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Landsat 5 true color image (RGB: 3, 2 and 1) of the study area. The studied lakes are: (1) El Longar; (2) Larga de Villacañas; (3) Tírez; (4) Peñahueca; (5) Grande de Quero; (6) Mermejuela; (7) Salicor; (8) Grande de Villafranca; (9) Las Yeguas; (10) Camino de Villafranca; (11) La Veguilla; (12) Manjavacas; and (13) Alcahozo.
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The Biosphere Reserve of La Mancha Húmeda is a wetland-rich area located in central Spain. This reserve comprises a set of temporary lakes, often saline, where water level fluctuates seasonally. Water inflows come mainly from direct precipitation and runoff of small lake watersheds. Most of these lakes lack surface outlets and behave as endorheic s...
Contexts in source publication
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... study is focused on a host of lakes located in the Biosphere Reserve of La Mancha Húmeda, a wetland-rich region, the largest in the Iberian Peninsula, comprising up to 30,000 ha holding wetlands and lakes [28] distributed within the provinces of Albacete, Ciudad Real, Cuenca, and Toledo, in the Castilla-La Mancha region (Central Spain) (Figure 1). In 1981, UNESCO designated the area as the Biosphere Reserve of La Mancha Húmeda within the Man and Biosphere Programme (MAB), a scientific program to promote improved relationships between people and their environments. ...
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... studied lakes are: (1) El Longar; (2) Larga de Villacañas; (3) Tírez; (4) Peñahueca; (5) Grande de Quero; (6) Mermejuela; (7) Salicor; (8) Grande de Villafranca; (9) Las Yeguas; (10) Camino de Villafranca; (11) La Veguilla; (12) Manjavacas; and (13) Alcahozo. In brackets, the lake identification number (from Figure 1); * 2 source: Guadiana River Basin Administration (http://www.chguadiana.es/); +, these lakes receive treated wastewater inputs that artificially increase the water supply. ...
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... El Longar; (2) Larga de Villacañas; (3) Tírez; (4) Peñahueca; (5) Grande de Quero; (6) Mermejuela; (7) Salicor; (8) Grande de Villafranca; (9) Las Yeguas; (10) Camino de Villafranca; (11) La Veguilla; (12) Manjavacas; and (13) Alcahozo. In brackets, the lake identification number (from Figure 1); * 2 source: Guadiana River Basin Administration (http://www.chguadiana.es/); +, these lakes receive treated wastewater inputs that artificially increase the water supply. ...
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... were unable to find close reference data to carry out this part of the study for lakes Grande de Villafranca and Mermejuela. (4) ´0.05 95 0.82 Salicor (7) ´0.05 94 0.15 * Synchronous reference and satellite data (same day); * 1 lake identification number listed in each bracket as in Figure 1. ...
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... were unable to find close reference data to carry out this part of the study for lakes Grande de Villafranca and Mermejuela. * Synchronous reference and satellite data (same day); * 1 lake identification number listed in each bracket as in Figure 1. ...
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... reference evapotranspiration (ET0) was used as an indicator of the behavior and intensity of the evaporation in our study area. The results of the flooded area estimation for Lake Alcahozo, which is completely dry in summer periods following the seasonal trend typical for this type of lake, are shown in Figure 10. Figure 11 shows that the flooded area was mainly driven by the precipitation pattern, and larger flooded areas were obtained for wetter years. ...
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... results of the flooded area estimation for Lake Alcahozo, which is completely dry in summer periods following the seasonal trend typical for this type of lake, are shown in Figure 10. Figure 11 shows that the flooded area was mainly driven by the precipitation pattern, and larger flooded areas were obtained for wetter years. In May 2013, the flooded area presented a maximum value, because lake water remaining from winter was further increased by the March rainfall peak. ...
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... May 2013, the flooded area presented a maximum value, because lake water remaining from winter was further increased by the March rainfall peak. In addition, this lake presents a dry season in summer, corresponding to the highest ET0 values (Figure 10). ...
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... reference evapotranspiration (ET 0 ) was used as an indicator of the behavior and intensity of the evaporation in our study area. The results of the flooded area estimation for Lake Alcahozo, which is completely dry in summer periods following the seasonal trend typical for this type of lake, are shown in Figure 10. Figure 11 shows that the flooded area was mainly driven by the precipitation pattern, and larger flooded areas were obtained for wetter years. ...
Context 10
... results of the flooded area estimation for Lake Alcahozo, which is completely dry in summer periods following the seasonal trend typical for this type of lake, are shown in Figure 10. Figure 11 shows that the flooded area was mainly driven by the precipitation pattern, and larger flooded areas were obtained for wetter years. In May 2013, the flooded area presented a maximum value, because lake water remaining from winter was further increased by the March rainfall peak. ...
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... Among the 670 SR that are part of the RHS identified with this methodology, 545 are selected to compute their volumes. Visual inspection confirms that the MWAE of each SR generally follow the contour lines of the DEM (Figure 2) despite the comparative lower resolution of Sentinel-2 (10 m vs 2 m) and the well known imperfection of water detection methods from multi-spectral data on the shores or caused by vegetation, especially in seasonal shallow temporary lakes [11]. Table 1 presents the proportions of total and selectedby removing DEM no-data -MWAE and their corresponding MWSC derived from GSW database and Sentinel-2 time series for the four Pleiades ground coverage. ...
... Although several methods to estimate water pixels from remote sensing sensors exist, such as single-band thresholds [10,11,19,20], image transformation [17,[21][22][23][24][25] or thematic classifications [26][27][28], water indices are widely used, since they are considered as less restrictive and more reproducible, especially for applications at large or even on a global scale [29][30][31]. Water indices are based on the capability of the water spectrum response in the near and middle infrared bands to identify flooded areas. ...
... The application of these indices in temporary lakes showed that discrepancies may appear among those using the middle infrared band in the detection of seasonal and permanent water. This is possibly due to the higher sensitivity of these wavelengths to the presence of water [13,19,20], thus causing mistakes in the water-cover pixel identification. ...
... More recently, Doña et al. [20] introduced a remote sensing methodology to estimate the changes in water coverage of lenitic (standing waters) ecosystems using Landsat-7 ETM+ images. This approach was based on an algorithm developed using genetic programing (GP) techniques that allows to discern between water and non-water pixels using the near infrared band of Landsat-7 and different thresholds. ...
This work aims to validate the wide use of an algorithm developed using genetic programing (GP) techniques allowing to discern between water and non-water pixels using the near infrared band and different thresholds. A total of 34 wetlands and shallow lakes of 18 ecological types were used for validation. These include marshes, salt ponds, and saline and freshwater, temporary and permanent shallow lakes. Furthermore, based on the spectral matching between Landsat and Sentinel-2 sensors, this methodology was applied to Sentinel-2 imagery, improving the spatial and temporal resolution. When compared to other techniques, GP showed better accuracy (over 85% in most cases) and acceptable kappa values in the estimation of water pixels (κ ≥ 0.7) in 10 of the 18 assayed ecological types evaluated with Landsat-7 and Sentinel-2 imagery. The improvements were especially achieved for temporary lakes and wetlands, where existing algorithms were scarcely reliable. This shows that GP algorithms applied to remote sensing satellite imagery can be a valuable tool to monitor water coverage in wetlands and shallow lakes where multiple factors cause a low resolution by commonly used water indices. This allows the reconstruction of hydrological series showing their hydrological behaviors during the last three decades, being useful to predict how their hydrological pattern may behave under future global change scenarios.
... The NDWI is a ratio of a combination of the near infrared (NIR = MODIS band 2 = ρ NIR = 841-876 nm) and green bands (GREEN = MODIS band 4 = ρ GREEN = 545-565 nm) ranging from −1 to +1 with large positive values highlighting surface water throughout much of the pixel. Researchers have applied this method to a variety of remote sensing instruments including MODIS with reasonable accuracy (e.g., Doña et al. 2016). Recently, Bowen et al. (2017) used the NDWI with Landsat satellite data to identify and remove pixels covered in water when creating area estimates of the salt crust on the Salt Flats as pixels covered in water complicate the calculation of salt-crust area. ...
Parks and protected areas are complex, and managers often need integrated social–ecological science-based information that illuminates the dynamic interactions between the biophysical and social processes. However, modeling and determining social–ecological connections are difficult due to disciplinary paradigms, divergent research questions, and data sets representing different scales. During this investigation, researchers sought to evaluate social–ecological linkages at a large salt pan (Bonneville Salt Flats) in western Utah (US). Specifically, the investigation evaluated how the changing level and location of salt-crust moisture and ponding water influenced visitors’ spatial distribution of use and important elements of their experience. The findings indicate that visitors travel more distance, spend more time recreating, and use the Salt Flats in higher densities during dry conditions. However, the results also highlight that importance levels ascribed to specific aspects of the visitor experience remained relatively stable regardless of changes in salt-crust moisture and ponding water. Illuminating such linkages is important because most natural resource issues in society, including resources at the Bonneville Salt Flats, are not solely ecological or social in nature but characterized by deeper enmeshment between the two.
... However, surface water studies based on very high resolution imagery usually focused on small regions due to size of the image footprint and data costs. Many attempts to monitor surface water changes with 10-30 m resolution optical data have been reported including spectral data from Landsat (Borro et al., 2014;Doña et al., 2016;Jin et al., 2017;Tan et al., 2019) and Sentinel-2 (Kaplan and Avdan, 2017;Yang et al., 2018) for specific locations. For larger spatial and longer time scales, several efforts exist to map and monitor surface water changes with Landsat data at regional (Halabisky et al., 2016;Heimhuber et al., 2016), continental (Mueller et al., 2016), and global scale (Donchyts et al., 2016;Pekel et al., 2016). ...
Wetlands are among the most biodiverse ecosystems in the world, due largely to their dynamic hydrology. Frequent observations by satellite sensors such as the Moderate Resolution Imaging Spectrometer (MODIS) allow for monitoring the seasonal, inter-annual and long-term dynamics of surface water extent. However, existing MODIS-based studies have only demonstrated this for large water bodies (>100 km²) despite the ecological importance of smaller-sized wetland systems. In this paper, we assessed if the temporal dynamics of surface water extent can be effectively captured with MODIS for a wide range of water body sizes, specifically 0.01 km² and larger. We constructed the temporal dynamics of surface water extent for 340 individual water bodies in the Mediterranean region between 2000 and 2017, using a previously developed 8-day 500 m MODIS surface water fraction (SWF) dataset. We then evaluated how MODIS SWF time series corresponded to water extent derived from a Landsat-based dataset and to satellite-altimetry derived water level data. Results showed that although correlations between MODIS- and Landsat-derived SWF were poor for relative static water bodies (r = 0.17), they increased for more dynamic water bodies (r = 0.81). For dynamic water bodies larger than 100 km², time series of water extent derived from MODIS SWF showed good correlation with both Landsat (r≥0.76) and water level data (ρ≥0.63). Our MODIS SWF dataset can also effectively monitor the variability of very small water bodies (<1 km²) when comparing with Landsat data as long as the temporal variability in their surface water area was high. We conclude that MODIS SWF is a useful product to help understand hydrological dynamics for both small and larger-sized water bodies, and to monitor their seasonal, intermittent, inter-annual and long-term changes.
... In order to model the target data, researchers used terrestrial station data as inputs, which have many limitations. Thus, many researchers tend to use satellite imagery [16][17][18][19][20][21][22][23]. In studying the model trees and neural network for modeling the rainfall, [12] it was concluded that tree models could be a suitable substitute for the neural network for precipitation modeling. ...
... MeikeKühnlein, in a study [16] about improving the accuracy of rainfall rates from optical satellite sensors with machine learning showed that, using machine learning methods can accurately improve the rates of precipitation, even up to hourly rates. Doña et al [20] used remote sensing to estimate the temporal variation of the flooded area, and their associated hydrological patterns related to the seasonality of precipitation and evapotranspiration. He applied several inverse modeling methods, such as two-band and multispectral indices, single-band threshold, classification methods, artificial neural network, support vector machine and genetic programming. ...
... Our results show that Poly-P concentrations were higher in most of the lakes with wastewater inputs; however, they were very low in non-polluted lakes such as lakes Alcahozo, Grande de Villafranca and Tirez. With regard to Lake Alcahozo, which has the lowest TPs values measured, this may be due to its small catchment and the absence of regular surface water inflows, except those coming from rainfall (Doña et al., 2016: Camacho et al., 2017. ...
The La Mancha Húmeda Biosphere Reserve is the largest wetland district in the Iberian Peninsula, containing many temporary saline lakes undergoing different anthropogenic pressures. Eleven of these lakes were selected to assess phosphorus (P) burial, fractionation and mobilization in their sediments. Wastewater inputs, wherever they occurred, favored accumulation of all P sedimentary fractions, which took place mainly via precipitation with divalent cations (i.e. calcium or magnesium) and as buried organic matter, both representing relatively occluded P forms, although they can also be involved in P release. P immobilization would be partially restrained in the volcanic lakes of the region, making them more prone to eutrophication. Our seasonal survey indicated that the natural drying and re-flooding of lakes replenishes the availability of labile-P in water, and often contributed to the lakes' productivity. However, artificial flooding alters redox conditions and thereby enhances a reductive dissolution of P. This further suggests that the internal P supply might not diminish in the polluted lakes after external input shortcomings, thus counteracting any restoration measures. On the other hand, condensed P, which occurs when the excess P is taken up by microorganisms and stored as polyphosphate, was also important in sediments. Aside from the wastewater purification performed in treatment plants, we propose additional countermeasures such as extending the use of tributary slow flow channels and/or artificial wetlands. These can act as “tertiary treatments” that would promote biogeochemical transformations involving P binding before treated wastewater is poured into the lakes.
... Remote sensing has been effectively applied in the monitoring of waters and wetlands around the world, for example, in the Amazon basin [80][81][82], in the African Great Lakes [12,[83][84][85][86][87][88][89], in the greater Everglades [90][91][92], in coastal Louisiana [93][94][95][96][97], in Alpine lakes on the Tibetan Plateau [32,[98][99][100][101][102], in tropical lakes [103], in river deltas [46,104], in Lake Baikal [105,106], and in large lakes in Europe and China [107][108][109][110][111][112]. ...
Monitoring of changing lake and wetland environments has long been among the primary focus of scientific investigation, technology innovation, management practice, and decision-making analysis. Floodpath lakes and wetlands are the lakes and associated wetlands affected by seasonal variations of water level and water surface area. Floodpath lakes and wetlands are, in particular, sensitive to natural and anthropogenic impacts, such as climate change, human-induced intervention on hydrological regimes, and land use and land cover change. Rapid developments of remote sensing science and technologies, provide immense opportunities and capacities to improve our understanding of the changing lake and wetland environments. This special issue on Remote Sensing of Floodpath Lakes and Wetlands comprise featured articles reporting the latest innovative research and reflects the advancement in remote sensing applications on the theme topic. In this editorial paper, we review research developments using state-of-the-art remote sensing technologies for monitoring dynamics of floodpath lakes and wetlands; discuss challenges of remote sensing in inventory, monitoring, management, and governance of floodpath lakes and wetlands; and summarize the highlights of the articles published in this special issue.
... The lakes in the MHBR region have high structural diversity, varying in size, shape and proximity to other wetlands (Doña et al., 2016;Gosálvez, Gil-Delgado, Vives-Ferrándiz, Sánchez, & Florín, 2012;Hera & Villarroya, 2013). Apart from hydroperiod and flooded surface area, lakes within 10 km were related to richness of waders, more intensely during the breeding season and most significantly over the common census period (Table S2). ...
Temporary inland wetlands are among the most threatened ecosystems worldwide, and recognition of the factors that determine species richness in different seasons is key for developing conservation plans for these systems. La Mancha Húmeda Biosphere Reserve in central Spain has many inland wetlands of this type, but the driving ecological processes of species richness are poorly understood.
This study examines the association of landscape and local variables with species richness patterns of inland wetlands of La Mancha Húmeda Biosphere Reserve during winter and the breeding season. The number of lakes in several increasing radii was a proxy of connectivity, and maximum flooded surface and shoreline length were surrogates of the species–area relationship.
Other landscape and local habitat variables, such as hydroperiod (length of inundation period), distance from human settlements, shoreline development index, vegetation surface cover, average lake depth, number of islands and surface area of islands were also analysed. Hierarchical partitioning analysis was used to evaluate the contribution of the environmental variables to explain the species richness of waders.
Species richness (26 species, four threatened in Europe) had different associations with the variables during the wintering and breeding seasons. The richness of breeding and wintering species was positively associated with hydroperiod and maximum flooded surface area respectively. No variable measured was negatively associated with species richness in any season.
Hydroperiod and flooded surface area are altered directly by human activities. Water extraction for irrigation reduces hydroperiod in some wetlands, whereas wastewater input extends hydroperiod in others, promoting wader concentrations in lakes flooded in summer and potentially favouring botulism outbreaks. In addition, the lack of protection and management on most of the lakes in La Mancha Húmeda Biosphere Reserve also favours encroachment of agriculture, destroying and degrading habitat for wintering and breeding waders.
... All of those features are associated with an unsupervised classification method of satellite digital data. In this context, IsoData [51] is one of the most used clustering algorithms when working with an unsupervised classification of satellite images data [52], and it is frequently used in water resources studies to delineate and determine changes in water boundaries [53][54][55][56] Note: * Layers of the products used which did not need to be calculated. ** Band electromagnetic range: MIR (b7): 2105-2155 nm; MIR (b6): 1628-1652 nm; NIR: 841-876 nm; R: 620-670 nm; G: 545-565 nm. ...
In arid and semi-arid regions, the climatic impact on lakes is especially critical, as they are scarce and play an important role as a primary source of the water supply. However, in some extended regions with those climatic conditions, the implementation of an in-situ monitoring program of high temporal resolution of the water resources is not possible due to its logistics and costs. Thus, developing an accurate methodology to monitor the evolution of water bodies is especially critical in these areas. For example, with remote sensing images, lake area fluctuation can be analyzed. The main objective of this study was to identify an efficient remote sensing methodology, with a temporal resolution that allows for analyzing intra-annual lake area variations. For detecting lakes area changes six Moderate Resolution Imaging Spectroradiometer (MODIS, National Aeronautics and Space Administration products) indexes and layers were analyzed and compared. We applied the methods to the Musters (deep) and Colhué Huapí (shallow) lakes, which are located in the extra-Andean Argentine Patagonia plains (semi-arid region). The MODIS products have not been accurate to detect the areal variations of the deep lake, probably because the spatial resolution of these images is not specific enough to identify the slight variation that these lakes usually have on the extension of their area. On the contrary, MODIS products have been accurate to analyze the areal changes of the shallow lake. The Colhué Huapí lake area fluctuated between 105 km2 to 797 km2. The Modified Normalized Difference Water Index (a combination of green and middle infrared electromagnetic spectrum), as well as two bands that include a different range of middle infrared surface reflectance (2105–2155 nm; 1628–1652 nm), were the most accurate to identify the variation of the lake area.
... Carroll et al. in [23] monitored the change in water bodies area in the North American high Northern latitudes for 20 years (1991-2011) by applying a decision tree classification algorithm to Landsat images. Doña et al. [22] applied several supervised and unsupervised classification methods on Landsat 7 images to measure the water area of thirteen shallow saline lakes in Spain. Based on their results, a generic programming method (a supervised classification algorithm) outperformed the others and provided more accurate water masks. ...
Our knowledge of the spatio-temporal variation of river hydrological parameters is surprisingly poor. In situ gauge stations are limited in spatial and temporal coverage, and their number has been decreasing during the past decades. On the other hand, remote sensing techniques have proven their ability to measure different parameters within the Earth system. Satellite imagery, for instance, can provide variations in river area with appropriate temporal sampling. In this study, we develop an automatic algorithm for water body area monitoring based on maximum a posteriori estimation of Markov random fields. The algorithm considers pixel intensity, spatial correlation between neighboring pixels, and temporal behavior of the water body to extract accurate water masks. We solve this optimization problem using the graph cuts technique. We also measure the uncertainty associated with the determined water masks. Our method is applied over three different river reaches of Niger and Congo rivers with different hydrological characteristics. We validate the obtained river area time series by comparing with in situ river discharge and satellite altimetric water level time series. Along the Niger River, we obtain correlation coefficients of 0.85–0.96 for river reaches and 0.65 for the Congo River, which is demonstrably an improvement over other river mask retrieval algorithms.
