Enhanced index for water body delineation and area calculation using Google Earth Engine: a case study of the Manchar Lake

Ismail, Muhammad Ali; Waqas, Maria; Ali, Amjad; Muzzamil, Mirza Muhammad; Abid, Uzair; Zia, Talha

doi:10.2166/wcc.2021.282

Cited by 13 publications

(8 citation statements)

References 34 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• WIBI = NDWI -NDBI, where NDBI = (SWIR1 -NIR) / (SWIR1 + NIR) (Ismail et al 2022) Fourth, we used single-band reflectance information to improve the classification of water and snow or ice. After examining the validation results in the Ny-Ålesund study area, we identified a need to improve this distinction.…”

Section: Proposed Proceduresmentioning

confidence: 99%

Supplementary material to "Arctic shoreline displacement with open satellite imagery and data fusion: A pilot study 1984–2022"

Nylén,

Calle,

Gonzales-Inca

2023

Preprint

View full text Add to dashboard Cite

Study areasWe used two contrasting coastal environments in the European Arctic to calibrate and validate the procedure for extracting shoreline displacement time series. First, the procedure was iteratively calibrated, tested and validated in the Tanafjorden area, at the Barents Sea coast of Norway (Figure S1). Second, the final procedure was validated in the Ny-Ålesund area at the Greenland Sea coast of the Svalbard archipelago (Figure S1).The first study area in Tanafjorden (Deanuvuotna in northern Sámi language, Tenovuono in Finnish; c. 70.5° N, 28.5° E; Figure S1), is located in the Fennoscandian Barents Sea coasts of northern Finnmark, Norway. The study area included south-western parts of the Nordkinn Peninsula (Nordkinnhalvøya), the Tana River delta, and north-western parts of the Varanger Peninsula (Varangerhalvøya). The area was selected since it was relatively easy to access during the COVID pandemic and provided varying landscape, including a major delta. We used sea level data from the nearest tide gauge, located in Honningsvåg c. 100 km from the study area and operated by the Norwegian Mapping Authority. The study area is characterised by steep fjord landscapes, the highly dynamic delta, lack of glaciers and sea ice, and mesotidal conditions.The Tanafjorden coast is in most parts dominated by steep cliffs. Where rivers enter the fjords, the topography is generally flatter and regular water level fluctuations create tidal flats and salt marshes. The largest river in the area is Tana River (Deatnu in northern Sámi language, Tenojoki in Finnish). It is the border river between Norway and Finland and one of the largest rivers in Norway. While there are some snowbeds in the mountains (patches of snow that melt late in the summer), there is no sea ice (data by National Snow and Ice Data Center), no glaciers (Andreassen et al., 2012), and no coastal permafrost in the area (Gisnås et al., 2017). Rivers entering the Barents Sea and their brackish deltas have an ice cover from mid-October to mid-May.The Tanafjorden study area has a humid subarctic climate. The annual mean temperature in Tanafjorden is around 1 °C and the mean annual precipitation around 500 mm (Norwegian Centre for Climate Services). The coast has a seasonal snow cover and is mainly free of snow from mid-June to mid-October. The area is characterized by a semidiurnal lunar tide with a period of 12 hours and 25 minutes and mean tidal range of 2.3 meters during spring tide (Norwegian Mapping Authority).

show abstract

Section: Proposed Proceduresmentioning

confidence: 99%

Supplementary material to "Arctic shoreline displacement with open satellite imagery and data fusion: A pilot study 1984–2022"

Nylén,

Calle,

Gonzales-Inca

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…By using GEE, it is possible to view and analyze all covers and uses of the earth with high spatial details in two and three dimensions. Many studies in different fields, such as vegetation monitoring [50], land use/cover maps [51] and their characteristics, such as the temperature of the earth's surface [52], monitoring of protected areas [53], monitoring of water resources and water bodies [54], agricultural products [55], and other types of remote sensing studies, have been carried out using this system.…”

Section: Google Earth Enginementioning

confidence: 99%

A Simulation Optimization Approach for Wetland Conservation and Management in an Agricultural Basin

Hatamkhani,

Moridi

2023

Sustainability

View full text Add to dashboard Cite

Decreasing water quantity and growth in water demand have increased the competition between satisfying societal water needs and protecting ecosystem requirements. Wetlands are some of the most productive ecosystems on Earth. They provide various services to people’s livelihoods, in addition to being suitable habitats for many plant and wildlife species. However, wetlands are under threat of loss and degradation due to anthropogenic activities, particularly the diversion of water for irrigation. The flow regime is usually considered the most crucial ecological factor and a key component of wetland management. So, determining the allocation of environmental requirements is a main factor for managing, restoring, and protecting wetlands, and it is crucial to reach a compromise for optimal water allocation between different sectors. For this purpose, in this research, a new approach is developed to achieve the optimal environmental flow of the wetland in an agricultural-dominated basin using a combination of remote sensing and the simulation optimization method. Waterbirds and vegetation are used as bioindicators of wetland ecosystems. First, using remote sensing data and analyses, we obtained the interrelation between the wetland water regime, vegetation, and waterbird characteristics using different time series of Landsat spectral indices. Then, by employing the long-term simulation optimization (WEAP-MOPSO) model, the optimal e-flow of the wetland is evaluated in such a way that the suitable ecological condition of the wetland is achieved and the wetland is able to provide its functions and services.

show abstract

“…Recent advancement in satellite based‐remote sensing information with different spatial, spectral, radiometric, and temporal resolutions has given new dimensions to the water bodies studies (Ismail et al., 2021). Following the increase in the availability of satellite images, and image processing techniques, numerous research studies have attempted to extract and delineate water bodies from these images (Ouma & Tateishi, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…In testing, we employed images from the Moderate Resolution Imaging Spectroradiometer satellite, providing less detailed images, and the Land Remote-Sensing Satellite, offering highly detailed imagery. In essence, this study enhances water resource management by effectively combining data from multiple sources, even in adverse conditions, potentially advancing environmental protection and management efforts.Recent advancement in satellite based-remote sensing information with different spatial, spectral, radiometric, and temporal resolutions has given new dimensions to the water bodies studies (Ismail et al, 2021). Following the…”

mentioning

confidence: 99%

See 1 more Smart Citation

Spatiotemporal Data Augmentation of MODIS‐Landsat Water Bodies Using Adversarial Networks

Filali Boubrahimi,

Neema,

Nassar

et al. 2024

Water Resources Research

View full text Add to dashboard Cite

With increasing demands for precise water resource management, there is a growing need for advanced techniques in mapping water bodies. The currently deployed satellites provide complementary data that are either of high spatial or high temporal resolutions. As a result, there is a clear trade‐off between space and time when considering a single data source. For the efficient monitoring of multiple environmental resources, various Earth science applications need data at high spatial and temporal resolutions. To address this need, many data fusion methods have been described in the literature, that rely on combining data snapshots from multiple sources. Traditional methods face limitations due to sensitivity to atmospheric disturbances and other environmental factors, resulting in noise, outliers, and missing data. This paper introduces Hydrological Generative Adversarial Network (Hydro‐GAN), a novel machine learning‐based method that utilizes modified GANs to enhance boundary accuracy when mapping low‐resolution MODIS data to high‐resolution Landsat‐8 images. We propose a new non‐saturating loss function for the Hydro‐GAN generator, which maximizes the log of discriminator probabilities to promote stable updates and aid convergence. By focusing on reducing squared differences between real and synthetic images, our approach enhances training stability and overall performance. We specifically focus on mapping water bodies using MODIS and Landsat‐8 imagery due to their relevance in water resource management tasks. Our experimental results demonstrate the effectiveness of Hydro‐GAN in generating high‐resolution water body maps, outperforming traditional methods in terms of boundary accuracy and overall quality.

show abstract

Enhanced index for water body delineation and area calculation using Google Earth Engine: a case study of the Manchar Lake

Cited by 13 publications

References 34 publications

Supplementary material to "Arctic shoreline displacement with open satellite imagery and data fusion: A pilot study 1984–2022"

Supplementary material to "Arctic shoreline displacement with open satellite imagery and data fusion: A pilot study 1984–2022"

A Simulation Optimization Approach for Wetland Conservation and Management in an Agricultural Basin

Spatiotemporal Data Augmentation of MODIS‐Landsat Water Bodies Using Adversarial Networks

Contact Info

Product

Resources

About