Estimativa de biomassa e carbono a partir de técnicas de sensoriamento remoto em área sob influência de empreendimentos termelétricos e mineração
DOI:
https://doi.org/10.26848/rbgf.v17.2.p1362-1374Keywords:
remote sensing, supervised classification, carbon, miningAbstract
The use of techniques using remote sensing images and data to obtain vegetation indices is already widespread for applications in plant ecosystems, which helps in decision-making. Similarly, the use of land use cover classifiers is also becoming popular in environmental data analysis research, as it provides spatial identification of different types of land cover, including urban infrastructure and mining, which can later be related to gas emissions into the atmosphere. The aim of this work was to analyze the land cover patterns obtained using NDVI, and to perform supervised classification in areas impacted by thermal power plants and mining, relating them to greenhouse gas emissions using remote sensing data. The study analyzed the application of the Random Forest supervised classification algorithm, which showed excellent statistical results, with a Kappa index of 0.83. The mapping of land use classes obtained allowed an assessment to be made in terms of occupation and use in the study area, data which was then compared with the behavior of CO2Flux, as well as with methane emissions estimated by the TROPOMI. The results revealed a relationship between the distribution of biomass and carbon and the distribution of agricultural and energy activities, these being the two areas with the highest methane values. On the other hand, CO2Flux values were higher in areas classified as forest and grassland vegetation.
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References
Achanta, R.; Süsstrunk, S. 2017. Superpixels and Polygons using Simple Non-Iterative Clustering. 2017 IEEE Conference on Computer Vision and Pattern Recognition [online]. Disponível: doi: 10.1109/CVPR.2017.520. Acesso: 23 out. 2023.
Alvares, C. A. et al. 2013. Köppen's climate classification map for Brazil. Meteorologische Zeitschrift [online] 22. Disponível: DOI: 10.1127/0941-2948/2013/0507. Acesso: 23 out. 2023.
Apituley, A. et al. 2021. Sentinel-5 precursor/TROPOMI Level 2 Product User Manual Methane. Royal Netherlands Meteorological Institute. Disponível: https://sentinel.esa.int/documents/247904/2474726/Sentinel-5P-Level-2-Product-User-Manual-Methane.pdf/1808f165-0486-4840-ac1d-06194238fa96. Acesso: 23 out. 2023.
Banko, G. 1998. A review of assessing the accuracy of classification of remotely sensed data and methods including remote sensing data in forest inventory. In International Institute for Applied Systems Analysis. Disponível: https://core.ac.uk/reader/33897040. Acesso: 23 out. 2023.
Baptista, G. M. M. 2003. Validação da modelagem de sequestro de carbono para ambientes tropicais de cerrado, por meio de dados AVIRIS e HYPERION. In: XI SBSR, Belo Horizonte. Anais [...]. São José dos Campos: INPE, 2003. p. 1037-1044. Disponível: http://marte.sid.inpe.br/col/ltid.inpe.br/sbsr/2002/09.07.21.45/doc/10_002.pdf. Acesso: 23 out. 2023.
Beamish, A. et al. 2020. Recent trends and remaining challenges for optical remote sensing of Arctic tundra vegetation: A review and outlook. Remote Sensing of Environment [online] 246. Disponível: https://doi.org/10.1016/j.rse.2020.111872. Acesso: 25 out. 2023.
Bizon, A. R. et al. 2020. Integration Statistical Systems for Land Cover Mapping in Southern Brazil. Geospatial Technologies and Geographic Information Science for Crisis Management (GIS), Blacksburg, VA, USA, mai. 2020.
Canavesi, V.; Ponzoni, F. J.; Valeriano, M. M. 2010. Estimativa de volume de madeira em plantios de Eucalyptus spp. utilizando dados hiperespectrais e dados topográficos. Revista Árvore [online] 34. Disponível: https://doi.org/10.1590/S0100-67622010000300018. Acesso: 01 nov. 2023.
Carvalho, W. S.; Filho, F. J. C. M.; Santos, T. L. 2021. Uso e cobertura do solo utilizando a Plataforma Google Earth Engine (GEE): Estudo de caso em uma Unidade de Conservação. Brazilian Journal of Development [online] 7. Disponível: DOI:10.34117/bjdv7n2-243. Acesso: 23 out. 2023.
Currihuinca, L. E. S.; Chaves, J. M.; Rocha, W. J. S. F.; Lobão, J. S. B.; Falcão, P. M. 2021. Identificação das Dunas do Atacama (Norte do Chile) a partir da avaliação de três algoritmos no Google Earth Engine. Revista Brasileira de Geografia Física [online] 14. Disponível: https://doi.org/10.26848/rbgf.v14.6.p3294-3315. Acesso: 25 out. 2023.
Gamon, J. A.; Penuelas, J.; Field, C. B. 1992. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment [online] 41. Disponível: https://doi.org/10.1016/0034-4257(92)90059-S. Acesso: 23 out. 2023.
Gong, S.; Shi, Y. 2021. Evaluation of comprehensive monthly-gridded methane emissions from natural and anthropogenic sources in China. Science of The Total Environment [online] 784. Disponível: https://doi.org/10.1016/j.scitotenv.2021.147116. Acesso: 23 out. 2023.
Hamud, A. M.; Shafri, H. Z. M.; Shaharum, N. S. N. 2021. Monitoring Urban Expansion and Land Use/Land Cover Changes In Banadir, Somalia Using Google Earth Engine (GEE). IOP Conf. Series: Earth and Environmental Science {online] 767. Disponível: DOI 10.1088/1755-1315/767/1/012041. Acesso: 23 out. 2023.
Huang, S.; Tang, L.; Hupy, J. P.; Wang, Y.; Shao, G. 2020. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research [online] 32. Disponível: https://doi.org/10.1007/s11676-020-01155-1. Acesso: 24 out. 2023.
IBGE – Instituto Brasileiro de Geografia e Estatística. MANUAL TÉCNICO DA VEGETAÇÃO BRASILEIRA 2012.
Köppen, W., 1936: Das geographische System der Klimate. KÖPPEN, W., R. GEIGER (Eds.): Handbuch der Klimatologie. Gebrüder Bornträger, Berlin, 1, 1–44, part C.
Kozicka, K.; Gozdowski, D.; Wójcik-Gront, E. 2021. Spatial-Temporal Changes of Methane Content in the Atmosphere for Selected Countries and Regions with High Methane Emission from Rice Cultivation. Atmosphere-Basel [online] 12. Disponível: https://doi.org/10.3390/atmos12111382. Acesso: 23 out. 2023.
Landis, J. R.; Koch, G. G. 1977. The measurement of observer agreement for categorical data. Biometrics [online] 33. Disponível: https://doi.org/10.2307/2529310. Acesso: 23 out. 2023.
Lima, J.; Lage-Pinto, F.; Bernini, E. 2023. Spatial–temporal distribution of mangrove species in the estuary of the Mamanguape river in the state of Paraíba, Brazil. Regional Studies in Marine Science [online] 66. Disponível: https://doi.org/10.1016/j.rsma.2023.103166. Acesso: 25 out. 2023.
Liu, Z.; Yang, J.; Huang, X. 2023. Landsat-derived impervious surface area expansion in the Arctic from 1985 to 2021. Science of The Total Environment [online]. Disponível: https://doi.org/10.1016/j.scitotenv.2023.166966. Acesso: 25 out. 2023.
Lou, P. et al. 2020. An Optimized Object-Based Random Forest Algorithm for Marsh Vegetation Mapping Using High-Spatial-Resolution GF-1 and ZY-3 Data. Remote Sensing [online] 12. Disponível: https://doi.org/10.3390/rs12081270. Acesso: 30 out. 2023.
Ni, Q.; Zhou, M.; Wang, J.; Wang, T.; Wang, G.; Wang, P. 2023. Intercomparison of CH4 Products in China from GOSAT, TROPOMI, IASI, and AIRS Satellites. Remote Sensing [online] 15. Disponivel: https://doi.org/10.3390/rs15184499. Acesso: 05 mar. 2024.
Peuker, K.; Fagundes, L. Emissões Fugitivas da Mineração e do Tratamento de Carvão Mineral. In: Primeiro Inventário Brasileiro de Emissões Antrópicas de Gases de Efeito Estufa, 2006.
RIO GRANDE DO SUL. Carta-compromisso com a agenda mundial para a descarbonização. Porto Alegre, 2021.
Ryu, J.; Oh, D.; Cho, J. 2021. Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor. Journal of Integrative Agriculture [online] 20. Disponível: https://doi.org/10.1016/S2095-3119(20)63410-4. Acesso: 24 out. 2023.
Santos, A. S. R. M. et al. 2019. Métodos de Classificação Supervisionada Aplicados no Uso e Ocupação do Solo no Município de Presidente Médice – RO. Biodiversidade [online] 18. Disponível: https://periodicoscientificos.ufmt.br/ojs/index.php/biodiversidade/article/view/8242. Acesso: 23 out. 2023.
Saunois, M. et al. 2020. The Global Methane Budget 2000–2017. Earth System Science Data [online] 12. Disponível: https://doi.org/10.5194/essd-12-1561-2020. Acesso: 23 out. 2023.
SEEG - Sistema de Estimativa de Emissões de Gases de Efeito Estufa. Base de dados: Emissões Totais, 2022.
Singh, R. P. et al. 2016. Normalized Difference Vegetation Index (NDVI) Based Classification to Assess the Change in Land Use/Land Cover (LULC) in Lower Assam, India. International Journal of Advanced Remote Sensing and GIS [online] 5. Disponível: https://doi.org/10.23953/cloud.ijarsg.74. Acesso: 23 out. 2023.
Trenchev, P.; Dimitrova, M.; Avetisyan, D. Huge. 2023. CH4, NO2 and CO Emissions from Coal Mines in the Kuznetsk Basin (Russia) Detected by Sentinel-5P. Remote Sensing [online] 15. Disponível: https://doi.org/10.3390/rs15061590. Acesso: 05 mar. 2024.
Wang, J.; Sun, C.; Wang, G.; Zou, M.; Tan, T.; Liu, K.; Chen, W.; Gao, X. 2020. A fibered near-infrared laser heterodyne radiometer for simultaneous remote sensing of atmospheric CO2 and CH4. Optics and Lasers in Engineering [online] 129. Disponível: https://doi.org/10.1016/j.optlaseng.2020.106083. Acesso: 23 out. 2023.
Wang, X. et al. 2019. Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images. Remote Sensing [online] 11. Disponível: https://doi.org/10.3390/rs11161927. Acesso: 25 out. 2023.
Weber, E. J. et al. Uso e Cobertura Vegetal do Estado do Rio Grande do Sul – Situação em 2015. 1ª Edição, Porto Alegre - UFRGS IB
Centro de Ecologia, 2018.
Wu, X.; Zhang, X.; Chuai, X.; Huang, X.; Wang, Z. 2019. Long-Term Trends of Atmospheric CH4 Concentration across China from 2002 to 2016. Remote Sensing [online] 11. Disponível: https://doi.org/10.3390/rs11050538. Acesso: 23 out. 2023.
Wuebbles, D. J.; Hayhoe, K. 2001. Atmospheric methane and global change. Earth-Science Reviews [online] 57. Disponível: https://doi.org/10.1016/S0012-8252(01)00062-9. Acesso: 23 out. 2023.
Zhang, F.; Yang, X. 2020. Improving land cover classification in an urbanized coastal area by random forests: The role of variable selection. Remote Sensing of Environment [online] 251. Disponível: https://doi.org/10.1016/j.rse.2020.112105. Acesso: 25 out. 2023.
Zhang, J.; Han, G.; Mao, H.; Pei, Z.; Ma, X.; Jia, W.; Gong, W. 2022. The Spatial and Temporal Distribution Patterns of XCH4 in China: New Observations from TROPOMI. Atmosphere [online] 13. Disponível: https://doi.org/10.3390/atmos13020177. Acesso: 23 out. 2023.
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