Derechos de autor 2020 Investigación e Innovación en Ingenierías
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Mapeo de cobertura terrestre utilizando aprendizaje máquina
Corresponding Author(s) : Cristian Muñoz-Ordóñez
camilomu@unicauca.edu.co
Investigación e Innovación en Ingenierías,
Vol. 8 Núm. 3 (2020): Numero especial - XV Jornadas iberoamericanas de Ingeniería de Software e Ingeniería del Conocimiento - JIISIC 2020
Resumen
Objetivo: Determinar las dinámicas de evolución de la cobertura terrestre sobre la Cuenca Alta del Río Cauca. Metodología: Esta investigación, presenta un proceso de clasificación de coberturas sobre la CARC utilizando imágenes Landsat-8 y Sentinel-1. Este análisis se realizó mediante la clasificación de características de imágenes satelitales ópticas y de radar, utilizando algoritmos de aprendizaje máquina. Resultados y conclusiones: se identificaron regiones que presentan afectación en sus coberturas vegetales mostrando la importancia de la utilización conjugada de satélites ópticos y de radar. La clasificación tuvo una precisión del 88.9% y un coeficiente kappa de 0.86
Palabras clave
Clasificación de imágenes satelitales, Percepción remota satelital, Aprendizaje máquina, Sistemas de producción combinada
[1]
C. Muñoz-Ordóñez, J. Muñoz-Ordóñez, L. Pencue-Fierro, y A. Figueroa, «Mapeo de cobertura terrestre utilizando aprendizaje máquina», Investigación e Innovación en Ingenierías, vol. 8, n.º 3, pp. 85–101, nov. 2020.
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Referencias
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M. A. Munnaf, G. Haesaert, M. Van Meirvenne, and A. M. Mouazen, “Site-specific seeding using multi-sensor and data fusion techniques: A review,” in Advances in Agronomy, vol. 161, Academic Press Inc., 2020, pp. 241–323.
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5. J. Muñoz, L. Pencue, A. Figueroa, and C. Guzmán, “Crop monitoring in high andean ecosystems of the upper basin of the palacé river using planet images,” in Advances in Intelligent Systems and Computing, Nov. 2018, vol. 687, pp. 155–169, doi: 10.1007/978-3-319-70187-5_12.
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16. Y. Lin, X. Hu, R. Qiu, Z. Zhang, Q. Lin, and J. Lin, “Responses of Landsat-based NDVI to Interaction of Vegetation and Influencing Factors,” Nongye Jixie Xuebao/Transactions Chinese Soc. Agric. Mach., vol. 49, no. 10, pp. 212–219, Oct. 2018, doi: 10.6041/j.issn.1000-1298.2018.10.024.
17. A. Ferro, “Advanced methods for the analysis of radar sounder and VHR SAR signals,” University of Trento, 2011.
18. E. Wali, M. Tasumi, and M. Moriyama, “Combination of linear regression lines to understand the response of sentinel-1 dual polarization SAR data with crop phenology-case study in Miyazaki, Japan,” Remote Sens., vol. 12, no. 1, 2020, doi: 10.3390/rs12010189.
19. M. A. Merchant, R. K. Warren, R. Edwards, and J. K. Kenyon, “An Object-Based Assessment of Multi-Wavelength SAR, Optical Imagery and Topographical Datasets for Operational Wetland Mapping in Boreal Yukon, Canada,” Can. J. Remote Sens., vol. 45, no. 3–4, pp. 308–332, Jul. 2019, doi: 10.1080/07038992.2019.1605500.
20. R. M. Haralick, I. Dinstein, and K. Shanmugam, “Textural Features for Image Classification,” IEEE Trans. Syst. Man Cybern., vol. SMC-3, no. 6, pp. 610–621, 1973, doi: 10.1109/TSMC.1973.4309314.
21. A. Darzi, I. Lang, A. Taklikar, H. Averbuch-Elor, and S. Avidan, “Co-occurrence Based Texture Synthesis,” May 2020, Accessed: May 25, 2020. [Online]. Available: http://arxiv.org/abs/2005.08186.
22. D. I. V. Danny Ibarra Vega, C. Peña Rincón, J. Valencia Calvo, J. M. Redondo, and G. Olivar Tost, “Water resources planning in bioethanol production from sugarcane,” Memorias, no. 1, pp. 30–35, Nov. 2018, doi: 10.22490/25904779.3055.
Referencias
M. A. Munnaf, G. Haesaert, M. Van Meirvenne, and A. M. Mouazen, “Site-specific seeding using multi-sensor and data fusion techniques: A review,” in Advances in Agronomy, vol. 161, Academic Press Inc., 2020, pp. 241–323.
2. G. Vargas, N. León, and Y. Hernández, “Agricultural Socio-economic Effects in Colombia due to Degradation of Soils,” in Sustainable Management of Soil and Environment, Springer Singapore, 2019, pp. 289–337.
3. C. Martinez, A. Jarvis, C. Navarrete, N. Riaño, and A. Figueroa-Casas, “Sector Agrícola de la Cuenca Alta del Río Cauca Altamente Vulnerable al Cambio Climático,” 2014. [Online]. Available: https://cdkn.org/wp-content/uploads/2012/08/Brief_General-Cuenca-alta-rio-cauca.pdf.
4. J. Muñoz, C. Cobos, and M. Mendoza, “Vegetation index based on genetic programming for bare ground detection in the Amazon,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Oct. 2018, vol. 10633 LNAI, pp. 259–271, doi: 10.1007/978-3-030-02840-4_21.
5. J. Muñoz, L. Pencue, A. Figueroa, and C. Guzmán, “Crop monitoring in high andean ecosystems of the upper basin of the palacé river using planet images,” in Advances in Intelligent Systems and Computing, Nov. 2018, vol. 687, pp. 155–169, doi: 10.1007/978-3-319-70187-5_12.
6. T. Talema and B. T. Hailu, “Mapping rice crop using sentinels (1 SAR and 2 MSI) images in tropical area: A case study in Fogera wereda, Ethiopia,” Remote Sens. Appl. Soc. Environ., vol. 18, Apr. 2020, doi: 10.1016/j.rsase.2020.100290.
7. E. L. Pencue-Fierro, Y. T. Solano-Correa, J. C. Corrales-Muñoz, and A. Figueroa-Casas, “A Semi-Supervised Hybrid Approach for Multitemporal Multi-Region Multisensor Landsat Data Classification,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 9, no. 12, pp. 5424–5435, Dec. 2016, doi: 10.1109/JSTARS.2016.2623567.
8. G. Lei, A. Li, J. Bian, and Z. Zhang, “The roles of criteria, data and classification methods in designing land cover classification systems: evidence from existing land cover data sets,” International Journal of Remote Sensing, vol. 41, no. 14. Taylor and Francis Ltd., pp. 5062–5082, Jul. 17, 2020, doi: 10.1080/01431161.2020.1724349.
9. S. Ji, Z. Zhang, C. Zhang, S. Wei, M. Lu, and Y. Duan, “Learning discriminative spatiotemporal features for precise crop classification from multi-temporal satellite images,” Int. J. Remote Sens., vol. 41, no. 8, pp. 3162–3174, Apr. 2020, doi: 10.1080/01431161.2019.1699973.
10. C. Huang et al., “Land Cover Mapping in Cloud-Prone Tropical Areas Using Sentinel-2 Data: Integrating Spectral Features with Ndvi Temporal Dynamics,” Remote Sens., vol. 12, no. 7, p. 1163, Apr. 2020, doi: 10.3390/rs12071163.
11. A. Kern, H. Marjanović, and Z. Barcza, “Spring vegetation green-up dynamics in Central Europe based on 20-year long MODIS NDVI data,” Agric. For. Meteorol., vol. 287, p. 107969, Jun. 2020, doi: 10.1016/j.agrformet.2020.107969.
12. M. K. Gumma et al., “Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system,” GIScience Remote Sens., vol. 55, no. 6, pp. 926–949, Nov. 2018, doi: 10.1080/15481603.2018.1482855.
13. R. Simoes et al., “Land use and cover maps for Mato Grosso State in Brazil from 2001 to 2017,” Sci. Data, vol. 7, no. 1, pp. 1–10, Dec. 2020, doi: 10.1038/s41597-020-0371-4.
14. M. C. A. Picoli et al., “Big earth observation time series analysis for monitoring Brazilian agriculture,” ISPRS J. Photogramm. Remote Sens., vol. 145, pp. 328–339, Nov. 2018, doi: 10.1016/j.isprsjprs.2018.08.007.
15. Y. Bai, J. Gao, and B. Zhang, “Monitoring of Crops Growth Based on NDVI and EVI,” Nongye Jixie Xuebao/Transactions Chinese Soc. Agric. Mach., vol. 50, no. 9, pp. 153–161, Sep. 2019, doi: 10.6041/j.issn.1000-1298.2019.09.017.
16. Y. Lin, X. Hu, R. Qiu, Z. Zhang, Q. Lin, and J. Lin, “Responses of Landsat-based NDVI to Interaction of Vegetation and Influencing Factors,” Nongye Jixie Xuebao/Transactions Chinese Soc. Agric. Mach., vol. 49, no. 10, pp. 212–219, Oct. 2018, doi: 10.6041/j.issn.1000-1298.2018.10.024.
17. A. Ferro, “Advanced methods for the analysis of radar sounder and VHR SAR signals,” University of Trento, 2011.
18. E. Wali, M. Tasumi, and M. Moriyama, “Combination of linear regression lines to understand the response of sentinel-1 dual polarization SAR data with crop phenology-case study in Miyazaki, Japan,” Remote Sens., vol. 12, no. 1, 2020, doi: 10.3390/rs12010189.
19. M. A. Merchant, R. K. Warren, R. Edwards, and J. K. Kenyon, “An Object-Based Assessment of Multi-Wavelength SAR, Optical Imagery and Topographical Datasets for Operational Wetland Mapping in Boreal Yukon, Canada,” Can. J. Remote Sens., vol. 45, no. 3–4, pp. 308–332, Jul. 2019, doi: 10.1080/07038992.2019.1605500.
20. R. M. Haralick, I. Dinstein, and K. Shanmugam, “Textural Features for Image Classification,” IEEE Trans. Syst. Man Cybern., vol. SMC-3, no. 6, pp. 610–621, 1973, doi: 10.1109/TSMC.1973.4309314.
21. A. Darzi, I. Lang, A. Taklikar, H. Averbuch-Elor, and S. Avidan, “Co-occurrence Based Texture Synthesis,” May 2020, Accessed: May 25, 2020. [Online]. Available: http://arxiv.org/abs/2005.08186.
22. D. I. V. Danny Ibarra Vega, C. Peña Rincón, J. Valencia Calvo, J. M. Redondo, and G. Olivar Tost, “Water resources planning in bioethanol production from sugarcane,” Memorias, no. 1, pp. 30–35, Nov. 2018, doi: 10.22490/25904779.3055.
2. G. Vargas, N. León, and Y. Hernández, “Agricultural Socio-economic Effects in Colombia due to Degradation of Soils,” in Sustainable Management of Soil and Environment, Springer Singapore, 2019, pp. 289–337.
3. C. Martinez, A. Jarvis, C. Navarrete, N. Riaño, and A. Figueroa-Casas, “Sector Agrícola de la Cuenca Alta del Río Cauca Altamente Vulnerable al Cambio Climático,” 2014. [Online]. Available: https://cdkn.org/wp-content/uploads/2012/08/Brief_General-Cuenca-alta-rio-cauca.pdf.
4. J. Muñoz, C. Cobos, and M. Mendoza, “Vegetation index based on genetic programming for bare ground detection in the Amazon,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Oct. 2018, vol. 10633 LNAI, pp. 259–271, doi: 10.1007/978-3-030-02840-4_21.
5. J. Muñoz, L. Pencue, A. Figueroa, and C. Guzmán, “Crop monitoring in high andean ecosystems of the upper basin of the palacé river using planet images,” in Advances in Intelligent Systems and Computing, Nov. 2018, vol. 687, pp. 155–169, doi: 10.1007/978-3-319-70187-5_12.
6. T. Talema and B. T. Hailu, “Mapping rice crop using sentinels (1 SAR and 2 MSI) images in tropical area: A case study in Fogera wereda, Ethiopia,” Remote Sens. Appl. Soc. Environ., vol. 18, Apr. 2020, doi: 10.1016/j.rsase.2020.100290.
7. E. L. Pencue-Fierro, Y. T. Solano-Correa, J. C. Corrales-Muñoz, and A. Figueroa-Casas, “A Semi-Supervised Hybrid Approach for Multitemporal Multi-Region Multisensor Landsat Data Classification,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 9, no. 12, pp. 5424–5435, Dec. 2016, doi: 10.1109/JSTARS.2016.2623567.
8. G. Lei, A. Li, J. Bian, and Z. Zhang, “The roles of criteria, data and classification methods in designing land cover classification systems: evidence from existing land cover data sets,” International Journal of Remote Sensing, vol. 41, no. 14. Taylor and Francis Ltd., pp. 5062–5082, Jul. 17, 2020, doi: 10.1080/01431161.2020.1724349.
9. S. Ji, Z. Zhang, C. Zhang, S. Wei, M. Lu, and Y. Duan, “Learning discriminative spatiotemporal features for precise crop classification from multi-temporal satellite images,” Int. J. Remote Sens., vol. 41, no. 8, pp. 3162–3174, Apr. 2020, doi: 10.1080/01431161.2019.1699973.
10. C. Huang et al., “Land Cover Mapping in Cloud-Prone Tropical Areas Using Sentinel-2 Data: Integrating Spectral Features with Ndvi Temporal Dynamics,” Remote Sens., vol. 12, no. 7, p. 1163, Apr. 2020, doi: 10.3390/rs12071163.
11. A. Kern, H. Marjanović, and Z. Barcza, “Spring vegetation green-up dynamics in Central Europe based on 20-year long MODIS NDVI data,” Agric. For. Meteorol., vol. 287, p. 107969, Jun. 2020, doi: 10.1016/j.agrformet.2020.107969.
12. M. K. Gumma et al., “Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system,” GIScience Remote Sens., vol. 55, no. 6, pp. 926–949, Nov. 2018, doi: 10.1080/15481603.2018.1482855.
13. R. Simoes et al., “Land use and cover maps for Mato Grosso State in Brazil from 2001 to 2017,” Sci. Data, vol. 7, no. 1, pp. 1–10, Dec. 2020, doi: 10.1038/s41597-020-0371-4.
14. M. C. A. Picoli et al., “Big earth observation time series analysis for monitoring Brazilian agriculture,” ISPRS J. Photogramm. Remote Sens., vol. 145, pp. 328–339, Nov. 2018, doi: 10.1016/j.isprsjprs.2018.08.007.
15. Y. Bai, J. Gao, and B. Zhang, “Monitoring of Crops Growth Based on NDVI and EVI,” Nongye Jixie Xuebao/Transactions Chinese Soc. Agric. Mach., vol. 50, no. 9, pp. 153–161, Sep. 2019, doi: 10.6041/j.issn.1000-1298.2019.09.017.
16. Y. Lin, X. Hu, R. Qiu, Z. Zhang, Q. Lin, and J. Lin, “Responses of Landsat-based NDVI to Interaction of Vegetation and Influencing Factors,” Nongye Jixie Xuebao/Transactions Chinese Soc. Agric. Mach., vol. 49, no. 10, pp. 212–219, Oct. 2018, doi: 10.6041/j.issn.1000-1298.2018.10.024.
17. A. Ferro, “Advanced methods for the analysis of radar sounder and VHR SAR signals,” University of Trento, 2011.
18. E. Wali, M. Tasumi, and M. Moriyama, “Combination of linear regression lines to understand the response of sentinel-1 dual polarization SAR data with crop phenology-case study in Miyazaki, Japan,” Remote Sens., vol. 12, no. 1, 2020, doi: 10.3390/rs12010189.
19. M. A. Merchant, R. K. Warren, R. Edwards, and J. K. Kenyon, “An Object-Based Assessment of Multi-Wavelength SAR, Optical Imagery and Topographical Datasets for Operational Wetland Mapping in Boreal Yukon, Canada,” Can. J. Remote Sens., vol. 45, no. 3–4, pp. 308–332, Jul. 2019, doi: 10.1080/07038992.2019.1605500.
20. R. M. Haralick, I. Dinstein, and K. Shanmugam, “Textural Features for Image Classification,” IEEE Trans. Syst. Man Cybern., vol. SMC-3, no. 6, pp. 610–621, 1973, doi: 10.1109/TSMC.1973.4309314.
21. A. Darzi, I. Lang, A. Taklikar, H. Averbuch-Elor, and S. Avidan, “Co-occurrence Based Texture Synthesis,” May 2020, Accessed: May 25, 2020. [Online]. Available: http://arxiv.org/abs/2005.08186.
22. D. I. V. Danny Ibarra Vega, C. Peña Rincón, J. Valencia Calvo, J. M. Redondo, and G. Olivar Tost, “Water resources planning in bioethanol production from sugarcane,” Memorias, no. 1, pp. 30–35, Nov. 2018, doi: 10.22490/25904779.3055.