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Wetland and floodplain inundation is well-known for its hydrological, ecological and environmental importance. Satellite remote sensing provides an effective and efficient tool for detecting inundation extent. This study compares and validates inundation maps derived from NASA’s Moderate Imaging Spectroradiometer (MODIS) imagery. The comparison was...
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... condition differs from its surrounding pixels. For example, a non-inundated pixel surrounded by eight inundated pixels is categorised as a scattered pixel. We regarded these scattered pixels as noise which indicates incorrect inundation condi- tions. We used a 3×3 moving window and applied a self- defined filtering method to remove the noise (Fig. 4). The number of cloud pixels within the window, which could remain after cloud masking and normal noise reduction filters, was examined. If there are more than four cloud pixels among the eight surrounding pixels in the 3×3 window, we then left the middle pixel as it was because there is not enough reference information for us to ...
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... cloud pixels within the window, which could remain after cloud masking and normal noise reduction filters, was examined. If there are more than four cloud pixels among the eight surrounding pixels in the 3×3 window, we then left the middle pixel as it was because there is not enough reference information for us to correct the status of the pixel (Fig. 4a). When there are less than four cloud edge-pixels, we applied the following rules to the central ...
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... The pixel will be set as a dry pixel if all non-cloud pixels are dry pixels (Fig. 4b); or & The pixel will be set as a wet pixel using the average OWL value of the surrounding non-cloud pixels (Fig. ...
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... This is known as the 8-day repeating cycle (i.e., MODIS 8-day composite) (Y. Chen et al., 2013). Landsat 8 consists of two data collection instruments called OLI and TRIS. ...
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.
... The MNDWI is calculated using MOD09A1 (version 006), which is described earlier. Among the various remotely sensed water indices (Mozumder et al., 2014;Rokni et al., 2014;Li et al., 2015), MNDWI has been acknowledged as one of the most accurate indicators for extracting water area variations (Ji et al., 2009;Chen et al., 2013). MNDWI, similar to NDVI, is a dimensionless index that ranges between −1 and 1. ...
Generating signals of reduced resilience in ecosystems is crucial for conservation and management endeavors. However, the practical implications of such systems are still limited due to the lack of high-frequency data and uncertainties associated with predicting complex systems such as ecosystems. This study aims to investigate the potential of time series analysis of remote sensing data in detecting signals of reduced resilience in mangrove forest ecosystems. Using time series analysis of remote sensing images, the resilience of mangrove forests was explored across two distinct study sites. One site (Qeshm Island) has been adversely affected by land-use and land-cover changes, while the other (Gabrik) serves as a reference ecosystem. The study uses data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to quantify three remotely sensed indices: the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Modified Vegetation Water Ratio (MVWR). In addition, Landsat data has been used to explore temporal alterations in land-use and land-cover change. To identify signals of reduced resilience, trend analyses of indicators such as autocorrelation (acf (1)) and standard deviation (SD) are applied. The findings revealed a notable decrease in resilience, signaled by significant upward trends in NDVI statistical metrics for Qeshm Island (Kendall’s τ of acf (1): 0.50 and SD: 0.90), contrasting with the pattern observed in Gabrik (Kendall’s τ of acf (1): −0.19 and SD: −0.19). These results align with our expectations derived from previous studies. Despite MNDWI significantly indicating reduced resilience in Qeshm Island (Kendall’s τ of acf (1): 0.86 and SD: 0.90), it also signaled decreased resilience in Gabrik (Kendall’s τ of acf (1): 0.79 and SD: 0.90). Moreover, MVWR failed to indicate signals of reduced resilience in both sites, specifically in Qeshm (Kendall’s τ of acf (1): −0.10 and SD: −0.07) and in Gabrik (Kendall’s τ of acf (1): −0.72 and SD: −0.12). These findings may be explained through quantitative analyses of land-use and land-cover change. While Qeshm Island and Gabrik share similarities in climate, geography, and annual rainfall, the analysis of land-use and land-cover change revealed significant differences between the two study areas. Qeshm Island underwent drastic increases in the built-up class by a 64.40% change between 1996 and 2014, whereas the built-up class expanded modestly by a 4.04% change in the Gabrik site. This study contributes to advancing our understanding of ecosystem dynamics. The findings of this study can be integrated with ecosystem management tools to enhance the effectiveness of conservation efforts. This is the first report of the successful application of remote sensing in generating signals of reduced resilience within mangrove forests in the Middle East.
... Other types of non-hazardous floodwaters shown in VIIRS flood maps include coastal wetlands and tidal marshes. Because the amount of water in the coastal wetlands and tidal marshes is highly dynamic (Lamb et al., 2021), some studies categorized wetland and floodplain or water bodies as the same land use type that mostly corresponds to the flooded areas (Chen et al., 2013;Notti et al., 2018), and some researches defined permanently and temporarily flooded coastal wetlands (Martín et al., 2020). When swollen rivers cause river levels to rise above their banks, floodwaters can inundate nearby low-lying areas. ...
... The Modified Normalized Difference Water Index (MNDWI) is calculated using MOD09A1 (version 006), which is described earlier. Among the various remotely sensed water indices (Mozumder, Tripathi et al. 2014, Rokni, Ahmad et al. 2014, Li, Chen et al. 2015, MNDWI has been acknowledged as the most accurate indicator for extracting water area variations (Ji, Zhang et al. 2009, Chen, Huang et al. 2013. MNDWI, similar to NDVI, is a dimensionless index that ranges from -1 to 1. ...
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... It is worth emphasizing the importance of considering the conservation status of small and intermittently flooded wetlands, which are often overlooked due to their size and intermittent nature (Chen et al. 2013). Despite their modest dimensions, these wetlands play a vital role in providing essential ecohydrological services to the surrounding communities (Carolissen 2022). ...
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... Identification error represents the probability that a pixel is classified. In contrast, omission error represents the percentage of a certain class is incorrectly identified on the map (Chen et al., 2013). Kappa is an index that estimates the correlation between two data (Landis and Koch, 1977;Jensen, 2005). ...
In recent years, urbanization and population have been increasing rapidly in coastal areas of Vietnam. Various development projects are carried out along the coastal areas, putting significant pressure on these areas leading to many coastal hazards such as coastline erosion, saltwater intrusion, seawater pollution,... Continuous monitoring of urbanization in coastal areas is essential to monitor the loss of natural areas due to urban development and to support planning activities, helping local authorities to raise awareness of environmental protection and people’s safety. These issues and concerns are the main focuses of this research.This study applies two methods to assess the effects of urbanization through land use change and coastline change in Rach Gia, one of the country’s fastest-growing cities in recent decades:(1)Assessment of land use changes: The study utilizes medium-resolution Landsat satellite imagery (30 m) observed at multiple time points from 2010 to 2020. An advanced machine learning method, the Support Vector Machine (SVM) algorithm, is employed to estimate land use area in conjunction with population survey data. The results demonstrate the proportion of urban land use and urban land use density relative to population growth rates, thereby confirming the influence of urbanization on land use patterns.(2)Assessment of coastal shoreline changes: A series of Landsat satellite images are used to detect changes and fluctuations in the Rach Gia coastline. The technology interprets
coastal changes using calculation indices, threshold images and shoreline analysis, integrated within the ArcGIS environment via the DSAS software provided by the United States Geological
Survey (USGS). Coastal changes are evaluated using a five-year cycle change index, including erosion and accretion processes from 2010 to 2020. The coastal change index is quantified by
three functional analysis variables within DSAS: End Point Rate (EPR), Nearshore Movement (NSM) and Linear Regression Rate (LRR). These variables clearly illustrate the dynamics of the
Rach Gia coastline during the study period. The research results demonstrate the advantages and reliability of the research methods and their ability to effectively and timely support the assessment of changes resulting from urbanization in coastal areas
... MNDWI is calculated using MOD09A1 (version 006), which is described earlier. Among the numerous remotely sensed water indices available (Mozumder, Tripathi et al. 2014, Rokni, Ahmad et al. 2014, Li, Chen et al. 2015, MNDWI has been recognized as the most accurate indicator for extracting water area variations (Ji, Zhang et al. 2009, Chen, Huang et al. 2013. MNDWI, similar to NDVI, is a dimensionless index that ranges from -1 to 1. ...
Detecting abrupt transitions in ecosystems, known as regime shifts, holds immense implications for conservation and management endeavors. This research aims to investigate the feasibility of developing an early warning system capable of identifying an upcoming critical transition within Mangrove Forest ecosystems. Employing a fusion of remote sensing analysis, time series analysis, and the critical slowing down theory, Mangrove Forests' state change was explored across two distinct study sites. One site has been adversely affected by disturbances stemming from land use and land cover changes, while the other serves as an unaffected reference ecosystem. The study uses data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite, quantifying three remotely sensed indices: the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Modified Vegetation Water Ratio (MVWR). Furthermore, temporal alterations in land-use and land cover are scrutinized using Landsat data from 1996, 2002, 2008, and 2014. To identify early warning signals of critical transitions, indicators such as autocorrelation, skewness, and standard deviation are applied. The results show the robust capabilities of remote sensing in generating early warning signals of critical transition in Mangrove Forests. NDVI outperformed MVWR and MNDWI as ecosystem state indicators. This study not only highlights the potential of remote in identifying the approaching regime shifts in Mangrove Forest ecosystems but also adds knowledge on ecosystem dynamics. This is the first report of the successful application of remote sensing in generating early warning signals for imminent critical transitions within Mangrove forests in the Middle East.
... Es de esperarse que los datos MODIS subestimen la extensión de la inundación en comparación con datos de mayor resolución espacial como Landsat o Sentinel (Chen et al. 2013). En esta tesis la mínima disponibilidad de datos por cada pixel fue del 86% y la calidad de dichos datos, varió entre la temporada de estiaje y lluvias, en donde la temporada de estiaje presentó una mejor calidad. ...
... In another study, Szabó et al. used the NVDI vegetation index for the estimation of sedimentation and vegetation in artificial lakes [36]. Chen et al. (2013) prepared a flooding map for the wetlands in Australia using the MNDWI index [37]. Sharifzadeh and Adhikari have studied the application of the SVM learning algorithm for the extraction of water from the TM images of the Landsat satellite located in the northwest Minnesota and east-central of Wisconsin and compared the results with the normal index of MNDWI. ...
... In another study, Szabó et al. used the NVDI vegetation index for the estimation of sedimentation and vegetation in artificial lakes [36]. Chen et al. (2013) prepared a flooding map for the wetlands in Australia using the MNDWI index [37]. Sharifzadeh and Adhikari have studied the application of the SVM learning algorithm for the extraction of water from the TM images of the Landsat satellite located in the northwest Minnesota and east-central of Wisconsin and compared the results with the normal index of MNDWI. ...
The construction of new breakwaters in Anzali port has had a significant impact on the water body of the Anzali international lagoon. The Anzali wetland is under threat from sediment influx from mountainous regions, and the study used satellite image processing to demonstrate how the construction of new breakwaters impedes the natural transfer of sediment from the lagoon to the sea. The methodology employed a hybrid approach combining two methods: normal water index (MNDWI) and supervised classification (SVM) to detect sediment accumulation in the wetland water zone. Following the construction of new breakwaters in 2009, an island formed and expanded exponentially in parts of Sorkhankol Wildlife Sanctuary's water body. This phenomenon is attributed to decreased water flow caused by increased cross-section current and volume of water, creating a dam-like function against channel flow leading to the sea. Consequently, sediments and suspended loads settle in Sorkhankol's water zone, leading to an increase in island area from 0.39 hectares to over 26 hectares during the studied period. Result showed Kappa coefficients by SVM algorithm for years 2002, 2010, 2012 and 2017 which were found to be 0.76, 0.62, 0.71 and 0.86 respectively indicating that SVM outperforms MNDWI in effectively monitoring landform changes.
... This, in turn, has facilitated the advancement of modeling and forecasting techniques, resulting in more accurate estimation and understanding of water resources and their dynamics at various scales. The exponential increase in the number of studies utilizing freely available satellite data for surface water monitoring in data-scarce regions, particularly drylands, highlights the growing interest and utility of these resources (Ozesmi and Bauer, 2002;Chen et al., 2013;Powell et al., 2019). To document and consolidate the hydrological research trends of the past decades, this review was conducted, encompassing various themes and providing key scientific findings and conclusions. ...
This paper provides an overview of the progress made in remote sensing of water resources in Africa, focusing on various applications such as precipitation estimation, land surface temperature analysis, soil moisture assessment , surface water extent measurement, surface runoff and streamflow analysis, water quality evaluation, land cover/land use mapping, and groundwater analysis. Specifically, the study sheds light on the remarkable progress made in remote sensing applications, showcasing scientific advancements and highlighting the challenges encountered. Moreover, it identifies crucial knowledge gaps that need to be addressed in order to further advance this field. The review's key findings indicate that the availability of sensors and observations, along with analytical models, has contributed to monitoring Africa's water resources at various scales. The availability and accessibility of hydrological data for monitoring and assessing water resources in Africa have been partially improved through the adoption of satellite data and processing technologies. Additionally, the emergence of various international remote sensing initiatives, North-South research collaborations, and projects has contributed to the research progress. Prominent satellite data series such as Landsat, MODIS, and GRACE have played significant roles in African hydrological research. However, the limited and malfunctioning in-situ hydrological monitoring networks in Africa have affected the accurate calibration and validation of remotely sensed hydrological models. Insufficient long-term rainfall and climate data pose challenges to long-term earth observation research on African water systems. The lack of high-resolution spatial and temporal data hampered accurate monitoring of hydrological processes at smaller scales. Despite the widespread use of rainfall satellite products, validation attempts over Africa, particularly in western and southern regions, have been limited. Furthermore, the reliability of hydrological satellite datasets is affected by factors such as the number and coverage of surface stations, retrieval algorithms, data integration techniques, and cloud cover. Overall, this work demonstrates the importance of earth observation in understanding Africa's hydrology, previously hindered by the lack of in-situ data. Nevertheless, efforts are therefore needed to enhance the adoption and application of remote sensing, particularly in groundwater and surface water estimation at smaller scales. Future research should focus on multi-source data integration, assimilation, and big data analytics using cloud computing and machine learning to address complex hydrological research questions at various scales.
