Vegetation health conditions assessment and mapping using AVIRIS-NG hyperspectral and field spectroscopy data for -environmental impact assessment in coal mining sites

Kayet, Narayan; Pathak, Khanindra; Singh, C. P.; Chowdary, V. M.; Bhattacharya, Bimal K.; Kumar, Dheeraj; Kumar, Sanjeev; Shaik, Ibrahim

doi:10.1016/j.ecoenv.2022.113650

Cited by 15 publications

(6 citation statements)

References 85 publications

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“…In this study, two methods were used for the evaluation of the model. One method drew the receiver operating characteristic (ROC) curve of the model and calculated the area under the ROC (AUC) values, 20 while the other calculated the recall rate and compared the model performance to evaluate the model. 21 The ROC curve of the model was drawn, and the evaluation method of AUC value was calculated, which was suitable for the performance evaluation of normal and chylous plasma classification models.…”

Section: Model Evaluation Methodsmentioning

confidence: 99%

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Detection of Chylous Plasma Based on Machine Learning and Hyperspectral Techniques

Liu,

Lai,

et al. 2024

Appl Spectrosc

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Chylous blood is the main cause of unqualified and scrapped blood among volunteer blood donors. Therefore, a diagnostic method that can quickly and accurately identify chylous blood before donation is needed. In this study, the GaiaSorter “Gaia” hyperspectral sorter was used to extract 254 bands of plasma images, ranging from 900 nm to 1700 nm. Four different machine learning algorithms were used, including decision tree, Gaussian Naive Bayes (GaussianNB), perceptron, and stochastic gradient descent models. First, the preliminary classification accuracies were compared with the original data, which showed that the effects of the decision tree and GaussianNB models were better; their average accuracies could reach over 90%. Then, the feature dimension reduction was performed on the original data. The results showed that the effects of the decision tree were better with a classification accuracy of 93.33%. the classification of chylous plasma using different chylous indices suggested that the accuracies of the decision trees model both before and after the feature dimension reductions were the best with over 80% accuracy. The results of feature dimension reduction showed that the characteristic bands corresponded to all kinds of plasma, thereby showing their classification and identification potential. By applying the spectral characteristics of plasma to medical technology, this study suggested a rapid and effective method for the identification of chylous plasma and provided a reference for the blood detection technology to achieve the goal of reducing wasting blood resources and improving the work efficiency of the medical staff.

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Section: Model Evaluation Methodsmentioning

confidence: 99%

Section: Data Analysis Methods and Evaluationmentioning

confidence: 99%

Detection of Chylous Plasma Based on Machine Learning and Hyperspectral Techniques

Liu,

Lai,

et al. 2024

Appl Spectrosc

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“…The produced minerals are essential for a country to know how much improvement it gives to the economy, infrastructure, military, and other aspects (Upadhyay et al, 2021). Nevertheless, there are studies made to discuss how mining activity brought not only positive effects but also some backlash, especially to the environment, such as global climate change (Tolvanen et al, 2019), biotic life (Kayet et al, 2022;Kløcker Larsen et al, 2022;Sun et al, 2022), and human health (Rentier & Cammeraat, 2022).…”

Section: Acid Mine Drainage Impactmentioning

confidence: 99%

Constructed Wetlands for Treatment of Acid Mine Drainage: A Review

et al. 2022

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The coal mining industry is an industrial activity that impacts the environment. This activity will generate acid mine drainage due to the interaction of water, air and sulfide minerals. Acid mine drainage is wastewater with low pH and heavy metals content. These conditions will be given some negatives impact on the environment and human health. The low-cost, applicable and simple method to solve acid mine drainage in mining areas is constructed wetlands. Hence, this paper aims to describe the potential of wetlands as a low-cost and applicable method for acid mine drainage treatment. This paper also describes the holistic information about an overview of constructed wetlands, acid mine drainage (AMD) production and their negative impacts, recent trends in constructed wetlands, recommendation components of wetlands, potential application in rural areas and future considerations

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“…Suir constructed a method that combined ground observation and remote sensing inversion, used hyperspectral images to estimate wetland plant-community quality and vegetation biomass, and developed a wetland vegetation condition indictor system for three Lake Ontario wetland areas [19]. Kayet evaluated the health of vegetation around a mining area using airborne hyperspectral data, simultaneously quantifying the impact of mining on vegetation health [20]. Amputu developed indicators for potential forage biomass and pasture cover types and used drone data to monitor the semi-arid grassland conditions in central Namibia [21].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Monitoring and Assessment of Global Vegetation Status and Changes during 2016–2020

Li,

Xin,

Zhao

et al. 2023

Sensors

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Vegetation plays a fundamental role within terrestrial ecosystems, serving as a cornerstone of their functionality. Presently, these crucial ecosystems face a myriad of threats, including deforestation, overgrazing, wildfires, and the impact of climate change. The implementation of remote sensing for monitoring the status and dynamics of vegetation ecosystems has emerged as an indispensable tool for advancing ecological research and effective resource management. This study takes a comprehensive approach by integrating ecosystem monitoring indicators and aligning them with the objectives of SDG15. We conducted a thorough analysis by leveraging global 500 m resolution products for vegetation Leaf Area Index (LAI) and land cover classification spanning the period from 2016 to 2020. This encompassed the calculation of annual average LAI, identification of anomalies, and evaluation of change rates, thereby enabling a comprehensive assessment of the global status and transformations occurring within major vegetation ecosystems. In 2020, a discernible rise in the annual Average LAI of major vegetation ecosystems on a global scale became evident when compared to data from 2016. Notably, the ecosystems demonstrating a slight increase in area constituted the largest proportion (34.23%), while those exhibiting a significant decrease were the least prevalent (6.09%). Within various regions, such as Eastern Europe, Central Africa, and South Asia, substantial increases in both forest ecosystem area and annual Average LAI were observed. Furthermore, Eastern Europe and Central America recorded significant expansions in both grassland ecosystem area and annual average LAI. Similarly, regions experiencing notable growth in both cropland ecosystem areas and annual average LAI encompassed Southern Africa, Northern Europe, and Eastern Africa.

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Vegetation health conditions assessment and mapping using AVIRIS-NG hyperspectral and field spectroscopy data for -environmental impact assessment in coal mining sites

Cited by 15 publications

References 85 publications

Detection of Chylous Plasma Based on Machine Learning and Hyperspectral Techniques

Detection of Chylous Plasma Based on Machine Learning and Hyperspectral Techniques

Constructed Wetlands for Treatment of Acid Mine Drainage: A Review

Remote Sensing Monitoring and Assessment of Global Vegetation Status and Changes during 2016–2020

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