Laboratório de Processamento de Imagens e Geoprocessamento (LAPIG/UFG) - https://www.lapig.iesa.ufg.br/

Introdução ao Processamento em nuvem atráves da plataforma Google Earth Engine

Agradecimentos

• Equipe LAPIG
• Iniciativa Mapbiomas
• Universidade Federal de Goiás
• Google / Earth Engine Team

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Pré-requisito

• Conhecimentos prévios de Sensoriamento Remoto e Sistemas de Informação Geográfica (SIG)
• Vincular/Registrar a conta do Gmail ao Google Earth Engine: Earth Engine account registration
• Adicionar o repositório de scripts GEE do LAPIG (Basta acessar apenas uma vez este link)

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Recomendado

• Realizar tutoriais da Google
• [Tutoriais:] --(https://courses.spatialthoughts.com/end-to-end-gee.html) --... UPDATING ...

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Scripts

S01 - Recuperar Imagens Orbitais

S02 - Aplicar Funções Redutoras

S03 - Gerar Mosaicos Livres de Nuvem

S04 - Gerar Mosaicos Sintetizados

S05 - Gerar Mosaicos de Índices Livres de Nuvem

S06 - Gerar Mosaicos Sintetizados de Índices

S07 - Gerar Estatística de Zona em dados Sintetizados

S08 - Gerar Estatística de Zona em dados Sintetizados de Índices

S09 - Gerar Estatística de Zona em Áreas de Pastagem

S10 - Classificação utilizando Random Forest

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Variáveis

Você pode aprender mais sobre as variáveis utilizadas no curso e como edita-las clicando aqui

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Glossário

• TOA: Reflectância de topo de atmosfera
• SR: Reflectância de superfície
• Chuva: Coloração padrão do dado. e.g. precipitação em mm/h
• ALT: Altitude/Elevação em metros
• TEMP: Temperatura de Superfície (LST)
• NDVI: Índice de vegetação com diferença normalizada (Nir-Red / Nir+Red)
• EVI: Índice de vegetação melhorado (2.5 * (Nir - Red)/ 1 + Nir + 6Red + 7.5Blue)
• ET/PET: Evapotranspiração total / Evapotranspiração potencial total em mm
• Agri: Composição colorida utilizada na detecção de áreas agrícolas (Swir/Nir/Red)
• False: Composição colorida utilizada para dar ênfase a vegetação (Nir/Swir/Red ou Nir/Red/Green)
• False20: Semelhante ao 'False', porém realiza a composição com as bandas de 20m de Sentinel 2
• True: Composição colorida que reproduz as cores "naturais" (Red/Green/Blue)
• SAR: Diferença de eixo de polarização (Radar - VV,VH,VH)
• OIL: Visualização da polarização VV (Radar) com realce específico para vazamentos de óleo

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Índices Espectrais (Under construction)

Você pode encontrar mais índices aqui: https://www.indexdatabase.de

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• ARI_1 & ARI_2 : Anthocyanin Reflectance Index - Aplicação:
  • Gitelson, A., M. Merzlyak, and O. Chivkunova. Optical Properties and Nondestructive Estimation of Anthocyanin Content in Plant Leaves. Photochemistry and Photobiology 71 (2001): 38-45.
  • https://www.harrisgeospatial.com/docs/LeafPigments.html

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• CAI : Cellulose Absorption Index - Aplicações:
  • Nagler, Pamela & Daughtry, Craig & Goward, Samuel. (2000). Plant Litter and Soil Reflectance. Remote Sensing of Environment. 71. 207-215. 10.1016/S0034-4257(99)00082-6.
  • Nagler, P. L., Inoue, Y., Glenn, E. P., Russ, A. L., & Daughtry, C. S. T. (2003). Celluloseabsorption index (CAI) to quantify mixed soil–plant litter scenes.Remote Sensing of Environment,87, 310–325.
  • Guerschman, J. -P., Hill, M. J., Barrett, D. J., Renzullo, L., Marks, A., & Botha, E. (2009). Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetationand soil in mixed tree–grassvegetationusing the EO-1 and MODIS sensors. RemoteSensing of Environment,113,928–945.

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• CRI1 & CRI2 : Carotenoid Reflectance Index - Aplicação:
  • Gitelson, A., et al. Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy. Photochemistry and Photobiology 75 (2002): 272-281.
  • https://www.harrisgeospatial.com/docs/LeafPigments.html

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• EVI_1 : Enhanced Vegetation Index - Aplicações:
  • Huete, A.; Didan, K.; Miura, T.; Rodriguez, E. P.; Gao, X.; Ferreira, L. G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83(2002) 195-213

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• EVI_2 : Enhanced Vegetation Index 2 - Aplicações:
  • Jiang, Z.; Huete, A.R.; Didan, K.; Miura, T. Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ., 112 (2008), pp. 3833-3845

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• LAI_1 & LAI_2 : Leaf Area Index - Aplicações:
  • Faris, A.A.; Reddy, Y.S. Estimation of urban heat island using Landsat ETM+ imagery at Chennai city – a case study. Int. J. Earth Sci. Eng., 3 (2010), pp. 332-340
  • Boegh, E., H. Soegaard, N. Broge, C. Hasager, N. Jensen, K. Schelde, and A. Thomsen. Airborne Multi-spectral Data for Quantifying Leaf Area Index, Nitrogen Concentration and Photosynthetic Efficiency in Agriculture. Remote Sensing of Environment 81, no. 2-3 (2002): 179-193.
  • https://www.sciencedirect.com/science/article/pii/S1110982315000551

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• MSI : Moisture Stress Index - Aplicações:
  • Hunt, E.R. and Rock, B.N. 1989. Detection of changes in leaf water content using near- and middle-infraredreflectances. Remote Sensing of Environment, 30, 43–54.

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• NDBI : Normalized Difference Build-up Index - Aplicações:
  • Zha, Y.; Gao, J. ; Ni, S. (2003) Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, International Journal of Remote Sensing, 24:3, 583-594, DOI: 10.1080/01431160304987

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• NDII : Normalized Difference Infrared Index - Aplicações:
  • Hardisky, M. A., Klemas, V., & Smart, R. M. (1983). The influences of soil salinity, growthform, and leaf moisture on the spectral reflectance ofSpartina alternifloracanopies.Photogrammetric Engineering and Remote Sensing,49,77–83.

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• NDVI : Normalized Difference Vegetation Index - Aplicações:
  • Rouse, J., R. Haas, J. Schell, and D. Deering. Monitoring Vegetation Systems in the Great Plains with ERTS. Third ERTS Symposium, NASA (1973): 309-317.
  • Tucker, C.J.; Elgin, H.J.-Jr.; McMurtrey, J.E.I.; Fran, C.J. Monitoring corn and soybean crop development with hand-held radiometer spectral data. Remote Sens. Environ., 8 (1979), pp. 237-248, 10.1016/0034-4257(79)90004-X

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• NDWI_1 & NDWI_2 : Normalized Difference Water Index - Aplicações:
  • Conteúdo de Água nas folhas (NDWI_1): Gao, B. NDWI — A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58 (1996), pp. 257-266
  • Água no ambiente (NDWI_2): McFEETERS, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, International Journal of Remote Sensing, 17:7, 1425-1432, DOI: 10.1080/01431169608948714

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• PRI : Photochemical Reflectance Index - Aplicações:
  • Penuelas, J., I. Filella, and J. Gamon. Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytologist 131 (1995): 291-296.
  • Gamon, J., L. Serrano, and J. Surfus. The Photochemical Reflectance Index: An Optical Indicator of Photosynthetic Radiation Use Efficiency Across Species, Functional Types and Nutrient Levels. Oecologia 112 (1997): 492-501.
  • https://www.harrisgeospatial.com/docs/LightUseEfficiency.html

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• PSRI : Plant Senescence Reflectance Index - Aplicações:
  • Marsett, R. C., Qi, J. G., Heilman, P., Biedenbender, S. H., Watson, M. C., Amer, S., et al.(2006).Remote sensing for grassland management in the arid Southwest. RangeEcology and Management,59, 530–540
  • https://www.harrisgeospatial.com/docs/LightUseEfficiency.html

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• PSSR : Pigment Specific Simple Ratio - Aplicações:
  • Blackburn, G. A. (1998). Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves, International Journal of Remote Sensing, 19:4, 657-675, DOI: 10.1080/014311698215919

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• RGR : Red-Green Ratio - Aplicações:
  • Gamon, J., and J. Surfus. Assessing Leaf Pigment Content and Activity With a Reflectometer. New Phytologist 143 (1999): 105-117.
  • Sims, D. A., & Gamon, J. A. (2002). Relationships between leaf pigment content andspectral reflectance across a wide range of species, leaf structures and developmentalstages. Remote Sensing of Environment,81,337–354.
  • https://www.harrisgeospatial.com/docs/LightUseEfficiency.html

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• RVSI : Red-Edge Vegetation Stress Index - Aplicações:
  • Merton, R. N. (1998). Monitoring community hysteresis using spectral shift analysis and the red-edgevegetation stress index. Proceedings of the Seventh Annual JPL Airborne Earth Science Workshop.NASA, Jet Propulsion Laboratory, Pasadena, California, USA. 12-16 January 1998.
  • Merton, R., and J. Huntington. Early simulation results of the ARIES-1 satellite sensor for multi-temporal vegetation research derived from AVIRIS. NASA Jet Propulsion Lab., Pasadena, CA, 1999.

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• SAVI: Soil Adjusted Vegetation Index - Aplicações:
  • Huete, A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295–309.

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• SATVI : Soil Adjusted Total Vegetation Index - Aplicações:
  • Marsett, R. C., Qi, J. G., Heilman, P., Biedenbender, S. H., Watson, M. C., Amer, S., et al.(2006). Remote sensing for grassland management in the arid Southwest. RangeEcology and Management,59, 530–540

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• SIPI_1 & SIPI_2 : Structure Insensitive Pigment Index - Aplicações:
  • Penuelas, J., F. Baret, and I. Filella. Semi-Empirical Indices to Assess Carotenoids/Chlorophyll-a Ratio from Leaf Spectral Reflectance. Photosynthetica 31 (1995): 221-230.
  • Blackburn, G. A. (1998). Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves, International Journal of Remote Sensing, 19:4, 657-675, DOI: 10.1080/014311698215919
  • https://www.harrisgeospatial.com/docs/LightUseEfficiency.html

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• VARI : Visible Atmospherically Resistant Index - Aplicações:
  • Gitelson, A., et al. Vegetation and Soil Lines in Visible Spectral Space: A Concept and Technique for Remote Estimation of Vegetation Fraction. International Journal of Remote Sensing 23 (2002): 2537−2562.
  • https://www.harrisgeospatial.com/docs/BroadbandGreenness.html#Visible

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• VIG : Visible Index Green - Aplicações:
  • Gitelson, A.A.; Kaufman, Y.J.; Stark, R.; Rundquist, D. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ., 80 (2002), pp. 76-87

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