Monitoring global cement plants from space

Yuyu Yang

doi:10.1016/j.rse.2023.113954

2024-03-01

PRE

DOI：10.1016/j.rse.2023.113954

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As an energy-intensive industry, cement production can emit significant air pollutants that negatively impact human health and the environment. Geospatial data on cement plants are crucial for managing the cement industry and designing effective policies to mitigate ever-increasing air pollution. However, the available fundamental asset-level datasets of global cement plants are generally incomplete and may not adequately satisfy the above requirements. To address this gap, (i) we proposed an innovative semi-automatic framework, which is based on the high-temperature signal from active kilns in time-series Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) imagery and the iconic characteristics of cement processing equipment in high-resolution imagery, to identify global cement plants, assess their operational status, and evaluate relevant activity levels. (ii) We compiled a state-of-the-art inventory of global cement plants, enriched with detailed attributes, using multi-source spaceborne observations/products. We identified 3075 cement plants with rotary/shaft kilns and pinpointed 1960 locations for cement grinding and storage facilities. (iii) We investigated the status of global cement plants using time-series Sentinel-2 MSI imagery. Our results show that from 2016 to date, 69.3% of cement plants are continuously operating, 4.3% were newly constructed, and 26.4% were decommissioned. (iv) We proposed two normalized metrics, namely the occurrence rate and the mean detected area, to assess the activity level of cement plants. The results showed that the rotary kilns in wet-process cement plants had the highest activity level due to the higher energy consumption. The activity level of global cement plants increased by -9% during 2016-2021 and then decreased by -4% in 2022, possibly due to the influence of COVID-19 and weak market demand. The proposed framework provides valuable insights for investigating the locations and dynamics of other industrial hot sources, such as those in steel mills, coke ovens, refineries, and oil/gas development areas, to improve the estimates of CO2 emissions and air pollutants and then to promote climate action.

Keyword:

Cement plants

Deep learning

Satellite

Landsat-8 OLI

Sentinel-2 MSI