Environmental Management & Policy Research Institute (EMPRI), Bengaluru, India; Department of Mining Engineering, India Indian Institute of Technology, Kharagpur, India.
Department of Mining Engineering, India Indian Institute of Technology, Kharagpur, India.
J Environ Manage. 2024 Sep;367:121935. doi: 10.1016/j.jenvman.2024.121935. Epub 2024 Aug 2.
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), 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 (0.90), the narrow-banded TC-greenness differenced VI was chosen as the best VI, and the coefficient (L) value (-7.75gm/m) 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 (0.71) and lowest RMSE (4.95 gm/m). 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 = 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.
本研究聚焦于利用植被指数（VIs）差异模型和 PRISMA 高光谱图像进行采煤场的粉尘检测和植被估算。研究结果通过地面调查光谱和叶片粉尘数据进行了验证。结果表明，窄带归一化植被指数（NDVI）、变换土壤调整植被指数（TSAVI）和缨帽变换绿化度（TC-greenness）的分离性（S）、判别系数（R）和概率（P）最高。这些指数已被用于植被组合（VC）指数分析。与其他 VC 指数相比，该 VC 指数显示出最高的差异（29.77%），这促使我们使用该指数来检测健康和受粉尘影响的区域。利用 VIs 差异模型（VIs diff models）、实验室粉尘量和叶片光谱回归分析，建立了叶片粉尘模型，用于估算和绘制粉尘对植被的影响。基于最高的 R（0.90），选择窄带 TC-greenness 差分 VI 作为最佳 VI，其系数（L）值（-7.75gm/m）用于估算采煤场叶片粉尘量。与基于其他指数的差分粉尘模型相比，窄带 TC-greenness 差分图像具有最高的 R（0.71）和最低的 RMSE（4.95 gm/m）。研究结果表明，粉尘含量较高的区域包括采矿运输道路、运输、铁路线、排土场、尾矿池、回填区和煤堆场侧面。本研究还表明，植被粉尘等级、叶片冠层光谱和与矿山的距离之间存在显著的负相关关系（R=0.84）。本研究为基于先进高光谱遥感（PRISMA）和野外光谱分析技术估算植被粉尘提供了一种新方法，可能有助于采矿场的植被粉尘监测和环境管理。
