Akar Ö, Güngör O (2012) Classification of multispectral images using Random Forest algorithm. Journal of Geodesy and Geoinformation 1:105–112. https://doi.org/10.9733/jgg.241212.1
Almeida P, Luchiari A (2017) Uso das imagens SAR R99B para mapeamento geomorfológico do canal do Ariaú no município de Iranduba-AM. Revista de Geografia (Recife) 34:209–229
ANM (2019) Catastro Minero Colombiano. In: Datos Abiertos Agencia Nacional de Minería ANM. https://www.anm.gov.co/?q=Datos_Abiertos_ANM
Azzari G, Lobell D (2017) Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring. Remote Sensing of Environment 202:64–74. https://doi.org/10.1016/j.rse.2017.05.025
Belgiu M, Dragut L (2016) Random forest in remote sensing: A review of applications and futuredirections. ISPRS Journal of Photogrammetry and Remote Sensing 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Blaes X, Vanhalle L, Defourny P (2005) Efficiency of crop identification based on optical and SAR image time series. Remote Sensing of Environment 352–365. https://doi.org/10.1016/j.rse.2005.03.010
Bocco G, Mendoza M, Velázquez A (2001) Remote sensing and GIS-based regional geomorphological mapping—a tool for land use planning in developing countries. Geomorphology 39:211–219
Bocco G, Mendoza M, Velázquez A, Torres A (1999) La regionalización geomorfológica como una alternativa de regionalización ecológica en México. El caso de Michoacán de Ocampo. Investigaciones Geográficas 40:7–22
Castañeda C, Ducrot D (2009) Land cover mapping of wetland areas in an agricultural landscape using SAR and Landsat imagery. Journal of Environmental Management 90:2270–2277. https://doi.org/10.1016/j.jenvman.2007.06.030
Colditz R (2015) An Evaluation of Different Training Sample Allocation Schemes for Discrete and Continuous Land Cover Classification Using Decision Tree-Based Algorithms. Remote Sensing 9655–9681. https://doi.org/10.3390/rs70809655
Congalton R (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 35–46
Domaç A, Süzen ML (2006) Integration of environmental variables with satellite images in regional scale vegetation classification. International Journal of Remote Sensing 27:1329–1350
Du P, Samat A, Waske B, Liu S, Li Z (2015) Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features. ISPRS Journal of Photogrammetry and Remote Sensing 38–53. https://doi.org/10.1016/j.isprsjprs.2015.03.002
Farr T, Rosen P, Caro E, Crippen R, Duren R, Hensley S, Kobrick M, Paller M, Rodriguez E, Roth L, Seal D, Shaffer S, Shimada J, Umland J, Werner M, Oskin M, Burbank D, Alsdorf D (2007) The shuttle radar topography mission. Reviews of Geophysics 45:1–33. https://doi.org/10.1029/2005RG000183
Flores-Anderson A, Herndon K, Cherrington E, Thapa R, Kucera L, Quyen N, Odour P, Wahome A, Temeson K, Mamane B, Saah D, Chishtie F, Limaye A (2019) Introduction and Rationale. In: Flores-Anderson A, Herndon K, Thapa R, Cherrington E (eds) The SAR handbook. SERVIR Global Science, Huntsville, EEUU, pp 13–20
Foody G (2004) Thematic Map Comparison: Evaluating the Statistical Significance of Differences in Classification Accuracy. Photogrammetric Engineering & Remote Sensing 627–633
Franklin S (1987) Geomorphometric processing of digital elevation models. Computers & Geosciences 13:603–609
Franklin S, Peddle D (1987) Texture analysis of digital image data using spatial cooccurrence. Computers & Geosciences 13:293–311
Giri C, Pengra B, Long J, Loveland T (2013) Next generation of global land cover characterization, mapping, and monitoring. International Journal of Applied Earth Observation and Geoinformation 25:30–37. https://doi.org/10.1016/j.jag.2013.03.005
Goosen D (1963) División fisiográfica de los Llanos Orientales. Rev Nac Agric 55:39–41
Goosen D (1964) Geomorfología de los Llanos Orientales. Rev Acad Col Ci Ex Fís Nat 12:129–139
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 18–27
Grohmann C, Riccomini C, Steiner S (2008) Aplicações dos modelos de elevação SRTM em geomorfologia. Rev Geogr Acadêmica 2:73–83
Hamilton S, Kellndorfer J, Lehner B, Tobler M (2007) Remote sensing of floodplain geomorphology as a surrogate for biodiversity in a tropical river system (Madre de Dios, Peru). Geomorphology 89:23–38. https://doi.org/10.1016/j.geomorph.2006.07.024
IDEAM (2010a) Las depresiones tectónicas. In: Flórez A (ed) Sistemas morfogénicos del territorio Colombiano. Instituto de Hidrología, Meteorología y Estudios Ambientales IDEAM, Bogotá, Colombia, pp 111–138
IDEAM (2010b) Leyenda Nacional de coberturas de la tierra: metodología CORINE Land Cover adaptada para Colombia escala 1:100.000. Instituto de Hidrología, Meteorología y Estudios Ambientales IDEAM, Bogotá, Colombia
IDEAM (2013) Zonificación hidrográfica de Colombia a escala 1:100.000. In: Instituto de Hidrología, Meteorología y Estudios Ambientales IDEAM. http://www.siac.gov.co/
IDEAM (2017) Ecosistemas Continentales, Costeros y Marinos de Colombia a escala 1:100.000. Versión 2.1. In: Instituto de Hidrología, Meteorología y Estudios Ambientales IDEAM. http://www.siac.gov.co/
IGAC (2014) Código para los levantamientos de suelos. Bogotá, Colombia
IGAC (2017) Cartografía básica del territorio colombiano (Escala 1:100.000). In: Instituto Geográfico Agustín Codazzi. ftp://[email protected]
Jaramillo A, Rangel-Ch JO (2014a) Los sistemas fluviales de la Orinoquía colombiana. In: Rangel-Ch JO (ed) Colombia Diversidad Biótica Vol. XIV: La región de la Orinoquia de Colombia. Universidad Nacional de Colombia, Instituto de Ciencias Naturales, Bogotá, Colombia, pp 71–99
Jaramillo A, Rangel-Ch JO (2014b) Las unidades del paisaje y los bloques del territorio en la Orinoquia. In: Rangel-Ch JO (ed) Colombia Diversidad Biótica XIV: La región de la Orinoquia de Colombia. Universidad Nacional de Colombia, sede Bogotá, Bogotá, Colombia, pp 101–152
Jin H, Stehman S, Mountrakis G (2014) Assessing the impact of training sample selection on accuracy of an urban classification: a case study in Denver, Colorado. International Journal of Remote Sensing 26:217–222. https://doi.org/10.1080/01431160412331269698
Kumar L, Mutanga O (2018) Google Earth Engine Applications Since Inception: Usage, Trends, and Potential. Remote Sensing 10:1–15. https://doi.org/10.3390/rs10101509
Liu C, Frazier P, Kumar L (2007) Comparative assessment of the measures of thematic classification accuracy. Remote Sensing of Environment 606–616. https://doi.org/10.1016/j.rse.2006.10.010
Lu D, Weng Q (2007) A survey of image classification methodsand techniques for improving classificationperformance. International Journal of Remote Sensing 28:823–870. https://doi.org/10.1080/01431160600746456
Meyer F (2019) Spaceborne Synthetic Aperture Radar: Principles, Data Access, and Basic Processing Techniques. In: Flores-Anderson A, Herndon K, Thapa R, Cherrington E (eds) The SAR handbook. SERVIR Global Science, Huntsville, EEUU, pp 21–44
Moreira A, Prats-Iraola P, Younis M, Krieger G, Hajnsek I, Papathanassiou K (2013) A Tutorial on Synthetic Aperture Radar. IEEE Geoscience and Remote Sensing Magazine 1:6–43
Mutanga O, Kumar L (2019) Google Earth Engine Applications. Remote Sensing 11:1–4. https://doi.org/10.3390/rs11050591
Niño L (2019) Aproximación socioeconómica sobre la Serranía de Manacacías (Meta) Orinoquía colombiana. In: Rangel-Ch JO, Andrade-C G, Jarro-F C, Santos-C G (eds) Colombia Diversidad Biótica Vol. XVII: La región de la Serranía de Manacacías (Meta) Orinoquía colombiana. Universidad Nacional de Colombia, Bogotá, Colombia, pp 601–628
Olofsson P, Foody G, Herold M, Stehman S, Woodcock C, Wulder M (2014) Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment 42–57. https://doi.org/10.1016/j.rse.2014.02.015
Pal M (2005) Random forest classifier for remote sensing classification. International Journal of Remote Sensing 26:217–222. https://doi.org/10.1080/01431160412331269698
Paradella W, Bignelli P, Veneziani P (1997) Airborne and spaceborne Synthetic Aperture Radar (SAR) integration with Landsat TM and gamma ray spectrometry for geological mapping in a tropical rainforest environment, the Carajás Mineral Province, Brazil. International Journal of Remote Sensing 18:1483–1501. https://doi.org/10.1080/014311697218232
Paradella W, Santos A, Veneziani P, Cunha E (2005) Radares Imageadores nas Geociências: Status e Perspectivas. In: Anais XII Simpósio Brasileiro de Sensoriamento Remoto. Instituto Nacional de Pesquisas Espaciais INPE, Goiânia, Brasil, pp 1847–1854
Perilla G, Mas J (2020) Google Earth Engine (GEE): una poderosa herramienta que vincula el potencial de los datos masivos y la eficacia del procesamiento en la nube. Investigaciones Geográficas 101:e59929. https://doi.org/10.14350/rig.59929
Peterseil J, Wrbka T, Plutzar C, Schmitzberger I, Kiss A, Szerencsits E, Reiter K, Schneider W, Suppan F, Beissmann H (2004) Evaluating the ecological sustainability of Austrian agricultural landscapes—the SINUS approach. Land Use Policy 307–320. https://doi.org/10.1016/j.landusepol.2003.10.011
Plourde L, Congalton R (2003) Sampling Method and Sample Placement:How Do They Affect the Accuracy ofRemotely Sensed Maps? Photogrammetric Engineering & Remote Sensing 69:289–297
Powell R, Matzke N, de Souza C, Clark M, Numata I, Hess L, Roberts D (2004) Sources of error in accuracy assessment of thematic land-cover maps in the Brazilian Amazon. Remote Sensing of Environment 221–234. https://doi.org/10.1016/j.rse.2003.12.007
Probst P, Wright M, Boulesteix A (2019) Hyperparameters and tuning strategies for random forest. WIREs Data Mining and Knowledge Discovery 9:e1301. https://doi.org/10.1002/widm.1301
Raed M, Gari J, Berlles J, Sedeño A, Porta P, Delise L, Vicini E, Sánchez L, Iriondo J, Yebrin J (1996) Métodos de clasificación supervisada y no supervisada de imágenes SAR ERS 1/2. In: Proceedings of an lnternational Seminar on The Use and Applications of ERS in Latin America. European Space Agency, Viña del Mar, Chile, pp 287–292
Rangel-Ch JO, Minorta-Cely V (2014) Los tipos de vegetación de la Orinoquia colombiana. In: Rangel-Ch JO (ed) Colombia Diversidad Biótica Vol. XIV: La región de la Orinoquia de Colombia. Universidad Nacional de Colombia, Instituto de Ciencias Naturales, Bogotá, Colombia, pp 533–612
Reiche J, Lucas R, Mitchell A, Verbesselt J, Hoekman D, Haarpaintner J, Kellndorfer J, Rosenqvist A, Lehmann E, Woodcock C, Seifert F, Herold M (2016) Combining satellite data for better tropical forest monitoring. Nature Climate Change 6:120–122
Romero-Ruiz M, Flantua S, Tansey K, Berrio J (2011) Landscape transformations in savannas of northern South America: Land use/cover changes since 1987 in the Llanos Orientales of Colombia. Applied Geography 32:766–776. https://doi.org/10.1016/j.apgeog.2011.08.010
Rosenfield G, Fitzpatrick-Lins K (1986) A coefficient of agreement as ameasure of thematic classification accuracy. Photogrammetric Engineering & Remote Sensing 223–227
Salovaara K, Thessler S, Malik R, Tuomisto H (2005) Classification of Amazonian primary rain forest vegetation using Landsat ETM+ satellite imagery. Remote Sensing of Environment 97:39–51. https://doi.org/10.1016/j.rse.2005.04.013
Schneiders A, Müller F (2017) A natural base for ecosystem services. In: Burkhard B, Maes J (eds) Mapping ecosystem services. Pensoft Publishers, Sofia, Bulgaria, pp 33–38
Segundo I, Bocco G, Velázquez A, Gajewski K (2017) On the relationship between landforms and land use in tropical dry developing countries. A GIS and multivariate statistical approach. Investigaciones Geográficas 93:3–19. https://doi.org/10.14350/rig.56438
SGC (2015) Propuesta metodológica sistemática para la generación de mapas geomorfológicos analíticos aplicados a la zonificación de amenaza por movimientos en masa escala 1:100.000. Anexo A: glosario de términos geomorfológicos. Bogotá, Colombia
SGC (2019) Banco de Información Petrolera BIP. In: Portal Servicio Geológico Colombiano SGC. http://srvags.sgc.gov.co/JSViewer/GEOVISOR_BIP/
Shetty S (2019) Analysis of Machine Learning Classifiers for LULC Classification on Google Earth Engine. University of Twente
Silva J, Alves P (2007) A utilização dos modelos SRTM na interpretação geomorfológica: técnicas e tecnologias aplicadas ao mapeamento geomorfológico do território brasileiro. In: Anais XIII Simpósio Brasileiro de Sensoriamento Remoto. Instituto Nacional de Pesquisas Espaciais INPE, Florianópolis, Brasil, pp 4261–4266
Smith M, Pain C (2009) Applications of remote sensing in geomorphology. Progress in Physical Geography 33:568–582
Souza P, Paradella W (2002) Recognition of the main geobotanical features along the Bragança mangrove coast (Brazilian Amazon Region) from Landsat TM and RADARSAT-1 data. Wetlands Ecology and Management 10:123–132
Stehman S (1992) Comparison of systematic and random sampling for estimating the accuracy of maps generated from remotely sensed data. Photogrammetric Engineering & Remote Sensing 58:1343–1350
Stehman S, Czaplewski R (1998) Design and Analysis for Thematic Map Accuracy Assessment: Fundamental Principles. Remote Sensing of Environment 331–344
Steinhardt U, Herzog F, Lausch A, Müller E, Lehmann S (1999) Hemeroby index for landscape monitoring and evaluation. In: Pykh Y, Hyatt D, Lenz R (eds) First International Conference on Environmental Indices: Systems Analysis Approach. Oxford, EOLSS Publ., St. Petersburg, Russia, pp 237–254
Stone T, Schlesinger P, Houghton R, Woodwell G (1994) A map of the vegetation of South America based on satellite imagery. Photogrammetric Engineering & Remote Sensing 60:541–551
Story M, Congalton R (1986) Accuracy assessment: A user’s perspective. Photogrammetric Engineering & Remote Sensing 397–399
Thenkabail PS, Hall J, Lin T, Ashton MS, Harris D, Enclona EA (2003) Detecting floristic structure and pattern across topographic and moisture gradients in a mixed species Central African forest using IKONOS and Landsat-7 ETM+ images. International Journal of Applied Earth Observation and Geoinformation 4:255–270
Topouzelis K, Psyllos A (2012) Oil spill feature selection and classification using decision tree forest on SAR image data. ISPRS Journal of Photogrammetry and Remote Sensing 135–143. https://doi.org/10.1016/j.isprsjprs.2012.01.005
Tsai Y, Stow D, Chen J, Lewison R, An L, Shi L (2018) Mapping Vegetation and Land Use Types in Fanjingshan National Nature Reserve Using Google Earth Engine. Remote Sensing 10:1–14. https://doi.org/10.3390/rs10060927
Vega L, Hirata Y, Ventura L, Serrudo N (2018) Natural Forest Mapping in the Andes (Peru): A Comparison of the Performance of Machine-Learning Algorithms. Remote Sensing 10:1–20. https://doi.org/10.3390/rs10050782
Walsh S, Butler D, Malanson G (1998) An overview of scale, pattern, process relationships in geomorphology: a remote sensing and GIS perspective. Geomorphology 21:183–205
Walz U, Stein C (2009) Indicators of hemeroby for the monitoring of landscapes in Germany. Journal for Nature Conservation 22:279–289
Waske B, Braun M (2009) Classifier ensembles for land cover mapping using multitemporal SAR imagery. ISPRS Journal of Photogrammetry and Remote Sensing 450–457. https://doi.org/10.1016/j.isprsjprs.2009.01.003
Xie Y, Sha Z, Yu M (2008) Remote sensing imagery in vegetation mapping: a review. Journal of Plant Ecology 1:9–23