@article{PereiraMedeirosFranckeetal.2019,
  author    = {Pereira, Bruno and Medeiros, Pedro Henrique Augusto and Francke, Till and Ramalho, Geraldo and F{\"o}rster, Saskia and De Araujo, Jose Carlos},
  title     = {Assessment of the geometry and volumes of small surface water reservoirs by remote sensing in a semi-arid region with high reservoir density},
  series = {Hydrological sciences journal = Journal des sciences hydrologiques},
  volume    = {64},
  journal   = {Hydrological sciences journal = Journal des sciences hydrologiques},
  number    = {1},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {0262-6667},
  doi       = {10.1080/02626667.2019.1566727},
  pages     = {66 -- 79},
  year      = {2019},
  abstract  = {Water fluxes in highly impounded regions are heavily dependent on reservoir properties. However, for large and remote areas, this information is often unavailable. In this study, the geometry and volume of small surface reservoirs in the semi-arid region of Brazil were estimated using terrain and shape attributes extracted by remote sensing. Regression models and data classification were used to predict the volumes, at different water stages, of 312 reservoirs for which topographic information is available. The power function used to describe the reservoir shapes tends to overestimate the volumes; therefore, a modified shape equation was proposed. Among the methods tested, four were recommended based on performance and simplicity, for which the mean absolute percentage errors varied from 24 to 39\%, in contrast to the 94\% error achieved with the traditional method. Despite the challenge of precisely deriving the flooded areas of reservoirs, water management in highly reservoir-dense environments should benefit from volume prediction based on remote sensing.},
  language  = {en}
}
@article{HeineFranckeRogassetal.2014,
  author    = {Heine, Iris and Francke, Till and Rogass, Christian and Medeiros, Pedro Henrique Augusto and Bronstert, Axel and F{\"o}rster, Saskia},
  title     = {Monitoring seasonal changes in the water surface areas of reservoirs using TerraSAR-X time series data in semiarid northeastern Brazil},
  series = {IEEE journal of selected topics in applied earth observations and remote sensing},
  volume    = {7},
  journal   = {IEEE journal of selected topics in applied earth observations and remote sensing},
  number    = {8},
  publisher = {Inst. of Electr. and Electronics Engineers},
  address   = {Piscataway},
  issn      = {1939-1404},
  doi       = {10.1109/JSTARS.2014.2323819},
  pages     = {3190 -- 3199},
  year      = {2014},
  abstract  = {The 933 km(2) Bengue catchment in northeastern Brazil is characterized by distinct rainy and dry seasons. Precipitation is stored in variously sized reservoirs, which is essential for the local population. In this study, we used TerraSAR-X SM(HH) data for an one-year monitoring of seasonal changes in the reservoir areas from July 2011 to July 2012. The monitoring was based on acquisitions in the ascending pass direction, complemented by occasional descending-pass images. To detect water surface areas, a histogram analysis followed by a global threshold classification was performed, and the results were validated using in situ GPS data. Distinguishing between small reservoirs and similar looking dark areas was difficult. Therefore, we tested several approaches for identifying misclassified areas. An analysis of the surface area dynamics of the reservoirs indicated high spatial and temporal heterogeneities and a large decrease in the total water surface area of the reservoirs in the catchment by approximately 30\% within one year.},
  language  = {en}
}