-157. https://www.geofocus. org/index.php/geofocus/article/view/291
Leal J. Pérez U. y Ortiz NE. Distribución espacial y temporal de deslizamientos (1999 – 2015) en la cuenca del río Combeima, Colombia. Revista Geográfica Venezolana. 2018 Ene; 59(2): 346 – 365. https://www.redalyc.org/journal/3477/347760473008/html/
Ganasri BP. y Ramesh H. (2016). Assessment of soil erosion by RUSLE model using remote sensing and GIS - A case study of Nethravathi Basin. Geoscience Frontiers. 2016 Nov; 7(6): 953 – 961. http://dx.doi.org/10.1016/j.gsf.2015.10.007
Opeyemi OA. Abidemi FH. y Otokiti V. (2019). Assessing the Impact of Soil Erosion on Residential Areas of Efon-Alaaye Ekiti, Ekiti-State, Nigeria. International Journal of Environmental Planning and Management. 2019; 5(1): 23–31. https://www.researchgate. net/publication/333532006_Assessing_the_Impact_of_Soil_Erosion_on_Residential_ Areas_of_Efon-Alaaye_Ekiti_Ekiti-State_Nigeria
lambeck NO. Reassessment of the potential risk of soil erosion by water on agricultural land in Germany: Setting the stage for site-appropriate decision-making in soil and water resources management. Ecological Indicators. 2020 Nov; 118: 106732. https://doi. org/10.1016/j.ecolind.2020.106732
Drzewiecki W. Wężyk P. Pierzchalski M. y Szafrańska B. Quantitative and qualitative assessment of soil erosion risk in Małopolska (Poland), supported by an object-based analysis of high- resolution satellite images. Pure Appl. Geophys. 2014 Abr; 171: 867-
https://doi.org/10.1007/s00024-013- 0669-7
Anderson W. y Jhonson T. Evaluating Global Land Degradation Using Ground-Based Measurements and Remote Sensing. In: E. Nkonya, V. Mirzabaev, A., J.Von Braun (Eds.), Economics of Land Degradation and Improvement – A Global Assessment for Sustainable Development (pp. 85 – 116). Cham, Switzerland: Springer Open. 2016. https://doi.org/10.1007/978-3-319-19168-3
Panagos P. Ballabio C. Borrelli P. Meusburger K. Klik A. Rousseva S. Tadic MP. Michaelides S. Hrabalikova M. Olsen P. Aalto J. Lakatos M. Rymszewicz A. Dumitrescu
A. Begueria S. y Alewell C. Rainfall erosivity in Europe. Science Total Environment. 2015 Abr; 511: 801-14. https://doi.org/10.1016/j.scitotenv.2015.01.008
United Nations General Assembly (UNGA). (2015). Transforming Our World: the 2030 Agenda for Sustainable Development. Resolution adopted by the General Assembly on 25 September 2015. https://sdgs.un.org/2030agenda
Efiong J. Imoke D. Nwabueze J. y James S. Geo-spatial modelling of landslide susceptibility in Cross River State of Nigeria. Scientific African. 2021; 14: e01032. https://doi.org/10.1016/j.sciaf.2021.e01032
Naciones Unidas. Aplicación de datos del mes: Erosión del suelo. 2021 https://www.un-spider.org/es/enlaces-y-recursos/fuentos-de-datos/daotm-erosion-suelo#USLE
Tsegaye K. Addis HK. y Hassen EE. Soil Erosion Impact Assessment using USLE/GIS Approaches to Identify High Erosion Risk Areas in the Lowland Agricultural Watershed of Blue Nile Basin, Ethiopia. International Annals of Science. 2020 Nov; 8(1): 120-129. https://doi.org/10.21467/ias.8.1.120- 129
Bastidas J. Nociones de hidrografía. Consejo de Publicaciones, Universidad de Los Andes. Mérida, Venezuela. 2007. 145 p. http://bdigital.ula.ve/storage/pdftesis/pregrado/tde_ arquivos/29/TDE-2010-05-20T05:09:40Z-1024/Publico/munoz_teran.pdf
Vivas L. Los Andes Venezolanos. Caracas: Academia Nacional de la Historia. 1992. https://catalogosiidca.csuca.org/Record/CR.UNA01000086866/Similar
Ewel, J. Madriz, A. y Tosi, J. Zonas de vida de Venezuela Memoria explicativa sobre el mapa ecológico. Caracas, Venezuela: Ediciones del Fondo Nacional de Investigaciones Agropecuarias. 1976. 270 pp.
La Marca, E. y Contreras, Y. Descubrimiento y reporte de las primeras estepas altiandinas en Venezuela. Revista Ibero-Afro-Americana de Geografía Física e Ambiente 2019; 1(1): 121-139. https://dialnet.unirioja.es/servlet/articulo?codigo=9262239
Alaska Satellite Facility. Terrain-Corrected (RTC). 2021 https://www.asf.alaska.edu/sar-data/palsar/terrain-corrected-rtc/
Instituto Geográfico Agustín Codazzi (IGAC). Descripción y Corrección de Productos Landsat 8 LDCM (Landsat Data Continuity Mission) Versión 1.0. Bogotá: Instituto Geográfico Agustín Codazzi. 2013. https://www.un-spider.org/sites/default/files/LDCM-L8.R1.pdf
EUROPEAN SPACE AGENCY (ESA). Sentinel MSI, Product types, level 1C. 2014. https://sentinels.copernicus.eu/web/sentinel/sentinel-data-access/sentinel-products/sentinel-
- data-products/collection-1-level-1c
EUROPEAN SPACE AGENCY (ESA). Sentinel−2 user Handbook (2nd ed.). European Space Agency Standard Document. 2015. https://sentinel.esa.int/documents/247904/685211/Sentinel- 2_User_Handbook
COPERNICUS. El programa Copernicus aplicado a la producción y gestión de la información geoespacial. Proyecto cofinanciado por la Comisión Europea mediante acuerdo 2018/SI2.810140/04. 2018. https://www.ign.es/web/resources/docs/IGNCnig/actividades/OBS/Programa_Marco_Copernicus_User_Uptake/12_Componente_ InSitu.pdf
GIS AG MAPS. Landsat 8 & Sentinel-2 Rare Imagery Comparison. 2017. https://www. gisagmaps.org/sentinel-2-landsat-8-cell-comp/#:~:text=Sentinel%2D2%20imagery%20 cells%20differ,partial%20imagery%20within%20a%20cell.
Camargo CE. Pacheco CE. y López R. Evaluación de métodos de corrección atmosférica y sombreado topográfico en imagen Landsat 8 OLI sobre un área montañosa semiárida. UD y la Geomática. 2021 Dic; 16: 23-39. https://revistas.udistrital.edu.co/index.php/UDGeo/article/view/17040
Teillet P. Guindon B. y Goodenough D. On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing. 1982; 8(2): 84-106. https://doi.org/ 10.1080/07038992.1982.10855028
Canty M. Image analysis, classification, and change detection in remote sensing with Algorithms for ENVI/IDL. CRC Press Taylor & Francis Group, LL. Boca Ratón, United States of America. 2009 Dic. 474 p. https://doi.org/10.1201/9781420087147
SIGIS. (2019). Digital Globe. https://www.sigis.com.ve/
Thenkabail PS. Remotely sensed data characterization, classification, and accuracies. CRC Press Taylor and Francis Group. 2015 Oct. 712p. https://doi.org/10.1201/b19294
Auerswald K. Fischer FK. Kistler M. Treisch M. Maier H. y Brandhuber R. Behavior of farmers in regard to erosion by water as reflected by their farming practices. Science of the Total Environment. 2018 Feb; 613–614: 1–9. https://doi.org/10.1016/j. scitotenv.2017.09.003
Fischer FK. Kistler M. Brandhuber R. Maier H. Treisch M. y Auerswald K. Validation of official erosion model-ling based on high-resolution radar rain data by aerial photo erosion classification. Earth Surf. Process. Landforms. 2017 Ago; 43(1): 187–194. https://doi.org/10.1002/esp.4216
Batista PVG. Davies J. Silva MLN. y Quinton JN. On the evaluation of soil erosion models: Are we doing enough? Earth-Science Reviews. 2019 Oct; 197: 102898. https://doi.org/10.1016/j.earscirev.2019.102898
Cardoza L. Aplicaciones de los sistemas de información geográfica en ingeniería civil utilizando el software GVSIG. Facultad De Ingeniería Y Arquitectura Escuela De Ingeniería Civil. Universidad de El Salvador. 2017. Trabajo Especial de Grado. 2017. 317 p. https://catalogosiidca.csuca.org/Record/UES.157312
Food and Agriculture Organization of the United Nations (FAO). Programa de Naciones Unidas para el Medio Ambiente (PNUMA), United Nations Educational, Scientific and Cultural Organization (UNESCO). (1980). Metodología provisional para la evaluación de la degradación de los suelos. Food & Agriculture Organization.
Guerra CA. Rosa IMD. Valentini E. Wolf F. Filipponi F. Karger DN. Nguyen Xuan A. Mathieu J. Lavelle P. y Eisenhauer N. Global vulnerability of soil ecosystems to erosion. Landscape Ecology. 2020 Mar; 35: 823-842. https://doi.org/10.1007/s10980-020-00984-z
Allafta H. y Opp C. Soil erosion assessment using the RUSLE model, remote sensing, and GIS in the Shatt Al-Arab Basin (Iraq-Iran). Applied Sciences. 2022 Jul; 12(15): 7776. https://doi.org/10.3390/app12157776
Al-Mamari M. Kantoush S. Al-Harrasi T. Al-Maktoumi A. Abdrabo K. Saber M. y Sumi
T. Assessment of sediment yield and deposition in a dry reservoir using field observations, RUSLE and remote sensing: Wadi Assarin, Oman. Journal of Hydrology. 2023 Feb; 617(A): 128982. https://doi.org/10.1016/j.jhydrol.2022.128982
Rosales A. y García P. La cuenca hidrográfica y su gestión integral. In A. Gabaldón, A. Rosales, E. Buroz, J. Córdova, G. Uzcátegui y L. Iskandar (Eds.), Agua en Venezuela: una riqueza escasa. Fundación Polar, Caracas, Venezuela. 2015. 867-914 p. https://bibliofep.fundacionempresaspolar.org/media/1378185/agua_ti_0_indice.pdf
Arnoldus H. Methodology used to determine the maximum potential average annual soil loss due to sheet and rill erosion in Morocco. Assessing Soil Degradation, FAO Soils Bulletin (FAO). 1977; 34: 39-48.
Pacheco HA. El índice de erosión potencial en la vertiente norte del Waraira Repano, estado Vargas, Venezuela. Cuadernos de geografía: revista colombiana de geografía. 2012 Jul; 21(2): 85 – 97. https://doi.org/10.15446/rcdg.v21n2.32215
Muñoz JL. Morante J. y Miranda P. Erosión potencial por reconversión productiva en subcuenca Llay-Llay, Chile. aplicación de unidades de respuesta a la erosión. Ciencia y Tecnología. 2014 ; 7(2): 35-47. https://dialnet.unirioja.es/servlet/articulo?codigo=5093737
Hämmerly RC. Paris RC. y Paz-González A. Assessment of domain areas for precipitation and evapotranspiration on the left bank of the Paraná watershed at Argentine territory. I: Thiessen polygons and kriging. Cadernos Lab. Xeolóxico de Laxe Coruña. 2019 Dic; 41: 75 – 97. https://doi.org/10.17979/cadlaxe.2019.41.1.5818
Ministerio del Ambiente y de los Recursos Naturales Renovables (MARNR). Sistemas Ambientales Venezolanos. Regiones naturales: 7A Depresión del Táchira, Proyecto Ven/79/001, Código II-2-7A. Proyecto VEN/79/001. MARNR. 1983.
Quiñonez E. y Dal Pozzo F. Distribución espacial del riesgo de degradación de los suelos por erosión hídrica en el estado Lara, Venezuela. Geoenseñanza. 2008 Ene; 13(1): 59 –70. https://www.redalyc.org/articulo.oa?id=36014579006
United States Department of Agriculture (USDA). Soil Texture Calculator. 2020. https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_054167
Food and Agriculture Organization of the United Nations (FAO), United Nations Educational, Scientific and Cultural Organization (UNESCO). Mapa mundial de suelos 1:5.000.0000. Food & Agriculture Organization. 1976.
Foster GR. Mccool DK. Renard, KG. y Moldenhauer WC. Conversion of the universal soil loss equation to SI metric units. J. Soil Water Conservation. 1981 Nov; 36(6): 355 – 359. https://www.jswconline.org/content/36/6/355
Alatorre LC. y Beguería S. (2009). Identification of eroded areas using remote sensing in a badlands landscape on marls in the central Spanish Pyrenees. Catena. 2009 Mar; 76(3):182-190. https://doi.org/10.1016/j.catena.2008.11.005
Wang H. Zhao W. Li C. y Pereira P. Vegetation greening partly offsets the water erosion risk in China from 1999 to 2018. Geoderma. 2021 Nov; 401: 115319. https://doi. org/10.1016/j.geoderma.2021.115319
Ampudia A. Sánchez G. y Jiménez F. Precisión diagnóstica del MMPI-2 con la personalidad delictiva: un análisis con la curva ROC. Revista de Psicología. 2017, 35(1): 167-192. http://dx. doi.org/http://doi.org/10.18800/psico.201701.006
Liang S. y Wang J. Advanced Remote Sensing: Terrestrial Information Extraction and Applications (2nd ed.). Academic Press. 2020. https://doi.org/10.1016/C2017-0-03489-4
Sánchez JM. Análisis de Calidad Cartográfica mediante el estudio de la Matriz de Confusión. Pensamiento matemático. 2016; 6(2): 9-26. https://dialnet.unirioja.es/servlet/articulo?codigo=5998855
Congalton, R.G. y Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2nd Edition, Lewis Publishers. 2020. https://www.scirp.org/reference/referencespapers?referenceid=1278841
Olofsson P, Foody G, Herold M, Stehmand S, Woodcocka C, Wulder M. Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment. 2014 May; 148: 42-57. https://doi.org/10.1016/j.rse.2014.02.015
Jensen J. Introductory Digital Image Processing: A Remote Sensing Perspective. (Upper Saddle River, NY: Prentice Hall. 526. 2005.
Cohen J. A coefficient of agreement for nominal scales. Educational and Psychological Measurement. 1960 Abr; 20(1): 37– 46. https://doi.org/10.1177/001316446002000104
Boca T, Rodríguez G. Métodos estadísticos de la evaluación de la exactitud de productos derivados de sensores remotos. Instituto de Clima y Agua, INTA Castelar. 2012.
GOFC-GOLD. A sourcebook of methods and procedures for monitoring and reporting anthropogenic greenhouse gas emissions and removals associated with deforestation, gains and losses of carbon stocks in forests remaining forests, and forestation. GOFC-GOLD Report version COP19-1. (GOFC-GOLD Land Cover Project Office, Wageningen University, The Netherlands). 2013 Mar. https://publications.jrc.ec.europa.eu/repository/handle/JRC76900
Olaya V. Sistemas de Información Geográfica. 2014. 854. Disponible en: https://www.icog.es/TyT/files/Libro_SIG.pdf
Landis J, Koch G. The Measurement of Observer Agreement for Categorical Data. Biometrics. 1977 Mar; 33(1): 159-174. Disponible en: https://www.jstor.org/stable/2529310
HARRIS GEOESPATIAL SOLUTION. Radiance and Scale Factors Background, Calculate Confusion Matrices. 2018.
Pearson K. On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine. 1901; 2(11): 559–572. https://doi.org/10.1080/14786440109462720
Boardman JW. Sedimentary Facies Analysis Using Imaging Spectrometry: A Geophysical Inverse Problem [Doctoral dissertation, University of Colorado Boulder]. ProQuest.1991. https://www.proquest.com/openview/961236e78bc11bb7cd328ebee fd98484/1?pq-origsite=gscholar&cbl=18750&diss=y
Peñuelas J. Gamon JA. Griffin KL. y Field CB. Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sensing of Environment. 1993 Nov; 46(2): 110-118. https://doi.org/10.1016/0034-4257(93)90088-F
Peñuelas J. Gamon, JA. Fredeen AL. Merino J. y Field CB. Reflectance indices associated with physiological changes in nitrogen and water-limited sunflower leaves. Remote sensing of Environment. 1994 May; 48(2): 135-146. https://doi. org/10.1016/0034-4257(94)90136-8
Rikimaru A. Roy PS. y Miyatake S. Tropical forest cover density mapping. Tropical ecology. 2002 Ene; 43(1): 39-47. https://www.researchgate.net/publication/228604101_ Tropical_forest_cover_density_mapping
Kauth RJ. y Thomas GS. The Tasseled Cap — A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat [Paper presentation]. Symposium on Machine Processing of Remotely Sensed Data, West Lafayette, IN: Purdue. 1976. https://docs.lib.purdue.edu/lars_symp/159/
Baig MHA. Zhang L. Shuai T. y Tong Q. Derivation of a Tasselled Cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters. 2014 Abr; 5(5): 423–431. https://doi.org/10.1080/2150704X.2014.915434
Shi T. y Xu H. Derivation of tasseled cap transformation coefficients for Sentinel-2 MSI at- sensor reflectance data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2019 Oct; 12(10): 4038-4048. https://doi.org/10.1109/JSTARS.2019.2938388
Jordan CF. Derivation of leaf-area index from quality of light on the forest floor. Ecology. 1969 Jul; 50(4): 663–666. https://doi.org/10.2307/1936256
Pearson RL. y Miller LD. Remote mapping of standing crop biomass for estimation of the productivity of the short-grass prairie, Pawnee National Grasslands, Colorado [Paper presentation]. Proceedings of the Eighth International Symposium on Remote Sensing of Environment, ERIM, Ann Arbor, United States. 1972.
Rouse J. Haas RH. Schell JA. y Deering DW. Monitoring vegetation systems in the Great Plains with ERTS [Paper presentation]. Procceding of the Third ERTS Symposium, Washington DC, United States. 1973. https://ntrs.nasa.gov/api/citations/19740022614/downloads/19740022614.pdf
Baret F. y Guyot G. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment. 1991 Feb; 35(2-3): 161-173. https://doi. org/10.1016/0034-4257(91)90009-U
Deering DW. Rouse JW. Iiaa RH. y Schell JA. Measuring forage production of grazing units from landsat MSS Data [Paper presentation]. Proceedings of the Tenth International Symposium on Remote Sensing of Environment, ERIM, Ann Arbor, United States. 1975.
Thiam AK. Geographic Information System and Re-mote Sensing Methods for Assessing and Monitoring Land Degradation in the Shale: The Case of Southern Mauritania [Doctoral dissertation, Darks University]. ProQuest Dissertations and Theses. 1997. https://ui.adsabs.harvard.edu/abs/1998PhDT........94T/abstract
Perry CR. y Lautenschlager LF. Functional Equivalence of Spectral Vegetation Indices. Remote Sensing of Environment. 1984 Ene; 14(1-3): 169-182. https://doi. org/10.1016/0034-4257(84)90013-0
Huete AR. A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment 1988 Ago; 25(3): 295-309. https://doi.org/10.1016/0034-4257(88)90106-X
Richardson AJ. y Wiegand CL. Distinguishing Vegetation from Soil Background Information. Photogramnetric Engineering and Remote Sensing. 1977 Dic; 43(12): 1541-1552.https://www.asprs.org/wp-content/uploads/pers/1977journal/dec/1977_ dec_1541-1552.pdf
Jackson RD. Slater PN. y Pinter P. Discrimination of growth and water stress in wheat by varius vegetation indices through clear and a turbid atmospheres. Remote Sensing of Enviroment. 1983 Jul; 13(3): 187 - 208. https://doi.org/10.1016/0034-4257(83)90039-1
Walther D. y Shabaani S. Large scale monitoring of rangelands vegetation using NOAA/AVHRR LAC data: application to the rainy seasons 1989/90 in northern Kenya. Range Management Handbook of Kenya; Ministry of Livestock Development: Nairobi, Kenya. 1991.
Qi J. Chehbouni A. Huete AR. Kerr YH. y Sorooshian S. A Modified Soil Adjusted Vegetation Index. Remote Sensing of Environment. 1994 May; 48(2): 119-126. https://doi. org/10.1016/0034-4257(94)90134-1
Celik N. Change Detection of Urban Areas in Ankara through Google Earth Engine [Paper presentation]. 41st Inter-national Conference on Telecommunications and Signal Processing (TSP), Athens, Greece. 2018 Jul. https://doi.org/10.1109/TSP.2018.8441377
Zhao H. y Chen X. Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. In International geoscience and remote sensing symposium. 3: 1666-1668. 2005 Jul. https://doi.org/10.1109/IGARSS.2005.1526319
Shobha G. y Rangaswamy S. Machine Learning. In N, Venkat. C.R., Gudivada. Rao, C.R. (Eds), Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications. Print Book & E-Book. 2018 Ago. 197-228 p. https://shop.elsevier.com/books/computational-analysis-and-understanding-of-natural-languages-principles-methods-and-applications/rao/978-0-444-64042-0
Morell-Monzó S. Estornell j. y Sebastiá-Frasquet, MT. Comparison of Sentinel-2 and High- Resolution Imagery for Mapping Land Abandonment in Fragmented Areas. Remote Sensing. 2020 Jun; 12(12): 2062. https://doi.org/10.3390/rs12122062
Nasir NSB. Mustafa FB. y Muhammad Yusoff SY. Spatial prediction of soil erosion risk using knowledge-driven method in Malaysia’s Steepland Agriculture Forested Valley. Environment, Development and Sustainability. 2024 Abr; 26: s10668-023-03251-8. https://doi.org/10.1007/s10668-023-03251-8
Demirel T. y Tüzün S. Multi criteria evaluation of the methods for preventing soil erosion using fuzzy ANP: The case of Turkey. [Conference presentation]. World Congress on Engineering, London, England. 2011 Jul; 2. https://www.iaeng.org/publication/WCE2011/WCE2011_pp1179-1183.pdf
Condori-Tintaya F. Pino-Vargas E. y Tacora-Villegas P. Pérdida de suelos por erosión hídrica en laderas semiáridas de la subcuenca Cairani-Camilaca, Perú. Idesia. 2022; 40(2): 7-15. https://dx.doi.org/10.4067/S0718-34292022000200007
Alewell C. Borrelli P. Meusburger K. y Panagos P. (2019). Using the USLE: chances, challenges and limitations of soil erosion modelling. Int. Soil Water Conserv. Res. 2019 Sep; 7(3): 203 – 225. https://doi.org/10.1016/j.iswcr.2019.05.004
Wang L. Huang J. Du Y. Hu Y. y Han P. Dynamic Assessment of Soil Erosion Risk Using Landsat TM and HJ Satellite Data in Danjiangkou Reservoir Area, China. Remote Sensing. 2023 Jul; 5(8): 3826-3848. https://doi.org/10.3390/rs5083826
Bernui G. Del Águila L. Sanes M. Prochazka R. y Bussalleu A. Evaluación de un test del aliento con carbono 13 para el diagnóstico de Helicobacter pylori. Rev. de Gastroenterología del Perú. 2022 Mar; 42(1): 13-9. http://dx.doi.org/10.47892/rgp.2022.421.1341
Meshesha DT. Tsunekawa A. Tsubo M. y Haregeweyn N. (2012). Dynamics and hotspots of soil erosion and management scenarios of the Central Rift Valley of Ethiopia. International Journal of Sediment Research, 27 (1), 84–99. https://doi. org/10.1016/S1001-6279(12)60018-3
Khosrokhani M. y Pradhan B. Spatio-temporal assessment of soil erosion at Kuala Lumpur metropolitan city using remote sensing data and GIS. Geomatics, Natural Hazards and Risk. 2012 Mar; 5(3): 252-270. http://dx.doi.org/10.1080/19475705.2013.794164
Papaiordanidis S. Gitas IZ. y Katagis T. Soil erosion prediction using the revised universal soil loss equation (RUSLE) in Google Earth Engine (GEE) cloud-based platform. Dokuchaev Soil Bulletin. 2019 ; 100: 36-52. https://doi.org/10.19047/0136-1694-2019-100-36-52
Kumar R. Deshmukh B. y Kumar A. (2022). Using Google Earth Engine and GIS for basin scale soil erosion risk assessment: A case study of Chambal river basin, central India. Journal of Earth System Science. 2022 Oct; 131(228). https://doi.org/10.1007/s12040-022-01977-z
- Zolgenisk Del Valle Malaver Rosas , Cristopher Camargo Roa, Arturo Osorio Marquina, Jesús Andrades Grassi , Cambios en la cobertura y usos de la tierra en el municipio de Antolín del Campo, estado de nueva Esparta – Venezuela, durante los períodos 2015 – 2018 y 2018 – 2022. , Revista de Ciencias: Vol. 26 Núm. 2 (2023)
Aceptado 2024-08-22
Publicado 2025-03-05
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.