Resumen

A floresta tropical sazonalmente seca é um tipo de ecossistema que combina características de florestas tropicais e áreas sazonalmente secas, as terras áridas compreendem mais de 40% da superfície terrestre, abrangendo diversos biomas e são encontradas em várias partes do mundo, incluindo América do Sul, África, Ásia e Austrália. Essas áreas desempenham um papel crucial na regulação do ciclo do carbono na preservação da biodiversidade, adaptando-se às condições sazonais específicas de cada local. As estimativas dos fluxos carbono em florestas sazonalmente secas possibilitam uma compreensão mais aprofundada dos padrões de fluxo de superfície em áreas com diversas fisionomias vegetais. Existem diversos estudos sobre florestas tropicais sazonalmente secas, abordando diferentes metodologias, elas se alternam entre três principais métodos:  O primeiro método envolve amostragens diretas, segundo método emprega o uso de equações alométricas, por fim, o terceiro método utiliza técnicas de sensoriamento remoto. Diante dos métodos predominantes, este artigo busca conduzir uma revisão bibliográfica sobre a determinação do balanço de carbono no bioma Caatinga por meio do sensoriamento remoto. O objetivo é analisar artigos publicados nos últimos vinte e três anos que possam facilitar a avaliação remota das trocas de CO2 em diversas áreas de florestas sazonalmente secas.

 

Citas

• Barcza, Z.; Kern, A.; Davis, K. J.; Haszpra, L. Analysis of the 21-years long carbon dioxide flux dataset from a Central European tall tower site. Agricultural and Forest Meteorology, v. 290, p. 108027, 2020. Elsevier.
• Beuchle, R.; Grecchi, R. C.; Shimabukuro, Y. E.; et al. Land cover changes in the Brazilian Cerrado and Caatinga biomes from 1990 to 2010 based on a systematic remote sensing sampling approach. Applied Geography, v. 58, p. 116–127, 2015.
• Cerqueira, D. B. De; W, Franca-Rocha. Relação entre tipos de vegetação e fluxo de CO2 no Bioma Caatinga: Estudo de caso em Rio de Contas - Ba. Anais XIII Simpósio Brasileiro de Sensoriamento Remoto, p. 2413–2419, 2007.
• Christian, B.; Joshi, N.; Saini, M.; et al. Seasonal variations in phenology and productivity of a tropical dry deciduous forest from MODIS and Hyperion. Agricultural and Forest Meteorology, v. 214–215, p. 91–105, 2015.
• Coops, N. C.; Black, T. A.; Jassal, R. (Paul) S.; Trofymow, J. A. (Tony); Morgenstern, K. Comparison of MODIS, eddy covariance determined and physiologically modelled gross primary production (GPP) in a Douglas-fir forest stand. Remote Sensing of Environment, v. 107, n. 3, p. 385–401, 2007.
• Corona-Núñez, R. O.; Campo, J.; Williams, M. Aboveground carbon storage in tropical dry forest plots in Oaxaca, Mexico. Forest Ecology and Management, v. 409, p. 202–214, 2018.
• Costa, G. B.; Mendes, K. R.; Viana, L. B.; et al. Seasonal Ecosystem Productivity in a Seasonally Dry Tropical Forest (Caatinga) Using Flux Tower Measurements and Remote Sensing Data. Remote Sensing, v. 14, n. 16, p. 3955, 2022.
• Fernandes, G. S. T.; Machado, I. L. S. S.; Guedes, F. R. C. M.; Sousa, M. K. M.; Lima, E. DE A. Gross primary productivity by remote sensing in the Serra das Confusões National Park, Piauí, Brazil. Remote Sensing Applications: Society and Environment, v. 29, p. 100890, 2023.
• FERREIRA, R. R.; MUTTI, P.; MENDES, K. R.; et al. An assessment of the MOD17A2 gross primary production product in the Caatinga biome, Brazil. https://doi.org/10.1080/01431161.2020.1826063, v. 42, n. 4, p. 1275–1291, 2020.
• Flores-Rentería, D.; Delgado-Balbuena, J.; Campuzano, E. F.; Curiel Yuste, J. Seasonal controlling factors of CO2 exchange in a semiarid shrubland in the Chihuahuan Desert, Mexico. Science of The Total Environment, v. 858, p. 159918, 2023. Elsevier.
• Fu, Z.; Gerken, T.; Bromley, G.; et al. The surface-atmosphere exchange of carbon dioxide in tropical rainforests: Sensitivity to environmental drivers and flux measurement methodology. Agricultural and Forest Meteorology, v. 263, p. 292–307, 2018.
• Gallon, M. M. P.; Sanches, L.;Paulo, S. R. Fluxo E Perfil De Dióxido De Carbono No Dossel Uma Floresta Tropical De Transição Amazônica. Disponível em: <http://www.rbmet.org.br/port/revista/revista_artigo.php?id_artigo=237>. Acesso em: 17/6/2019.
• Gao, Y.; Yu, G.; Li, S.; et al. A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau. Ecological Modelling, v. 304, p. 34–43, 2015..
• Gomes, D. Da S.; Santos, S. K. Dos; Silva, J. H. C. S.; et al. CO2flux e temperatura da superfície edáfica em áreas de caatinga. Revista Brasileira de Geografia, v. 01, p. 758–769, 2023.
• Gomes, V. P.; Galvíncio, J. D.; Moura, M. S. B.; et al. Hyperspectral remote sensing applied for analysis of the resilience indicators and biome caatinga susceptibility to climate change. Revista Brasileira de Geografia Física, v. 9, n. 4, p. 1122–1136, 2016.
• Jesus, J. B. De; Kuplich, T. M.; Barreto, Í. D. De C.; Gama, D. C. Dual polarimetric decomposition in Sentinel-1 images to estimate aboveground biomass of arboreal caatinga. Remote Sensing Applications: Society and Environment, v. 29, p. 100897, 2023.
• Jia, X.; Mu, Y.; Zha, T.; et al. Seasonal and interannual variations in ecosystem respiration in relation to temperature, moisture, and productivity in a temperate semi-arid shrubland. Science of the Total Environment, v. 709, p. 136210, 2020.
• Jiang, Y.; Zhang, J.; Xu, X.; Dong, Z. A. GPP assimilation model for the southeastern Tibetan Plateau based on CO2 eddy covariance flux tower and remote sensing data. International Journal of Applied Earth Observation and Geoinformation, v. 23, p. 213–225, 2013.
• Leal De Oliveira, M. B.; Barbosa Santos, J.; Manzi, O. Trocas De Energia E Fluxo De Carbono Entre A Vegetação De Caatinga E Atmosfera No Nordeste Brasileiro. 2005.
• Liu, J. F.; Chen, S. P.; Han, X. G. Modeling gross primary production of two steppes in Northern China using MODIS time series and climate data. Procedia Environmental Sciences, v. 13, p. 742–754, 2012.
• Lloyd, J.; Taylor, J. A. On the Temperature Dependence of Soil Respiration. Functional Ecology, v. 8, n. 3, p. 315, 1994.
• Marshall, M.; Tu, K.; Brown, J. Optimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems. Remote Sensing of Environment, v. 217, p. 258–271, 2018.
• Maselli, F.; Cherubini, P.; Chiesi, M.; et al. Start of the dry season as a main determinant of inter-annual Mediterranean forest production variations. Agricultural and Forest Meteorology, v. 194, p. 197–206, 2014.
• Meacham-Hensold, K.; Montes, C. M.; Wu, J.; et al. High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity. Remote Sensing of Environment, v. 231, p. 111176, 2019.
• Mendes, K. R.; Campos, S.; Mutti, P. R.; et al. Assessment of SITE for CO2 and Energy Fluxes Simulations in a Seasonally Dry Tropical Forest (Caatinga Ecosystem). Forests 2021, Vol. 12, Page 86, v. 12, n. 1, p. 86, 2021.
• Monteith, J. L. Solar Radiation and Productivity in Tropical Ecosystems. The Journal of Applied Ecology, v. 9, n. 3, p. 747, 1972. Disponível em: <https://www.jstor.org/stable/2401901?origin=crossref>. Acesso em: 17/6/2019.
• Moura, M. S. B. De; Galvincio, J. D.; Brito, L. T. De L.; et al. Clima e água de chuva no Semi-Árido. Petrolina, 2007.
• Njoroge, B.; Li, Y.; Wei, S.; et al. An Interannual Comparative Study on Ecosystem Carbon Exchange Characteristics in the Dinghushan Biosphere Reserve, a Dominant Subtropical Evergreen Forest Ecosystem. Frontiers in Plant Science, v. 12, n. October, 2021.
• De Oliveira, M. L.; Dos Santos, C. A. C.; De Oliveira, G.; et al. Remote sensing-based assessment of land degradation and drought impacts over terrestrial ecosystems in Northeastern Brazil. Science of The Total Environment, v. 835, p. 155490, 2022.
• De Oliveira, M. L.; Dos Santos, C. A. C.; De Oliveira, G.; Perez-Marin, A. M.; Santos, C. A. G. Effects of human-induced land degradation on water and carbon fluxes in two different Brazilian dryland soil covers. Science of The Total Environment, v. 792, p. 148458, 2021.
• De Oliveira, M. L.; Dos Santos, C. A. C.; Santos, F. A. C.; et al. Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing. Forests, v. 14, n. 4, 2023a.
• De Oliveira, M. L.; Dos Santos, C. A. C.; Santos, F. A. C.; et al. Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing. Forests 2023, Vol. 14, Page 828, v. 14, n. 4, p. 828, 2023b.
• Park, H.; Im, J.; Kim, M. Improvement of satellite-based estimation of gross primary production through optimization of meteorological parameters and high resolution land cover information at regional scale over East Asia. Agricultural and Forest Meteorology, v. 271, p. 180–192, 2019.
• Sarkar, D P.; Shankar, B.; R. Parida, B. Machine learning approach to predict terrestrial gross primary productivity using topographical and remote sensing data. Ecological Informatics, v. 70, n. December 2021, p. 101697, 2022.
• .
• Ren, Y.; Zhang, F.; Kung, H.; et al. Using the vegetation-solar radiation (VSr) model to estimate the short-term gross primary production (GPP) of vegetation in Jinghe county, XinJiang, China. Ecological Engineering, v. 107, p. 208–215, 2017.
• Rezende, L. F. C.; Arenque-Musa, B. C.; Moura, M. S. B.; et al. Calibração da velocidade máxima de carboxilação (Vcmax), utilizando técnicas de mineração de dados e dados de ecofisiologia da região semiárida Brasileira, para uso em Modelos de Vegetação Globais Dinâmicos. Brazilian Journal of Biology, v. 76, n. 2, p. 341–351, 2016.
• Running, S. W.; Zhao, M. User’s Guide Daily GPP and Annual NPP (MOD17A2/A3) Products NASA Earth Observing System MODIS Land Algorithm. 2015.
• Sánchez, A. S.; Almeida, M. B.; Torres, E. A.; et al. Alternative biodiesel feedstock systems in the Semi-arid region of Brazil: Implications for ecosystem services. Renewable and Sustainable Energy Reviews, v. 81, p. 2744–2758, 2018.
• Santos, J. C.; Leal, I. R.; Almeida-Cortez, J. S.; Fernandes, G. W.; Tabarelli, M. Caatinga: The Scientific Negligence Experienced by a Dry Tropical Forest. Tropical Conservation Science, v. 4, n. 3, p. 276–286, 2011.
• Santos, S. D. A.; Correia, M. D. F.; Aragão, M. R. Da S.; Silva, P. K. De O. Aspectos da Variabilidade Sazonal da Radiação, Fluxos de Energia e CO2 em Área de Caatinga (Seasonal Variability Aspects of Radiation and Fluxes of Energy and CO2 in a Caatinga Area). Revista Brasileira de Geografia Física, v. 5, n. 4, p. 761, 2012.
• Schubert, P.; Lagergren, F.; Aurela, M.; et al. Modeling GPP in the Nordic forest landscape with MODIS time series data—Comparison with the MODIS GPP product. Remote Sensing of Environment, v. 126, p. 136–147, 2012.
• Schulz, K.; Guschal, M.; Kowarik, I.; et al. Grazing, forest density, and carbon storage: towards a more sustainable land use in Caatinga dry forests of Brazil. Regional Environmental Change, v. 18, n. 7, p. 1969–1981, 2018.
• Seixas, H. T.; Brunsell, N. A.; Moraes, E. C.; De Oliveira, G.; Mataveli, G. Exploring the ecosystem resilience concept with land surface model scenarios. Ecological Modelling, v. 464, p. 109817, 2022.
• Silva, B. B. Da; Galvíncio, J. D.; Montenegro, S. M. G. L.; et al. Determinação por sensoriamento remoto da produtividade primária bruta do perímetro irrigado São Gonçalo - PB. Revista Brasileira de Meteorologia, v. 28, n. 1, p. 57–64, 2013.
• Silva, N. B. J.; D. Galvíncio, J.; Queiroga M. R.; Moura, M. Modelos da Produtividade Primária Bruta em área de floresta tropical em sazonalmente seca, usando dados reflectância da vegetação de caatinga. Revista Brasileira de Geografia Física v, v. 14, p. 3775–3784, 2021.
• Silva, P. F. Da; Lima, J. R. De S.; Antonino, A. C. D.; et al. Seasonal patterns of carbon dioxide, water and energy fluxes over the Caatinga and grassland in the semi-arid region of Brazil. Journal of Arid Environments, v. 147, p. 71–82, 2017..
• Da Silveira, H. L. F.; Galvão, L. S.; Sanches, I. D. A.; De Sá, I. B.; Taura, T. A. Use of MSI/Sentinel-2 and airborne LiDAR data for mapping vegetation and studying the relationships with soil attributes in the Brazilian semi-arid region. International Journal of Applied Earth Observation and Geoinformation, v. 73, p. 179–190, 2018.
• .
• Souza, L.; Souza, L. S. B. De; Moura, M. S. B. De; Sediyama, G. C.; Silva, T. G. F. Da. Balanço de Radiação em Ecossistema de Caatinga Preservada Durante um Ano de Seca no Semiárido Pernambucano (Radiation Balance in Caatinga Ecosystem Preserved for a Year Drought in Semiarid Pernambucano). Revista Brasileira de Geografia Física, v. 8, n. 1, p. 041–055, 2015.
• Sun, J.; Zhou, T. C.; Liu, M.; Et Al. Water and heat availability are drivers of the aboveground plant carbon accumulation rate in alpine grasslands on the Tibetan Plateau. Global Ecology and Biogeography, v. 29, n. 1, p. 50–64, 2020.
• Sun, Z.; Wang, X.; Yamamoto, H.; et al. Spatial pattern of GPP variations in terrestrial ecosystems and its drivers: Climatic factors, CO2 concentration and land-cover change, 1982–2015. Ecological Informatics, v. 46, p. 156–165, 2018. Elsevier.
• Sun, Z.; Wang, X.; Zhang, X.; et al. Evaluating and comparing remote sensing terrestrial GPP models for their response to climate variability and CO2 trends. Science of The Total Environment, v. 668, p. 696–713, 2019. 9.
• Tabarelli, M.; Leal, I. R.; Scarano, F. R.; Silva, J. M. C. Da. Caatinga: legado, trajetória e desafios rumo à sustentabilidade. Ciência e Cultura, v. 70, n. 4, p. 25–29, 2018.
• Tong, X.; Mu, Y.; Zhang, J.; Meng, P.; Li, J. Water stress controls on carbon flux and water use efficiency in a warm-temperate mixed plantation. Journal of Hydrology, v. 571, p. 669–678, 2019.
• Vieira, L. A. F.; Tabarelli, M.; Souza, G.; Queiroz, R. T.; Santos, B. A. Divergent herb communities in drier and chronically disturbed areas of the Brazilian Caatinga. Perspectives in Ecology and Conservation, 2022.
• Vilas, C.; Macedo, G.; Baptista, D. M.; et al. Journal of Environmental do Bioma Caatinga Validation of a spectral model for CO 2 fluxes estimation in areas of the Caatinga Biome. Journal of Environmental Analysis and Progress, v. 08, n. 03, p. 226–239, 2023.
• Wang, H.; Shao, W.; Hu, Y.; Cao, W.; Zhang, Y. Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland. Remote Sensing 2023, Vol. 15, Page 3475, v. 15, n. 14, p. 3475, 2023..
• Wang, J.; Wu, C.; Zhang, C.; et al. Improved modeling of gross primary productivity (GPP) by better representation of plant phenological indicators from remote sensing using a process model. Ecological Indicators, v. 88, p. 332–340, 2018.
• Yang, Y.; Shang, S.; Guan, H.; Jiang, L. A novel algorithm to assess gross primary production for terrestrial ecosystems from MODIS imagery), A novel algorithm to assess gross primary production forterrestrial ecosystems from MODIS imagery. J. Geophys. Res. Biogeosci, v. 118, p. 590–605, 2013.
• Yu, T.; Zhang, Q.; Sun, R. Comparison of Machine Learning Methods to Up-Scale Gross Primary Production. Remote Sensing 2021, Vol. 13, Page 2448, v. 13, n. 13, p. 2448, 2021.
• Zhang, F.; Lu, X.; Huang, Q.; Jiang, F. Impact of different ERA reanalysis data on GPP simulation. Ecological Informatics, v. 68, p. 101520, 2022.
• Zhang, Q.; Cheng, Y.-B.; Lyapustin, A. I.; et al. Estimation of crop gross primary production (GPP): I. impact of MODIS observation footprint and impact of vegetation BRDF characteristics. Agricultural and Forest Meteorology, v. 191, p. 51–63, 2014.
• Zheng, C.; Tang, X.; Gu, Q.; et al. Climatic anomaly and its impact on vegetation phenology, carbon sequestration and water-use efficiency at a humid temperate forest. Journal of Hydrology, v. 565, p. 150–159, 2018.
• Zhu, W.; Zhao, C.; Xie, Z. An end-to-end satellite-based GPP estimation model devoid of meteorological and land cover data. Agricultural and Forest Meteorology, v. 331, n. 19, p. 109337, 2023.

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