利用 SDGSAT-1 TIS 数据识别京津冀地区工业热源的潜力

Remote. Sens. Pub Date : 2024-02-22 DOI:10.3390/rs16050768
Yanmei Xie, Caihong Ma, Yindi Zhao, Dongmei Yan, Bo Cheng, Xiaolin Hou, Hongyu Chen, Bihong Fu, Guangtong Wan
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引用次数: 1

摘要

对工业热源进行探测和分类对于工业可持续发展至关重要。可持续发展科学卫星 1 号(SDGSAT-1)热红外光谱仪(TIS)数据首次用于探测工业热源生产区,以解决难以识别燃烧温度低、规模小的工厂的问题。本研究利用 SDGSAT-1 TIS 和 Landsat 8/9 Operational Land Imager (OLI) 数据提出了一种新的工业热源识别和分类模型,以提高工业热源识别的精度和粒度。首先,利用 SDGSAT-1 TIS 和 Landsat 8/9 OLI 数据提取多种特征(热特征和光学特征)。其次,构建了基于支持向量机(SVM)和多种特征的工业热源识别模型。然后,根据生产区识别结果与谷歌地球图像之间的拓扑相关性生成并验证工业热源。最后,根据兴趣点(POI)数据将工业热源分为六类。新模型被应用于中国京津冀(BTH)地区。结果表明(1) 多重特征提高了工业热源生产区与背景之间的区分和识别精度。(2) 与主动火点(ACF)数据(375 m)和 Landsat 8/9 热红外传感器(TIRS)数据(100 m)相比,SDGSAT-1 TIS 夜间数据(30 m)有助于更准确地探测工业热源生产区。(3) 利用我们的模型在 BTH 区域探测到的工业热源数量是 Ma 和 Liu 报告的 2~6 倍。利用 TIS 数据首次发现了一些热排放低、面积小的工业热源(53 家热电厂)。(4) 水泥厂生产区的亮度温度最高,达到 301.78 K,而热电厂的亮度温度最低,平均为 277.31 K。提出了一种估算工业企业热能和空气污染排放的新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Potential of Using SDGSAT-1 TIS Data to Identify Industrial Heat Sources in the Beijing-Tianjin-Hebei Region
It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with low combustion temperatures and small scales. In this study, a new industrial heat source identification and classification model using SDGSAT-1 TIS and Landsat 8/9 Operational Land Imager (OLI) data was proposed to improve the accuracy and granularity of industrial heat source recognition. First, multiple features (thermal and optical features) were extracted using SDGSAT-1 TIS and Landsat 8/9 OLI data. Second, an industrial heat source identification model based on a support vector machine (SVM) and multiple features was constructed. Then, industrial heat sources were generated and verified based on the topological correlation between the identification results of the production areas and Google Earth images. Finally, the industrial heat sources were classified into six categories based on point-of-interest (POI) data. The new model was applied to the Beijing–Tianjin–Hebei (BTH) region of China. The results showed the following: (1) Multiple features enhance the differentiation and identification accuracy between industrial heat source production areas and the background. (2) Compared to active-fire-point (ACF) data (375 m) and Landsat 8/9 thermal infrared sensor (TIRS) data (100 m), nighttime SDGSAT-1 TIS data (30 m) facilitate the more accurate detection of industrial heat source production areas. (3) Greater than 2~6 times more industrial heat sources were detected in the BTH region using our model than were reported by Ma and Liu. Some industrial heat sources with low heat emissions and small areas (53 thermal power plants) were detected for the first time using TIS data. (4) The production areas of cement plants exhibited the highest brightness temperatures, reaching 301.78 K, while thermal power plants exhibited the lowest brightness temperatures, averaging 277.31 K. The production areas and operational statuses of factories could be more accurately identified and monitored with the proposed approach than with previous methods. A new way to estimate the thermal and air pollution emissions of industrial enterprises is presented.
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