Classification (of information)
; Digital storage
; Mean square error
; Remote sensing
; Reservoir management
; Reservoirs (water)
; Surveying
; 3-D reconstruction algorithms
; Digital elevation model
; Dynamic classification
; Effective management
; Irrigation reservoirs
; Root mean square errors
; Satellite remote sensing
; Scan line correctors
; Information management
; algorithm
; classification
; digital elevation model
; in situ measurement
; information
; Landsat
; monitoring
; remote sensing
; reservoir
; satellite imagery
; storage
; surface area
; topography
; Jordan
; Syrian Arab Republic
; Yarmouk Basin
英文摘要:
In river basins with water storage facilities, the availability of regularly updated information on reservoir level and capacity is of paramount importance for the effective management of those systems. However, for the vast majority of reservoirs around the world, storage levels are either not measured or not readily available due to financial, political, or legal considerations. This paper proposes a novel approach using Landsat imagery and digital elevation models (DEMs) to retrieve information on storage variations in any inaccessible region. Unlike existing approaches, the method does not require any in situ measurement and is appropriate for monitoring small, and often undocumented, irrigation reservoirs. It consists of three recovery steps: (i) a 2-D dynamic classification of Landsat spectral band information to quantify the surface area of water, (ii) a statistical correction of DEM data to characterize the topography of each reservoir, and (iii) a 3-D reconstruction algorithm to correct for clouds and Landsat 7 Scan Line Corrector failure. The method is applied to quantify reservoir storage in the Yarmouk basin in southern Syria, where ground monitoring is impeded by the ongoing civil war. It is validated against available in situ measurements in neighbouring Jordanian reservoirs. Coefficients of determination range from 0.69 to 0.84, and the normalized root-mean-square error from 10 to 16g % for storage estimations on six Jordanian reservoirs with maximal water surface areas ranging from 0.59 to 3.79g km2.
Department of Civil Engineering and Water Engineering, Université Laval, Québec, QC, Canada; Department of Civilg and Environmental Engineeringg and Earth Science, University of Notre Dame, Notre Dame, IN, India; Department of Engineering, School of Engineering and Computing Sciences, Texas A and M University - Corpus Christi, Corpus Christi, TX, United States
Recommended Citation:
Avisse N,, Tilmant A,, François Müller M,et al. Monitoring small reservoirs' storage with satellite remote sensing in inaccessible areas[J]. Hydrology and Earth System Sciences,2017-01-01,21(12)