A Review of Wetland Remote Sensing <s> Water Quality in the Wetland <s> A method has been developed, based on spectral mixture analysis, to estimate the concentration of suspended sediment in surface waters of the Amazon River wetlands from Landsat MSS and TM images. Endmembers were derived from laboratory reflectance measurements of water-sediment mixtures with a range of sediment concentrations. Using these reference spectra, we applied a linear mixture analysis to multispectral images after accounting for instrument and atmosphere gains and offsets. Sediment concentrations were estimated for individual pixels from the mixture analysis results based on a nonlinear calibration curve relating laboratory sediment concentrations and reflectance to endmember fractions. The uncertainty in the sediment concentrations derived from this analysis for three Amazon images is predicted to be within ±20 mg/L, and the concentrations fall within a range of concentrations of suspended sediment that were measured at several times and places in the field over the past 15 years. The emphasis of our work is to use the patterns of sediment concentrations to compute the approximate volumes of sediment that are transferred between the main channel and floodplain of the Amazon River. However, the methodology can be applied universally if the optical properties of water and sediment at the site are known, and it is, therefore, useful for the study of suspended sediment concentrations in surface waters of wetlands elsewhere. <s> BIB001 </s> A Review of Wetland Remote Sensing <s> Water Quality in the Wetland <s> To assess riparian influences on lake functioning and develop simple tools for lake health monitoring, the association of remotely-sensed riparian factors and water chemistry with the breakdown rate of reed litter was examined in Lake Vico, a volcanic lake in central Italy. The decomposition rate of Phragmites australis was determined as leaf mass loss from litterbags at 6 locations during summer 2000. Water temperature, dissolved oxygen, nitrogen and phosphorus concentrations, pH, conductivity, percent organic matter in sediments and effective fetch were also assessed at each site. Riparian factors (i.e. shoreline complexity, riparian vegetation cover, and distance from shoreline) were estimated from satellite images (Landsat VII). External organic loadings largely explained spatial heterogeneity in lake metabolism. Decomposition was fastest in the northern area of the lake that drained most of the watershed, including a larger portion in wetland area. Rates correlated exponentially with soluble reactive phosphorus (SRP) concentration, and were positively associated with total-P (TP) concentration and organic matter percentage in sediments, which, in turn, were influenced by the riparian-related factors and waveexposure (fetch). A relationship existed between the index of riparian load, RL, which combined the three riparian-related factors, and both decomposition rate and SRP, suggesting that the riparian index is a promising cost-saving tool for monitoring lake function using remotely-sensed data. <s> BIB002 </s> A Review of Wetland Remote Sensing <s> Water Quality in the Wetland <s> Changes in wetland ecosystems may result from the interactions of endogenous processes with exogenous factors such as environmental fluctuations and anthropogenic influences. Since mid-2004, the Rio Cruces wetland, a Ramsar site located in southern Chile (40°S), exhibited a sudden increase in mortality and emigration of the largest breeding population of Black-necked swans in the Neotropics, a massive demise of the dominant macrophyte Egeria densa (the main food of swans and several aquatic birds), and a seasonal appearance of turbid waters. We compared annual variation in rainfall, river flow, and radiation over the period 2000–2005 to assess the role of environmental factors on these wetland changes. Those factors, with the exception of a decrease in river flow during 2004, did not show significant inter-annual differences. However, when comparing Landsat images, we found in the visible and near-infrared spectrum, a corresponding increase and decrease in water reflectance for 2005 with respect to 2003 and 2001, respectively. These results may reflect the appearance of turbid waters and the decrease in cover of E. densa. All temporal changes were restricted to the northern and central zones of the wetland. In addition, spatial analysis showed a gradient in turbidity across the wetland waters, which was enhanced by estuarine influence during spring-tides. Censuses of aquatic birds (1999–2005) showed that only herbivorous birds exhibited a pronounced decrease in population abundance after mid-2004, while piscivorous birds continued normal cycling, with even some positive trends in abundance during 2004–2005. Population declines in herbivorous birds may be related to the demise of E. densa and suspension of sediments during periods of reduced river flow (2004) that gave rise to the turbidity in the wetland waters. Environmental changes could be related to changes in water quality after a new pulp mill, built upstream of the wetland, initiated operations during February 2004. <s> BIB003 </s> A Review of Wetland Remote Sensing <s> Water Quality in the Wetland <s> In remotely located watersheds or large waterbodies, monitoring water quality parameters is often not feasible because of high costs and site inaccessibility. A cost-effective remote sensing-based methodology was developed to predict water quality parameters over a large and logistically difficult area. Landsat spectral data were used as a proxy, and a neural network model was developed to quantify water quality parameters, namely chlorophyll-a, turbidity, and phosphorus before and after ecosystem restoration and during the wet and dry seasons. The results demonstrate that the developed neural network model provided an excellent relationship between the observed and simulated water quality parameters. These correlated for a specific region in the greater Florida Everglades at R 2 > 0.95 in 1998–1999 and in 2009–2010 (dry and wet seasons). Moreover, the root mean square error values for phosphorus, turbidity, and chlorophyll-a were below 0.03 mg L−1, 0.5 NTU, and 0.17 mg m−3, respectively, at the neural network training and validation phases. Using the developed methodology, the trends for temporal and spatial dynamics of the selected water quality parameters were investigated. In addition, the amounts of phosphorus and chlorophyll-a stored in the water column were calculated demonstrating the usefulness of this methodology to predict water quality parameters in complex ecosystems. <s> BIB004 </s> A Review of Wetland Remote Sensing <s> Water Quality in the Wetland <s> Abstract Water clarity is a reliable indicator of lake productivity and an ideal metric of regional water quality. Clarity is an indicator of other water quality variables including chlorophyll-a, total phosphorus and trophic status; however, unlike these metrics, clarity can be accurately and efficiently estimated remotely on a regional scale. Remote sensing is useful in regions containing a large number of lakes that are cost prohibitive to monitor regularly using traditional field methods. Field-assessed lakes generally are easily accessible and may represent a spatially irregular, non-random sample of a region. We developed a remote monitoring program for Maine lakes > 8 ha (1511 lakes) to supplement existing field monitoring programs. We combined Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) brightness values for TM bands 1 (blue) and 3 (red) to estimate water clarity (secchi disk depth) during 1990–2010. Although similar procedures have been applied to Minnesota and Wisconsin lakes, neither state incorporates physical lake variables or watershed characteristics that potentially affect clarity into their models. Average lake depth consistently improved model fitness, and the proportion of wetland area in lake watersheds also explained variability in clarity in some cases. Nine regression models predicted water clarity (R 2 = 0.69–0.90) during 1990–2010, with separate models for eastern (TM path 11; four models) and western Maine (TM path 12; five models that captured differences in topography and landscape disturbance. Average absolute difference between model-estimated and observed secchi depth ranged 0.65–1.03 m. Eutrophic and mesotrophic lakes consistently were estimated more accurately than oligotrophic lakes. Our results show that TM bands 1 and 3 can be used to estimate regional lake water clarity outside the Great Lakes Region and that the accuracy of estimates is improved with additional model variables that reflect physical lake characteristics and watershed conditions. <s> BIB005 </s> A Review of Wetland Remote Sensing <s> Water Quality in the Wetland <s> The number, size, and distribution of inland freshwater lakes present a challenge for traditional water-quality assessment due to the time, cost, and logistical constraints of field sampling and laboratory analyses. To overcome this challenge, Landsat imagery has been used as an effective tool to assess basic water-quality indicators, such as Secchi depth SD, over a large region or to map more advanced lake attributes, such as cyanobacteria, for a single waterbody. The overarching objective of this research application was to evaluate Landsat Thematic Mapper TM for mapping nine water-quality metrics over a large region and to identify hot spots of potential risk. The second objective was to evaluate the addition of landscape pattern metrics to test potential improvements in mapping lake attributes and to understand drivers of lake water quality in this region. Field-level in situ water-quality measurements were collected across diverse lakes n = 42 within the Lower Peninsula of Michigan. A multicriteria statistical approach was executed to map lake water quality that considered variable importance, model complexity, and uncertainty. Overall, band ratio radiance models performed well R2 = 0.65–0.81 for mapping SD, chlorophyll-a, green biovolume, total phosphorus TP, and total nitrogen TN with weaker R2 = 0.37 ability to map total suspended solids TSS and cyanobacteria levels. In this application, Landsat TM and pattern metrics showed poor ability to accurately map non-purgable organic carbon NPOC and diatom biovolume, likely due to a combination of gaps in temporal overpass and field sampling and lack of signal sensitivity within broad spectral channels of Landsat TM. The composition and configuration of croplands, urban, and wetland patches across the landscape were found to be moderate predictors of lake water quality that can complement lake remote-sensing data. Of the 4071 lakes, over 4 ha in the Lower Peninsula, approximately two-thirds, were identified as mesotrophic n = 2715. This application highlights how an operational tool might support lake decision-making or assessment protocols to identify hot spots of potential risk. <s> BIB006 </s> A Review of Wetland Remote Sensing <s> Water Quality in the Wetland <s> As human activities influence land cover changes, the environment on human life such as water quality, has been impacted. In particular, huge constructions or reclamation projects are responsible for dramatic land cover changes. The Saemangeum area in South Korea has been one of the largest reclamation projects to progress nearly in two decades. In this study, Landsat-5 Thematic Mapper and Landsat-7 Enhanced Thematic Mapper Plus images were used to classify land cover types in the Saemangeum area. A change detection method was utilized to determine the impacts of the reclamation project. While wetland, grassland, and urban areas were increased, forest, water, and agricultural areas were decreased during the reclamation progress. Water quality analysis related to the land cover changes was conducted to determine the influence of reclamation construction on the environment. Chemical oxygen demand and suspended sediment variability were significantly impacted by the sea current changes after the dyke construction. On the contrary, water temperature and dissolved oxygen were affected by the seasonal influences rather than the reclamation construction. Total nitrogen and total phosphorus were influenced by the fertilizers and pesticides as a result of agricultural activity. The trends of suspended sediment from Landsat images were similar with those from the ground observation sites and also impacted by the dyke construction. <s> BIB007 </s> A Review of Wetland Remote Sensing <s> Water Quality in the Wetland <s> Abstract Coastal mangrove ecosystems are under duress worldwide because of urban development, sea-level rise, and climate change, processes that are capable of changing the salinity and nutrient concentration of the water utilized by the mangroves. This study correlates long-term water chemistry in mangrove environments, located in Everglades National Park, with mangrove spectral reflectance measurements made at both the leaf and canopy scales. Spectral reflectance measurements were collected using a handheld spectrometer for leaf-level measurements and Landsat 5TM data for regional coverage. Leaf-level reflectance data were collected from three mangrove species (i.e., red, black and white mangroves) across two regions; a tall mangrove (~ 18 m) and dwarf mangrove (1–2 m) region. The reflectance data were then used to calculate a wide variety of biophysical reflectance indices (e.g., NDVI, EVI, SAVI) to determine signs of stress. Discrete, quarterly water samples from the surface water, groundwater, and pore water (20 and 85 cm depths) and daily autonomous surface water samples were collected at each site and analyzed for major anions (Cl − and SO 4 2− ), cations (Na + , K + , Mg 2 + , and Ca 2 + ), total nitrogen (TN) and total phosphorus (TP). Mangrove sites that exhibited the highest salinity and ionic concentrations in the surface and subsurface water also had the lowest near-infrared reflectance at both the leaf and satellite levels. Seasonal reflectance responses were measured in the near-infrared (NIR) wavelengths at both the leaf and canopy scales and were strongly correlated with nutrient and ionic concentrations in the surface and subsurface water, even though there was no significant separability between the three mangrove species. Study sites that experienced the greatest variability in surface and subsurface water ionic concentrations also exhibited the greatest fluctuations in NIR spectral reflectance. Landsat 5TM images were able to detect tall and dwarf mangroves by the differences in spectral indices (e.g., NDVI, NDWI, and EVI) because of the variability in the background conditions amongst the environments. In addition, Landsat 5TM images spanning 16 years (1993–2009) were successfully used to estimate the seasonal variability in ionic concentrations in the surface water across the Florida Coastal mangrove ecotone. This study has shown that water chemistry can be estimated indirectly by measuring the change in spectral response at the leaf- or satellite-scale. Furthermore, the results of this research may be extrapolated to similar coastal mangrove systems throughout the Caribbean and world-wide wherever red, black, and white mangroves occur. <s> BIB008 </s> A Review of Wetland Remote Sensing <s> Water Quality in the Wetland <s> River Beas originates in the Himalayas and merges into river Sutlej at Harike, a Ramsar wetland. This river is a habitat of the endangered freshwater dolphin, Platanista gangetica minor R. Twenty-five water quality parameters, including eight heavy metals, were studied at four sampling sites over a stretch of 63 km between Beas and Harike towns for pre-monsoon, post-monsoon and winter seasons. Principal component analysis of the data proved to be an effective tool for data reduction as the first three principal components of all the water quality parameters explained 100% variance. Factor analysis delineated three factors underlying the water quality. Factor 1 comprised pollution-related parameters like BOD, COD, DO, PO4(-3) and hardness. Factor 2 was a natural water quality determinant and explained maximum variance in turbidity, alkalinity and TDS. Factor 3 comprised NO3(-1), a fertilizer-related parameter. Reflectance values from bands 2 (green), 3 (red) and 4 (near infra-red) of Landsat (TM) digital data were regressed on PO4(-3), turbidity and TDS using multiple linear regression analysis. PO4(-3) contributed positively to the spectral radiance, whereas TDS contributed negatively. Beta regression analysis revealed that PO4(-3) had a positive relation with BOD, whereas turbidity and TDS were negatively regressed with BOD. Artificial neural network models were fitted to the data. Correlations between the target values from ANN for turbidity, BOD and bands 2 (green), 3 (red) and 4 (near infra-red) were highly significant. <s> BIB009
Landsat data used in water quality monitoring can be grouped into two categories, one uses the reflectance changes of the Landsat bands to monitor water quality BIB001 BIB003 BIB004 BIB006 BIB009 BIB005 BIB008 and the other one employs Landsat data to identify land cover types and then determine the relationship with water quality of field survey BIB007 BIB002 . The transport of sediment influences the type of biology and geomorphic processes. Mertes et al. BIB001 used spectral mixture analysis of Landsat MSS and TM data to estimate the suspended sediment concentrations in Amazon River wetland; the endmembers used in the spectral mixture analysis were obtained in the laboratory. Mccullough et al. BIB005 used Landsat TM bands 1 and 3 to estimate water clarity. Lagos et al. BIB003 argued that the increase and decrease of the water reflectance in the visible and near-infrared bands, respectively, were the results of turbid water and the decrease of water plants. Kumar et al. [144] used the reflectance of Landsat TM bands 2, 3, and 4 to regress water pollution parameters. A neural network model that uses Landsat reflectance data were proposed by Chebud et al. BIB004 to quantify water quality parameters. Landsat TM images could also be used to map water quality and then identify the potential risk BIB006 . Choi and Han BIB007 and Costantini et al. BIB002 used Landsat data to identify the land cover types around water and then evaluated water quality to determine how land cover changes affect environment.