An enhanced spatiotemporal fusion method – Implications for coal fire monitoring using satellite imagery

Ghosh, Raktim; Gupta, P. K.; Tolpekin, V.A.; Srivastav, S. K.

doi:10.1016/j.jag.2020.102056

Cited by 30 publications

(23 citation statements)

References 53 publications

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“…This approach is called the multisource-based or hybrid approach. The auxiliary picture must have the same wavelength and spatial resolution as the target image in this method [13]. The spatiotemporal method of cloud removal is an example of a hybrid approach that Zhu [14] has been developed.…”

Section: Research Historymentioning

confidence: 99%

A PSNR Review of ESTARFM Cloud Removal Method with Sentinel 2 and Landsat 8 Combination

Tarigan¹,

Isa²

2021

IJACSA

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Remote sensing images with high spatial and temporal resolution (HSHT) for GIS land use monitoring are crucial data sources. When trying to get HSHT resolution images, cloud cover is a typical problem. The effects of cloud cover reduction using the ESTARFM, one of spatiotemporal image fusion technique, is examined in this study. By merging two satellite photos of low-resolution and medium-resolution images, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Method (ESTARFM), predicts the reflectance value of the cloud cover region. ESTARFM, on the other hand, employs both medium and high-resolution satellite pictures in this study. Using Sentinel 2 and Landsat 8, the Peak Signal Noise Ratio (PSNR) statistical methods are then utilized to evaluate the ESTARFM. The PSNR explain ESTARFM cloud removal performance by comparing the level of similarity of the reference image with the reconstructed image. In remote sensing, this hypothesis was established to get high-quality HSHT pictures. Based on this study, Landsat 8 images that have been cloud removed with ESTARFM may be classed as good. The PSNR value of 21.8 to 26 backs this up, and the ESTARFM result seems good on visual examination.

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Section: Research Historymentioning

confidence: 99%

A PSNR Review of ESTARFM Cloud Removal Method with Sentinel 2 and Landsat 8 Combination

Tarigan¹,

Isa²

2021

IJACSA

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“…Spatiotemporal fusion (STF) methods have been considered as a practical solution to the above issues [11,12], as they combine images with fine spatial but rough temporal resolution, such as Landsat images (referred to herein as "fine" images), with images with fine temporal but rough spatial resolution, such as MODIS images (referred to herein as "coarse" images) to produce satellite images with both high spatial and high temporal resolution. Thus far, various STF methods have been developed and widely utilized in Earth surface monitoring [13][14][15][16][17], and the generation of real-time quantitative remote sensing products, including evapotranspiration [18][19][20], surface temperature [21][22][23][24][25], leaf area indexing [26][27][28], surface soil moisture [29], and ocean color imaging products [30]. However, different STF methods, which are generally categorized into weighting-function-, unmixing-, hybrid, and learning-based STF methods, still have distinct applicability in the prediction of complex land surface temporal changes, including phenological change (PC) and LC prediction.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep Learning-Based Spatiotemporal Fusion Method for Combining Satellite Images with Different Resolutions

et al. 2021

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Spatiotemporal fusion (STF) is considered a feasible and cost-effective way to deal with the trade-off between the spatial and temporal resolution of satellite sensors, and to generate satellite images with high spatial and high temporal resolutions. This is achieved by fusing two types of satellite images, i.e., images with fine temporal but rough spatial resolution, and images with fine spatial but rough temporal resolution. Numerous STF methods have been proposed, however, it is still a challenge to predict both abrupt landcover change, and phenological change, accurately. Meanwhile, robustness to radiation differences between multi-source satellite images is crucial for the effective application of STF methods. Aiming to solve the abovementioned problems, in this paper we propose a hybrid deep learning-based STF method (HDLSFM). The method formulates a hybrid framework for robust fusion with phenological and landcover change information with minimal input requirements, and in which a nonlinear deep learning-based relative radiometric normalization, a deep learning-based superresolution, and a linear-based fusion are combined to address radiation differences between different types of satellite images, landcover, and phenological change prediction. Four comparative experiments using three popular STF methods, i.e., spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC, as benchmarks demonstrated the effectiveness of the HDLSFM in predicting phenological and landcover change. Meanwhile, HDLSFM is robust for radiation differences between different types of satellite images and the time interval between the prediction and base dates, which ensures its effectiveness in the generation of fused time-series data.

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“…Advancements in remote sensing techniques have contributed significantly to tackling these challenges by facilitating large-scale coal fire monitoring [16][17][18]. Diverse remote sensing methods have been used in coal fire monitoring, including analyses of subsidence related to coal fires [19,20], identification of surface temperature anomalies [21][22][23][24], and mapping of geo-environmental indicators of coal fires [25,26].…”

Section: Introductionmentioning

confidence: 99%

Detecting and Analyzing the Evolution of Subsidence Due to Coal Fires in Jharia Coalfield, India Using Sentinel-1 SAR Data

et al. 2021

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Public safety and socio-economic development of the Jharia coalfield (JCF) in India is critically dependent on precise monitoring and comprehensive understanding of coal fires, which have been burning underneath for more than a century. This study utilizes New-Small BAseline Subset (N-SBAS) technique to compute surface deformation time series for 2017–2020 to characterize the spatiotemporal dynamics of coal fires in JCF. The line-of-sight (LOS) surface deformation estimated from ascending and descending Sentinel-1 SAR data are subsequently decomposed to derive precise vertical subsidence estimates. The most prominent subsidence (~22 cm) is observed in Kusunda colliery. The subsidence regions also correspond well with the Landsat-8 based thermal anomaly map and field evidence. Subsequently, the vertical surface deformation time-series is analyzed to characterize temporal variations within the 9.5 km2 area of coal fires. Results reveal that nearly 10% of the coal fire area is newly formed, while 73% persisted throughout the study period. Vulnerability analyses performed in terms of the susceptibility of the population to land surface collapse demonstrate that Tisra, Chhatatanr, and Sijua are the most vulnerable towns. Our results provide critical information for developing early warning systems and remediation strategies.

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An enhanced spatiotemporal fusion method – Implications for coal fire monitoring using satellite imagery

Cited by 30 publications

References 53 publications

A PSNR Review of ESTARFM Cloud Removal Method with Sentinel 2 and Landsat 8 Combination

A PSNR Review of ESTARFM Cloud Removal Method with Sentinel 2 and Landsat 8 Combination

A Hybrid Deep Learning-Based Spatiotemporal Fusion Method for Combining Satellite Images with Different Resolutions

Detecting and Analyzing the Evolution of Subsidence Due to Coal Fires in Jharia Coalfield, India Using Sentinel-1 SAR Data

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