Detection of Coal Seam Fires in Summer Seasons from Landsat 8 OLI/TIRS in Dhanbad

Mukherjee, Jit; Mukherjee, Jayanta; Chakravarty, Debashish

doi:10.1007/978-981-13-0020-2_46

Cited by 5 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The latest Landsat-8 OLI sensor can acquire multispectral images covering several spectral wavelength ranges, where the near-infrared (NIR) band (0.85-0.88 μm) and two short wave infrared (SWIR) bands (1.57-1.65 μm and 2.11-2.29 μm) are quite often used in burned areas identification. A variety of methods have been employed for burned area detection including spectral indices (Tucker, 1979), surface temperature inversion (Mukherjee et al, 2018), Principal Component Analysis (PCA) (Richards, 1984), images classification (Mitri & Gitas, 2004), neural network (Gómez & Martín, 2011) and spectral mixture analysis (Smith et al, 2007). Spectral features are more intuitive and effective in identifying different land-cover objects, thus the spectral index-based method becomes popular in burned area detection due to its simple implementation and high accuracy.…”

Section: Introductionmentioning

confidence: 99%

A novel fire index-based burned area change detection approach using Landsat-8 OLI data

Liu

Zheng

Dalponte

et al. 2020

European Journal of Remote Sensing

View full text Add to dashboard Cite

Change detection from multi-temporal remote sensing images is an effective way to identify the burned areas after forest fires. However, the complex image scenario and the similar spectral signatures in multispectral bands may lead to many false positive errors, which make it difficult to exact the burned areas accurately. In this paper, a novel-burned area change detection approach is proposed. It is designed based on a new Normalized Burn Ratio-SWIR (NBRSWIR) index and an automatic thresholding algorithm. The effectiveness of the proposed approach is validated on three Landsat-8 data sets presenting various fire disaster events worldwide. Compared to eight index-based detection methods that developed in the literature, the proposed approach has the best performance in terms of class separability (2.49, 1.74 and 2.06) and accuracy (98.93%, 98.57% and 99.51%) in detecting the burned areas. Simultaneously, it can also better suppress the complex irrelevant changes in the background.

show abstract

Section: Introductionmentioning

confidence: 99%

A novel fire index-based burned area change detection approach using Landsat-8 OLI data

Liu

Zheng

Dalponte

et al. 2020

European Journal of Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Therefore, the study of the distribution of LST plays important role in the detection of coal fire risk from remotely sensed data. It has been observed by Mukherjee et al (2018) that, during the summer season, water bodies have high temperatures, thus affecting the performance of detection of fire risks. This might lead to the false detection of coal fire risks.…”

Section: Distribution Of Lsts In the Na Duong Coal Fieldmentioning

confidence: 99%

Delineation of Coal Fire Risk Areas From Landsat-8 Tirs Data: A Case Study of Na Duong Coalfield (North-East of Vietnam)

VU¹,

NGUYEN²,

HOANG³

et al. 2023

View full text Add to dashboard Cite

Coal fires are a persistent threat to major coal-producing countries in the world. Thus, it is very important to delineate the potential risks of coal fires. This study presents a method for the delineation of coal fire risk areas from Landsat-8 TIRS data. Land surface temperatures (LSTs) were first retrieved from the Landsat-8 TIRS images. The degree of spatial autocorrelation among these LSTs was then identified using local Moran's I statistic. Thermal-related anomalies for the delineation of coal fire risk areas were identified by setting the MEAN+2*SDEV (SDEV is the standard deviation), MEAN+3*SDEV, and MEAN+4*SDEV formulas as thresholds on the local Moran's I statistic. These coal fire risk areas were finally validated using known coal fire sites and cross-validated by comparing them with those obtained from hot spot analysis. A case study of the Na Duong coal field (northern Vietnam) has shown that coal fire risks at moderate and high levels were mainly detected in the center of the coal field. The higher values of local Moran's I statistic, the higher levels of coal fire risks. These coal fire risks were mainly concentrated around known coal fire sites. These results reveal that Landsat-8 TIRS data can effectively delineate coal fire risk areas.

show abstract

“…Image processing methods include the use of specific algorithms such as principal component analysis (PCA) [14] and mixed spectral analysis methods [15] to extract targeted information and improve the accurate detection of burned areas. Integrated methods increase detection accuracy by using multisource data, including temperature [16] and fire point data, as well as ground and remote sensing image data at varying resolutions [17]. The spectral index method is commonly used for detecting forest fire areas based on changes in reflectance characteristics in various spectral bands [18].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Index for Monitoring Burned Vegetation by Combining Image Texture Features with Vegetation Indices

Fan,

Yao,

Tang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

The detection and monitoring of burned areas is crucial for vegetation recovery, loss assessment, and anomaly analysis. Although vegetation indices (VIs) have been widely used, accurate vegetation detection is challenging due to potential confusion in the spectra of different types of land cover and the interference of shadow effects caused by terrain. In this work, a novel Vegetation Anomaly Spectral Texture Index (VASTI) is proposed, which leverages the merits of both spectral and spatial texture features to identify abnormal pixels for extracting burned vegetation areas. The performance of the VASTI and its components, the Global Environmental Monitoring Index (GEMI), the Enhanced Vegetation Index (EVI), and the texture feature Autocorrelation (AC) were assessed based on a global dataset previously established, which contains 1774 pairs of samples from 10 different sites. The results illustrated that, compared with the GEMI and EVI, the VASTI improved the user’s accuracy (UA), producer’s accuracy (PA), and kappa coefficient across the ten study areas by approximately 5% to 10%. Compared to AC, the VASTI improved the accuracy of abnormal vegetation detection by 13% to 25%. The improvements were mainly caused by the fact that the incorporation of texture features can reduce spectral confusion between pixels. The innovation of the VASTI is that it considers the relationship between anomalous pixels and surrounding pixels by explicitly integrating spatial texture features with traditional spectral features.

show abstract

Detection of Coal Seam Fires in Summer Seasons from Landsat 8 OLI/TIRS in Dhanbad

Cited by 5 publications

References 25 publications

A novel fire index-based burned area change detection approach using Landsat-8 OLI data

A novel fire index-based burned area change detection approach using Landsat-8 OLI data

Delineation of Coal Fire Risk Areas From Landsat-8 Tirs Data: A Case Study of Na Duong Coalfield (North-East of Vietnam)

A Hybrid Index for Monitoring Burned Vegetation by Combining Image Texture Features with Vegetation Indices

Contact Info

Product

Resources

About