Temporal analysis of drought coverage in a watershed area using remote sensing spectral indexes
PDF-en

Keywords

Geostatistics
Remote sensing
Seasonality
Drought index
Vegetation index

How to Cite

DUTRA, D. J.; ELMIRO, M. A. T. .; COELHO, C. W. G. A. .; NERO, M. A. .; TEMBA, P. da C. . Temporal analysis of drought coverage in a watershed area using remote sensing spectral indexes. Sociedade & Natureza, [S. l.], v. 33, 2021. DOI: 10.14393/SN-v33-2021-59505. Disponível em: https://seer.ufu.br/index.php/sociedadenatureza/article/view/59505. Acesso em: 3 dec. 2024.

Abstract

The development of several time series analysis programs using satellite images has provided many applications based on resources from geostatistics field. Currently, the use of statistical tests applied to vegetation indexes has enabled the analysis of different natural phenomena, such as drought events in watershed areas. The objective of this article is to provide a comparative analysis between NDVI and EVI vegetation index data made available by MOD13Q1 project of MODIS sensor for drought mapping using vegetation condition index (VCI) in the Serra Azul stream sub-basin, MG. The methodology adopted the Cox-Stuart statistical test for seasonality analysis and Pearson's linear correlation to verify the influence of different indexes on delimitation of drought in a watershed. The results indicated the NDVI vegetation index as more efficient than EVI in spatial characterization of studied watershed region, mainly in identification of seasonality. The VCI proved to be highly feasible for monitoring drought in study period between 2013 and 2018, allowing the effective delimitation of drought conditions in the Serra Azul stream sub-basin. In addition, the effectiveness of MODIS sensor data in characterizing drought events that affected the study area was proven.

https://doi.org/10.14393/SN-v33-2021-59505
PDF-en

References

ARANTES, T. B. et al. Effectiveness of BFAST algorithm to characterize time series of dense forest, agriculture and pasture in the amazon region. Theoretical and applied engineering, v. 1, n. 1, p. 10–19, 2017.

BANIYA, B. et al. Spatial and temporal variation of drought based on satellite derived vegetation condition index in Nepal from 1982–2015. Sensors (Switzerland), v. 19, n. 2, 2019. https:/doi.org/10.3390/s19020430

BHUIYAN, C.; KOGAN, F. N. Monsoon variation and vegetative drought patterns in the Luni Basin in the rain-shadow zone. International Journal of Remote Sensing, v. 31, n. 12, p. 3223–3242, 2010. https://doi.org/10.1080/01431160903159332

BONIFACIO, R.; DUGDALE, G.; MILFORD, J. R. Sahelian rangeland production in relation to rainfall estimates from Meteosat. International Journal of Remote Sensing, v. 14, n. 14, p. 2695–2711, 1993.

BRANCO, E. R. F. Ocorrências de seca e tendências da vegetação na reserva biológica de sooretama e zona de amortecimento, no estado do Espírito Santo, Brasil. Dissertação de mestrado ed. Jerônimo Monteiro: Universidade Federal do Espirito Santo, 2016.

CHAVES, M. E. D.; MATAVELI, G. A. V.; JUSTINO, R. C. Uso da modelagem estatística para monitoramento da vegetação no Parque Nacional da Serra da Canastra, Minas Gerais. Caderno de Geografia, v. 24, n. 1, p. 120–132, 2014. https://doi.org/10.5752/P.2318-2962.2014v24nespp120

COHEN, J. No Statistical Power Analysis for the Behavioral Sciences. Second ed. Mahwah: Lawrence Erlbaum, 1998.

COVELE, P. A. Aplicação de índices das condições de vegetação no monitoramento em tempo quase real da seca em Moçambique usando NOAA-AVHRR. GEOUSP - Espaço e Tempo, n. 29, p. 85–95, 2011.

COX, D. R.; STUART, A. Some quick sign tests for trend in location and dispersion. Biometrika, v. 42, n. 1/2, p. 80, 1955.

CUNHA, A. P. M. A. et al. Avaliação de indicador para o monitoramento dos impacos da seca em áreas de pastagens no Semiárido do Brasil. Revista Brasileira de Cartografia, v. 69, n. 1, p. 89–106, 2017.

DANCEY, C. P.; REIDY, J. Estatística sem Matemática para Psicologia: usando SPSS para Windows. Porto Alegre: Penso, 2006.

DAVIES, N.; CHATFIELD, C. The analysis of time series: an introduction. 6. ed. New York Washington: CHAPMAN & HALL/CRC, 1990. v. 74. https://doi.org/10.2307/3619403

DECHANT, C. M.; MORADKHANI, H. Analyzing the sensitivity of drought recovery forecasts to land surface initial conditions. Journal of Hydrology, v. 526, p. 89–100, 2015. http://dx.doi.org/10.1016/j.jhydrol.2014.10.021

DECHANT, C. M.; MORADKHANI, H. Toward a reliable prediction of seasonal forecast uncertainty: Addressing model and initial condition uncertainty with ensemble data assimilation and Sequential Bayesian Combination. Journal of Hydrology, v. 519, n. PD, p. 2967–2977, 2014. http://dx.doi.org/10.1016/j.jhydrol.2014.05.045

DETZEL, D. H. M. et al. Estacionariedade das afluências às usinas hidrelétricas brasileiras. Revista Brasileira de Recursos Hídricos, v. 16, n. 3, p. 95–111, 2011. https://doi.org/10.21168/rbrh.v16n3.p95-111

DU, L. et al. A comprehensive drought monitoring method integrating MODIS and TRMM data. International Journal of Applied Earth Observation and Geoinformation, v. 23, n. 1, p. 245–253, 2013. http://dx.doi.org/10.1016/j.jag.2012.09.010

DUBREUIL, V. et al. Os tipos de climas anuais no Brasil: Uma aplicação da classificação de Köppen de 1961 a 2015. Confins. Revue franco- brésilienne de géographie/Revista franco-brasilera de geografia, v. 37, 2018.

DUFT, D. G.; PICOLI, M. C. A. Uso de imagens do sensor MODIS para identificação da seca na cana-de-açúcar através de índices espectrais. Scientia Agraria, v. 19, n. 1, p. 52, 2018. https://doi.org/10.5380/rsa.v19i1.54005

DUTRA, D. J. Uso so sensoriamento remoto para análise de eventos de seca em bacias hidrográficas: estudo de caso na sub-bacia do ribeirão Serra Azul. MG. Dissertação de mestrado ed. Belo Horizonte: Universidade Federal de Minas Gerais (Dissertação). Programa de Pós-graduação em Análise e Modelagem de Sistemas Ambientais da Universidade Federal de Minas Gerais, 2021.

DUTRA, D. J.; BRIANEZI, D.; COELHO, C. W. G. A. Uso de Geotecnologias para Análise da Dinâmica da Vegetação da Sub-bacia do Ribeirão Serra Azul, MG. Anuário do Instituto de Geociências - UFRJ, v. 43, n. 4, p. 283–292, 18 dez. 2020. http://dx.doi.org/10.11137/2020_4_283_292

DUTRA, D. J.; ELMIRO, M. A. T.; GARCIA, R. A. Comparative analysis of methods applied in vegetation cover delimitation using Landsat 8 images. Sociedade & Natureza, v. 32, n. July, p. 699–710, 9 out. 2020. https://doi.org/10.14393/SN-v32-2020-56139

EYOH, A.; OKEKE, F.; EKPA, A. Assessment of the effectiveness of the Vegetation Condition Index (VCI) as an indicator for monitoring drought condition across the Niger Delta region of Nigeria using AVHRR / MODIS NDVI. European Journal of Earth and Environment, v. 6, n. 1, p. 12–18, 2019.

FIGUEIREDO FILHO, D. B.; SILVA JÚNIOR, J. A. Desvendando os mistérios do coeficiente de correlação de Pearson (r). Revista Política Hoje, v. 18, n. 1, p. 115–146, 2009.

FLORENZANO, T. G. Iniciação em sensoriamento remoto. 3o edição ed. São Paulo: Oficina de Textos, 2013.

FORMAGGIO, A. R.; SANCHES, I. D. Índices espectrais de vegetação x agricultura. Sensoriamento remoto em agricultura. 1. ed. São Paulo: Oficina de Textos, 2017a. p. 95–113.

FORMAGGIO, A. R.; SANCHES, I. D. Sensoriamento remoto em agricultura. 1o ed. São José dos Campos: Oficina de Textos, 2017b.

GOMES, A. R. S. et al. Estudo da Relação entre a Variabilidade dos índices de Vegetação e Temperatura da Região Nordeste do Brasil. Revista Brasileira de Meteorologia, v. 34, n. 3, p. 359–368, 2019. https://doi.org/10.1590/0102-7786343051

GOW, L. J. et al. A detection problem: Sensitivity and uncertainty analysis of a land surface temperature approach to detecting dynamics of water use by groundwater-dependent vegetation. Environmental Modelling and Software, v. 85, p. 342–355, 2016. https://doi.org/10.1016/j.envsoft.2016.09.003

GU, L. et al. The contribution of internal climate variability to climate change impacts on droughts. Science of the Total Environment, v. 684, p. 229–246, 2019. https://doi.org/10.1016/j.scitotenv.2019.05.345

HUETE, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensisng of Enviroment, v. 83, p. 195–213, 2002.

IBGE. Dados vetoriais. Avaible in: https://www.ibge.gov.br/geociencias-novoportal/informacoes-ambientais/climatolo gia /15817-clima.html?=&t=downloads. Access in: jul, 2020.

JIAO, W. et al. A new station-enabled multi-sensor integrated index for drought monitoring. Journal of Hydrology, v. 574, n. April, p. 169–180, 2019. https://doi.org/10.1016/j.jhydrol.2019.04.037

KÄFER, P. S.; REX, F. E. Avaliação espectral e temporal de remanescentes da mata atlântica com dados Spot-Vgt e variáveis meteorológicas. BIOFIX Scientific Journal, v. 5, n. 1, p. 13–22, 2020. https://doi.org/10.5380/biofix.v5i1.67235

KAMBLE, D. B. et al. Drought assessment for kharif rice using standardized precipitation index (SPI) and vegetation condition index (VCI). Journal of Agrometeorology, v. 21, n. 2, p. 182–187, 2019.

KOGAN, F. N. Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, v. 15, n. 11, p. 91–100, 1995. https://doi.org/10.1016/0273-1177(95)00079-T

LEIVAS, J. F. et al. Monitoramento da seca 2011/2012 no nordeste brasileiro a partir do satélite Spot-vegetation e TRMM. Revista Engenharia Na Agricultura - Reveng, v. 22, n. 3, p. 211–221, 2014. https://doi.org/10.13083/1414-3984.v22n03a03

MINAS GERAIS. Portaria IGAM no 014, de 08 de abril de 2015. Declara situação crítica de escassez hídrica superficial na porção hidrográfica localizada no reservatório Serra Azul e a sua bacia de contribuição.: Belo Horizonte. , 2015

MORETTIN, P. A.; TOLOI, C. M. C. Análise de séries temporais. 4. ed. New Jersey: Blucher, 2006.

MORETTIN, P. A.; TOLOI, C. M. C. Modelos para previsão de séries temporais. Rio de Janeiro: Instituto de Matemática Pura e Aplicada, 1981.

NORA, E. L. D.; SANTOS, J. E. Análise da dinâmica sazonal de duas formaçoes florestais do bioma Mata Atlântica com base em índices de vegetação. Perspectiva, v. 34, n. 125, p. 41–51, 2010.

PARANHOS, R. et al. Desvendando os Mistérios do Coeficiente de Correlação de Pearson: o Retorno. Leviathan (São Paulo), n. 8, p. 66, 2014. https://doi.org/10.11606/issn.2237-4485.lev.2014.132346

PEARSON, K. The grammar of science. Nature, v. 46, n. 1185, p. 247–247, 1982. https://doi.org/10.1038/046247b0

PEARSON, K.; FISCHER, R.; INMAN, H. F. Karl Pearson and R. A. Fischer on Statistical Test: A 1935 exchange from nature. The American Statistician, v. 48, n. 1, p. 2–11, 1994. https://doi.org/10.1080/00031305.1994.10476010

PONZONI, F. J.; SHIMABUKURO, Y. E.; KUPLICH, T. M. Sensoriamento remoto da vegetação. São José dos Campos: Oficina de Textos, 2015.

POTTER, C. et al. Major disturbance events in terrestrial ecosystems detected using global satellite data sets. Global Change Biology, v. 9, n. 7, p. 1005–1021, 2003. https://doi.org/10.1046/j.1365-2486.2003.00648.x

QUESADA, H. B. et al. Análise da vegetação ripária em bacia hidrográfica utilizando Índice de Vegetação Normalizada (NDVI) no município de Maringá-PR. Geo UERJ, v. 0, n. 31, p. 439–455, 2017. https://doi.org/10.12957/geouerj.2017.26737

SAUSEN, T. M.; LACRUZ, M. S. P. Sensoriamento remoto para desastres. 1. ed. São Paulo: Oficina de Textos, 2015.

SOARES, A. Geoestatística para as ciências da terra e do ambiente. Rio de Janeiro: Instituto Superior Técnico, 2000.

UTTARUK, Y.; LAOSUWAN, T. Drought detection by application of remote sensing technology and vegetation phenology. Journal of Ecological Engineering, v. 18, n. 6, p. 115–121, 2017. https://doi.org/10.12911/22998993/76326

VIEIRA, S. et al. Geoestatistical theory and application to variability of some agronomical properties. Hilgardia, v. 51, n. 3, p. 1–75, 1983. https://doi.org/10.3733/hilg.v51n03p075

XU, Z. et al. Trends in Global Vegetative Drought from Long-Term Satellite Remote Sensing Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 13, p. 815–826, 2020. https://doi.org/10.1109/JSTARS.2020.2972574

YULISTYA, V. D.; WIBOWO, A.; KUSRATMOKO, E. Assessment of agricultural drought in paddy field area using Vegetation Condition Index (VCI) in Sukaresmi District, Cianjur Regency. IOP Conference Series: Earth and Environmental Science, v. 311, n. 1, 2019. https://doi.org/10.1088/1755-1315/311/1/012020

ZAMBRANO, F. et al. Sixteen years of agricultural drought assessment of the biobío region in chile using a 250 m resolution vegetation condition index (VCI). Remote Sensing, v. 8, n. 6, p. 1–20, 2016. https://doi.org/10.3390/rs8060530

ZHANG, L. et al. Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices. Remote Sensing of Environment, v. 190, p. 96–106, 2017. http://dx.doi.org/10.1016/j.rse.2016.12.010

Authors hold the Copyright for articles published in this journal, and the journal holds the right for first publication. Because they appear in a public access journal, articles are licensed under Creative Commons Attribution (BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...