Analysis of the Taquari Megafan through radiometric indices

https://doi.org/10.1016/j.jsames.2022.104034Get rights and content

Highlights

  • The choice of radiometric index affects the distinction of mapped water features.

  • MNDWI accurately identifies water bodies and channels.

  • NDWI-Gao was very responsive to alternating cycles of wetter and drier vegetation.

  • The vegetation that makes up the Taquari megafan is unevenly affected by drought.

Abstract

The complex landscapes of tropical monsoonal lowlands make water resource estimates difficult, given sparsely distributed monitoring networks. Satellite radiometry has facilitated increased access to environmental data. Here, we use radiometric indices to analyze the Taquari megafan and identify features of the Pantanal subregions that characterize it. Recent periods of strong droughts and floods were selected in the region for statistical analysis using the following radiometric indices: NDWI-Gao, NDWI-McFeeters and MNDWI. The behavior of the NDWI-Gao showed that the Paiaguás and Nhecolândia subregions had similar vegetation moisture values in 2011 and 2012, but surprisingly, the two subregions did not attain maximum values of vegetation moisture higher than values in the Taquari subregion during severe flooding in 2011. The Paiaguás and Nhecolândia subregions were more correlated in terms of vegetation moisture than open water features. The MNDWI analysis of the Taquari subregion, however, revealed the highest levels of liquid water in the vegetation and the highest values for water bodies/courses. The permanently flooded state of the Taquari subregion may explain why the vegetation appears to be moister in the NDWI-Gao. The NDWI-Gao data were consistent for dry vegetation in 2020, recording values of −0.7, while other dry periods such as 2010 and 2013 recorded values of −0.4. The vegetation responded quickly to the alternation of drought/flood cycles, registering high values of liquid water content in the period 2011–2012 and reduced values in 2012–2013.

Introduction

The Taquari megafan, the largest fluvial fan in the Pantanal floodplain, is sui generis as it has a circular geometry and diameter of ∼250 km (ASSINE, 2009), which makes it one of the largest of its type in the world (LATRUBESSE et al., 2005). According to Zani and De Fátima Rossetti (2012), megafans are large sedimentary deposits found in continental basins, specifically designating fluvial fans spanning >1,000 km2. Both fluvial fans and megafans are characterized by distributary drainage and frequent main channel avulsion phenomena (ZANI, 2008).

With such a large area, the Taquari megafan system has been subdivided according to compositional features, such as vegetation cover, water availability and soil composition. The Pantanal floodplain consists of at least two subregions, so assessing the Taquari megafan within subregions allows for more detailed analyses. The use of remote sensing is an efficient technology, enabling analyzes to be carried out at different scales (local, regional, continental and global) within the Pantanal floodplain. Mapping at different scales and possibly on a repeatable basis is an advantage in remote sensing (SCHMUGGE et al., 2002). In addition, there are currently a variety of remote sensing products available online free of charge, which allows for the development of research.

Using the available remote sensing techniques, we can analyze aspects of moisture or water resources, soil, vegetation, and other natural components of the megafan and its subregions to characterize and understand this Pantanal region. The Taquari watershed suffers from erosion and silting of the main river, resulting in severe flooding, ecological deterioration and economic losses (QUERNER et al., 2005). Thus, the generation of data from radiometric indices can help assess the impacts resulting from this silting process, such as widespread flooding that has occurred in the region since 1970 (ABDON, 2017).

We employed geotechnology to analyze the Taquari megafan using radiometric indices and to identify characteristics of the Pantanal subregions that will ultimately help improve water resources and sediment management in this area.

Section snippets

Study area

The Pantanal is an active sedimentary basin formed by a mosaic of alluvial fans deposited in the Pleistocene, flooded perennially by monsoon precipitation and surrounded by plateaus (ASSINE and SOARES, 2004; ALHO, 2008). The Pantanal region is divided into several subregions, because the Pantanal lowlands are not flooded homogeneously throughout its vast extent.

Of the various Pantanal subregional classifications, Mioto et al. (2012) divided the Pantanal into 18 subregions, of which 3 make up

Results and discussions

Remote sensing techniques represent a great advantage for studies in the Pantanal region, since it is generally difficult for field validation. We used radiometric indices analyze the entire megafan and compute statistical weightings for the subregions. It is noteworthy that with the spatial resolution of the MODIS image, some targets were not distinguishable, such as the lagoons (baías and salt pans) of Nhecolândia that suffered from the homogenization of the digital number of the pixel.

The

Conclusions

A large part of the Taquari megafan was covered by some type of vegetation ranging from herbaceous or arboreal to aquatic. The NDWI-Gao results were therefore most suitable for interpretation and generated less spectral confusion. The “liquid water in vegetation” factor provided a satisfactory response both in drier and moister areas. In terms of vegetation moisture, the Nhecolândia and Paiaguás sub-regions presented similar values, indicating that the vegetation in these areas were similarly

CRediT authorship contribution statement

Luciana Escalante Pereira: Writing – original draft, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Edward Limin Lo: Writing – review & editing, Resources. Antônio Conceição Paranhos Filho: Writing – review & editing, Writing – original draft, Validation, Supervision, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We thank the Brazilian National Council for Scientific and Technological Development (CNPq) for a Research Productivity fellowship to A.C. Paranhos Filho (process 305013/2018–1). The Foundation for the Support and Development of Teaching, Science, and Technology in Mato Grosso do Sul (FUNDECT) supported L.E. Pereira, and the US National Science Foundation Graduate Research Fellowship Program and University of Kentucky assistantships supported E.L. Lo. This study was made possible by the support

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