TY  - JOUR
A1  - Heine, Iris
A1  - Francke, Till
A1  - Rogass, Christian
A1  - Medeiros, Pedro Henrique Augusto
A1  - Bronstert, Axel
A1  - Förster, Saskia
T1  - Monitoring seasonal changes in the water surface areas of reservoirs using TerraSAR-X time series data in semiarid northeastern Brazil
JF  - IEEE journal of selected topics in applied earth observations and remote sensing
N2  - 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.
KW  - Image classification
KW  - monitoring
KW  - radar imaging
KW  - remote sensing
KW  - synthetic aperture radar (SAR)
Y1  - 2014
U6  - https://doi.org/10.1109/JSTARS.2014.2323819
SN  - 1939-1404
SN  - 2151-1535
VL  - 7
IS  - 8
SP  - 3190
EP  - 3199
PB  - Inst. of Electr. and Electronics Engineers
CY  - Piscataway
ER  - 
TY  - JOUR
A1  - Pereira, Bruno
A1  - Medeiros, Pedro Henrique Augusto
A1  - Francke, Till
A1  - Ramalho, Geraldo
A1  - Förster, Saskia
A1  - De Araujo, Jose Carlos
T1  - Assessment of the geometry and volumes of small surface water reservoirs by remote sensing in a semi-arid region with high reservoir density
JF  - Hydrological sciences journal = Journal des sciences hydrologiques
N2  - 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.
KW  - reservoir volume
KW  - high-density reservoir network
KW  - water height-area-volume curve
KW  - semi-arid
KW  - remote sensing
Y1  - 2019
U6  - https://doi.org/10.1080/02626667.2019.1566727
SN  - 0262-6667
SN  - 2150-3435
VL  - 64
IS  - 1
SP  - 66
EP  - 79
PB  - Routledge, Taylor & Francis Group
CY  - Abingdon
ER  -