Lập bản đồ ba thập kỷ biến đổi thực vật tự nhiên trong thảo nguyên Brazil bằng dữ liệu Landsat xử lý trên nền tảng Google Earth Engine
Tóm tắt
Từ khóa
#Cerrado #Landsat #Google Earth Engine #thực vật tự nhiên #biến đổi khí hậu #phân loại máy học #rừng #thảo nguyên #môi trườngTài liệu tham khảo
Bourlière, F. (1983). Present-day savannas: An overview. Tropical Savannas, Elsevier.
Scholes, 1996, The carbon budget of tropical savannas, woodlands and grasslands, Sci. Comm. Probl. Environ. Int. Counc. Sci. Unions, 56, 69
Solbrig, O.T. (1996). The diversity of the savanna ecosystem. Biodiversity and Savanna Ecosystem Processes, Springer.
Gillson, 2004, Evidence of Hierarchical Patch Dynamics in an East African Savanna?, Landsc. Ecol., 19, 883, 10.1007/s10980-004-0248-5
Marchant, 2010, Understanding complexity in savannas: Climate, biodiversity and people, Curr. Opin. Environ. Sustain., 2, 101, 10.1016/j.cosust.2010.03.001
Sano, E.E., Rosa, R., Scaramuzza, C.A.M., Adami, M., Bolfe, E.L., Coutinho, A.C., Esquerdo, J.C.D.M., Maurano, L.E.P., da Narvaes, I.S., and de Oliveira Filho, F.J.B. (2019). Land use dynamics in the Brazilian Cerrado in the period from 2002 to 2013. Pesqui. Agropecuária Bras., 54.
Sano, S.M., and Almeida, S.P. (1998). Fitofisionomia do Bioma Cerrado. Cerrado: Ambiente e Flora, Embrapa.
Mittermeier, R.A., Turner, W.R., Larsen, F.W., Brooks, T.M., and Gascon, C. (2011). Global Biodiversity Conservation: The Critical Role of Hotspots. Biodiversity Hotspots, Springer.
Strassburg, 2017, Moment of truth for the Cerrado hotspot, Nat. Ecol. Evol., 1, 99, 10.1038/s41559-017-0099
Bustamante, 2012, Potential impacts of climate change on biogeochemical functioning of Cerrado ecosystems, Braz. J. Biol., 72, 655, 10.1590/S1519-69842012000400005
Spera, 2016, Land-use change affects water recycling in Brazil’s last agricultural frontier, Glob. Chang. Biol., 22, 3405, 10.1111/gcb.13298
INPE (Instituto Nacional de Pesquisas Espaciais) (2018, November 10). Programa de Monitoramento da Amazônia e Demais Biomas—Bioma Cerrado. Available online: http://terrabrasilis.dpi.inpe.br/downloads/.
Rocha, 2011, Detecção de desmatamentos no bioma Cerrado entre 2002 e 2009: Padrões, tendências e impactos, Rev. Bras. Cart., 3, 341
Dias, 2015, Effects of land cover change on evapotranspiration and streamflow of small catchments in the Upper Xingu River Basin, Central Brazil, J. Hydrol. Reg. Stud., 4, 108, 10.1016/j.ejrh.2015.05.010
Sano, 2010, Land cover mapping of the tropical savanna region in Brazil, Environ. Monit. Assess., 166, 113, 10.1007/s10661-009-0988-4
Glenn, 2008, Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us about the Landscape, Sensors, 8, 2136, 10.3390/s8042136
Jacon, 2017, Seasonal characterization and discrimination of savannah physiognomies in Brazil using hyperspectral metrics from Hyperion/EO-1, Int. J. Remote Sens., 38, 4494, 10.1080/01431161.2017.1320443
Hill, M.J., and Hanan, N.P. (2011). Remote Sensing of Global Savana Fire Occurrence, Extent, and Properties. Ecosystem Function in Savannas: Measurement and Modeling at Landscape to Global Scales, CRC Press.
Gomes, 2020, Effects and behaviour of experimental fires in grasslands, savannas, and forests of the Brazilian Cerrado, For. Ecol. Manage., 458, 117804, 10.1016/j.foreco.2019.117804
Ferreira, 2004, Assessing the seasonal dynamics of the Brazilian Cerrado vegetation through the use of spectral vegetation indices, Int. J. Remote Sens., 25, 1837, 10.1080/0143116031000101530
Ratana, 2005, Analysis of Cerrado Physiognomies and Conversion in the MODIS Seasonal–Temporal Domain, Earth Interact., 9, 1, 10.1175/1087-3562(2005)009<0001:AOCPAC>2.0.CO;2
Ferreira, 2007, Spectral linear mixture modelling approaches for land cover mapping of tropical savanna areas in Brazil, Int. J. Remote Sens., 28, 413, 10.1080/01431160500181507
Beuchle, 2015, Land cover changes in the Brazilian Cerrado and Caatinga biomes from 1990 to 2010 based on a systematic remote sensing sampling approach, Appl. Geogr., 58, 116, 10.1016/j.apgeog.2015.01.017
Schwieder, 2016, Mapping Brazilian savanna vegetation gradients with Landsat time series, Int. J. Appl. Earth Obs. Geoinf., 52, 361
Hill, 2017, Relationships between vegetation indices, fractional cover retrievals and the structure and composition of Brazilian Cerrado natural vegetation, Int. J. Remote Sens., 38, 874, 10.1080/01431161.2016.1271959
Rufin, 2015, Mining dense Landsat time series for separating cropland and pasture in a heterogeneous Brazilian savanna landscape, Remote Sens. Environ., 156, 490, 10.1016/j.rse.2014.10.014
Brazil, M. (2015). TerraClass: Mapeamento do Uso e Cobertura do Cerrado: Projeto TerraClass Cerrado 2013.
Fbds, F.B., and Para, D.S. (2019, November 01). Projeto de Mapeamento em Alta Resolução dos Biomas Brasileiros. Available online: http://geo.fbds.org.br/.
Ministério da Ciência, Tecnologia e Inovação (2015). III Inventário Brasileiro de Emissões e Remoções Antrópicas de Gases de Efeito Estufa não Controlados pelo Protocolo de Montreal.
IBGE (2017). Monitoramento da Cobertura e uso da Terra—2000, 2010, 2012, 2014, 2015—Em Grade Territorial Estatística, IBGE.
Wulder, 2016, The global Landsat archive: Status, consolidation, and direction, Remote Sens. Environ., 185, 271, 10.1016/j.rse.2015.11.032
Phiri, D., and Morgenroth, J. (2017). Developments in Landsat Land Cover Classification Methods: A Review. Remote Sens., 9.
Gorelick, 2017, Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18, 10.1016/j.rse.2017.06.031
Parente, 2019, Assessing the pasturelands and livestock dynamics in Brazil, from 1985 to 2017: A novel approach based on high spatial resolution imagery and Google Earth Engine cloud computing, Remote Sens. Environ., 232, 111301, 10.1016/j.rse.2019.111301
IBGE (2019). Biomas e Sistema Costeiro-Marinho do Brasil: Compatível com a Escala 1:250.000, IBGE.
Assad, E.D. (1994). Chuva nos Cerrados: Análise e Espacialização, Embrapa-CPAC.
Coutinho, L.M. (2002). O bioma do cerrado. Eugen Warming e o Cerrado Brasileiro: Um Século Depois, UNESP.
Roitman, I., Bustamante, M.M.C., Haidar, R.F., Shimbo, J.Z., Abdala, G.C., Eiten, G., Fagg, C.W., Felfili, M.C., Felfili, J.M., and Jacobson, T.K.B. (2018). Optimizing biomass estimates of savanna woodland at different spatial scales in the Brazilian Cerrado: Re-evaluating allometric equations and environmental influences. PLoS ONE, 13.
Housman, I., Chastain, R., and Finco, M. (2018). An Evaluation of Forest Health Insect and Disease Survey Data and Satellite-Based Remote Sensing Forest Change Detection Methods: Case Studies in the United States. Remote Sens., 10.
Souza, 2005, Combining spectral and spatial information to map canopy damage from selective logging and forest fires, Remote Sens. Environ., 98, 329, 10.1016/j.rse.2005.07.013
Diniz, C., Cortinhas, L., Nerino, G., Rodrigues, J., Sadeck, L., Adami, M., and Souza-Filho, P. (2019). Brazilian Mangrove Status: Three Decades of Satellite Data Analysis. Remote Sens., 11.
Swain, 1977, The decision tree classifier: Design and potential, IEEE Trans. Geosci. Electron., 15, 142, 10.1109/TGE.1977.6498972
Safavian, 1991, Separating and tracking multiple beacon sources for deep space optical communications, Free. Laser Commun. Technol. XXII, 21, 660
Zhu, 2014, Continuous change detection and classification of land cover using all available Landsat data, Remote Sens. Environ., 144, 152, 10.1016/j.rse.2014.01.011
Parente, L., and Ferreira, L. (2018). Assessing the Spatial and Occupation Dynamics of the Brazilian Pasturelands Based on the Automated Classification of MODIS Images from 2000 to 2016. Remote Sens., 10.
Nogueira, S.H.M., Parente, L.L., and Ferreira, L.G. (2017, January 6–9). Temporal Visual Inspection: Uma Ferramenta Destinada À Inspeção Visual De Pontos Em Séries Históricas De Imagens De Sensoriamento Remoto. Proceedings of the Anais do XXVII Congresso Brasileiro de Cartografia e XXVI Exposicarta, Rio de Janeiro, Brazil.
Stehman, 2014, Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes, Int. J. Remote Sens., 35, 4923, 10.1080/01431161.2014.930207
Olofsson, 2014, Good practices for estimating area and assessing accuracy of land change, Remote Sens. Environ., 148, 42, 10.1016/j.rse.2014.02.015
Macedo, 2012, Decoupling of deforestation and soy production in the southern Amazon during the late 2000s, Proc. Natl. Acad. Sci. USA, 109, 1341, 10.1073/pnas.1111374109
Zalles, 2019, Near doubling of Brazil’s intensive row crop area since 2000, Proc. Natl. Acad. Sci. USA, 116, 428, 10.1073/pnas.1810301115
Azzari, 2017, Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring, Remote Sens. Environ., 202, 64, 10.1016/j.rse.2017.05.025
Deines, 2019, Mapping three decades of annual irrigation across the US High Plains Aquifer using Landsat and Google Earth Engine, Remote Sens. Environ., 233, 111400, 10.1016/j.rse.2019.111400
Grecchi, 2013, Assessing the spatio-temporal rates and patterns of land-use and land-cover changes in the Cerrados of southeastern Mato Grosso, Brazil, Int. J. Remote Sens., 34, 5369, 10.1080/01431161.2013.788798
Ferreira, 2011, Use of Orbital LIDAR in the Brazilian Cerrado Biome: Potential Applications and Data Availability, Remote Sens., 3, 2187, 10.3390/rs3102187
Zimbres, 2020, Savanna vegetation structure in the Brazilian Cerrado allows for the accurate estimation of aboveground biomass using terrestrial laser scanning, For. Ecol. Manage., 458, 117798, 10.1016/j.foreco.2019.117798
Noojipady, 2017, Forest carbon emissions from cropland expansion in the Brazilian Cerrado biome, Environ. Res. Lett., 12, 025004, 10.1088/1748-9326/aa5986