Mapping Of Water Bodies Research Articles

Role of IRS-1C in Developing Remote Sensing Applications for Water Management in India

Efficient water management is essential for water and food security leading to socio-economic development. In order to build resilience and achieve water security, planning and management of water resources projects needs to be based on realistic assessment of related aspects. This requires comprehensive, reliable and easily accessible information on demand, availability and utilisation of water resources. Observational capabilities from ground monitoring systems and supplementary/complementary data from satellite remote sensing provide unique opportunities for data integration and value addition supporting water management decisions. While LANDSAT-1 initiated various remote sensing based activities with respect to management of hydrology and water resources across the globe, the launch of Indian remote sensing satellite, IRS-1A in 1988, along with its follow-up satellites, provided fillip to these activities mainly in India. The launch of IRS-1C in December 1995, with unique combination of sensors, has greatly improved extensive utilisation of satellite remote sensing for various facets of water management. This article presents a review of various applications carried out using IRS-1C data while highlighting the capabilities and advantage of IRS-1C sensors achieving significant improvement in mapping water bodies, inventory and monitoring of irrigated crops, irrigation system performance evaluation, assessment of reservoir sedimentation, mapping and monitoring of snow cover, snowmelt runoff forecasting, watershed developmental planning and management, etc.

Analysis of Surface Water Extraction and Change Detection Over Barur Lake, India

Water is one of the most essential and devastating element in the world. The lack of access to water and its abundance can be a serious threat to human life. These two circumstances may create a difficult situation against the sustainability of human life. Inadequate access to water causes drought, affects agriculture and on other side high level water availability in urban areas affects daily life. Hence, mapping and analysing of the surface water of the earth benefits from such impacts. Therefore, this study analysis evaluates the performance of available Landsat-8 OLI images for mapping water bodies and to obtaining precise Normalized Differential Water Index (NDWI) extraction. Change detection analysis of the obtained data shows that the area of the surface water body keeps on changing every year. The surface area of the lake has undergone a drastic change in the year 2019.

Vulnerability of the Caspian Sea shoreline to changes in hydrology and climate

During the past three decades, sea water level (SWL) in the Caspian Sea has declined by about 2 m and sea area has decreased by about 15 000 km2. This has affected coastal communities, the environment and economically important gulfs of the sea (e.g. Dead Kultuk). To assess the effects of coastline change and evaluate zones vulnerable to desiccation, we simulated SWL using total inflow from feeder rivers and precipitation and evaporation over the sea. We determined potential vulnerable areas of the sea over the past 80 years by comparing the minimum and maximum annual water body maps (for 1977 and 1995). We then determined the linear regression between SWL rise and covered potential vulnerable area (CVA), using annual Normalised Difference Water Index (NDWI) maps and SWL data from 1977 to 2018. Combining SWL-CVA regression and SWL simulation model enabled us to determine desiccated areas in different regions of the Caspian Sea due to changes in precipitation, evaporation and total inflow. The results showed that 25 000 km2 of the sea is potentially vulnerable to SWL fluctuations in terms of desiccation, with 70% of this vulnerable area located in Kazakhstan. Potential vulnerable area per kilometre coastline was found to be 6 km2 in Kazakhstan, 4 km2 in Russia and whole of Caspian Sea, 1.5 km2 in Iran, 1 km2 in Azerbaijan and 0.5 km2 in Turkmenistan. The results also indicated that SWL in the Caspian Sea is sensitive to evaporation and that e.g. a 37.5 mm decrease in mean annual net precipitation would lead to a 1875 km2 decrease in the sea area, while a 1 km3 decrease in mean annual inflow would lead to a 1400 km2 decrease in the sea area. Thus the developed framework enabled the spatial consequences of changes in water balance parameters on sea area to be quantified. It can be used to assess future changes in SWL and sea area due to anthropogenic activities and climate change.

Nation-Scale Mapping of Coastal Aquaculture Ponds with Sentinel-1 SAR Data Using Google Earth Engine

Global rapid expansion of the coastal aquaculture industry has made great contributions to enhance food security, but has also caused a series of ecological and environmental issues. Sustainable management of coastal areas requires the explicit and efficient mapping of the spatial distribution of aquaculture ponds. In this study, a Google Earth Engine (GEE) application was developed for mapping coastal aquaculture ponds at a national scale with a novel classification scheme using Sentinel-1 time series data. Relevant indices used in the classification mainly include the water index, texture, and geometric metrics derived from radar backscatter, which were then used to segment and classify aquaculture ponds. Using this approach, we classified aquaculture ponds for the full extent of the coastal area in Vietnam with an overall accuracy of 90.16% (based on independent sample evaluation). The approach, enabling wall-to-wall mapping and area estimation, is essential to the efficient monitoring and management of aquaculture ponds. The classification results showed that aquaculture ponds are widely distributed in Vietnam’s coastal area and are concentrated in the Mekong River Delta and Red River delta (85.14% of the total area), which are facing the increasing collective risk of climate change (e.g., sea level rise and salinity intrusion). Further investigation of the classification results also provides significant insights into the stability and deliverability of the approach. The water index derived from annual median radar backscatter intensity was determined to be efficient at mapping water bodies, likely due to its strong response to water bodies regardless of weather. The geometric metrics considering the spatial variation of radar backscatter patterns were effective at distinguishing aquaculture ponds from other water bodies. The primary use of GEE in this approach makes it replicable and transferable by other users. Our approach lays a solid foundation for intelligent monitoring and management of coastal ecosystems.

Water Body Extraction from Sentinel-3 Image with Multiscale Spatiotemporal Super-Resolution Mapping

Water body mapping is significant for water resource management. In the view of 21 spectral bands and a short revisit time of no more than two days, a Sentinel-3 OLCI (Ocean and Land Colour Instrument) image could be the optimum data source in the near-real-time mapping of water bodies. However, the image is often limited by its low spatial resolution in practice. Super-resolution mapping (SRM) is a good solution to generate finer spatial resolution maps than the input data allows. In this paper, a multiscale spatiotemporal super-resolution mapping (MSST_SRM) method for water bodies is proposed, particularly for Sentinel-3 OLCI images. The proposed MSST_SRM method employs the integrated Normalized Difference Water Index (NDWI) images calculated from four near-infrared (NIR) bands and Green Band 6 of the Sentinel-3 OLCI image as input data and combined the spectral, multispatial, and temporal terms into one objective function to generate a fine water body map. Two experiments in the Tibet Plate and Daye lakes were employed to test the effectiveness of the MSST_SRM method. Results revealed that by using multiscale spatial dependence under the framework of spatiotemporal super-resolution Mapping, MSST_SRM could generate finer water body maps than the hard classification method and the other three SRM-based methods. Therefore, the proposed MSST_SRM method shows marked efficiency and potential in water body mapping using Sentinel-3 OLCI images.

Gainers and losers of surface and terrestrial water resources in China during 1989\u20132016

Data and knowledge of the spatial-temporal dynamics of surface water area (SWA) and terrestrial water storage (TWS) in China are critical for sustainable management of water resources but remain very limited. Here we report annual maps of surface water bodies in China during 1989–2016 at 30m spatial resolution. We find that SWA decreases in water-poor northern China but increases in water-rich southern China during 1989–2016. Our results also reveal the spatial-temporal divergence and consistency between TWS and SWA during 2002–2016. In North China, extensive and continued losses of TWS, together with small to moderate changes of SWA, indicate long-term water stress in the region. Approximately 569 million people live in those areas with deceasing SWA or TWS trends in 2015. Our data set and the findings from this study could be used to support the government and the public to address increasing challenges of water resources and security in China.

Integration of hyperspectral and LiDAR data for mapping small water bodies

Inland water bodies are globally threatened by environmental degradation and climate change. On the other hand, new water bodies can be designed during landscape restoration (e.g. after coal mining). Effective management of new water resources requires continuous monitoring; in situ surveys are, however, extremely time-demanding. Remote sensing has been widely used for identifying water bodies. However, the use of optical imagery is constrained by accuracy problems related to the difficulty in distinguishing water features from other surfaces with low albedo, such as tree shadows. This is especially true when mapping water bodies of different sizes. To address these problems, we evaluated the potential of integrating hyperspectral data with LiDAR (hereinafter “integrative approach”). The study area consisted of several spoil heaps containing heterogeneous water bodies with a high variability of shape and size. We utilized object-based classification (Support Vector Machine) based on: (i) hyperspectral data; (ii) LiDAR variables; (iii) integration of both datasets. Besides, we classified hyperspectral data using pixel-based approaches (K-mean, spectral angle mapper). Individual approaches (hyperspectral data, LiDAR data and integrative approach) resulted in 2–22.4 % underestimation of the water surface area (i.e, omission error) and 0.4–1.5 % overestimation (i.e., commission error).The integrative approach yielded an improved discrimination of open water surface compared to other approaches (omission error of 2 % and commission error of 0.4 %). We also evaluated the success of detecting individual ponds; the integrative approach was the only one capable of detecting the water bodies with both omission and commission errors below 10 %. Finally, the assessment of misclassification reasons showed a successful elimination of shadows in the integrative approach. Our findings demonstrate that the integration of hyperspectral and LiDAR data can greatly improve the identification of small water bodies and can be applied in practice to support mapping of restoration process.

Rio Doce Acoustic Surveys of Fish Biomass and Aquatic Habitat

ABSTRACTFollowing the failure of the Fundão mine tailings dam in Brazil, approximately 32 million cubic meters of Fe ore tailings were released into the downstream riverine system. The postevent monitoring surveys implemented the use of noninvasive acoustic methods to improve the understanding of the fish biomass distribution patterns and aquatic habitat condition of the impacted reaches of the Rio Gualaxo do Norte, Rio do Carmo, and Rio Doce. The primary focus of the program was to collect hydroacoustic measurements of fish biomass, which was accompanied by sonar imaging of aquatic habitats using high‐resolution side scan and downward imaging sonar at each site visited. The biannual surveys began in April 2017 and were fundamentally a multiple control–impact design because before data (prior to the dam failure event) were not available. Up to 22 sites were visited for each survey, including reservoir and river sites. Results indicate similar levels of instream habitat between control and impact river and reservoir sites. Average volumetric biomass was not significantly different between impact sites and their corresponding controls in the August 2018 survey (latest to date). Preliminary temporal analysis of the biomass data set collected indicates that either stable or increasing levels of biomass are detected at tailings impacted sites within the Rio Doce. Integr Environ Assess Manag 2020;16:615–621. © 2020 Hydrobiology QLD Pty Ltd. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC)

Potential of Large-Scale Inland Water Body Mapping from Sentinel-1/2 Data on the Example of Bavaria\u2019s Lakes and Rivers

The mapping of water bodies is an important application area of satellite-based remote sensing. In this contribution, a simple framework based on supervised learning and automatic training data annotation is shown, which allows to map inland water bodies from Sentinel satellite data on large scale, i.e. on state level. Using the German state of Bavaria as an example and different combinations of Sentinel-1 SAR and Sentinel-2 multi-spectral imagery as inputs, potentials and limits for the automatic detection of water surfaces for rivers, lakes, and reservoirs are investigated. Both quantitative and qualitative results confirm that fully automatic large-scale inland water body mapping is generally possible from Sentinel data; whereas, the best result is achieved when all available surface-related bands of both Sentinel-1 and Sentinel-2 are fused on a pixel level. The main limitation arises from missed smaller water bodies, which are not observed in bands with a resolution of about 20 m. Given the simplicity of the proposed approach and the open availability of the Sentinel data, the study confirms the potential for a fully automatic large-scale mapping of inland water with cloud-based remote sensing techniques.

Modelling Flood-Induced Wetland Connectivity and Impacts of Climate Change and Dam

Hydrological connectivity between rivers and wetlands is considered one of the key critical factors for the integrity of floodplain landscapes. This study is a comprehensive modelling exercise on quantifying flood-induced wetland connectivity and the potential impacts of climate and water storage in an unregulated river basin in northern Australia. Flood inundation was simulated using a two-dimensional hydrodynamic model and the connectivities between wetlands and rivers were calculated using geoprocessing tools in ArcGIS. Wetlands in the floodplain were identified using waterbody maps derived from satellite imagery. A broadly representative sample of 20 wetlands were selected from 158 wetlands in the Mitchell basin considering location, size and spatial distribution. Five flood events ranging from 1 in 2 to 1 in 100 years were investigated to evaluate how connectivity changes with flood magnitude. Connectivities were assessed for the current condition as well as for two scenarios of future climate (Cwet and Cdry) and one scenario of dam storage. Results showed that a 1 in 100 years event inundated about 5450 km2 of land compared to 1160 km2 for a 1 in 2 years event. Average connectivity of wetlands in the Mitchell basin varies from 1 to 5 days for the floods of 1 in 2 to 1 in 26 years. As expected, a large flood produces longer duration of connectivity relative to a small flood. Results also showed that reduction in mean connectivity under a dryer climate (up to 1.8 days) is higher than the possibility of increase under a wet climate (up to 1 day). The impacts of a water storage, in the headwater catchment, are highly pronounced in terms of inundation and wetland connectivity (e.g., mean connectivity reduced by 1.7 days). The relative change in connectivity is higher for a small flood compared to that of a large event. These results demonstrate that there is a possibility of both increase and decease in connectivity under future climate. However, any water storage will negatively impact the connectivity between floodplain waterbodies and thus reduce the material exchange resulting in a reduction in primary and secondary productions in rivers and wetlands.

Diatomological mapping of water bodies in Chongqing section of the Yangtze River and Jialing River.

The diagnosis of drowning is one of the most difficult in forensic medicine. Forensic diatomology has been proposed to be useful in solving the diagnosis of drowning and considered to be a reliable indicator of the site of drowning. The Yangtze River and Jialing River are the main rivers in the Chongqing area (China), and a large number of corpses are found in the rivers every year. However, the distribution of diatoms in the rivers was not fully studied. In the presented study, a Microwave Digestion-Vacuum Filtration-Scanning Electron Microscopy (MD-VF-SEM) method was performed to acquire the qualitative and quantitative data of diatoms of water samples collected from 10 different sites of the Yangtze River and Jialing River in Chongqing section during different seasons. Our study not only created the diatomological maps of water bodies in Chongqing section of the Yangtze River and Jialing River for the first time but also identified some seasonal and site-specific diatoms that can be taken as markers of particular sites or seasons of drowning. The results of our study may provide forensic scientists helpful reference in solving the drowning cases.

Super‐resolution mapping of hyperspectral satellite images using hybrid genetic algorithm

To assess the rate of sedimentation and the consequent reduction in the storage capacity, periodical capacity surveys of multi-purpose reservoirs is essential. Hydrographic surveys and acoustic surveys are time-consuming and expensive. The limited availability and high cost of the high-resolution images require a different methodology to accurately estimate the water-spread area of the reservoir. In this study, 30 m resolution hyperspectral image (hyperion) and multi-spectral image (The Earth Observing One (EO-1) advanced land imager) are used to estimate the water-spread area of the Peechi Reservoir, South India. A hybrid genetic algorithm (GA)-based super-resolution mapping approach is developed and demonstrated, which incorporates the multi-objective GA and Hopfield neural network (HNN). The hybrid GA-based super-resolution mapping approach gives a global optimum solution in half of the original computation time. Furthermore, mapping approach gives an error of 6.38% for the multi-spectral image and a lesser error of 3.86% for the hyperspectral image, while the HNN-based super-resolution mapping approach gives an error of 8.23% for the multi-spectral image and 5.71% for the hyperspectral image. Thus, in this work, an efficient technique based on hybrid GA is presented, which is a useful tool for accurate mapping of water bodies at the sub-pixel scale using hyperspectral imagery.

A Multi-Scale Water Extraction Convolutional Neural Network (MWEN) Method for GaoFen-1 Remote Sensing Images

Automatic water body extraction method is important for monitoring floods, droughts, and water resources. In this study, a new semantic segmentation convolutional neural network named the multi-scale water extraction convolutional neural network (MWEN) is proposed to automatically extract water bodies from GaoFen-1 (GF-1) remote sensing images. Three convolutional neural networks for semantic segmentation (fully convolutional network (FCN), Unet, and Deeplab V3+) are employed to compare with the water bodies extraction performance of MWEN. Visual comparison and five evaluation metrics are used to evaluate the performance of these convolutional neural networks (CNNs). The results show the following. (1) The results of water body extraction in multiple scenes using the MWEN are better than those of the other comparison methods based on the indicators. (2) The MWEN method has the capability to accurately extract various types of water bodies, such as urban water bodies, open ponds, and plateau lakes. (3) By fusing features extracted at different scales, the MWEN has the capability to extract water bodies with different sizes and suppress noise, such as building shadows and highways. Therefore, MWEN is a robust water extraction algorithm for GaoFen-1 satellite images and has the potential to conduct water body mapping with multisource high-resolution satellite remote sensing data.

Flooded wetlands mapping from Sentinel-2 imagery with spectral water index: a case study of Kampinos National Park in central Poland

Flood monitoring of wetlands and floodplains is a new issue in remote sensing, as compared to the mapping of open water bodies. The method based on spectral water indices, calculated on the basis of green, red and shortwave infrared bands, is one of the most popular methods for the recognition of a water body in multispectral images. The recently introduced Sentinel-2 satellite can provide multispectral images with high spatial resolution. This new data set is potentially of great importance for flood mapping, due to its free access and the frequent revisit capabilities. In this study, three popular water indices (Modified Normalized Difference Water Index, Normalized Difference Pond Index and Normalized Difference Turbidity Index) were used. The efficiency of the proposed method was tested experimentally using the Sentinel-2 image for the Kampinos National Park in Poland. The experiment compared four extraction algorithms including three based on individual water indicators and one on a combination of them. The results showed that the 10-metre false colour composite produced significantly improved the recognition of flooding in wetland areas by comparison with single spectral water indices. In this way, flooded wetlands were mapped based on the Sentinel-2 data set for the years 2017–2018.

A Novel Water Change Tracking Algorithm for Dynamic Mapping of Inland Water Using Time-Series Remote Sensing Imagery

Fine-scale mapping of inland water is important for regional or global ecosystem monitoring; however, due to the spectral heterogeneity of inland water, large-scale dynamic mapping of these water bodies is usually challenging. In this article, we propose a water change tracking (WCT) algorithm, that uses only optical data, for accurate inland water mapping. Simultaneously, time-series of Landsat imagery are used as an example for illustrating the effectiveness of the method. First, time-series of Minimum Normalized Water Score (MNWS) images and corresponding ‘bad-pixel’ (cloud, ice and snow) masks were developed using time-series of Landsat imagery. Secondly, based on the MNWS time-series stack and ‘bad-pixel’ masks, time-series trajectories were formed to track changes in water bodies. A 30-m Dynamic Inland Water Body Map (DIWBM) of China was then produced to test the robustness of the WCT algorithm. Eight test sites were selected, and the water detection algorithms used in the Global 1 arc-second Water Body Map (G1WBM) and the function of mask (FMASK) were applied to our data to provide a comparison with the DIWBM results. The results indicated that the DIWBM gave a better performance of 96.20% against 85.91% for the G1WBM results and against 76.36% for the FMASK results. The DIWBM achieved user's accuracies of 98.36% and 92.53%, and producer's accuracies of 97.45% and 95.39% for permanent and temporary water, respectively. The proposed method was also better at identifying small water bodies. It is concluded, therefore, that the WCT algorithm is a promising method for large-scale dynamic inland water mapping.

High-Resolution Bathymetry Mapping of Water Bodies: Development and Implementation

Traditionally, bathymetry mapping of ponds, lakes, and rivers have used techniques which are low in spatial resolution. Waste stabilisation ponds (WSPs) are utilised worldwide for wastewater treatment, and throughout their operation require periodic sludge surveys. Sludge accumulation in WSPs can impact performance by reducing the effective volume of the pond, and altering the pond hydraulics and wastewater treatment efficiency. Traditionally, sludge heights, and thus sludge volume, have been measured using techniques such as the 'sludge judge' and the 'white towel' test. Both of these methods have low spatial resolution, are subjective in terms of precision and accuracy, are labour intensive, and require a high level of safety precautions. A sonar device fitted to a remotely operated vehicle (ROV) can improve the resolution and accuracy of sludge height measurements, as well as reduce labour and safety requirements. This technology is readily available; however, despite its applicability, it has not been previously assessed for use on WSPs. This study aimed to design, build, and assess the performance of an ROV to measure sludge height in WSPs. Profiling of several WSPs has shown that the ROV with autonomous sonar device is capable of providing bathymetry with greatly increased spatial resolution in a greatly reduced profiling time. To date, the ROV has been applied on in excess of 400 WSPs across Australia, several large lakes, stormwater retention ponds, river beds, and drinking water reservoirs. ROVs, such as the one built in this study, will be useful for not only determining sludge profiles, but also in calculating sludge accumulation rates and in evaluating pond hydraulic efficiency. As demonstrated, this technology is not limited to application in wastewater management, with the potential for wider application in the monitoring of other small to medium sized water bodies, including reservoirs, lakes, channels, recreational water bodies, river beds, mine tailing dams and commercial ports.

A CYGNSS‐Based Algorithm for the Detection of Inland Waterbodies

AbstractThe Cyclone Global Navigation Satellite System (CYGNSS) is a new constellation of eight low Earth orbiting spacecrafts that receive both direct and reflected signals from GPS satellites. Coherent reflection of the GPS signal from standing water over land results in a high surface reflectivity signal in the CYGNSS data. An image processing algorithm is presented, which leverages the surface reflectivity signal to produce a watermask of inland waterbodies at 0.01° × 0.01° spatial resolution. The watermask is compared to hand‐drawn maps of inland waterbodies, as well as to the MODIS watermask product. We find that the CYGNSS watermask provides accurate, time‐varying maps that are able to resolve changes in lake and river position and extent. With CYGNSS' short return time, watermasks can be generated using as little as half a month of data to produce near‐real‐time maps of flooding events.

Potential of global thresholding methods for the identification of surface water resources using Sentinel-2 satellite imagery and normalized difference water index

The aim of this study is to investigate the performance of 15 automatic thresholding methods, namely Huang and Wang’s fuzzy thresholding method, intermode thresholding method, isodata thresholding method, Li and Tam’s thresholding method, maximum entropy thresholding method, mean thresholding method, minimum error thresholding method, minimum thresholding method, moment-preserving thresholding method, Otsu’s thresholding method, percentile (p-tile) thresholding method, Renyi’s entropy thresholding method, Shanbhag’s thresholding method, triangle thresholding method, and Yen’s thresholding method, for mapping water body using Sentinel-2 data based on normalized difference water index. Three different types of surface water bodies, such as a natural lake (Lake Burdur), a dam reservoir (Aslantaş Dam Reservoir), and a part of a river (Aras River), are chosen to reveal the potential of 15 thresholding methods. The reference water body maps of each test site were generated by manual digitization of high-resolution Google Earth images. The thresholding methods were assessed using the statistical measures, namely overall accuracy (OA), Kappa coefficient, producer’s accuracy, user’s accuracy, and misclassification error (ME). The accuracy analyses of 15 thresholding methods were carried out separately for each test site, and then the overall accuracies were calculated to determine the best method. The obtained OA results showed that minimum thresholding method was the best method among these 15 algorithms with 0.0008 ME, 99.92% OA, and 0.9758 Kappa coefficient. On the other hand, Shanbhag’s method provided the lowest overall accuracies as 0.3133 ME, 68.67% OA, and 0.3190 Kappa coefficient.

FLOOD MAPPING AND PERMANENT WATER BODIES CHANGE DETECTION USING SENTINEL SAR DATA

Abstract. Producing flood maps that can be carried out quickly for disaster management applications is essential to reduce the human and socio-economic losses. In addition, mapping and change detection of water bodies as an essential natural resource is imperative for robust operations and sustainable management. Synthetic Aperture Radar (SAR) sensors with long wavelengths have a high potential for delineating the extent of the flooded areas and providing timely and accurate maps of surface water for risk mitigation and disaster or sustainable management. In this study, multi-temporal Sentinel-1 C-band SAR images were utilized to investigate the performance of the sensor backscatter image on permanent water bodies monitoring and flooded areas mapping. Lake Urmia as a permanent water system and two floods in Golestan and Khuzestan provinces of Iran have been investigated. The backscatter values of an image acquired before the event that is referred as an Archive image and another one after the event as a Crisis image are analysed. As a preliminary result, it is concluded that with overlaying of the two bands from Archive and Crisis images and creating a color composite image, the permanent water bodies have a uniformly dark return due to the very low backscatter in both images. The flooded areas and changes in water level show relatively higher backscatter in the Crisis image, whereas the other land cover features indicate very high backscatter values with tones of grey. Therefore, Sentinel-1 SAR data provides beneficial information on flood risk management and change detection.

A flood of information: Using Sentinel-3 water colour products to assure continuity in the monitoring of water quality trends in the Great Barrier Reef (Australia)

An operational method to assess trends in marine water composition and ecosystem health during flood periods has been developed for the Great Barrier Reef (GBR), Queensland, Australia. This method integrates satellite water colour data with field water quality and ecosystem monitoring data and involves the classification of Moderate-Resolution Imaging Spectroradiometer (MODIS satellite) pixels into six distinct water bodies using a “wet season” colour scale developed specifically for the GBR. Using this information, several monitoring and reporting products have been derived and are operationally implemented into a long-term water quality monitoring program for the GBR. However, MODIS sensors are aging and a long-term monitoring solution is needed. This study reviewed the water colour monitoring products currently used in the GBR. It tested the feasibility to transition these methods from historical MODIS satellite imagery to the new Sentinel-3 satellite of the European Space Agency and from the wet season colour scale to the historical Forel-Ule colour scale, using a freely-distributed Forel Ule (FU) Satellite Toolbox. Monitoring products derived from both satellites and colour scales showed very similar patterns across two case study regions of the GBR, the Wet Tropics and Burdekin marine regions, over the 2017-18 wet season. The results obtained in this study highlighted the potential of using FU Sentinel-3 imagery for the mapping of GBR marine water bodies, including flood conditions. Furthermore, the operational monitoring products and frameworks developed for the GBR are likely to provide valuable foundations for analysis of FU Sentinel-3 data in the future. Such satellite water colour datasets and frameworks will be instrumental to better understand the impact of floods and reduced water clarity on marine ecosystems, as well as to support water quality management and facilitate catchment management policy in the GBR and worldwide.