Integration of satellite remote sensing data in underground coal fire detection: A case study of the Fukang region, Xinjiang, China

Yan, Song Y.; Shi, Ke; Li, Yang; Liu, Jinglong; Zhao, Hongfeng

doi:10.1007/s11707-019-0757-9

Cited by 30 publications

(15 citation statements)

References 22 publications

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“…This scenario is apparent from the map (Figure 6a) since most thermally anomalous regions are associated with a nearby subsidence zone, even though they may not overlap. Similar phenomena have been reported by the authors of [76] while integrating surface thermal anomalies and subsidence regions to derive the coal fire map. They have observed that most of the coal fire locations identified in field surveys are either nearby or on the boundaries of thermally anomalous regions identified in TIR images.…”

Section: Coal Fire Mapping Using Tir Imagesupporting

confidence: 85%

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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Section: Coal Fire Mapping Using Tir Imagesupporting

confidence: 85%

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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“…In terms of land cover type mapping, Kaplan and Avdan [27] fused Sentinel-1 and Sentinel-2 data to map wetlands in Turkey while Slagter et al [28] fused Sentinel-1 and Sentinel-2 data to map wetlands in South Africa. Kannaujiya et al [29] integrated electrical resistivity tomography and ground-penetrating radar to map landslides in Kunjethi, while Yan et al [30] integrated Landsat-8 optical data and Sentinel-1A to detect underground coal fires in China. Venter et al [31] assessed the efficacy of mapping hyperlocal Tair over Oslo in Norway by integrating Sentinel, Landsat, and light detection and ranging (LiDAR) data with crowdsourced Tair measurements.…”

Section: Introductionmentioning

confidence: 99%

Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

et al. 2021

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Mapping crop types and land cover in smallholder farming systems in sub-Saharan Africa remains a challenge due to data costs, high cloud cover, and poor temporal resolution of satellite data. With improvement in satellite technology and image processing techniques, there is a potential for integrating data from sensors with different spectral characteristics and temporal resolutions to effectively map crop types and land cover. In our Malawi study area, it is common that there are no cloud-free images available for the entire crop growth season. The goal of this experiment is to produce detailed crop type and land cover maps in agricultural landscapes using the Sentinel-1 (S-1) radar data, Sentinel-2 (S-2) optical data, S-2 and PlanetScope data fusion, and S-1 C2 matrix and S-1 H/α polarimetric decomposition. We evaluated the ability to combine these data to map crop types and land cover in two smallholder farming locations. The random forest algorithm, trained with crop and land cover type data collected in the field, complemented with samples digitized from Google Earth Pro and DigitalGlobe, was used for the classification experiments. The results show that the S-2 and PlanetScope fused image + S-1 covariance (C2) matrix + H/α polarimetric decomposition (an entropy-based decomposition method) fusion outperformed all other image combinations, producing higher overall accuracies (OAs) (>85%) and Kappa coefficients (>0.80). These OAs represent a 13.53% and 11.7% improvement on the Sentinel-2-only (OAs < 80%) experiment for Thimalala and Edundu, respectively. The experiment also provided accurate insights into the distribution of crop and land cover types in the area. The findings suggest that in cloud-dense and resource-poor locations, fusing high temporal resolution radar data with available optical data presents an opportunity for operational mapping of crop types and land cover to support food security and environmental management decision-making.

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“…Vegetation density was detected using a Normalized Difference Vegetation Index with the following formula [14]:…”

Section: Spectral Transformation Of Landsat 7 Etm + and Landsat mentioning

confidence: 99%

Spatial Monitoring of Urban Development Direction using Landsat 7 ETM+ and Landsat 8 OLI/TIRS in Balikpapan City, Indonesia

Dewantoro*,

Natani,

Islamiah

2020

IJITEE

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 Abstract: The development of urban areas in the city of Balikpapan increases over time and is characterized by increasing population. The growth and development of urban areas needs to be monitored so that the control function on area spatial can be implemented. This research aims to determine the direction of urban areas and measure the density of the built-up as a leading indicator of the development of urban areas in Balikpapan. The method used in this study is the multispasio-temporal analysis of remote sensing data of Landsat 7 ETM + and Landsat 8 OLI/TIRS which contain a combination of spectral transformation, classification supervised Maximum Likelihood, accuracy assessment and statistical analysis. The results showed the trend of urban development from 2001 to 2019 towards east and northeast with the highest built-up density located in the sub-district of Balikpapan Tengah by 82.07% and followed by the sub-district of Balikpapan Kota by 76.94%. The largest land conversion took place on the bare soil with low vegetation density class to be vegetation with the converted area of 7095.91 ha or approximately 14.10% followed by the bare soil with low vegetation density class to be built-up with the converted area of 5826.86 ha or about 11.58% of the total area of Balikpapan city during the period from 2001 to 2019. The accuracy of urban development map in 2001 reaches 92.39 % and the year 2019 reaches 95.69 %, while the accuracy of land cover map in 2001 reaches 85.57% and the year 2019 reaches 87.28 %.

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Integration of satellite remote sensing data in underground coal fire detection: A case study of the Fukang region, Xinjiang, China

Cited by 30 publications

References 22 publications

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

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

Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

Spatial Monitoring of Urban Development Direction using Landsat 7 ETM+ and Landsat 8 OLI/TIRS in Balikpapan City, Indonesia

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