Uso del clasificador Random Forest para clasificar el uso y la cobertura del suelo utilizando datos Sentinel 1 y 2 en una región rural del bioma del Bosque Atlántico.
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https://doi.org/10.5902/2236499487967Palabras clave:
Random Forest, Uso y Cobertura del Suelo, Classificación de Imágenes, Radar, ÓpticoResumen
El uso de mapas de uso y cobertura del suelo es fundamental para el monitoreo ambiental, lo que requiere el uso de técnicas de teledetección. Con esto en mente, el presente trabajo tuvo como objetivo utilizar los atributos de Coeficiente de Retrodispersión, Descomposición Polarimétrica y Coherencia Interferométrica del sensor Sentinel 1 y las bandas R, G, B, NIR y los índices de vegetación NDVI y SAVI del sensor Sentinel 2 para identificar el mejor combinación de variables de entrada del algoritmo de clasificación Random Forest (RF) utilizando precisión, en un área de los “Campos de Cima da Serra”, perteneciente al bioma de la Mata Atlántica. El trabajo identificó que el uso de los tres atributos de Sentinel 1 en conjunto con las bandas ópticas de Sentinel 2 tuvo mejor precisión (93%), aunque el uso de solo las bandas ópticas obtuvo un 89% de precisión. Sin embargo, al utilizar solo atributos SAR obtuvo la precisión más baja (67%). El desarrollo de esta metodología servirá de base para la continuidad de esta investigación, utilizando técnicas más robustas como el análisis de series de tiempo vía SITS (Satellite Image Time Series Analysis), con la generación de resultados para el monitoreo del bosque atlántico en la región sur. del país y subsidio para pruebas de monitoreo del bioma pampa, debido a su alta capacidad de análisis de series de tiempo desde diferentes plataformas en un paquete de código abierto.
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ADRIAN, J.; SAGAN, V.; MAIMAITIJIANG, M. Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, [S. l.], v. 175, p. 215–235, mai. 2021. DOI: 10.1016/j.isprsjprs.2021.02.018. DOI: https://doi.org/10.1016/j.isprsjprs.2021.02.018
ALIN, A. Multicollinearity. WIREs Interdisciplinary Reviews: Computational Statistics, [S. l.], v. 2, n. 3, p. 370–374, 25 mai. 2010. DOI: https://doi.org/10.1002/wics.84
ALJEBREEN, M et al. Land Use and Land Cover Classification Using River Formation Dynamics Algorithm With Deep Learning on Remote Sensing Images. IEEE Access, [S. l.], v. 12, n. 99, p. 11147-11156, 2024. DOI: 10.1109/ACCESS.2023.3349285. DOI: https://doi.org/10.1109/ACCESS.2023.3349285
AL-RUZOUQ, R. et al. Multi-scale correlation-based feature selection and random forest classification for LULC mapping from the integration of SAR and optical Sentinel images. In: Remote Sensing Technologies and Applications in Urban Environments, 4., 2019, Strasbourg. Proceedings... Strasbourg: SPIE, 2019. p. 1. DOI: 10.1117/12.2533123. DOI: https://doi.org/10.1117/12.2533123
BELGIU, M.; DRĂGUŢ, L. Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, [S. l.], v. 114, p. 24–31, abr. 2016. DOI: 10.1016/j.isprsjprs.2016.01.011. DOI: https://doi.org/10.1016/j.isprsjprs.2016.01.011
BOLDRINI, I. L. D. Biodiversidade Dos Campos Do Planalto Das Araucárias. Brasília: Ministério do Meio Ambiente, 2009.
BREIMAN, L. Random Forests. Machine Learning, [S. l.], v. 45, p. 5–32, 2001. DOI: 10.1023/A:1010933404324. DOI: https://doi.org/10.1023/A:1010933404324
BRESSANE, A.; GOMES, I. G.; DA ROSA, G. C. S.; BRANDELIK, C. C. M.; SILVA, M. B.; SIMINSKI, A.; NEGRI, R. G. Computer-aided classification of successional stage in subtropical Atlantic Forest: a proposal based on fuzzy artificial intelligence. Environ Monit Assess, [S. l.], v. 195, n. 1, p. 184, 2023. DOI: 10.1007/s10661-022-10799-x. DOI: https://doi.org/10.1007/s10661-022-10799-x
CHACHONDHIA, P.; SHAKYA, A.; KUMAR, G. Performance evaluation of machine learning algorithms using optical and microwave data for LULC classification. Remote Sensing Applications: Society and Environment, [S. l.], v. 23, p. 100599, ago. 2021. DOI: 10.1016/j.rsase.2021.100599. DOI: https://doi.org/10.1016/j.rsase.2021.100599
CHAVES, M. E. D.; SOARES, A. R.; MATAVELI, G. A. V; SANCHEZ, A. H.; SANCHES, I. D. A Semi-Automated Workflow for LULC Mapping via Sentinel-2 Data Cubes and Spectral Indices. Automation, [S. l.], v. 4, n. 1, p. 94–109, 23 fev. 2023. DOI: 10.3390/automation4010007. DOI: https://doi.org/10.3390/automation4010007
CLERICI, N.; VALBUENA CALDERÓN, C. A.; POSADA, J. M. Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia. Journal of Maps, [S. l.], v. 13, n. 2, p. 718–726, 30 nov. 2017. DOI 10.1080/17445647.2017.1372316. DOI: https://doi.org/10.1080/17445647.2017.1372316
CONGALTON, R. G. Assessing the accuracy of Remotely Sensed data: principles and practices. Boca Raton: Lewis, 1999. p. 34-46. DOI: https://doi.org/10.1201/9781420048568
CONGALTON, R. G.; MEAD, R. A. A quantitative method to test for consistency and correctness in photointerpretation. Photogrammetric Engineering and Remote Sensing, [S. l.], v. 49, n. 1, p. 69-74, 1983.
DINIZ, J. M. F. DE S.; GAMA, F. F.; ADAMI, M. Evaluation of polarimetry and interferometry of sentinel-1A SAR data for land use and land cover of the Brazilian Amazon Region. Geocarto International, [S. l.], v. 37, n. 5, p. 1482–1500, 4 mar. 2022. DOI: 10.1080/10106049.2020.1773544. DOI: https://doi.org/10.1080/10106049.2020.1773544
FENG, Q.; YANG, J.; ZHU, D.; LIU, J.; GUO, H.; BAYARTUNGALAG, B.; LI, B. Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta. Remote Sensing, [S. l.], v. 11, n. 9, p. 1006, 28 abr. 2019. DOI: 10.3390/rs11091006. DOI: https://doi.org/10.3390/rs11091006
GU, Z.; ZENG, M. The use of artificial intelligence and satellite remote sensing in land cover change detection: review and perspectives. Sustainability, [S. l.], v. 16, n. 1, p. 274, 2023. DOI: 10.3390/su16010274. DOI: https://doi.org/10.3390/su16010274
GUPTA, P.; SHUKLA, D. P. Combined Optical and SAR remote sensing for LULC mapping of Imphal valley using Machine Learning Algorithm. In: 2023 INTERNATIONAL CONFERENCE ON MACHINE INTELLIGENCE FOR GEOANALYTICS AND REMOTE SENSING (MIGARS), 2023, [S. l.]. Proceedings... [S. l.]: IEEE, 27 jan. 2023. DOI: 10.1109/MIGARS57353.2023.10064582. DOI: https://doi.org/10.1109/MIGARS57353.2023.10064582
ESA. Copernicus Open Access Hub. 2024. Disponível em: https://scihub.copernicus.eu/. Acesso em: 29 nov. 2024.
HUDSON, W. D. Correct Formulation of the Kappa Coefficient of Agreement. Photogrammetric Engineering & Remote Sensing, [S. l.], v. 53, n. 4, p. 421-422, 1987.
HUETE, A. R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, [S. l.], v. 25, n. 3, p. 295-309, 1988. DOI: 10.1016/0034-4257(88)90106-X. DOI: https://doi.org/10.1016/0034-4257(88)90106-X
KHALIL, R. Z.; SAAD-UL-HAQUE. InSAR coherence-based land cover classification of Okara, Pakistan. The Egyptian Journal of Remote Sensing and Space Science, [S. l.], v. 21, n. supl. 1, p. S23–S28, jul. 2018. DOI 10.1016/j.ejrs.2017.08.005. DOI: https://doi.org/10.1016/j.ejrs.2017.08.005
LIMA, D. R. M.; DLUGOSZ, F. L.; IURK, M. C.; PESCK, V. A. Uso de NDVI e SAVI para Caracterização da Cobertura da Terra e Análise Temporal em Imagens RapidEye. Revista Espacios, online, v. 38, n. 36, p. 7-21, 2017.
MIRANDA, M. S.; JÚNIOR V. A. S.; KÖRTING T. S.; MONTEIRO E. C. S.; E SILVA J. Q. AI4LUC: deep learning and automated mask labelling to support land use and land cover mapping in the Cerrado biome, Remote Sensing Letters, [S. l.], v. 15, n 8, p. 850–860, ago. 2024. DOI: 10.1080/2150704X.2024.2382845. DOI: https://doi.org/10.1080/2150704X.2024.2382845
MISHRA, D.; PATHAK, G.; SINGH, B. P.; MOHIT; SIHAG, P.; RAJEEV; SINGH, K.; SINGH, S. Crop classification by using dual-pol SAR vegetation indices derived from Sentinel-1 SAR-C data. Environmental Monitoring and Assessment, [S. l.], v. 195, n. 1, p. 115, 2023. DOI: 10.1007/s10661-022-10591-x. DOI: https://doi.org/10.1007/s10661-022-10591-x
MONSALVE-TELLEZ, J. M.; TORRES-LEÓN, J. L.; GARCÉS-GÓMEZ, Y. A. Evaluation of SAR and Optical Image Fusion Methods in Oil Palm Crop Cover Classification Using the Random Forest Algorithm. Agriculture, [S. l.], v. 12, n. 7, p. 955, 1 jul. 2022. DOI: 10.3390/agriculture12070955. DOI: https://doi.org/10.3390/agriculture12070955
NAIK, N.; CHANDRASEKARAN, K.; MEENAKSHI SUNDARAM, V.; PANNEER, P. Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India. Environ Monit Assess, [S. l.], v. 196, n. 6, p. 1-34, 2024. DOI: 10.1007/s10661-024-12598-y. DOI: https://doi.org/10.1007/s10661-024-12598-y
PARADELLA, W. R.; MURA, J. C.; GAMA, F. F. Monitoramento DInSAR para mineração e geotecnia. 1. ed. São Paulo: Oficina de Textos, 2021.
PEDREGOSA et al. Scikit-learn: Machine Learning in Python. Journal of machine learning research, [S. l.], v. 12, n. 85, p. 2825-2830, 2011.
RACZKO, E.; ZAGAJEWSKI, B. Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images. European Journal of Remote Sensing, [S. l.], v. 50, n. 1, p. 144–154, 9 jan. 2017. DOI: 10.1080/22797254.2017.1299557. DOI: https://doi.org/10.1080/22797254.2017.1299557
RENNÓ, C. D.; SILVA, T. S. F. Planilha: Índice Kappa. 2023. Disponível em: https://www.dpi.inpe.br/~camilo/estatistica/xls/kappa10cond.xlsx. Acesso em: 24 outubro 2023.
ROUSE JR, J. W.; HASS, R. H.; SCHELL, J. A.; DEERING, D. W. Monitoring vegetation systems in the Great Plains with ERTS. In: PROCEEDINGS OF THE THIRD EARTH RESOURCES TECHNOLOGY SATELLITE-1 SYMPOSIUM, 3., 1973, Washington D.C. Anais... Washington D.C.: NASA. p. 309.
SOLÓRZANO, J. V.; MAS, J. F.; GALLARDO-CRUZ, J. A.; GAO, Y.; DE OCA, A. F. M. Deforestation detection using a spatio-temporal deep learning approach with synthetic aperture radar and multispectral images. ISPRS Journal of Photogrammetry and Remote Sensing, [S. l.], v. 199, p. 87-101, 2023. DOI: 10.1016/j.isprsjprs.2023.03.017. DOI: https://doi.org/10.1016/j.isprsjprs.2023.03.017
SOUTHWORTH, J.; SMITH, A. C.; SAFAEI, M.; RAHAMAN, M.; ALRUZUQ, A.; TEFERA, B. B.; ... & HERRERO, H. V. Machine learning versus deep learning in land system science: a decision-making framework for effective land classification. Frontiers in Remote Sensing, [S. l.], v. 5, p. 1-25, 2024. DOI: 10.3389/frsen.2024.1374862. DOI: https://doi.org/10.3389/frsen.2024.1374862
SOUZA, C.; AZEVEDO, T. MapBiomas general “Handbook”. São Paulo: MapBiomas, 2017.
SHEYKHMOUSA, M.; MAHDIANPARI, M.; GHANBARI, H.; MOHAMMADIMANESH, F.; GHAMISI, P.; HOMAYOUNI, S. Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, [S. l.], v. 13, p. 6308–6325, 2020. DOI: 10.1109/JSTARS.2020.3026724. DOI: https://doi.org/10.1109/JSTARS.2020.3026724
SCHULZ, D.; YIN, H.; TISCHBEIN, B.; VERLEYSDONK, S.; ADAMOU, R.; KUMAR, N. Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel. ISPRS Journal of Photogrammetry and Remote Sensing, [S. l.], v. 178, p. 97–111, ago. 2021. DOI 10.1016/j.isprsjprs.2021.06.005. DOI: https://doi.org/10.1016/j.isprsjprs.2021.06.005
TALUKDAR, S.; SINGHA, P.; MAHATO, S.; PAL, S.; LIOU, Y. A.; RAHMAN, A. Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations— A Review. Remote Sensing, [S. l.], v. 12, n. 7, p. 1135, 2 abr. 2020. DOI: 10.3390/rs12071135. DOI: https://doi.org/10.3390/rs12071135
TAVARES, P.; BELTRÃO, N. E. S.; GUIMARÃES, U. S.; TEODORO, A. C. Integration of Sentinel-1 and Sentinel-2 for Classification and LULC Mapping in the Urban Area of Belém, Eastern Brazilian Amazon. Sensors, [S. l.], v. 19, n. 5, p. 1140, 6 mar. 2019. DOI: 10.3390/s19051140. DOI: https://doi.org/10.3390/s19051140
TRINDADE, P. M. P.; PEIXOTO, D. W. B.; SILVEIRA, G. V.; KUPLICH, T. M.; NARVAES, I. S. Mapeamento de cobertura e uso da terra no bioma Pampa utilizando diferentes sensores orbitais e classificador Random Forest. GEOgrafias, [S. l.], v. 19, n. 2, jul./dez. 2023. DOI: 10.35699/2237-549X.2023.46915. DOI: https://doi.org/10.35699/2237-549X.2023.46915
WANG, W.; GADE, M.; STELZER, K.; KOHLUS, J.; ZHAO, X.; FU, K. A Classification Scheme for Sediments and Habitats on Exposed Intertidal Flats with Multi-Frequency Polarimetric SAR. Remote Sensing, [S. l.], v. 13, n. 3, p. 360, 2021. DOI: 10.3390/rs13030360. DOI: https://doi.org/10.3390/rs13030360
WANG, J.; BRETZ, M.; DEWAN, M. A. A.; & DELAVAR, M. A. Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects. Science of The Total Environment, v. 822, p. 153559, 2022. DOI: 10.1016/j.scitotenv.2022.153559. DOI: https://doi.org/10.1016/j.scitotenv.2022.153559
ZUHLKE, M.; FOMFERRA, N., BROCKMANN, C., PETERS, M., VECI, L., MALIK, J., & REGNER, P. SNAP (sentinel application platform) and the ESA sentinel 3 toolbox. In: SENTINEL-3 FOR SCIENCE WORKSHOP, 3., 2015, Venice. Proceedings... Venice: ESA, 2015. p. 21.
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