Abstract
Understanding the dynamics of the earth’s surface variation patterns has been critical for climate change adaptation and mitigation. During the last decades, detecting these events through remote sensing allowed us to improve the conventional analysis toward an integrated space–time analysis. This chapter proposes a spatiotemporal exploratory analysis of the information from SPI, SPEI and links its results into remote sensing information of NDVI using computer vision algorithms for pattern recognition and tracking. This analysis was carried out in three phases. First, a 20-year analysis of vegetation-based indices (NDVI) and meteorological drought indices (SPI, SPEI), to identify and compare the water anomalies over Central America dry corridor, using ERA5 climatological information and satellite images for the period 2000–2020. These results are used to assess the spatiotemporal variations of meteorological stress and vegetation water stress. All this is analyzed considering the conditions along the phenological cycle. The implementation of the spatiotemporal drought methodology proposed by Corzo and Vitali, 2018, and its results used as input time series, through LOWESS smoothing proposed by Jong (Remote Sens Environ 115(2):692–702, 2011). The final comparison uses statistical metrics such as spatial correlation. Drought units are identified for each meteorological drought index and are compared among them, and together with the NDVI normalization, a vegetation-based drought index (vegetation condition index or VCI) is estimated. This step allows representing the phenological conditions of vegetative water stress without interferences of temporality and consistency. Finally, the VCI is classified in categorical ranges that allow the comparison of drought units to the SPI in different lags (1, 3, 6) and SPEI (1, 3, 6). By this, establishing meteorological relationships with the vegetative surface dynamics and generating the trajectories (tracking) of each drought cluster observed with the VCI. Finally, a validation of the trajectories are also compared. All validation show that his methodology allows using directly inferred drought from remote sensing as a meteorological drought index, in similar way as SPI. The spatiotemporal changes monitoring and evaluation associated with land cover and water sources, and derivation of drought index based on vegetative condition is an essential component of this chapter’s contribution.
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Acknowledgements
We would like to thank the IHE-Delft and Escuela Colombiana de IngenieriaJulio Garavito for funding this research. Also, thanks for the support to the hydro informatics research group, Dr. Gerald Augusto Corzo Perez, especially to reviewers and editors for their valuable comments, which helped to improve the quality of this manuscript.
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Hernández, K.A.S., Perez, G.A.C. (2022). A Comparative Analysis of Spatiotemporal Drought Events from Remote Sensing and Standardized Precipitation Indexes in Central America Dry Corridor. In: Singh, V.P., Yadav, S., Yadav, K.K., Corzo Perez, G.A., Muñoz-Arriola, F., Yadava, R.N. (eds) Application of Remote Sensing and GIS in Natural Resources and Built Infrastructure Management. Water Science and Technology Library, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-031-14096-9_5
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