Monitoring riverine traffic from space: The untapped potential of remote sensing for measuring human footprint on inland waterways

Śmigaj, Magdalena; Hackney, Christopher; Diem, Phan Kieu; Trí, Văn Phạm Đăng; Ngoc, Nguyen Thi; Bui, Duong Du; Darby, S. E.; Leyland, Julian

doi:10.1016/j.scitotenv.2022.160363

Cited by 14 publications

(7 citation statements)

References 42 publications

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“…Suspended load of rivers can now be monitored using remote sensing and sediment transport models now commonly integrate some key human drivers, e.g., landuse change and the location and operation of dams. Some of the greatest uncertainties related to sediment transport concern the transport of bed load, sand mining rates and impacts [255][256][257] , and in general the poor availability of sediment data, which hinders effective model calibration and determining a reliable baseline. The capacity to model accelerated subsidence is, with few exceptions, very limited.…”

Section: Discussionmentioning

confidence: 99%

Data, knowledge, and modeling challenges for science-informed management of river deltas

Schmitt

Minderhoud

2023

One Earth

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Section: Discussionmentioning

confidence: 99%

Data, knowledge, and modeling challenges for science-informed management of river deltas

Schmitt

Minderhoud

2023

One Earth

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“…limate change can considerably impact socioeconomic development, human well-being, and social services worldwide [1], [2], [3], [4], [5]. To address these impacts, the Intergovernmental Panel for Climate Change (IPCC) was established through the joint efforts between the World Meteorological Organization (WMO) and the United Nations (UN).…”

Section: Introductionmentioning

confidence: 99%

Investigating the Future Flood and Drought Shifts in the Transboundary Srepok River basin Using CMIP6 Projections

Tran,

Do,

Nguyen

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Quantifying the intensity and frequency of climatic extremes under the impacts of climate change is crucial for effective water resource management. In this study, we utilize the Soil and Water Assessment Tool (SWAT) hydrological model, robust indices, e.g., Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) as well as the Interquartile Range (IQR) method for a comprehensive analysis of the river flow response to future climate scenarios towards 2090. Four General Circulation Models (GCMs) under two Shared Socioeconomic Pathways (SSPs) have been used, including BCC-CSM2-MR, CanESM5, MIROC6, and MRI-ESM2-0. We aim to reveal the future impacts of extreme events and their potential consequences for local livelihoods and human well-being in the Srepok River basin-a major tributary of the Mekong River basin in Southeast Asia. Our findings include (1) a significant discrepancy between extreme events found with more flood events projected towards 2090; (2) a shift in precipitation patterns with an increase in intensity is observed; and (3) a correlation between climatic extremes and regional characteristics has been identified. This work provides valuable insights into the anticipated changes in climatic extremes under the impacts of climate change and serves as the scientific basis for stakeholders and decision-makers to develop adaptative strategies and sustainable plans to enhance the region's resilience.

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“…Although several studies have estimated the SM budget using official statistics for smaller regions, official statistics in Southeast Asia, especially in Cambodia and Vietnam, are believed to significantly underestimate the accurate figures due to illegal SM (Tuyen, 2023;Witness, 2010). Few studies Hackney et al, 2021;Smigaj et al, 2023) have attempted to use satellite images to map SM activities and estimate budgets without solely relying on traditional field/official statistics-based approaches. Hackney et al (2021) estimated the extraction rate in Cambodia by manually counting Sand Transport Boats (STB) from a monthly composite of PlanetScope imagery and the boat's carrying capacity.…”

Section: Introductionmentioning

confidence: 99%

“…However, the specific challenge of mapping SM operations and budgets presents unique complexities, necessitating the precise classification of SM vessels. Smigaj et al (2023) automatically applied a DL model to map SM boats using PlanetScope imagery. Despite the innovative attempt, they have not quantified the SM budget.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning framework to map riverbed sand mining budgets in large tropical deltas

Kumar,

Park,

Tran

et al. 2023

Preprint

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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 (percentage 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.

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Monitoring riverine traffic from space: The untapped potential of remote sensing for measuring human footprint on inland waterways

Cited by 14 publications

References 42 publications

Data, knowledge, and modeling challenges for science-informed management of river deltas

Data, knowledge, and modeling challenges for science-informed management of river deltas

Investigating the Future Flood and Drought Shifts in the Transboundary Srepok River basin Using CMIP6 Projections

A Deep Learning framework to map riverbed sand mining budgets in large tropical deltas

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