Mathematical modelling of the intra-annual behaviour of NDVI in the Caatinga Biome, Brazil
DOI:
https://doi.org/10.5902/1980509837279Keywords:
Semi-arid, Vegetation Index, Cauchy function, Gap series fillingAbstract
Vegetation indexes from remote sensing images are often used for land-cover monitoring and identification of biomass changes. They are also very useful to describe the relationships between the phenological cycle and the carbon sequestration, which are climate change indicators. The Caatinga land-cover is very heterogeneous, making hard the understanding of the land cover processes in different scales (spatial and temporal), due to seasonalities and human actions. The Landsat series products usually can describe spatial land-cover variations, with a low temporal scale, so far. This study aims to improve the temporal representation of the land cover by Landsat images for a Caatinga area. This article presents an evaluation, using a mathematical approach of three-parameter functions to describe the Normalized Difference Vegetation Index (NDVI). Each function performance was evaluated by the reduced chi-square (χ²) and determination coefficient (R2) parameters. The analysis is performed for an annual period and considers land-cover changes by anthropic action. The Cauchy function seems the best option and presented an adjust up to 83% of the total (years and places), with an R² (average) of 0.82. The parameters of this function can be a valuable source for environmental studies in the Caatinga biome, supporting temporal analysis of the vegetation.
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References
ATKINSON, P. M. et al.Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sensing of Environment, [s. l.], v. 123, p. 400-417, 2012.
BECK, P. S. A. et al. Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI. Remote SensingofEnvironment, [s. l.], v. 100, p. 321-334, 2006.
CADIER, E.; FREITAS, B. J.; LEPRUN, J. C. Bacia Experimental de Sumé: instalação e primeiros resultados. Recife: SUDENE, 1983. 87 p.
CAI, Z. et al. Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data. Remote Sensing, [s. l.], v. 9, n. 12, p. 1271-1289, 2017.
CHEN, J. et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote SensingofEnvironment, [s. l.], v. 91, n. 3/4, p. 332-344, 2004.
CUNHA, J. E. B. L. Monitoramento ambiental por sensoriamento remoto: avaliação, automação e aplicação ao bioma Caatinga utilizando séries históricas Landsat. 2018. Tese (Doutorado em Recursos Naturais) – Universidade Federal de Campina Grande, Campina Grande, 2018.
FOGA, S. et al. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, [s. l.], v. 194, p. 379-390, 2017.
HIRD, J. N.; MCDERMID, G. J. Noise reduction of NDVI time series: an empirical comparison of selected techniques. Remote SensingofEnvironment, [s. l.], v. 113, n. 1, p. 248-258, 2009.
JENSEN, J. R. Sensoriamento remoto do ambiente: uma perspectiva em recursos terrestres.2. ed.São José dos Campos: Parêntese, 2009. 598 p.
JONES, G. H.; VAUGHAN, A. R. Remote sensing of vegetation: principles, techniques and applications. Oxford: Oxford University Press, 2010. 353 p.
JÖNSSON, P.; EKLUNDH, L. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing, [s. l.], v. 40, n. 8, p. 1824-1832, 2002.
JULIEN, Y.; SOBRINO, J. A. Comparison of cloud-reconstruction methods for time series of composite NDVI data. Remote Sensing of Environment, [s. l.], v. 114, n. 3, p. 618-625, 2010.
JULIEN, Y.; SOBRINO, J. A. Optimizing and comparing gap-filling techniques using simulated NDVI time series from remotely sensed global data.International Journal of Applied Earth Observation and Geoinformation, [s. l.], v. 76, p. 93-111,2019.
KANDASAMY, S. et al. A comparison of methods for smoothing and gap filling time series of remote sensing observations - application to MODIS LAI products. Biogeosciences, [s. l.], v. 10, p. 4055-4071, 2013.
KUPLICH, T. M.; MOREIRA, A.; FONTANA, D. C. Série temporal de índice de vegetação sobre diferentes tipologias vegetais no Rio Grande do Sul. Revista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande, v. 17, n. 10, p. 1116-1123, 2013.
LEROUX, L. et al. Driving forces of recent vegetation changes in the Sahel: lessons learned from regional and local level analyses. Remote Sensing of Environment, [s. l.], v. 191, p. 38-54, 2017.
LIU, R. et al. Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability. Remote Sensing of Environment, [s. l.], v. 189, p. 164-179, 2017.
LOURENÇO, R. V. et al. Análise temporal do NDVI sob condições de caatinga preservada. In:INOVAGRI INTERNATIONAL MEETING, 3., Fortaleza. Anais […]. Fortaleza: [s. n.], 2015. p. 808-814.
MARKERT, K. et al. Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing. Remote Sensing, [s. l.], v. 10, n. 6, p. 909-928, 2018.
MARIANO, D. A. et al. Use of remote sensing indicators to assess effects of drought and human-induced land degradation on ecosystem health in Northeastern Brazil. Remote SensingofEnvironment, [s. l.], v. 213, p. 129-143, 2018.
PARAÍBA. Agência Executiva de Gestão das Águas. [Website]. João Pessoa, 2018.Disponível em: http://www.aesa.pb.gov.br/aesa-website/.Acessoem: 15 jun. 2018.
PASTOR-GUZMANA, J.; DASHA, J.; ATKINSON, M. P. Remote sensing of mangrove forest phenology and its environmental drivers. Remote SensingofEnvironment, [s. l.], v. 205, p. 71-84, 2018.
SILVA, C. R. daet al. Analysis of the phenology dynamics of Brazilian caatinga species with NDVI time series. Cerne, Lavras, v. 24, n. 1, p. 48-58, 2018.
SILVA, W. P. daet al. “LAB Fit Ajuste de Curvas”: um software em português para tratamento de dados experimentais. Revista Brasileira de Ensino de Física, São Paulo, v. 26, n. 4, p. 419-427, 2004.
TESTA, S. et al. MODIS-derived EVI, NDVI and WDRVI time series to estimate phenological metrics in French deciduous forests. International Journal of Applied Earth Observation and Geoinformation, [s. l.], v. 64, p. 132-144, 2018.
UNITED STATES GEOLOGICAL SURVEY. Landsat Surface Reflectance Quality Assessment/Landsat Missions. [S. l., 2018]. Disponível em: https://landsat.usgs.gov/landsat-surface-reflectance-quality-assessment/. Acessoem: 17 dez. 2018.
VERBESSELT, J. et al. Remotely sensed resilience of tropical forests. Nature Climate Change, [s. l.], v. 6, n. 11, p. 1028-1031, 2016.
ZHANG, Q. et al. Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013). Agriculturaland Forest Meteorology, [s. l.], v. 248, p. 408-417, 2018.
