Hyperspectral imaging data atmospheric correction challenges and solutions using QUAC and FLAASH algorithms

Vibhute, Amol D.; Kale, K. V.; Dhumal, Rajesh K.; Mehrotra, S. C.

doi:10.1109/mami.2015.7456604

Cited by 28 publications

(12 citation statements)

References 5 publications

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“…2021, 13, x FOR PEER REVIEW 6 of 22 racy of FLAASH model is higher than that of QUAC model. The application of QUAC model is simpler than that of FLAASH, and it has less dependence on input parameters and calibration accuracy of instruments [34,35]. FLAASH is a first-principle atmospheric correction tool that corrects wavelengths in the visible through near-infrared and shortwave infrared regions.…”

Section: Waterbody Extractionmentioning

confidence: 99%

“…(1) Determination of reservoir boundaries Using the Atmospheric Correction Module, it can be accurately compensated for atmospheric effects. In this study, the atmospheric correction using the FLAASH model [34,35,68] was performed and Landsat 8 images were fused using the Gram-Schmidt Pan Sharpening method [69][70][71][72]. After calculating the NDWI using the images after atmospheric correction and image fusion, zero was used as the segmentation threshold to extract the water body, and the manual editing was used to complete the extracted water body boundaries in the ArcGIS software.…”

Section: Diffusion Of Waste In the Reservoirs Of Two Hydroelectric Plantsmentioning

confidence: 99%

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Watch Out for the Tailings Pond, a Sharp Edge Hanging over Our Heads: Lessons Learned and Perceptions from the Brumadinho Tailings Dam Failure Disaster

Cheng

Cui

et al. 2021

Remote Sensing

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A catastrophic tailings dam failure disaster occurred in Brumadinho, Brazil on 25 January 2019, which resulted in over 270 casualties, 24,000 residents evacuated, and a huge economic loss. Environmental concerns were raised for the potential pollution of water due to tailings waste entering the Paraopeba River. In this paper, a detailed analysis has been carried out to investigate the disaster conditions of the Brumadinho dam failure using satellite images with different spatial resolutions. Our in-depth analysis reveals that the hazard chain caused by this failure contained three stages, namely dam failure, mudflow, and the hyperconcentrated flow in the Paraopeba River. The variation characteristics of turbidity of the Rio Paraopeba River after the disaster have also been investigated using high-resolution remote sensing images, followed by a qualitative analysis of the impacts on the downstream reservoir of the Retiro Baixo Plant that was over 300 km away from the dam failure origin. It is believed that, on the one hand, the lack of dam stability management at the maintenance stage was the main cause of this disaster. On the other hand, the abundant antecedent precipitation caused by extreme weather events should be a critical triggering factor. Furthermore, the spatiotemporal pattern mining of global tailings dam failures revealed that the Brumadinho dam disaster belonged to a Consecutive Hot Spot area, suggesting that the regular drainage inspection, risk assessment, monitoring, and early warning of tailings dam in Consecutive Hot Spot areas still need to be strengthened for disaster mitigation.

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Section: Waterbody Extractionmentioning

confidence: 99%

Section: Diffusion Of Waste In the Reservoirs Of Two Hydroelectric Plantsmentioning

confidence: 99%

Watch Out for the Tailings Pond, a Sharp Edge Hanging over Our Heads: Lessons Learned and Perceptions from the Brumadinho Tailings Dam Failure Disaster

Cheng

Cui

et al. 2021

Remote Sensing

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“…Atmospheric correction was applied using the Quick Atmospheric Correction (QUAC) algorithm provided in the ENVI software [26][27][28]. QUAC is an approximate, in-scene atmospheric approach that normalizes images based on the statistical properties of object spectra found within an image [29].…”

Section: Atmospheric Correctionmentioning

confidence: 99%

Applicability of Earth Observation for Identifying Small-Scale Mining Footprints in a Wet Tropical Region

et al. 2017

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Abstract:The unpredictable climate in wet tropical regions along with the spatial resolution limitations of some satellite imageries make detecting and mapping artisanal and small-scale mining (ASM) challenging. The objective of this study was to test the utility of Pleiades and SPOT imagery with an object-based support vector machine (OB-SVM) classifier for the multi-temporal remote sensing of ASM and other land cover including a large-scale mine in the Didipio catchment in the Philippines. Historical spatial data on location and type of ASM mines were collected from the field and were utilized as training data for the OB-SVM classifier. The classification had an overall accuracy between 87% and 89% for the three different images-Pleiades-1A for the 2013 and 2014 images and SPOT-6 for the 2016 image. The main land use features, particularly the Didipio large-scale mine, were well identified by the OB-SVM classifier, however there were greater commission errors for the mapping of small-scale mines. The lack of consistency in their shape and their small area relative to pixel sizes meant they were often not distinguished from other land clearance types (i.e., open land). To accurately estimate the total area of each land cover class, we calculated bias-adjusted surface areas based on misclassification values. The analysis showed an increase in small-scale mining areas from 91,000 m 2 -or 0.2% of the total catchment area-in March 2013 to 121,000 m 2 -or 0.3%-in May 2014, and then a decrease to 39,000 m 2 -or 0.1%-in January 2016.

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“…Radiometric calibration was performed on all image data used in this work as recommended by the USGS website. An atmospherically correction was then done using the widely used method, FLAASH (Fast Line-of-sight Atmospheric Analysis of Hypercubes) provided by ENVI (Exelis, Boulder, CO), as shown in Figure 2 (Nazeer et al, 2014;Souza Jr. et al, 2013;Vibhute et al, 2015).…”

Section: Landsat Oli Datamentioning

confidence: 99%

Assessment of The trophic state and Chlorophyll-a concentrations using Landsat OLI in Karaoun reservoir, Lebanon

Fadel¹,

Faour²,

Slim³

2016

LSJ

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Harmful algal blooms have become a worldwide environmental problem. A regular and cost-effective monitoring of these blooms is highly needed by lakes managers. Satellite remote sensing imagery like Landsat Operational Land Imager (OLI) can be used to assess and monitor chlorophyll-a in water bodies over large areas in a cost-effective way. In this study, the accuracy of Landsat OLI to estimate chlorophyll-a was examined. Four field campaigns and cloud free images

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Hyperspectral imaging data atmospheric correction challenges and solutions using QUAC and FLAASH algorithms

Cited by 28 publications

References 5 publications

Watch Out for the Tailings Pond, a Sharp Edge Hanging over Our Heads: Lessons Learned and Perceptions from the Brumadinho Tailings Dam Failure Disaster

Watch Out for the Tailings Pond, a Sharp Edge Hanging over Our Heads: Lessons Learned and Perceptions from the Brumadinho Tailings Dam Failure Disaster

Applicability of Earth Observation for Identifying Small-Scale Mining Footprints in a Wet Tropical Region

Assessment of The trophic state and Chlorophyll-a concentrations using Landsat OLI in Karaoun reservoir, Lebanon

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