Big Data and Multiple Methods for Mapping Small Reservoirs: Comparing Accuracies for Applications in Agricultural Landscapes

Jones, Sarah K.; Fremier, Alexander K.; DeClerck, Fabrice; Smedley, David; Pieck, Aline Ortega; Mulligan, Mark

doi:10.3390/rs9121307

Cited by 29 publications

(23 citation statements)

References 56 publications

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“…The surface wetness is directly derived from each image of the Landsat time-series using Xu's Normalized Differenced Water Index (NDWI) [74], a band ratio using the green and first short-wave infrared (SWIR I) band. Xu's version of the NDWI (also termed Modified NDWI) has been successfully used to detect flooded areas from remotely sensed data and mostly outperformed water indices based on different band combinations for land/water differentiation [75][76][77]. The NDWI band ratio is dimensionless and mathematically varies from −1 to 1.…”

Section: Dynamics Of Omongwa Pan Surfacementioning

confidence: 99%

Analyses of Namibian Seasonal Salt Pan Crust Dynamics and Climatic Drivers Using Landsat 8 Time-Series and Ground Data

2020

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Salt pans are highly dynamic environments that are difficult to study by in situ methods because of their harsh climatic conditions and large spatial areas. Remote sensing can help to elucidate their environmental dynamics and provide important constraints regarding their sedimentological, mineralogical, and hydrological evolution. This study utilizes spaceborne multitemporal multispectral optical data combined with spectral endmembers to document spatial distribution of surface crust types over time on the Omongwa pan located in the Namibian Kalahari. For this purpose, 49 surface samples were collected for spectral and mineralogical characterization during three field campaigns (2014–2016) reflecting different seasons and surface conditions of the salt pan. An approach was developed to allow the spatiotemporal analysis of the salt pan crust dynamics in a dense time-series consisting of 77 Landsat 8 cloud-free scenes between 2014 and 2017, covering at least three major wet–dry cycles. The established spectral analysis technique Sequential Maximum Angle Convex Cone (SMACC) extraction method was used to derive image endmembers from the Landsat time-series stack. Evaluation of the extracted endmember set revealed that the multispectral data allowed the differentiation of four endmembers associated with mineralogical mixtures of the crust’s composition in dry conditions and three endmembers associated with flooded or muddy pan conditions. The dry crust endmember spectra have been identified in relation to visible, near infrared, and short-wave infrared (VNIR–SWIR) spectroscopy and X-ray diffraction (XRD) analyses of the collected surface samples. According these results, the spectral endmembers are interpreted as efflorescent halite crust, mixed halite–gypsum crust, mixed calcite quartz sepiolite crust, and gypsum crust. For each Landsat scene the spatial distribution of these crust types was mapped with the Spectral Angle Mapper (SAM) method and significant spatiotemporal dynamics of the major surface crust types were observed. Further, the surface crust dynamics were analyzed in comparison with the pan’s moisture regime and other climatic parameters. The results show that the crust dynamics are mainly driven by flooding events in the wet season, but are also influenced by temperature and aeolian activity in the dry season. The approach utilized in this study combines the advantages of multitemporal satellite data for temporal event characterization with advantages from hyperspectral methods for the image and ground data analyses that allow improved mineralogical differentiation and characterization.

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Section: Dynamics Of Omongwa Pan Surfacementioning

confidence: 99%

Analyses of Namibian Seasonal Salt Pan Crust Dynamics and Climatic Drivers Using Landsat 8 Time-Series and Ground Data

2020

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“…Landsat data was successfully used for water bodies observation and identification of their shorelines many times, e.g. (Bhagat and Sonawane, 2011;Jiang et al, 2014;Jones et al, 2017;Nandi et al, 2016).…”

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

confidence: 99%

“…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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“…Ogilvie et al: Surface water monitoring in small water bodies 30 m topographic corrections, as did the Landsat imagery. Intensity-hue-saturation (IHS) pansharpening (Carper et al, 1990) was used on SWIR bands to allow water detection at 10 m (Du et al, 2016;Kaplan and Avdan, 2018) with the MNDWI and to compare performance with the 20 and 30 m MNDWI. The optimal MNDWI threshold on Sentinel-2 imagery was independently calibrated against k-means (unsupervised) classification (Jain, 2010) of flooded areas, and was substantially lower (−0.2) than with Landsat imagery (−0.09).…”

Section: Comparison With Sentinel-2 Imagerymentioning

confidence: 99%

Authors' response to reviewer #2 C. Cudennec

Ogilvie¹

2018

Preprint

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Hydrometric monitoring of small water bodies (1-10 ha) remains rare, due to their limited size and large numbers, preventing accurate assessments of their agricultural potential or their cumulative influence in watershed hydrology. Landsat imagery has shown its potential to support mapping of small water bodies, but the influence of their limited surface areas, vegetation growth, and rapid flood dynamics on long-term surface water monitoring remains unquantified. A semi-automated method is developed here to assess and optimize the potential of multi-sensor Landsat time series to monitor surface water extent and mean water availability in these small water bodies. Extensive hydrometric field data (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) for seven small reservoirs within the Merguellil catchment in central Tunisia and SPOT imagery are used to calibrate the method and explore its limits. The Modified Normalised Difference Water Index (MNDWI) is shown out of six commonly used water detection indices to provide high overall accuracy and threshold stability during high and low floods, leading to a mean surface area error below 15 %. Applied to 546 Landsat 5, 7, and 8 images over 1999-2014, the method reproduces surface water extent variations across small lakes with high skill (R 2 = 0.9) and a mean root mean square error (RMSE) of 9300 m 2 . Comparison with published global water datasets reveals a mean RMSE of 21 800 m 2 (+134 %) on the same lakes and highlights the value of a tailored MNDWI approach to improve hydrological monitoring in small lakes and reduce omission errors of flooded vegetation. The rise in relative errors due to the larger proportion and influence of mixed pixels restricts surface water monitoring below 3 ha with Landsat (Normalised RMSE = 27 %). Interferences from clouds and scan line corrector failure on ETM+ after 2003 also decrease the num-ber of operational images by 51 %, reducing performance on lakes with rapid flood declines. Combining Landsat observations with 10 m pansharpened Sentinel-2 imagery further reduces RMSE to 5200 m 2 , displaying the increased opportunities for surface water monitoring in small water bodies after 2015.

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Big Data and Multiple Methods for Mapping Small Reservoirs: Comparing Accuracies for Applications in Agricultural Landscapes

Cited by 29 publications

References 56 publications

Analyses of Namibian Seasonal Salt Pan Crust Dynamics and Climatic Drivers Using Landsat 8 Time-Series and Ground Data

Analyses of Namibian Seasonal Salt Pan Crust Dynamics and Climatic Drivers Using Landsat 8 Time-Series and Ground Data

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

Authors' response to reviewer #2 C. Cudennec

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