Assessment and estimation of coal dust impact on vegetation using VIs difference model and PRISMA hyperspectral data in mining sites.

Autor: Kayet N; Environmental Management & Policy Research Institute (EMPRI), Bengaluru, India; Department of Mining Engineering, India Indian Institute of Technology, Kharagpur, India. Electronic address: narayankayet@gmail.com., Pathak K; Department of Mining Engineering, India Indian Institute of Technology, Kharagpur, India., Singh CP; Space Applications Centre (SAC), ISRO, Ahmedabad, India., Chaturvedi RK; Birla Institute of Technology and Science (BITs), Pilani, Goa, India., Brahmandam AS; Department of Mining Engineering, India Indian Institute of Technology, Kharagpur, India., Mandal C; Indian Institute of Techology, Kharagpur, India.
Jazyk: angličtina
Zdroj: Journal of environmental management [J Environ Manage] 2024 Sep; Vol. 367, pp. 121935. Date of Electronic Publication: 2024 Aug 02.
DOI: 10.1016/j.jenvman.2024.121935
Abstrakt: This work focuses on dust detection, and estimation of vegetation in coal mining sites using the vegetation indices (VIs) differences model and PRISMA hyperspectral imagery. The results were validated by ground survey spectral and foliar dust data. The findings indicate that the highest Separability (S), Coefficient of discrimination (R 2 ), and lowest Probability (P) values were found for the narrow-banded Narrow-banded Normalized Difference Vegetation Index (NDVI), Transformed Soil Adjusted Vegetation Index (TSAVI), and Tasselled Cap Transformation Greenness (TC-greenness) indices. These indices have been utilized for the Vegetation Combination (VC) index analysis. Compared to other VC indices, this VC index revealed the highest difference (29.77%), which led us to employ this index for the detection of healthy and dust-affected areas. The foliar dust model was developed for the estimation and mapping of dust impact on vegetation using the VIs differences models (VIs diff models), laboratory dust amounts, and leaf spectral regression analysis. Based on the highest R 2 (0.90), the narrow-banded TC-greenness differenced VI was chosen as the best VI, and the coefficient (L) value (-7.75gm/m 2 ) was used for estimating the amount of foliar dust in coal mining sites. Compared to other indices-based difference dust models, the narrow-banded TC-greenness difference image had the highest R 2 (0.71) and lowest RMSE (4.95 gm/m 2 ). According to the findings, the areas with the highest dust include those with mining haul roads, transportation, rail lines, dump areas, tailing ponds, backfilling, and coal stockyard sides. This study also showed a significant inverse relationship (R 2  = 0.84) among vegetation dust classes, leaf canopy spectrum, and distance from mines. This study provides a new way for estimating dust on vegetation based on advanced hyperspectral remote sensing (PRISMA) and field spectral analysis techniques that may be helpful for vegetation dust monitoring and environmental management in mining sites.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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