Now showing 1 - 7 of 7
  • Publication
    Metadata only
    New systematically measured sand mining budget for the Mekong delta reveals rising trends and significant volume underestimations
    (2022)
    Gruel, Charles-Robin
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    Switzer, Adam D.
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    Sonu, Kumar
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    Ho, Huu Loc
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    Sameh, Kantoush
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    Doan, Van Binh
    ;
    Feng, Lian
    The river beds of the Mekong Delta are some of the most intensively sand mined places in the world. However, sand mining budgets remain limited to rough and indirect estimates. Here, we provide a first systematic, field-based estimation of the Mekong Delta’s sand mining budget. This budget overcomes the limitations of relying on officially declared statistics and bathymetric surveys of short channel reaches. We applied Sentinel-1 radar imagery to monitor the distribution of sand mining activities using boat metrics-driven mining intensity maps correlated with a field-based bathymetry difference map which were derived from two extensive bathymetric surveys conducted in 2014 and 2017. The two surveys cover ∼ 100 km in the Tiền River, reaching approximately 15% of the Mekong Delta. We then extrapolated the Tiền River findings to the broader Vietnamese Mekong Delta from 2015 to 2020 and measured a continuous increase of the extraction budget by ∼ 25% between 2015 (38 Mm3/yr) and 2020 (47 Mm3/yr). We estimated a total sand mining budget of 254 Mm3 during the 6-year study period with an average annual rate of ∼ 42 Mm3. Our field-based annual rates are higher than both official declarations provided and estimates from previous studies which implies that a substantial portion of the sand mining budget remains unaccounted for. Riverbed sand mining remains a key threat to the Mekong Delta as it contributes to a multitude of other environmental threats including dam construction effects on sedimentation, ongoing subsidence, sea level rise and recurring saltwater intrusion. This study offers a new approach that can be implemented elsewhere to allow for systematic monitoring and quantification of sand mining activities that are vital for assessing future projections on environmental impacts.
    WOS© Citations 13Scopus© Citations 15  49
  • Publication
    Metadata only
    Flood risk mapping during the extreme February 2021 flood in the Juruá River, western Brazilian Amazonia, state of Acre
    (MDPI, 2024)
    Mantovani, Jose
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    Alcantara, Enner
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    Marengo, Jose A.
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    Londe, Luciana
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    Cunha, Ana Paula
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    Tomasella, Javier

    Cruzeiro do Sul, a municipality in Northwestern Brazil is recurrently impacted by floods, particularly along the Juruá River. This study presents a comprehensive flood risk analysis by integrating geoprocessing, remote sensing, and hydraulic modeling techniques. Our objectives are to simulate flood extents, identify high-risk areas, and guide sustainable territorial management. Our findings illustrate that the flood impacts are distributed across urban (27%), agricultural (55%), and forest/grassland (17%) landscapes. Historical records and literature reviews also underscore a recurring pattern of extreme floods in the municipality, notably during February’s La Niña events. Some vulnerable urban neighborhoods were identified: Vila Cruzeirinho, Centro, Miritizal, and Da Várzea. These areas are especially susceptible due to their proximity to the river and increased surface runoff during high flood events. By amalgamating various data sources and methods, this research aids decision making for flood mitigation and urban development, fostering resilience against recurrent flooding events in Cruzeiro do Sul.

      5
  • Publication
    Open Access
    Spatiotemporal changes in mulberry-dyke-fish ponds in the Guangdong-Hong Kong-Macao Greater Bay Area over the past 40 years
    (2021)
    Zhang, Wenxin
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    Cheng, Zihao
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    Qiu, Junliang
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    Ran, Lishan
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    Xie, Xuetong
    ;
    Yang, Xiankun
    Mulberry-dyke-fish pond ecosystems are a representative traditional eco-agriculture in the Guangdong‒Hong Kong‒Macao Greater Bay Area (GBA). Investigations about the changes in the systems and their relevant water environments under the background of rapid urbanization can provide valuable information to formulate sustainable protection and development strategies. Using the Landsat images obtained after 1986, this study combined supervised classification and visual interpretation approaches, as well as water intensity index and synthesized index to identify the spatial patterns of changes in the ponds in the GBA over the past 40 years. The results indicated that during the period 1986‒2013, the total surface area of the ponds in the GBA increased significantly and peaked in 2013 with a total increase of 84.63%; After that, the total surface area showed a downward trend with a total decrease of approximately 31.34%. The year of 2013 was identified as the milestone of the changes. The results proved that human activities have continuously influenced the spatial distribution and size of fish ponds in the past 40 years. The fish ponds had transformed from near-natural ponds with different sizes and a near-natural random distribution in the early stage into an artificial distribution and an artificial shape. Land use changes, industrial transfer, Government guidance and financial motives were the major drivers to the changes. If no effective measures are taken, this shrinking trend in the ponds will remain in the future.
    WOS© Citations 6Scopus© Citations 7  52  82
  • Publication
    Metadata only
    A deep learning framework to map riverbed sand mining budgets in large tropical deltas
    (Taylor & Francis, 2023)
    Suno Kumar
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    Tran, Dung Duc
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    Wang, Jinyu
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    Ho, Huu Loc
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    Feng, Lian
    ;
    Sameh, A. Kantoush
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    Van Binh, Doan
    ;
    Li, Dongfeng
    ;
    Switzer, Adam D.

    Rapid urbanization has dramatically increased the demand for river sand, leading to soaring sand extraction rates that often exceed natural replenishment in many rivers globally. However, our understanding of the geomorphic and social-ecological impacts arising from Sand Mining (SM) remains limited, primarily due to insufficient data on sand extraction rates. Conventionally, bathymetry surveys and compilation of declared amounts have been used to quantify SM budgets, but they are often costly and laborious, or result in inaccurate quantification. Here, for the first time, we developed a Remote Sensing (RS)-based Deep Learning (DL) framework to map SM activities and budgets in the Vietnamese Mekong Delta (VMD), a global SM hotspot. We trained a near real-time object detection system to identify three boat classes in Sentinel-1 imagery: Barge with Crane (BC), Sand Transport Boat (STB), and other boats. Our DL model achieved a 96.1% Mean Average Precision (mAP) across all classes and 98.4% for the BC class, used in creating an SM boat density map at an Intersection over Union (IoU) threshold of 0.50. Applying this model to Sentinel-1, 256,647 boats were detected in the VMD between 2014–2022, of which 17.4% were BC. Subsequently, the annual SM budget was estimated by correlating it with a recent riverbed incision map. Our results showed that, between 2015–2022, about 366 Mm3 of sand has been extracted across the VMD. The annual budget has progressively increased from 34.92 Mm3 in 2015 to 53.25 Mm3 in 2022 (by 52%), with an annual increment of around 2.79 Mm3. At the provincial-scale, Dong Thap, An Giang, Vinh Long, Tien Giang, and Can Tho were the locations of intensive mining, accounting for 89.20% of the total extracted volume in the VMD. Finally, our estimated budgets were validated with previous research that yielded a correlation coefficient of 0.99% (with bias of 2.65%). The automatic DL framework developed in this study to quantify SM budgets has a high potential to be applied to other deltas worldwide also facing intensive SM.

    Scopus© Citations 2  3
  • Publication
    Open Access
    Inferring floodplain bathymetry using inundation frequency
    (2020) ;
    Emadzadeh, Adel
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    Enner, Alcantara
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    Yang, Xiangyu
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    Ho, Huu Loc
    This study proposes a new method to retrieve the bathymetry of turbid-water floodplains from the inundation frequency (IF) data derived from over 32 years of composite optical remote sensing data. The new method was tested and validated over the Curuai floodplain in the lower Amazon River, where the entire bathymetry was surveyed in 2004, and water level gauge data has been available since 1960. The depth was estimated based on the relationship derived from IF and surveyed depth data, and the results were compared to those retrieved from bare-Earth DEM. We further assessed the sensitivity of the approach by analyzing the deepest part of the lake (i. e., permanent water body ~ 8m) with high IF, as well as the effect of gradual sedimentation in the lake over time. The results showed that the model is highly accurate and sensitive to IF changes even in the permanent water body areas, suggesting that this model can be used in other seasonal lakes worldwide with turbid-waters, where large-scale bathymetry surveys are not feasible due to high operation costs.
    WOS© Citations 7Scopus© Citations 8  271  107
  • Publication
    Open Access
    Characterizing channel-floodplain connectivity using satellite altimetry: Mechanism, hydrogeomorphic control, and sediment budget
    In this study, a mechanism of channel-floodplain seasonal connectivity over a full hydrological year is assessed mainly utilizing satellite radar altimetry data (Jason-2) in a floodplain along the Amazon River. The proposed observation-based approach employs the concurrent measurement of water levels (WLs) over river and floodplain, analyzing seasonal changes in water surface height differences between the two water bodies. Hydrological connectivity thresholds at different stages during the rising phase were identified, and then validated using field data and remote sensing-driven surface suspended sediment maps. Successful decoupling of the two indiscrete flooding processes during the rising phase: channelized and overbank dispersion processes, is one of the major outcomes of this study. Different roles of the connectivity processes on floodplain hydrogeomorphology are highlighted that channelized flows determine inundation frequency, residence time and development of positive topographic features in the floodplain; while overbank flows contribute good part of the seasonal water storage and sediment budget in the floodplain, and tends to smooth positive topography built by channelized flows. The zones of overbank flooding, however, are rather localized due to the well-developed natural levee complex and stable channel-dominated floodplain along the river bank. Lastly, the presented approach is straightforward based on the publicly available operational dataset and therefore it may be readily adapted by non-remote sensing experts. Thus, along with the emergence of new radar altimetry platforms, such as ICESat-2 or Jason-3 that could measure WL of smaller lakes, the proposed approach offers the potential to contribute to research on channel-floodplain systems in other rivers at a global scale.
    WOS© Citations 29Scopus© Citations 36  289  167
  • Publication
    Open Access
    A pathway to the automated global assessment of water level in reservoirs with Synthetic Aperture Radar (SAR)
    (2020) ;
    Merino, Eder
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    Lewis, Quinn W.
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    Lindsey, Eric O.
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    Yang, Xiankun
    Global measurements of reservoir water levels are crucial for understanding Earth’s hydrological dynamics, especially in the context of global industrialization and climate change. Although radar altimetry has been used to measure the water level of some reservoirs with high accuracy, it is not yet feasible unless the water body is sufficiently large or directly located at the satellite’s nadir. This study proposes a gauging method applicable to a wide range of reservoirs using Sentinel–1 Synthetic Aperture Radar data and a digital elevation model (DEM). The method is straightforward to implement and involves estimating the mean slope–corrected elevation of points along the reservoir shoreline. We test the model on six case studies and show that the estimated water levels are accurate to around 10% error on average of independently verified values. This study represents a substantial step toward the global gauging of lakes and reservoirs of all sizes and in any location where a DEM is available.
    WOS© Citations 7  61  85