International Journal of Applied Earth Observation and Geoinformation
Empirical models for estimating the suspended sediment concentration in Amazonian white water rivers using Landsat 5/TM
Introduction
Suspended sediments affect the biological processes of aquatic ecosystems (Sioli, 1984, Kerr, 1995, Wetzel, 2001), floodplain evolution (Nanson and Croke, 1992, Walling and He, 1998, Pierce and King, 2008), and siltation of reservoirs, among other engineering problems (Thornton et al., 1981, Thornton, 1990).
In the fluvial system of the Amazon River, suspended sediments are important regulators of ecological processes in rivers and floodplains (McClain and Naiman, 2008, Junk et al., 2011). As indicators of geomorphological processes in the drainage basins, particularly in the Andes (Aalto et al., 2006, McClain and Naiman, 2008, Baby et al., 2009), suspended sediments may be possible indicators of disruption of the balance of drainage basins subject to deforestation.
Traditional systems for monitoring the suspended load in rivers are based on water samples collected in situ, from predetermined sections, and subsequent laboratory analysis. Despite the accuracy of these methods, the cost and time of acquisition of these samples are high, and the necessary logistics are quite complex, which results in a sample distribution that is limited in time and space (Ritchie et al., 1987; Ritchie and Shiebe, 2000; Costa et al., 2012). Furthermore, in remote areas such as Amazonian rivers, sampling locations are often chosen on the basis of ease of access, and not by spatial representativeness of the sample. This method of data acquisition, however, is not always adequate to answer questions involving the spatial and temporal variability in the suspended sediment concentration (SSC) in the rivers.
Therefore, the SSC database of rivers of the Amazon Basin is characterized by low spatial and temporal sampling frequency. The public domain data provided by ANA (Brazilian National Water Agency; available at: www.ana.gov.br) exemplify this: there are 97 sampling stations throughout the Amazon basin, with 2.72 samples per year on average for each station. Data provided by the HYBAM program (http://www.ore-hybam.org) have higher sampling frequency, but include narrower time coverage and are limited to larger Amazonian rivers. Beyond these limitations, in situ sampling may contain errors related to local effects, as noted by Espinoza Villar et al. (2012).
The literature validates the application of radiometric data from satellites to estimate SSC (Curran and Novo, 1988, Mertes, 2002, Wang and Lu, 2010) and show many studies based on Landsat images (Khorram, 1985, Aranuvachapun and Walling, 1988, Mertes et al., 1993, Mertes, 1994, Dekker et al., 2002, Islam et al., 2004, Wang et al., 2009). Most of the studies in Case 2 waters, however, refer to reservoirs, estuarine, lacustrine and coastal environments (Wang et al., 2009). The few studies in rivers are restricted to small sections usually covering only one scene, and involving a limited number of dates. Notable exceptions are Martinez et al. (2009), Mangiarotti et al. (2013) and Espinoza Villar et al. (2013).
Data from the Landsat program ensures a wider range of spatial and temporal scales for evaluating SSC, given the far-reaching historic series of well calibrated images (Markham and Helder, 2012), as well as free access (Wulder et al., 2012, www.dgi.inpe.br). Currently there are good prospects for the extension of the Landsat program (Irons et al., 2012, Loveland and Dwyer, 2012), confirming the importance of the new techniques for the application of Landsat/TM data. Taking into account the limitations of the in situ monitoring for the Amazon region, this paper presents a series of empirical models to estimate SSC in Amazonian white water rivers, developed from the integration of in situ data and radiometric data extracted from the Landsat 5/TM sensor.
Section snippets
Study area
The Amazonian rivers are classified into three main types: clear water, black water and white water rivers (Sioli, 1984). Each type of water has a distinct optical behavior that is related to the physical characteristics of the drainage area. White water rivers are dominated by suspended sediments from intense erosion processes that occur in the Andean chain (McClain and Naiman, 2008). The clear water rivers originate in cratonic terrain located to the north and south of the Amazon Basin, and
Choice of in situ data and images
In situ data were acquired at the SSC databases from ANA (www.ana.gov.br) and the HYBAM program (www.ore-hybam.org), for white water rivers only (Table A1 – see Appendix A). Due to the spatial resolution of the TM sensor, data from stations of rivers with a width narrower than 100 m were not used, preventing mixing of the spectral response among the water, sand bars and river banks. On the other hand, this threshold does not prevent adjacency effects produced by the atmospheric scattering, an
Global model
The distribution of actual SSC against estimated SSC (Fig. 6A) had a linear trend which was statistically significant (Table 2). Cross-validation statistics showed that SSC estimates were very similar, regardless of the data set used in the development and testing of the model (Fig. 6B). The model, however, has high variance, resulting in high average errors of SSC estimates and standard deviation.
Five variables were used for the global model, all significant (Table 3). The band ratios were
Conclusions
The global model for estimating SSC in Amazonian white water rivers does not provide estimates as accurate as the regional models. Estimation errors increased with increasing levels of SSC. The patterns of including SWIR and visible bands of shorter wavelengths in the regional models, with negative coefficients, are strong evidence that the models are capable of dealing with effects not related with the suspended components in the water.
The model development reported here is one of the largest
Acknowledgements
This work would not be possible without the free availability of satellite images and in situ data, and the authors are grateful for the free distribution policy of Landsat images by NASA and INPE, and for the distribution of in situ data by ANA and Hybam. We thank Mauricio Carvalho Mathias de Paulo for assistance with the automation of image calibration. Otávio Cristiano Montanher thanks CAPES for the scholarship supporting his master's degree, and CNPq grant 551034/2011-4 for the PCI
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