{"title":"利用带有光谱指数和机器学习算法的sentinel-2图像评估露天圈养石灰石矿区","authors":"V. C., U. G","doi":"10.3233/kes-230065","DOIUrl":null,"url":null,"abstract":"Limestone mining is a significant economic activity in India, accounting for around 10% of the GDP however, it has certain negative environmental consequences. The objective of this study is to determine the spatial distribution area of captive limestone mines using remote sensing datasets, spectral index, and machine learning algorithms and compare their area estimation with industrial field survey reports for the financial year 2019. The study area includes a limestone resource area of 2226.16 ha with an excavation area of 487.10 ha in 2019. In the present research, we used a high-resolution Sentinel-2A satellite dataset to map and compute the active mining area by implementing the Normalised Vegetation Index (NDVI), Iterative Self-Organizing Data Analysis Technique (ISODATA), K-Nearest Neighbours (KNN), and Random Forest (RF) algorithms in the QGIS 3.18 software tool. The RF classifier estimated a limestone mine area of 379.57 ha with user accuracy (UA) of 97.25% and producer accuracy (PA) of 99.18% with a kappa coefficient value of 0.957. The mine area was estimated at 417.47 ha with a UA of 98.99% and PA of 99.10% and kappa value of 0.947 of the KNN method, The NDVI method estimated 469.92 ha with a UA of 93.63% and PA of 92.04% and kappa value 0.685. This research confirmed that the RF classifier well performed in classification with overall accuracy (OA) of 95.79% to KNN (OA of 94.78%), NDVI (OA of 79.84%) classifiers, and ISODATA poor in classification with OA of 64.16%. This research assists limestone mine owners and environmental engineers in making environmentally sustainable decisions, eco-friendly mine design, and monitoring.","PeriodicalId":44076,"journal":{"name":"International Journal of Knowledge-Based and Intelligent Engineering Systems","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessment of open-pit captive limestone mining areas using sentinel-2 imagery with spectral indices and machine learning algorithms\",\"authors\":\"V. C., U. G\",\"doi\":\"10.3233/kes-230065\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Limestone mining is a significant economic activity in India, accounting for around 10% of the GDP however, it has certain negative environmental consequences. The objective of this study is to determine the spatial distribution area of captive limestone mines using remote sensing datasets, spectral index, and machine learning algorithms and compare their area estimation with industrial field survey reports for the financial year 2019. The study area includes a limestone resource area of 2226.16 ha with an excavation area of 487.10 ha in 2019. In the present research, we used a high-resolution Sentinel-2A satellite dataset to map and compute the active mining area by implementing the Normalised Vegetation Index (NDVI), Iterative Self-Organizing Data Analysis Technique (ISODATA), K-Nearest Neighbours (KNN), and Random Forest (RF) algorithms in the QGIS 3.18 software tool. The RF classifier estimated a limestone mine area of 379.57 ha with user accuracy (UA) of 97.25% and producer accuracy (PA) of 99.18% with a kappa coefficient value of 0.957. The mine area was estimated at 417.47 ha with a UA of 98.99% and PA of 99.10% and kappa value of 0.947 of the KNN method, The NDVI method estimated 469.92 ha with a UA of 93.63% and PA of 92.04% and kappa value 0.685. This research confirmed that the RF classifier well performed in classification with overall accuracy (OA) of 95.79% to KNN (OA of 94.78%), NDVI (OA of 79.84%) classifiers, and ISODATA poor in classification with OA of 64.16%. 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引用次数: 0
摘要
石灰石开采是印度一项重要的经济活动,占GDP的10%左右,然而,它有一定的负面环境后果。本研究的目的是利用遥感数据集、光谱指数和机器学习算法确定圈养石灰石矿山的空间分布面积,并将其面积估算与2019财政年度的工业现场调查报告进行比较。研究区石灰石资源面积2226.16 ha, 2019年挖掘面积487.10 ha。在本研究中,我们利用高分辨率Sentinel-2A卫星数据集,在QGIS 3.18软件工具中实现归一化植被指数(NDVI)、迭代自组织数据分析技术(ISODATA)、k近邻(KNN)和随机森林(RF)算法,绘制和计算了活跃矿区。该分类器估计石灰石矿区面积为379.57 ha,用户精度(UA)为97.25%,生产者精度(PA)为99.18%,kappa系数为0.957。KNN法估算矿区面积为417.47 ha, UA为98.99%,PA为99.10%,kappa值为0.947;NDVI法估算矿区面积为469.92 ha, UA为93.63%,PA为92.04%,kappa值为0.685。本研究证实,RF分类器对KNN分类器(OA为94.78%)、NDVI分类器(OA为79.84%)的分类总体准确率(OA)为95.79%,而ISODATA分类器的分类准确率较差,OA为64.16%。本研究有助于石灰石矿主和环境工程师做出环境可持续的决策、生态友好型矿山设计和监测。
Assessment of open-pit captive limestone mining areas using sentinel-2 imagery with spectral indices and machine learning algorithms
Limestone mining is a significant economic activity in India, accounting for around 10% of the GDP however, it has certain negative environmental consequences. The objective of this study is to determine the spatial distribution area of captive limestone mines using remote sensing datasets, spectral index, and machine learning algorithms and compare their area estimation with industrial field survey reports for the financial year 2019. The study area includes a limestone resource area of 2226.16 ha with an excavation area of 487.10 ha in 2019. In the present research, we used a high-resolution Sentinel-2A satellite dataset to map and compute the active mining area by implementing the Normalised Vegetation Index (NDVI), Iterative Self-Organizing Data Analysis Technique (ISODATA), K-Nearest Neighbours (KNN), and Random Forest (RF) algorithms in the QGIS 3.18 software tool. The RF classifier estimated a limestone mine area of 379.57 ha with user accuracy (UA) of 97.25% and producer accuracy (PA) of 99.18% with a kappa coefficient value of 0.957. The mine area was estimated at 417.47 ha with a UA of 98.99% and PA of 99.10% and kappa value of 0.947 of the KNN method, The NDVI method estimated 469.92 ha with a UA of 93.63% and PA of 92.04% and kappa value 0.685. This research confirmed that the RF classifier well performed in classification with overall accuracy (OA) of 95.79% to KNN (OA of 94.78%), NDVI (OA of 79.84%) classifiers, and ISODATA poor in classification with OA of 64.16%. This research assists limestone mine owners and environmental engineers in making environmentally sustainable decisions, eco-friendly mine design, and monitoring.