Monitoring water quality and hydrological patterns of wetlands using recent techniques in remote sensing

Wetlands contribute to a wealth of ecosystem services, including the regulation of the hydrological cycle for flood and drought control, and provide water supply, wildlife refuges, aesthetic enjoyment and recreational opportunities, among others. According to the Ramsar Convention, wetlands, in a broad sense, include all lakes and rivers, underground aquifers, swamps and marshes, wet grasslands, peatlands, oases, estuaries, deltas and tidal flats, mangroves and other coastal areas, coral reefs and all constructed sites, such as fish ponds, rice paddies, reservoirs and salt pans. Although these areas have a critical value to sustainable development, they are detrimentally impacted by urban growth, agricultural land reclamation and derived pollution. The water quality and the ecological status of these aquatic ecosystems can deteriorate due to, among others, eutrophication.Lake eutrophication is a critical issue in the interplay of water supply, environmental management, and ecosystem conservation. Integrated sensing, monitoring, and modeling for a holistic lake water quality assessment and ecological status with respect to multiple constituents is in acute need. On the other hand the water balance and hydrological variations are intimately tied to potential changes in a lentic ecosystem. Understanding the dynamics of water in lakes helps the goal of conservation and recovery of these valuable ecosystems. This is especially relevant given several environmental initiatives, such as the European Water Framework Directive (WFD), which came into force in 2000, and the Habitats Directive, delivered in 1992. These directives require each member state in the European Union to achieve a good ecological/conservation status for their water bodies and associated habitats and species, forcing the establishment of conservation actions. Remote sensing techniques can be used to estimate water quality variables such as the concentration of chlorophyll-a, of total suspended particles, and water transparency. The first part of thisThesis describes empirical algorithms for the estimation of these variables using Landsat Thematic Mapper (TM) data. In this case, the ground data were taken from several Spanish lakes covering a variety of trophic statuses, ranging from oligotrophic to hypereutrophic. The studied lakes were la Albufera de Valencia and lakes and ponds of the Southeast Regional Park in Madrid. Empirical equations were obtained to estimate chlorophyll-a from the ratio in reflectance values between bands 2 and 4 of TM, transparency (Secchi disk) from reflectance in band 2, and total suspended particles from reflectance in band 4. The spectral equivalence between TM and Deimos-1 was also tested. By applying the proposed algorithms to this sensor, the temporal resolution is improved by up to 3 days, and this also increases spatial resolution to 22 m. The algorithms were validated using 3 Deimos-1 scenes of la Albufera de Valencia together with ground measurements. Results of this validation showed root mean square errors of 40 mg/m3 for chlorophyll-a concentration (Mean absolute difference percentage MADP = 22%), 10 mg/l for total suspended particles concentration (MADP=15%) and 0.10 m for SD (MADP=40%). Then, results were acceptable in terms of chlorophyll-aand total suspended particles concentration estimation with MADP values of ±22% and ±15%, respectively. In any case, these results show the potential of Deimos-1 as a substitute of TM in water quality monitoring in small/medium water bodies, providing continuity to 3 decades of TM imagery. Following this work, we developed an integrated algorithm for data fusion and mining of satellite remote sensing images to generate daily estimates of some water quality parameters of interest, such as chlorophyll-a concentrations and water transparency, to be applied for the assessment of the water quality of la Albufera de Valencia. In this case, remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Thematic Mapper (TM) and Enhance Thematic Mapper (ETM+) images were fused to carry out an integrative near-real time water quality assessment on a daily basis. Landsat images are useful to study the spatial variability of the water quality parameters, due to its spatial resolution of 30 m, in comparison to the low spatial resolution (250/500 m) of MODIS. While Landsat offers a high spatial resolution, the low temporal resolution of 16 days is a significant drawback to achieve a near real-time monitoring system. This gap may be bridged by using MODIS images that have a high temporal resolution of 1 day, in spite of its low spatial resolution. Synthetic Landsat images were fused for dates with no Landsat overpass over the study area. Finally, with a suite of ground truth data, a few genetic programming (GP) models were derived to estimate the water quality using the fused surface reflectance data as inputs. The GP model for chlorophyll-a estimation showed a Root Mean Square Error (RMSE) = 8 mg/m3(MADP=6%), and the GP model for water transparency estimation using Secchi disk showed a RMSE = 4 cm (MADP=9%). This methodology improves the traditional effort by means of linear regression techniques, as shown above. The spatio-temporal variations of water transparency and chlorophyll-a concentration can be then monitored simultaneously on a daily basis throughout the lake for environmental management. Sentinel-2 offers the opportunity to continue with this inland water quality monitoring task, thanks to its 5-day revisit cycle and 10-30 m spatial resolution. In the framework of the Sentinel-2 preparatory activities, the ESA developed the SPOT5 take-5 experiment. From early April to the end of August 2015, SPOT-5 satellite was relocated in a 5-day orbit, before being decommissioned. Based on the spectral matching between both VNIR sensors, SPOT-5 was used to simulate Sentinel-2 products and show the benefits of its high spatial resolution to monitor small water bodies.Then, we developed algorithms to estimate the water quality parameters from SPOT-5 images in three water bodies belonging to the Jucar river basin. Several experimental campaigns were carried out concurrent with SPOT-5 overpasses or close in date to accomplish this aim. Chlorophyll-a concentration, transparency and total suspended particles concentration were measured in la Albufera de Valencia. Again, genetic programming models were used to generate nonlinear regression equations between ground measurements and reflectance values from the SPOT-5 spectral bands. Results showed MADP values of ± 8%, ± 5% and ± 10% in the estimation of chlorophyll-a concentration, transparency and total suspended particles concentration, respectively. Focusing on la Albufera de Valencia Lake, results are similar to those already reported in the previous work. These results show the potential of Sentinel-2 to monitor and study the spatio-temporal trend of these water quality parameters. On the other hand, remote sensing technologies also facilitate the content-based mapping over space and time, leading to multitemporal change detection of the hydrological variations in wetlands. Mapping surface water bodies allows for the investigation of water balance dynamics by providing information on the temporal and spatial variations of surface water coverage, this being especially relevant under the current climate change scenario. The Biosphere Reserve of La Mancha Humeda is currently the main wetland area in the Iberian Peninsula. This Lake District is one of the wetland complexes most threatened by anthropogenic activity, mainly by groundwater overexploitation due to excessive use for irrigated agriculture. This area is an important refuge for endangered waterfowl species, following the protection criteria for birds in Europe, and also holds endangered habitats. Because of its natural and ethnographic values, it was designated a Biosphere Reserve.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 systems, where water withdrawal is mainly due to evaporation, causing salt accumulation in the lake beds. Remote sensing was also used to estimate the temporal variation of the flooded area in these lakes and their associated hydrological patterns related to the seasonality of precipitation and evapotranspiration. Landsat 7 ETM+ images for the reference period 2013–2015 were jointly used with ground-truth datasets. 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, were applied to retrieve information on the variation of the flooded areas. Results were compared to ground-truth data, and the classification errors were evaluated by means of the kappa coefficient. Comparative analyses demonstrated that the genetic programming approach yielded the best results, with a kappa value of 0.98 and a total error of omission-commission of 2%. The dependence of the variations in the water-covered area on precipitation and evaporation was also investigated. The results show the potential of the tested techniques to monitor the hydrological patterns of temporary lakes in semiarid areas, which might be useful for management strategy-linked lake conservation and specifically to accomplish the goals of both the European Water Framework Directive and the Habitats Directive.