DATA FUSION STRATEGY FOR MAPPING ENVIRONMENT AND CLIMATE VARIABLES OF BRAZIL

Authors

Keywords:

Brazil, Environment, Geography, Cartography, Machine Learning, Sustainable Development

Abstract

The study aims at utilizes the machine learning methods in cartography with a case study of climate and environmental mapping of Brazil. Rapid advances in machine learning applied to Earth observations have resulted in the application of scripting and programming languages fir cartographic visualization and modelling. This research applies the GMT scripting toolset for advanced environmental mapping of Brazil. The data incldues TerraClimate dataset of 2020. The GMT is an advanced cartographic tools that operates mapping from the console using scripts. Selected snippets of used codes are presented in the research for technical explanation of the workflow. The results show correlation among the parameters and demonstrate climate and environmental trends notable for different biomes of Brazil: Amazônia, Caatinga, Cerrado, Pampa and Pantanal. The study presents 10 new maps made using GMT. Based on the obtained results, the increase of precipitation is notable in the Amazônia, along with the highest temperatures in the northern Brazil (Negro river basin) which corresponds to the increase in soil moisture and runoff. The evapotranspiration is generally higher in the southern regions of than those in the north. On the contrast, the Caatinga region shows the minimal values of evapotranspiration, soil moisture and runoff. The main advantage of scripting cartography, demonstrated in this research, consists in automated data processing which pushes environmental studies towards being data-driven. Automated mapping technically facilitates workflow due to fast and smooth handling of various formats and types of data. The results contribute to the environmental analysis of climate in Brazil that has applications in agricultural and food studies and shows technical use of GMT.

Downloads

Download data is not yet available.

References

References

XIMENES, A.C.; AMARAL, S., VIEIRA MONTEIRO, A.M.; ALMEIDA, R.M.; VALERIANO, D.M. Mapping the terrestrial ecoregions of the Purus-Madeira interfluve in the Amazon Forest using machine learning techniques. Forest Ecology and Management, Vol. 488, 118960, 2021. https://doi.org/10.1016/j.foreco.2021.118960

CHAVES, M.E.D.; DE C. ALVES, M.; SÁFADI, T.; DE OLIVEIRA, M.S.; PICOLI, M.C.A.; SIMOES, R.E.O.; MATAVELI, G.A.V. Time-weighted dynamic time warping analysis for mapping interannual cropping practices changes in large-scale agro-industrial farms in Brazilian Cerrado, Science of Remote Sensing, Vol. 3, 100021, 2021. https://doi.org/10.1016/j.srs.2021.100021

SUETOVA, I.; USHAKOVA, L.; LEMENKOVA, P. Geoinformation mapping of the Barents and Pechora Seas, Geography and Natural Resources, Vol. 4, p. 138–142, 2005. https://doi.org/10.6084/m9.figshare.7435535

KLAUČO, M.; GREGOROVÁ, B.; STANKOV, U.; MARKOVIĆ, V.; LEMENKOVA, P. Determination of ecological significance based on geostatistical assessment: a case study from the Slovak Natura 2000 protected area, Central European Journal of Geosciences, Vol. 5, n. 1, p. 28–42, 2013. https://doi.org/10.2478/s13533-012-0120-0

LEMENKOVA, P.; SAGA GIS for Computing Multispectral Vegetation Indices by Landsat TM for Mapping Vegetation Greenness, Contemporary Agriculture, Vol. 70, n. 1–2, p. 67–75, 2021. https://doi.org/10.2478/contagri-2021-0011

ARVOR, D.; DUBREUIL, V.; RONCHAIL, J.; SIMÕES, M.; FUNATSU, B.M. Spatial patterns of rainfall regimes related to levels of double cropping agriculture systems in Mato Grosso (Brazil). International Journal of Climatology, 34, 2622–2633, 2014. https://doi.org/10.1002/joc.3863

FONSECA, L.M.G.; KÖRTING, T.S.; DO N. BENDINI, H.; GIROLAMO-NETO, C.D.; NEVES, A.K.; SOARES, A.R.; TAQUARY, E.C.; MARETTO, R.V. Pattern Recognition and Remote Sensing techniques applied to Land Use and Land Cover mapping in the Brazilian Savannah, Pattern Recognition Letters, Vol. 148, p. 54-60, 2021. https://doi.org/10.1016/j.patrec.2021.04.028

DE LIMA, C.E.S.; DE OLIVEIRA COSTA, V.S.; DOMICIANO GALVÍNCIO, J.; DA SILVA, R.M.; GUIMARÃES SANTOS, C.A. Assessment of automated evapotranspiration estimates obtained using the GP-SEBAL algorithm for dry forest vegetation (Caatinga) and agricultural areas in the Brazilian semiarid region. Agricultural Water Management, Vol. 250, 106863, 2021. https://doi.org/10.1016/j.agwat.2021.106863

KLAUČO, M.; GREGOROVÁ, B.; STANKOV, U.; MARKOVIĆ, V.; LEMENKOVA, P. Landscape metrics as indicator for ecological significance: assessment of Sitno Natura 2000 sites, Slovakia, Ecology and Environmental Protection. Proceedings of the International Conference (Belarusian State University, March 19–20, 2014). Minsk, Belarus, 85–90, 2014. https://doi.org/10.6084/m9.figshare.7434200

LEMENKOVA P.; PROMPER C.; GLADE T. Economic Assessment of Landslide Risk for the Waidhofen a.d. Ybbs Region, Alpine Foreland, Lower Austria, In Eberhardt et al. (eds.). 11th International Symposium on Landslides & the 2nd North American Symposium on Landslides & Engineered Slopes (NASL). Protecting society through improved understanding: June 2–8, 2012. Canada, Banff, p. 279–285, 2012. https://doi.org/10.6084/m9.figshare.7434230

SCHENKE, H.W.; LEMENKOVA, P. Zur Frage der Meeresboden-Kartographie: Die Nutzung von AutoTrace Digitizer für die Vektorisierung der Bathymetrischen Daten in der Petschora-See, Hydrographische Nachrichten, Vol. 81, p. 16–21, 2008. https://doi.org/10.6084/m9.figshare.7435538

UTSUMI, A.G.; TARLÉ PISSARRA, T.C.; ROSALEN, D.L.; MARTINS FILHO, M.V.; SILVA ROTTA, L.H. Gully mapping using geographic object-based image analysis: A case study at catchment scale in the Brazilian Cerrado, Remote Sensing Applications: Society and Environment, Vol. 20, 100399, 2020. https://doi.org/10.1016/j.rsase.2020.100399

GAUGER, S., KUHN, G., GOHL, K., FEIGL, T., LEMENKOVA, P., HILLENBRAND, C. (2007). Swath-bathymetric mapping. Reports on Polar and Marine Research, 557, 38–45. https://doi.org/10.6084/m9.figshare.7439231

KLAUČO, M.; GREGOROVÁ, B.; STANKOV, U.; MARKOVIĆ, V.; LEMENKOVA, P. Land planning as a support for sustainable development based on tourism: A case study of Slovak Rural Region, Environmental Engineering and Management Journal, Vol. 2, n. 16, p. 449–458, 2017. https://doi.org/10.30638/eemj.2017.045

LEMENKOVA, P.; Data-driven insights into correlation among geophysical setting, topography and seafloor sediments in the Ross Sea, Antarctic, Caderno de Geografia, Vol. 31, n. 64, p. 1–20, 2021. https://doi.org/0.5752/p.2318-2962.2021v31n64p1

SILVA COSTA, S. C.; KAZMERSKI, L. L.; DINIZ, A. S. A. C.; Impact of soiling on Si and CdTe PV modules: Case study in different Brazil climate zones, Energy Conversion and Management, Vol. X, n. 10, p. 100084, 2021. https://doi.org/10.1016/j.ecmx.2021.100084

MANCINI, M.; WEINDORF, D.C.; GODINHO SILVA, S.H.; CHAKRABORTY, S.; DOS SANTOS TEIXEIRA, A.F.; GUIMARÃES GUILHERME, L.R.; CURI, N. Parent material distribution mapping from tropical soils data via machine learning and portable X-ray fluorescence (pXRF) spectrometry in Brazil. Geoderma, Vol. 354, 113885, 2019. https://doi.org/10.1016/j.geoderma.2019.113885

LEMENKOVA, P.; Scripting cartographic methods of GMT for mapping the New Britain and San Cristobal Trenches, Solomon Sea, Papua New Guinea, Revista da Casa da Geografia de Sobral, Vol. 22, n. 3, p. 122–142, 2020. https://doi.org/10.35701/rcgs.v22n3.717

LEMENKOVA, P. GRASS GIS for topographic and geophysical mapping of the Peru-Chile Trench, Forum Geografic, Vol. 19, n. 2, p. 143–157, 2020. https://doi.org/10.5775/fg.2020.009.d

ARRUDA, V.L.S.; PIONTEKOWSKI, V.J.; ALENCAR, A.; PEREIRA, R.S.; MATRICARDI, E.A.T. An alternative approach for mapping burn scars using Landsat imagery, Google Earth Engine, and Deep Learning in the Brazilian Savanna. Remote Sensing Applications: Society and Environment, Vol. 22, 100472, 2021. https://doi.org/10.1016/j.rsase.2021.100472

ZUCATELLI, P.J.; NASCIMENTO, E.G.S.; SANTOS, A.Á.B.; ARCE, A.M.G.; MOREIRA, D.M. An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay. Energy, Vol. 230, 120842, 2021. https://doi.org/10.1016/j.energy.2021.120842

LEMENKOVA, P. Statistical Analysis of the Mariana Trench Geomorphology Using R Programming Language, Geodesy and Cartography, Vol. 45, p. 57–84, 2019. https://doi.org/10.3846/gac.2019.3785

LEMENKOVA, P. Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation, Aquatic Sciences and Engineering, Vol. 34, p. 51–60, 2019. https://doi.org/10.26650/ASE2019547010

ANTOLIN, L. A. S.; HEINEMANN, A. B.; MARIN, F.R.; Impact assessment of common bean availability in Brazil under climate change scenarios, Agricultural Systems, Vol. 191, 103174, 2021, https://doi.org/10.1016/j.agsy.2021.103174

DUBREUIL, V.; FANTE, K.P.; PLANCHON, O.; SANT'ANNA NETO, J.L. Climate change evidence in Brazil from Köppen's climate annual types frequency. International Journal of Climatology, Vol. 39, p. 1446–1456, 2019. https://doi.org/10.1002/joc.5893

LEMENKOVA, P. Exploring structured scripting cartographic technique of GMT for ocean seafloor modeling: A case of the East Indian Ocean, Maritime Technology and Research, Vol. 3, n. 2, p. 162–184, 2021. https://doi.org/10.33175/mtr.2021.248158

LEMENKOVA, P.; Cartographic Processing of the Multi-Source Geospatial Datasets by GMT for Mapping Variability in Geologic Setting and Bathymetry of the Pacific Ocean, Mercator, Vol. 20, p. 1-18, 2021. https://doi.org/10.4215/rm2021.e20013

DE NADAI FERNANDES, E.A.; SARRIÉS, G.A.; BACCHI, M.A.; MAZOLA, Y.T.; GONZAGA, C.L.; SARRIÉS, S.R.V. Trace elements and machine learning for Brazilian beef traceability. Food Chemistry, Vol. 333, 127462, 2020. https://doi.org/10.1016/j.foodchem.2020.127462

HUDSON, R. A.; LIBRARY OF CONGRESS. Federal Research Division. (1998) Brazil: A Country Study. Washington, D.C.: Federal Research Division, Library of Congress: For sale by the Supt. of Docs., U.S. G.P.O. [Pdf] Retrieved from the Library of Congress, https://www.loc.gov/item/97036500/

INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATISTICA (IBGE). Geografia do Brasil 5 vols. Rio de Janeiro: 1989.

UEREYEN, S.; KUENZER, C. A Review of Earth Observation-Based Analyses for Major River Basins. Remote Sensing, 11, 2951, 2019. https://doi.org/10.3390/rs11242951

FRAPPART, F.; CALMANT, S.; CAUHOPE, M.; SEYLER, F.; CAZENAVE, A. Preliminary results of ENVISAT RA-2-derived water levels validation over the Amazon basin. Remote Sensing of Environment, 100(2), 252–264, 2006. https://doi.org/10.1016/j.rse.2005.10.027

DA SILVA, J.S.; SEYLER, F.; CALMANT, S.; ROTUNNO FILHO, O.C.; ROUX, E.; MAGALHÃES ARAÚJO, A.A.; GUYOT, J.L. Water level dynamics of Amazon wetlands at the watershed scale by satellite altimetry, International Journal of Remote Sensing, 33(11), 3323-3353, 2012. https://doi.org/10.1080/01431161.2010.531914

BIRKETT, C. M., MERTES, L. A. K., DUNNE, T., COSTA, M. H., JASINSKI, M. J. Surface water dynamics in the Amazon Basin: Application of satellite radar altimetry, Journal of Geophysical Research, 107(D20), 8059, 2002. https://doi.org/10.1029/2001JD000609

DA SILVA SOITO, J.L.; VASCONCELOS FREITAS, M.A. Amazon and the expansion of hydropower in Brazil: Vulnerability, impacts and possibilities for adaptation to global climate change. Renewable and Sustainable Energy Reviews, Vol. 15, Issue 6, pp. 3165-3177, 2011. https://doi.org/10.1016/j.rser.2011.04.006

MENDES, C.A.B.; BELUCO, A.; CANALES, F.A. Some important uncertainties related to climate change in projections for the Brazilian hydropower expansion in the Amazon, Energy, Vol. 141, p. 123-138, 2017. https://doi.org/10.1016/j.energy.2017.09.071

AYRES, J. M.; DA FONSECA, G. A. B.; RYLANDS, A. B.; QUEIROZ, H. L.; PINTO, L. P.; MASTERSON, D.; CAVALCANTI, R. B. Os Corredores Ecológicos das Florestas Tropicais do Brasil. Sociedade Civil Mamirauá, 2005 256p.: il., mapas, 2005. ISBN 85-85924-12-8.

LEAL, I.R.; DA SILVA, J.M.C.; TABARELLI, M.; LACHER, T.E.JR. Changing the Course of Biodiversity Conservation in the Caatinga of Northeastern Brazil. Conservation Biology, 19, 701-706, 2005. https://doi.org/10.1111/j.1523-1739.2005.00703.x

BARROSO, F.R.G.; DOS SANTOS GOMES, V.; CARVALHO, C.E.; LEDRU, M.P.; FAVIER, C.; ARAÚJO, F.S.; BREMOND, L. Phytoliths from soil surfaces and water reservoirs of the Brazilian semi-arid Caatinga. Journal of South American Earth Sciences, Vol. 108, 103180, 2021. https://doi.org/10.1016/j.jsames.2021.103180

BARROS DE SÁ, C.S.; KENJI SHIOSAKI, R.; DOS SANTOS, A.M.; DA SILVA CAMPOS, M.A. Salinization causes abrupt reduction in soil nematode abundance in the Caatinga area of the Submedio San Francisco Valley, Brazilian semiarid region. Pedobiologia, Vol. 85–86, 150729, 2021. https://doi.org/10.1016/j.pedobi.2021.150729

CREMON, É.H.; PEREIRA, A.C.; DE PAULA, L.D.L.; NUNES, E.D. Geological and terrain attributes for predicting soil classes using pixel- and geographic object-based image analysis in the Brazilian Cerrado, Geoderma, Vol. 401, 115315, 2021. https://doi.org/10.1016/j.geoderma.2021.115315

OLIVEIRA, P. S.; MARQUIS, R. J. The Cerrados of Brazil, New York Chichester, West Sussex: Columbia University Press, 2002. https://doi.org/10.7312/oliv12042

ALLAN, C. The Cerrados of Brazil: Ecology and Natural History of a Neotropical Savanna. Economic Botany 57(4), 656, 2003. https://doi.org/10.1663/0013-0001(2003)057[0656:DFABRE]2.0.CO;2

LUO, D.; CALDAS, M.M.; GOODIN, D.G. Estimating environmental vulnerability in the Cerrado with machine learning and Twitter data. Journal of Environmental Management, Vol. 289, 112502, 2021. https://doi.org/10.1016/j.jenvman.2021.112502

SILVA, T.R.; PENA, J.C.; MARTELLO, F.; BETTIOL, G.M.; SANO, E.E.; MASCIA VIEIRA, D.L. Not only exotic grasslands: The scattered trees in cultivated pastures of the Brazilian Cerrado. Agriculture, Ecosystems & Environment, Vol. 314, 107422, 2021. https://doi.org/10.1016/j.agee.2021.107422

DE LIMA, D.O.; CROUZEILLES, R.; VIEIRA, M.V. Integrating strict protection and sustainable use areas to preserve the Brazilian Pampa biome through conservation planning. Land Use Policy, Vol. 99, 104836, 2020. https://doi.org/10.1016/j.landusepol.2020.104836

MARENGO J.A., OLIVEIRA G.S., ALVES L.M. (2015) Climate Change Scenarios in the Pantanal. In: BERGIER I., ASSINE M. (eds). Dynamics of the Pantanal Wetland in South America. The Handbook of Environmental Chemistry, Vol 37. Springer, Cham. https://doi.org/10.1007/698_2015_357

GUERRA, A.; REIS, L.K.; BORGES, F.L.G.; OJEDA, P.T.A.; PINEDA, D.A.M.; MIRANDA, C.O.; FURTADO DE LIMA MAIDANA, D.P.; ROCHA DOS SANTOS, T.M.; SHIBUYA, P.S.; MARQUES, M.C.M.; LAURANCE, S.G.W.; GARCIA, L.C. Ecological restoration in Brazilian biomes: Identifying advances and gaps, Forest Ecology and Management, Vol. 458, 117802, 2020. https://doi.org/10.1016/j.foreco.2019.117802

FREIRE DA SILVA, E.F.; DE MORAES NOVO, E.M.L.; DE LUCIA LOBO, F.; FARIA BARBOSA, C.C.; TRESSMANN CAIRO, C.; NOERNBERG, M.A.; DA SILVA ROTTA, L.H. A machine learning approach for monitoring Brazilian optical water types using Sentinel-2 MSI. Remote Sensing Applications: Society and Environment, 100577, 2021. https://doi.org/10.1016/j.rsase.2021.100577

COSTA, W.; FONSECA, L.; KÖRTING T. Classifying Grasslands and Cultivated Pastures in the Brazilian Cerrado Using Support Vector Machines, Multilayer Perceptrons and Autoencoders. In: Perner P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2015. Lecture Notes in Computer Science, vol 9166, 2015. Springer, Cham. https://doi.org/10.1007/978-3-319-21024-7_13

ABATZOGLOU, J., DOBROWSKI, S., PARKS, S., HEGEWISCH, K.C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci Data 5, 170191, 2018. https://doi.org/10.1038/sdata.2017.191

DE CASTILHO BERTANI, T.; DE FÁTIMA ROSSETTI, D.; GURGEL ALBUQUERQUE, P.C. Object-based classification of vegetation and terrain topography in Southwestern Amazonia (Brazil) as a tool for detecting ancient fluvial geomorphic features. Computers & Geosciences, Vol. 60, p. 41-50, 2013. https://doi.org/10.1016/j.cageo.2013.06.013

LEMENKOV, V.; LEMENKOVA, P. Using TeX Markup Language for 3D and 2D Geological Plotting. Foundations of Computing and Decision Sciences, Vol. 46, n. 3, p. 43–69, 2021. https://doi.org/10.2478/fcds-2021-0004

SCHWALBERT, R.A.; AMADO, T.; CORASSA, G.; POTT, L.P.; VARA PRASAD, P.V.; CIAMPITTI, I.A. Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil. Agricultural and Forest Meteorology. Vol. 284, 107886, 2020. https://doi.org/10.1016/j.agrformet.2019.107886

LEMENKOVA, P. Geomorphological modelling and mapping of the Peru-Chile Trench by GMT, Polish Cartographical Review, Vol. 51, n. 4, p. 181–194, 2019. https://doi.org/10.2478/pcr-2019-0015

LEMENKOVA, P. Geophysical Modelling of the Middle America Trench using GMT, Annals of Valahia University of Targoviste. Geographical Series, Vol. 19, n. 2, p. 73–94, 2019. https://doi.org/10.6084/m9.figshare.12005148

LEMENKOVA, P.; Gobi Altai, Khangai and Khentii Mountains mapped by a mixed-method cartographic approach for comparative geophysical analysis, Mongolian Geoscientist, Vol. 26, n. 52, p. 62–79, 2021. https://doi.org/10.5564/mgs.v26i52.1512

DA SILVA, E. H. F. M.; SILVA ANTOLIN, L. A.; ZANON, A. J. ANDRADE, A. S.; DE SOUZA, H. A.; DOS SANTOS CARVALHO, K.; VIEIRA, N. A.; MARIN, F. R.; Impact assessment of soybean yield and water productivity in Brazil due to climate change, European Journal of Agronomy, Vol. 129, n. 126329, 2021. https://doi.org/10.1016/j.eja.2021.126329

DEPAULA, G. The distributional effect of climate change on agriculture: Evidence from a Ricardian quantile analysis of Brazilian census data. Journal of Environmental Economics and Management, Vol. 104, 102378, 2020. https://doi.org/10.1016/j.jeem.2020.102378

CARAUTA, M.; TROOST, C.; GUZMAN-BUSTAMANTE, I.; HAMPF, A.; LIBERA, A.; MEURER, K.; BÖNECKE, E.; FRANKO, U.; DE ARAGÃO RIBEIRO RODRIGUES, R.; BERGER, T.; Climate-related land use policies in Brazil: How much has been achieved with economic incentives in agriculture? Land Use Policy, Vol. 109, n. 105618, 2021,. https://doi.org/10.1016/j.landusepol.2021.105618

VILANOVA, R.S.; DELGADO, R.C.; DE ANDRADE, C.F.; DOS SANTOS, G.L.; MAGISTRALI, I.C.; DE OLIVEIRA, C.M.M.; TEODORO, P.E.; SILVA, G.F.C.; DA SILVA JUNIOR, C.A.; DE ÁVILA RODRIGUES, R. Vegetation degradation in ENSO events: Drought assessment, soil use and vegetation evapotranspiration in the Western Brazilian Amazon. Remote Sensing Applications: Society and Environment, Vol. 23, 100531, 2021. https://doi.org/10.1016/j.rsase.2021.100531

FERREIRA, L.B.; DA CUNHA, F.F.; DE OLIVEIRA, R.A.; FILHO, E.I.F. Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM – A new approach, Journal of Hydrology, Vol. 572, p. 556-570, 2019. https://doi.org/10.1016/j.jhydrol.2019.03.028

MONTOYA, M.A.; ALLEGRETTI, G.; SLEIMANN BERTUSSI, L.A.; TALAMINI, E. Renewable and Non-renewable in the energy-emissions-climate nexus: Brazilian contributions to climate change via international trade. Journal of Cleaner Production, Vol. 312, 127700, 2021. https://doi.org/10.1016/j.jclepro.2021.127700

HAMPF, A.C.; STELLA, T.; BERG-MOHNICKE, M.; KAWOHL, T.; KILIAN, M.; NENDEL, C. Future yields of double-cropping systems in the Southern Amazon, Brazil, under climate change and technological development, Agricultural Systems, Vol. 177, 102707, 2020. https://doi.org/10.1016/j.agsy.2019.102707

EVANGELISTA-VALE, J.C.; WEIHS, M.; JOSÉ-SILVA, L.; ARRUDA, R.; SANDER, N.L.; GOMIDES, S.C.; MACHADO, T.M.; PIRES-OLIVEIRA, J.C.; BARROS-ROSA, L.; CASTUERA-OLIVEIRA, L.; MIRANDA MATIAS, R.A.; MARTINS-OLIVEIRA, A.T.; SÃO BERNARDO, C.S., SILVA-PEREIRA, I.; CARNICER, C.; CARPANEDO, R.S.; EISENLOHR, P.V. Climate change may affect the future of extractivism in the Brazilian Amazon. Biological Conservation, Vol. 257, 109093, 2021. https://doi.org/10.1016/j.biocon.2021.109093

CAVIGLIA-HARRIS, J. L. Agricultural innovation and climate change policy in the Brazilian Amazon: Intensification practices and the derived demand for pasture, Journal of Environmental Economics and Management, Vol. 90, p. 232-248, 2018. https://doi.org/10.1016/j.jeem.2018.06.006

ALVES, L.M.; CHADWICK, R.; MOISE, A.; BROWN, J.; MARENGO, J.A. Assessment of rainfall variability and future change in Brazil across multiple timescales. International Journal of Climatology, Vol. 41 (Suppl. 1): E1875– E1888, 2021. https://doi.org/10.1002/joc.6818

XAVIER, A.C.; KING, C.W.; SCANLON, B.R. Daily gridded meteorological variables in Brazil (1980–2013). International Journal of Climatology, Vol. 36, p. 2644–2659, 2016. https://doi.org/10.1002/joc.4518

CARRANZA, T.; MANICA, A.; KAPOS, V.; BALMFORD, A. Mismatches between conservation outcomes and management evaluation in protected areas: A case study in the Brazilian Cerrado. Biological Conservation, Vol. 173, p. 10–16, 2014. https://doi.org/10.1016/j.biocon.2014.03.004

CARRANZA, T.; BALMFORD, A.; KAPOS, V.; MANICA, A. Protected Area Effectiveness in Reducing Conversion in a Rapidly Vanishing Ecosystem: The Brazilian Cerrado. Conservation Letters, Vol. 7, p. 216–223, 2014. https://doi.org/10.1111/conl.12049

D´AMICO, A.R..; CORTES FIGUEIRA, J.E.; CÂNDIDO-JR, J.F.; DRUMOND, M.A. Environmental diagnoses and effective planning of Protected Areas in Brazil: Is there any connection? PLoS ONE, Vol. 15(12), p. e0242687, 2020. https://doi.org/10.1371/journal.pone.0242687

RYLANDS, A.B.; BRANDON, K. Brazilian protected areas. Conservation Biology, Vol. 19, p. 612–618. https://doi.org/10.1111/j.1523-1739.2005.00711.x

ROCHA, G. F.; GUIMARÃES FERREIRA, L.; CLEMENTINO FERREIRA, N.; EDUARDO FERREIRA, M. Detecção de desmatamentos no bioma Cerrado entre 2002 e 2009: padrões, tendências e impactos. Revista Brasileira de Cartografia, Vol. 63, n. 3, p. 341–349, 2012.

Downloads

Published

2022-02-03

How to Cite

Lemenkova, P. (2022). DATA FUSION STRATEGY FOR MAPPING ENVIRONMENT AND CLIMATE VARIABLES OF BRAZIL. Tecno-Lógica, 26(1), 15-34. Retrieved from https://online.unisc.br/seer/index.php/tecnologica/article/view/16793

Issue

Vol. 26 No. 1 (2022)

Section

Environmental Technology

License

A submissão de originais para este periódico implica na transferência, pelos autores, dos direitos de publicação impressa e digital. Os direitos autorais para os artigos publicados são do autor, com direitos do periódico sobre a primeira publicação. Os autores somente poderão utilizar os mesmos resultados em outras publicações indicando claramente este periódico como o meio da publicação original. Em virtude de sermos um periódico de acesso aberto, permite-se o uso gratuito dos artigos em aplicações educacionais e científicas desde que citada a fonte conforme a licença CC-BY da Creative Commons. Licença Creative Commons Creative Commons Atribuição 4.0 Internacional.