Combining Landsat observations with hydrological modelling for improved surface water monitoring of small lakes

Ogilvie, Andrew; Belaud, Gilles; Massuel, Sylvain; Mulligan, Mark; Goulven, Patrick Le; Malaterre, Pierre‐Olivier; Calvez, Roger

doi:10.1016/j.jhydrol.2018.08.076

Cited by 28 publications

(17 citation statements)

References 74 publications

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“…Additional validation using available river discharge data and model calibration against observations is recommended for regional studies. Data assimilation using in situ or satellite-derived observations of water surface area would also be useful for improving the ability of CaMa-Flood to estimate water surface extent (Bates, 2012;Ogilvie et al, 2018;Schumann et al, 2009). Although the water surface area estimated using CaMa-Flood deviates from that of Landsat, CaMa-Flood offers great advantages over satellite results related to the following aspects.…”

Section: Discussionmentioning

confidence: 99%

Toward Improved Comparisons Between Land‐Surface‐Water‐Area Estimates From a Global River Model and Satellite Observations

Zhou

Prigent

Yamazaki

2021

Water Resources Research

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Land surface water area (hereafter LSWA) is of paramount importance to the survival of all life forms (Karpatne et al., 2016). Water not only provides habitat for aquatic organisms but also affects various aspects of human life, such as for agricultural, domestic and industrial purposes (Vörösmarty & Sahagian, 2000). LSWA is highly dynamic and variations therein can be used as a direct indicator of climate change (Williamson et al., 2009) or human-induced changes (Pekel et al., 2016). LSWA is thus an essential variable in ecological, hydrological, climatic, and economic studies (Hirabayashi et al., 2013;Raymond et al., 2013;Willner et al., 2018). For such applications, accurate water information at adequate spatiotemporal resolution is crucial.Estimation of LSWA relies on three methods: ground surveys, remote sensing, and models. Among these methods, ground surveys cannot fully describe the water dynamics due to their slow updating frequency (Carroll et al., 2009;Lehner & Döll, 2004) and the significant cost of covering a large spatial domain. Remote sensing using satellites is an outstanding method that can provide regular large-scale observations of water surfaces. Various satellites have been used to identify LSWA, including Landsat (

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Section: Discussionmentioning

confidence: 99%

Toward Improved Comparisons Between Land‐Surface‐Water‐Area Estimates From a Global River Model and Satellite Observations

Zhou

Prigent

Yamazaki

2021

Water Resources Research

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“…Five reservoirs with different sizes (and water storing capacity) were selected in this study (Figure 1) combining Landsat images and hydrological studies [32] . These reservoirs are located in the climatic Mediterranean area of Spain: La Pedrera and Beniarrés, both in the province of Alicante, Cubillas and Negratín in the province of Granada and La Serena in the province of Badajoz.…”

Section: Methodsmentioning

confidence: 99%

Using Landsat Images to Determine Water Storing Capacity in Mediterranean Environments

et al. 2021

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Reservoirs play an important role in water management and are key elements for water supply. Monitoring is needed in order to guarantee the quantity and quality of stored water. However, this task is sometimes not easy. The objective of this study was to develop a procedure for predicting volume of stored water with remote sensing in water bodies under Mediterranean climate conditions. To achieve this objective,multispectral Landsat 7 and 8 images (NASA) were analyzed for the following five reservoirs: La Serena,La Pedrera, Beniarrés, Cubillas and Negratín (Spain). Reservoirs water surface was computed with the spectral angle mapper (SAM) algorithm.After that, cross-validation regression models were computed in order to assess the capability of water surface estimations to predict stored water in each of the reservoirs. The statistical models were trained with Landsat 7 images and were validated by using Landsat 8 images. Our results suggest a good capability of water volume prediction from free satellite imagery derived from surface water estimations. Combining free remote sensing images and open source GIS algorithms can be a very useful tool for water management and an integrated and efficient way to control water storage,especially in low accessible sites.

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“…Small water inland bodies identification from remotely sensed data was emphasized as a specific issue by several authors, e.g. (Cermakova, Komarkova and Sedlak, 2019;Haas, Bartholomé and Combal, 2009;Jones et al, 2017;Li et al, 2015;Ogilvie et al, 2018;Pásler, Komárková and Sedlák, 2015). In the Czech Republic, e.g.…”

Section: State Of the Art Water Bodies And Land Cover Changesmentioning

confidence: 99%

Spectral Enhancement of Imagery for Small Inland Water Bodies Monitoring: Utilization of UAV-Based Data

Komárková

Čermáková

Sedlák

et al. 2019

J INFORM SYSTEMS ENG

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The article describes a way for identification of land cover types and consequently land cover changes around a small water body, which is based on spectral enhancement of RGB UAV-based data. A middleclass unmanned aerial vehicle (UAV)-DJI Phantom 3 Pro, was used for data collection. UAV represents a cheap and on-demand available solution for remote data sensing. Its utilization is limited by weather conditions and particular legal regulations must be followed. The article is focused on a monitoring of a small water body and its surrounding by spectral enhancement. Spectral indices, which are calculated only from the visible bands, are used to identify particular land cover types: Color Index of Vegetation Extraction (CIVE), Excess Green (ExG), Excess Red (ExR), Green Leaf Index (GLI), Normalized Green-Red Difference Index (NGRDI), Red-Green-Blue Vegetation Index (RGBVI), Visible Atmospherically Resistant Index (VARI), and ExG-ExR difference. Low pass filtering was used for post-processing and results were simply visualised in a form of classified raster (by natural breaks-Jenks). Even this simple spectral enhancement of imagery supports its visual interpretation. Visible spectral indices highlight particular land cover types, namely green vegetation and water surface but other types of land cover can be distinguished as well.

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Combining Landsat observations with hydrological modelling for improved surface water monitoring of small lakes

Cited by 28 publications

References 74 publications

Toward Improved Comparisons Between Land‐Surface‐Water‐Area Estimates From a Global River Model and Satellite Observations

Toward Improved Comparisons Between Land‐Surface‐Water‐Area Estimates From a Global River Model and Satellite Observations

Using Landsat Images to Determine Water Storing Capacity in Mediterranean Environments

Spectral Enhancement of Imagery for Small Inland Water Bodies Monitoring: Utilization of UAV-Based Data

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