Study of the Buried Basin C-H, Based on the Multi-Source Remote Sensing Data

Xu, Xiaojian; Hu, Teng; Zhang, Kang; Du, Xiuli; Zhang, Lin

doi:10.3390/rs14215284

Cited by 4 publications

(3 citation statements)

References 47 publications

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“…NDBI has shown a strong positive correlation with the LST and is pertinent to evaluate thermal profiles, especially in dense urban areas (Berger et al, 2017;Dai et al, 2018). MNDWI as developed by Xu (2005) is a water-index that uses the green (GREEN) and the short wave infrared (SWIR) bands from RS data. MNDWI has been found to be more effective in highlighting waterbodies among the different types of water-related indices (Szabó et al, 2016).…”

Section: Image Processing and Study Variablesmentioning

confidence: 99%

Quantifying thermal variation around gray infrastructure in urban India

Subramanian

2023

Front. Water

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IntroductionDense cities in developing nations face rapid urban sprawl. This alters the local ecology and contributes significantly to the local temperature variation. Gray infrastructure (GI) includes vital processes of sewage treatment and wastewater pumping stations. GI is attributed to large greenhouse gas emissions and high energy utilization, contributing to the local urban heat island effect. A knowledge gap exists in assessing GI contribution to the local temperature variation in megacities of developing nations like India.MethodologyIn this study, the Thermal Variance Index (TVI) was derived around a buffer zone for 7 Sewage Treatment Plants (STPs) in Mumbai. Landsat 8 remote sensing imagery was used with summer and winter variation for alternate years from 2014 to 2021.Results and discussionThree STPs set within densely built surroundings showed a cooling profile. Four STPs located among wetlands displayed a heating profile. The surrounding built spaces showed significant influence on the TVI recorded. The STP Cooling Effect (CE) was further quantified by deducing its Cooling Range (CR) and Cooling Intensity (CI). STPs within densely built areas showed higher Cooling Range and Cooling Intensity. Regression analysis models indicated a high positive correlation for the Normalized Difference Built-up Index (NDBI), Landscape Shape Index (LSI), and STP capacity. Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and STP area showed a strong negative correlation.

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Section: Image Processing and Study Variablesmentioning

confidence: 99%

Quantifying thermal variation around gray infrastructure in urban India

Subramanian

2023

Front. Water

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“…The historical analysis of MHWs in the East Sea (Wang et al, 2022), mechanisms and properties of MHWs in the East Asian Marginal Seas (Choi et al, 2022;Lee et al, 2023) have been studied. Future analysis of MHWs in China's marginal Seas and Adjacent offshore waters using CMIP5 GCMs (Yao et al, 2020) and using CMIP6 GCMs (Xue et al, 2023) have also been studied. But a systematic future projection specially focusing on the East Sea using the latest CMIP6 is limited.…”

Section: Introductionmentioning

confidence: 99%

Future projection of marine heat waves in a global marine hotspot Case of East/Japan Sea

Deegala,

Chung

2024

Preprint

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The future analysis of the Marine Heat Waves (MHWs) has high uncertainty due to the significant shortcomings of the Global Climate Models (GCMs) in simulating the Sea Surface Temperature (SST) and the MHWs. This study suggests a more systematic approach to do the future projection of MHWs. Our study area is East/Japan Sea which is a large marine ecosystem exposed to rapid warming of the ocean. This study found the models; ACCESS-CM2, BCC-CSM2-MR ACCESS-ESM1, and GFDL-ESM4 from Coupled Model Intercomparison Project sixth phase (CMIP6) are the best performing GCMs in the East Sea by analyzing their grid-wise performance during the historical period (1985–2014). Using the ensemble mean from the selected models, the future MHW metrices of frequency, maximum intensity, and duration during the near future (2041–2070) and far future (2071–2100) was investigated. Following the state-of-art, shifting baseline approach was utilized to identify the MHWs and 30 years were used as the climatology period for each historical and future periods. The time series results from the ensemble mean indicated that high emission scenarios (SSP3-7.0, and SSP5-8.5) would have higher trends than that of low emission scenarios (SSP1-2.6, and SSP2-4.5) as well as that of historical observations. The high emission scenarios would have lower values in the beginning of their respective climatology period when compared to that of low emission scenarios but rather higher values toward the end of the period. The average MHW metrices of near and far futures shows certain shifts compared to that of historical but the numerical values are almost similar to that of historical period.

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“…In ungauged basins, remote sensing serves as a valuable source of data for hydrological simulation and prediction. For instance, Du [16] utilized satellite-retrieved glacier, precipitation, temperature, and water level data to simulate a runoff time series in the Tibetan Plateau. In addition, remote sensing-based observations can extend the scale of disaster monitoring, such as drought from the local scale to a larger regional scale.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Technology in the Construction of Digital Twin Basins: Applications and Prospects

Liu

2023

Water

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A digital twin basin serves as a virtual representation of a physical basin, enabling synchronous simulation, virtual–real interaction, and iterative optimization. The construction of a digital twin basin requires a basin database characterized by large-scale coverage, high-precision, high-resolution, and low-latency attributes. The advancements in remote sensing technology present a new technical means for acquiring essential variables of the basin. The purpose of this paper was to provide a comprehensive overview and discussion of the retrieval principle, data status, evaluation and inter-comparison, advantages and challenges, applications, and prospects of remote sensing technology in capturing seven essential variables, i.e., precipitation, surface temperature, evapotranspiration, water level, river discharge, soil moisture, and vegetation. It is indicated that remote sensing can be applied in some digital twin basin functions, such as drought monitoring, precipitation forecasting, and water resources management. However, more effort should be paid to improve the data accuracy, spatiotemporal resolution, and latency through data merging, data assimilation, bias correction, machine learning algorithms, and multi-sensor joint retrieval. This paper will assist in advancing the application of remote sensing technology in constructing a digital twin basin.

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Study of the Buried Basin C-H, Based on the Multi-Source Remote Sensing Data

Cited by 4 publications

References 47 publications

Quantifying thermal variation around gray infrastructure in urban India

Quantifying thermal variation around gray infrastructure in urban India

Future projection of marine heat waves in a global marine hotspot Case of East/Japan Sea

Remote Sensing Technology in the Construction of Digital Twin Basins: Applications and Prospects

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