Geomorphology, Land-Use, and Hemeroby of Foothills in Colombian Orinoquia: Classification and Correlation at a Regional Scale

References

• Akar, Ö., and O. Güngör. 2012. Classification of multispectral images using Random Forest algorithm. Journal of Geodesy and Geoinformation 1 (2):105–12. doi: 10.9733/jgg.241212.1.
   Google Scholar
• Almeida, P., and A. Luchiari. 2017. Uso Das Imagens SAR R99B Para Mapeamento Geomorfológico Do Canal Do Ariaú No Município de Iranduba-AM. Revista de Geografia (Recife) 34 (1):209–29.
   Google Scholar
• ANM. 2019. Catastro Minero Colombiano. Datos Abiertos Agencia Nacional de Minería. ANM. https://www.anm.gov.co/?q=Datos_Abiertos_ANM.
   Google Scholar
• Azzari, G., and D. Lobell. 2017. Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring. Remote Sensing of Environment 202:64–74. doi: 10.1016/j.rse.2017.05.025.
   Web of Science ®Google Scholar
• Belgiu, M., and L. Dragut. 2016. Random Forest in remote sensing: A review of applications and futuredirections. ISPRS Journal of Photogrammetry and Remote Sensing 114:24–31. doi: 10.1016/j.isprsjprs.2016.01.011.
   Web of Science ®Google Scholar
• Blaes, X., L. Vanhalle, and P. Defourny. 2005. Efficiency of crop identification based on optical and SAR image time series. Remote Sensing of Environment 96:352–65. doi: 10.1016/j.rse.2005.03.010.
   Web of Science ®Google Scholar
• Bocco, G., M. Mendoza, and A. Velázquez. 2001. Remote sensing and GIS-based regional geomorphological mapping—A tool for land use planning in developing countries. Geomorphology 39:211–9.
   Web of Science ®Google Scholar
• Bocco, G., M. Mendoza, A. Velázquez, and A. Torres. 1999. La Regionalización Geomorfológica Como Una Alternativa de Regionalización Ecológica En México. El Caso de Michoacán de Ocampo. Investigaciones Geográficas 40:7–22.
   Google Scholar
• Castañeda, C., and D. Ducrot. 2009. Land cover mapping of wetland areas in an agricultural landscape using SAR and landsat imagery. Journal of Environmental Management 90 (7):2270–7. doi: 10.1016/j.jenvman.2007.06.030.
   PubMed Web of Science ®Google Scholar
• 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 7:9655–81. doi: 10.3390/rs70809655.
   Web of Science ®Google Scholar
• Congalton, R. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37:35–46.
   Web of Science ®Google Scholar
• Domaç, A., and M. L. Süzen. 2006. Integration of environmental variables with satellite images in regional scale vegetation classification. International Journal of Remote Sensing 27 (7):1329–50.
   Web of Science ®Google Scholar
• Du, P., A. Samat, B. Waske, S. Liu, and Z. Li. 2015. Random Forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features. ISPRS Journal of Photogrammetry and Remote Sensing 105:38–53. doi: 10.1016/j.isprsjprs.2015.03.002.
   Web of Science ®Google Scholar
• Farr, T., P. Rosen, E. Caro, R. Crippen, R. Duren, S. Hensley, M. Kobrick, et al. 2007. The shuttle radar topography mission. Reviews of Geophysics 45 (2):1–33. doi: 10.1029/2005RG000183.
   Web of Science ®Google Scholar
• Flores-Anderson, A., K. Herndon, E. Cherrington, R. Thapa, L. Kucera, N. Quyen, P. Odour, A. Wahome, K. Tenneson, B. Mamane, et al. 2019. Introduction and rationale. In The SAR handbook, edited by A. I. Flores-Anderson, K. E. Herndon, R. B. Thapa, and E. Cherrington, 13–20. Huntsville, AL: SERVIR Global Science.
   Google Scholar
• Foga, S., P. Scaramuzza, S. Guo, Z. Zhu, R. Dilley, T. Beckmann, G. Schmidt, J. Dwyer, J. Hughes, and B. Laue. 2017. Cloud detection algorithm comparison and validation for operational landsat data products. Remote Sensing of Environment 194:379–90. doi: 10.1016/j.rse.2017.03.026.
   Web of Science ®Google Scholar
• Foody, G. 2004. Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy. Photogrammetric Engineering & Remote Sensing 70:627–33.
   Web of Science ®Google Scholar
• Franklin, S. 1987. Geomorphometric processing of digital elevation models. Computers & Geosciences 13 (6):603–9.
   Web of Science ®Google Scholar
• Franklin, S., and D. Peddle. 1987. Texture analysis of digital image data using spatial cooccurrence. Computers & Geosciences 13 (3):293–311.
   Web of Science ®Google Scholar
• Giri, C., B. Pengra, J. Long, and T. Loveland. 2013. Next generation of global land cover characterization, mapping, and monitoring. International Journal of Applied Earth Observation and Geoinformation 25:30–7. doi: 10.1016/j.jag.2013.03.005.
   Web of Science ®Google Scholar
• Goosen, D. 1963. División Fisiográfica de Los Llanos Orientales. Revista Nacional de Agricultura 55:39–41.
   Google Scholar
• Goosen, D. 1964. Geomorfología de Los Llanos Orientales. Revista de la Acadeia Colombiana de Cienicas Exactas, Físicas y Naturales 12:129–39.
   Google Scholar
• Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. 2017. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27.
   Web of Science ®Google Scholar
• Grohmann, C., C. Riccomini, and S. Steiner. 2008. Aplicações Dos Modelos de Elevação SRTM Em Geomorfologia. Rev. Geogr. Acadêmica 2 (2):73–83.
   Google Scholar
• Hamilton, S., J. Kellndorfer, B. Lehner, and M. Tobler. 2007. Remote sensing of floodplain geomorphology as a surrogate for biodiversity in a tropical river system (Madre de Dios, Peru). Geomorphology 89:23–38. doi: 10.1016/j.geomorph.2006.07.024.
   Web of Science ®Google Scholar
• IDEAM. 2010a. Las Depresiones Tectónicas. In Sistemas Morfogénicos Del Territorio Colombiano, edited by A. Flórez, 111–38. Bogotá, Colombia: Instituto de Hidrología, Meteorología y Estudios Ambientales IDEAM.
   Google Scholar
• IDEAM. 2010b. Leyenda Nacional de Coberturas de La Tierra: Metodología CORINE Land Cover Adaptada Para Colombia Escala 1:100.000. Edited by Néstor Martínez and Uriel Murcia. Bogotá, Colombia: Instituto de Hidrología, Meteorología y Estudios Ambientales IDEAM.
   Google Scholar
• IDEAM. 2013. Zonificación y Codificación de Unidades Hidrográficas e Hidrogeológicas de Colombia. Bogotá, Colombia: Instituto de Hidrología, Meteorología y Estudios Ambientales IDEAM.
   Google Scholar
• IDEAM. 2017. Ecosistemas Continentales, Costeros y Marinos de Colombia a Escala 1:100.000. Versión 2.1. Bogotá, Colombia: Instituto de Hidrología, Meteorología y Estudios Ambientales IDEAM. http://www.siac.gov.co/.
   Google Scholar
• IGAC. 2004. El Meta: Un Territorio de Oportunidades. Bogotá, Colombia: Gobernación del Meta, Instituto Geográfico Agustín Codazzi IGAC.
   Google Scholar
• IGAC. 2014. Código Para Los Levantamientos de Suelos. I40100-06/14.V1. Bogotá, Colombia. Instituto Geográfico Agustín Codazzi IGAC.
   Google Scholar
• IGAC. 2017. Cartografía Básica Del Territorio Colombiano (Escala 1:100.000). Bogotá, Colombia: Instituto Geográfico Agustín Codazzi. ftp://[email protected].
   Google Scholar
• Jaramillo, A., and J. O. Rangel-Ch. 2014a. Las Unidades Del Paisaje y Los Bloques Del Territorio En La Orinoquia. In Colombia Diversidad Biótica XIV: La Región de La Orinoquia de Colombia, edited by J. O. Rangel-Ch, 101–52. Bogotá, Colombia: Universidad Nacional de Colombia, sede Bogotá.
   Google Scholar
• Jaramillo, A., and J. O. Rangel-Ch. 2014b. Los Sistemas Fluviales de La Orinoquía Colombiana. In Colombia Diversidad Biótica Vol. XIV: La Región de La Orinoquia de Colombia, edited by Jesús Orlando Rangel-Ch, 71–99. Bogotá, Colombia: Universidad Nacional de Colombia, Instituto de Ciencias Naturales.
   Google Scholar
• Jin, H., S. Stehman, and G. Mountrakis. 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 (1):217–22. doi: 10.1080/01431160412331269698.
   Google Scholar
• Kumar, L., and O. Mutanga. 2018. Google earth engine applications since inception: Usage, trends, and potential. Remote Sensing 10:1–15. doi: 10.3390/rs10101509.
   Web of Science ®Google Scholar
• Lawson, R. 2007. Small sample confidence intervals for the odds ratio. Communications in Statistics - Simulation and Computation 33 (4):1095–113. doi: 10.1081/SAC-200040691.
   Google Scholar
• Liu, C., P. Frazier, and L. Kumar. 2007. Comparative assessment of the measures of thematic classification accuracy. Remote Sensing of Environment 107:606–16. doi: 10.1016/j.rse.2006.10.010.
   Web of Science ®Google Scholar
• Lu, D., and Q. Weng. 2007. A survey of image classification methodsand techniques for improving classificationperformance. International Journal of Remote Sensing 28 (5):823–70. doi: 10.1080/01431160600746456.
   Web of Science ®Google Scholar
• Méndez, W., Z. González, J. Suárez, M. Arauno, M. Vielma, and H. Maiz. 2016. Geomorfología de Los Abanicos Aluviales Del Piedemonte Norte Del Macizo El Ávila, Estado Vargas, Venezuela. Revista de Investigación 40 (87):95–128.
   Google Scholar
• Meyer, F. 2019. Spaceborne synthetic aperture radar: Principles, data access, and basic processing techniques. In The SAR handbook, edited by Africa Flores-Anderson, Kelsey Herndon, Rajesh Thapa, and Emil Cherrington, 21–44. Huntsville, EEUU: SERVIR Global Science.
   Google Scholar
• Moreira, A., P. Prats-Iraola, M. Younis, G. Krieger, I. Hajnsek, and K. Papathanassiou. 2013. A tutorial on synthetic aperture radar. IEEE Geoscience and Remote Sensing Magazine 1 (1):6–43.
   Web of Science ®Google Scholar
• Mutanga, O., and L. Kumar. 2019. Google earth engine applications. Remote Sensing 11:1–4. doi: 10.3390/rs11050591.
   Web of Science ®Google Scholar
• Niño, L., J. O. Rangel-Ch, G. Andrade-C, C. Jarro-F, and G. Santos-C. 2019. Aproximación Socioeconómica Sobre La Serranía de Manacacías (Meta) Orinoquía Colombiana. In Colombia Diversidad Biótica Vol. XVII: La Región de La Serranía de Manacacías (Meta) Orinoquía Colombiana, 601–28. Bogotá, Colombia: Universidad Nacional de Colombia.
   Google Scholar
• Olofsson, P., G. Foody, M. Herold, S. Stehman, C. Woodcock, and M. Wulder. 2014. Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment 148:42–57. doi: 10.1016/j.rse.2014.02.015.
   Web of Science ®Google Scholar
• Pal, M. 2005. Random Forest classifier for remote sensing classification. International Journal of Remote Sensing 26 (1):217–22. doi: 10.1080/01431160412331269698.
   Web of Science ®Google Scholar
• Paradella, W., P. Bignelli, and P. Veneziani. 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 (7):1483–501. doi: 10.1080/014311697218232.
   Web of Science ®Google Scholar
• Paradella, W., A. Santos, P. Veneziani, and E. Cunha. 2005. Radares Imageadores Nas Geociências: Status e Perspectivas. In Anais XII Simpósio Brasileiro de Sensoriamento Remoto, 1847–1854. Goiânia, Brasil: Instituto Nacional de Pesquisas Espaciais INPE.
   Google Scholar
• Perilla, G., and J. Mas. 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: E 59929. doi: 10.14350/rig.59929.
   Google Scholar
• Peterseil, J., T. Wrbka, C. Plutzar, I. Schmitzberger, A. Kiss, E. Szerencsits, K. Reiter, W. Schneider, F. Suppan, and H. Beissmann. 2004. Evaluating the ecological sustainability of Austrian agricultural landscapes—The SINUS approach. Land Use Policy 21:307–20. doi: 10.1016/j.landusepol.2003.10.011.
   Web of Science ®Google Scholar
• Plourde, L., and R. Congalton. 2003. Sampling method and sample placement: How do they affect the accuracy of remotely sensed maps? Photogrammetric Engineering & Remote Sensing 69 (3):289–97.
   Web of Science ®Google Scholar
• PNN. 2014. Propuesta de Declaratoria Área Protegida Alto Manacacías. Bogotá, Colombia: Parques Nacionales Naturales de Colombia PNN y Corporación Para El Desarrollo Sostenible del Área Manejo Especial la Macarena CORMACARENA.
   Google Scholar
• Powell, R., N. Matzke, C. de Souza, M. Clark, I. Numata, L. Hess, and D. Roberts. 2004. Sources of error in accuracy assessment of thematic land-cover maps in the Brazilian Amazon. Remote Sensing of Environment 90:221–34. doi: 10.1016/j.rse.2003.12.007.
   Web of Science ®Google Scholar
• Probst, P., M. Wright, and A. Boulesteix. 2019. Hyperparameters and tuning strategies for Random Forest. WIREs Data Mining and Knowledge Discovery 9 (3):e1301. doi: 10.1002/widm.1301.
   Web of Science ®Google Scholar
• Raed, M., J. Gari, J. Berlles, A. Sedeño, P. Porta, L. Delise, E. Vicini, L. Sánchez, J. Iriondo, and J. Yebrin. 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, 287–92. Viña del Mar, Chile: European Space Agency.
   Google Scholar
• Rangel-Ch, J. O., and V. Minorta-Cely. 2014. Los Tipos de Vegetación de La Orinoquia Colombiana. In Colombia Diversidad Biótica Vol. XIV: La Región de La Orinoquia de Colombia, edited by Jesús Orlando Rangel-Ch, 533–612. Bogotá, Colombia: Universidad Nacional de Colombia, Instituto de Ciencias Naturales.
   Google Scholar
• Reiche, J., R. Lucas, A. Mitchell, J. Verbesselt, D. Hoekman, J. Haarpaintner, J. Kellndorfer, et al. 2016. Combining satellite data for better tropical forest monitoring. Nature Climate Change 6 (2):120–2.
   Web of Science ®Google Scholar
• Rita, H., and A. Komonen. 2008. Odds ratio: An ecologically sound tool to compare proportions. Annales Zoologici Fennici 45:66–72. doi: 10.5735/086.045.0106.
   Web of Science ®Google Scholar
• Robertson, K., and M. Castiblanco. 2011. Amenazas Fluviales En El Piedemonte Amazónico Colombiano. Cuadernos de Geografía 20 (2):125–37.
   Google Scholar
• Romero-Ruiz, M., S. Flantua, K. Tansey, and J. Berrio. 2011. Landscape transformations in savannas of northern South America: Land use/cover changes since 1987 in the Llanos Orientales of Colombia. Applied Geography 32:766–76. doi: 10.1016/j.apgeog.2011.08.010.
   Web of Science ®Google Scholar
• Rosenfield, G., and K. Fitzpatrick-Lins. 1986. A coefficient of agreement as a measure of thematic classification accuracy. Photogrammetric Engineering & Remote Sensing 52:223–7.
   Web of Science ®Google Scholar
• Salovaara, K., S. Thessler, R. Malik, and H. Tuomisto. 2005. Classification of Amazonian primary rain forest vegetation using Landsat ETM + satellite imagery. Remote Sensing of Environment 97:39–51. doi: 10.1016/j.rse.2005.04.013.
   Web of Science ®Google Scholar
• Schneiders, A., and F. Müller. 2017. A natural base for ecosystem services. In Mapping ecosystem services, edited by B. Burkhard and J. Maes, 33–8. Sofia, Bulgaria: Pensoft Publishers.
   Google Scholar
• Segundo, I., G. Bocco, A. Velázquez, and K. Gajewski. 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. doi: 10.14350/rig.56438.
   Google Scholar
• 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. Bogotá, Colombia: Anexo A: Glosario de Términos Geomorfológicos.
   Google Scholar
• SGC. 2019. Banco de Información Petrolera BIP. Portal Servicio Geológico Colombiano SGC. http://srvags.sgc.gov.co/JSViewer/GEOVISOR_BIP/.
   Google Scholar
• Shetty, S. 2019. Analysis of machine learning classifiers for LULC classification on Google earth engine. University of Twente. Enschede, Netherlands.
   Google Scholar
• Silva, J., and P. Alves. 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, 4261–6. Florianópolis, Brasil: Instituto Nacional de Pesquisas Espaciais INPE.
   Google Scholar
• Smith, M., and C. Pain. 2009. Applications of remote sensing in geomorphology. Progress in Physical Geography 33 (4):568–82.
   Web of Science ®Google Scholar
• Souza, P., and W. Paradella. 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 (2):123–32.
   Google Scholar
• 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 (9):1343–50.
   Web of Science ®Google Scholar
• Stehman, S., and R. Czaplewski. 1998. Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sensing of Environment 64:331–44.
   Web of Science ®Google Scholar
• Steinhardt, U., F. Herzog, A. Lausch, E. Müller, and S. Lehmann. 1999. Hemeroby index for landscape monitoring and evaluation. In First international conference on environmental indices: Systems analysis approach, edited by Y. Pykh, D. Hyatt, and R. Lenz, 237–54. St. Petersburg, Russia; Oxford: EOLSS Publ.
   Google Scholar
• Stone, T., P. Schlesinger, R. Houghton, and G. Woodwell. 1994. A map of the vegetation of south America based on satellite imagery. Photogrammetric Engineering & Remote Sensing 60 (5):541–51.
   Web of Science ®Google Scholar
• Story, M., and R. Congalton. 1986. Accuracy assessment: A user’s perspective. Photogrammetric Engineering & Remote Sensing 52:397–9.
   Web of Science ®Google Scholar
• Thenkabail, P. S., J. Hall, T. Lin, M. S. Ashton, D. Harris, and E. A. Enclona. 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 (3):255–70.
   Google Scholar
• Topouzelis, K., and A. Psyllos. 2012. Oil spill feature selection and classification using decision tree forest on SAR image data. ISPRS Journal of Photogrammetry and Remote Sensing 68:135–43. doi: 10.1016/j.isprsjprs.2012.01.005.
   Web of Science ®Google Scholar
• Tsai, Y., D. Stow, J. Chen, R. Lewison, L. An, and L. Shi. 2018. Mapping vegetation and land use types in Fanjingshan National Nature Reserve using Google earth engine. Remote Sensing 10:1–14. doi: 10.3390/rs10060927.
   Web of Science ®Google Scholar
• Vega, L., Y. Hirata, L. Ventura, and N. Serrudo. 2018. Natural forest mapping in the Andes (Peru): A comparison of the performance of machine-learning algorithms. Remote Sensing 10:1–20. doi: 10.3390/rs10050782.
   Web of Science ®Google Scholar
• Walsh, S., D. Butler, and G. Malanson. 1998. An overview of scale, pattern, process relationships in geomorphology: A remote sensing and GIS perspective. Geomorphology 21:183–205.
   Web of Science ®Google Scholar
• Walz, U., and C. Stein. 2009. Indicators of hemeroby for the monitoring of landscapes in Germany. Journal for Nature Conservation 22:279–89.
   Google Scholar
• Waske, B., and M. Braun. 2009. Classifier ensembles for land cover mapping using multitemporal SAR imagery. ISPRS Journal of Photogrammetry and Remote Sensing 64:450–7. doi: 10.1016/j.isprsjprs.2009.01.003.
   Web of Science ®Google Scholar
• Xie, Y., Z. Sha, and M. Yu. 2008. Remote sensing imagery in vegetation mapping: A review. Journal of Plant Ecology 1 (1):9–23.
   Web of Science ®Google Scholar