In recent years, the process of globalization has continued to deepen and the volume of international trade in goods has increased constantly. Due to the advantages of large transportation volume and low freight rate, maritime transportation accounts for more than 90% of international freight transportation. Coastal ports have become important nodes for trade between countries, and the cargo handling capacity (also known as Cargo throughout) is the most basic and important indicator for measuring the development status of coastal ports. As defined by China Ports & Harbors Association, the cargo handling capacity is “the weight of loaded and unloaded goods that enter and exit the port area by waterway during the reporting period,” usually in tons. The index has been widely used to evaluate the scale and comprehensive capacity of sea ports [1
]. The calculation methods of cargo handling capacity include calculating the inbound throughput of the cargo that enters the port by waterway and is then unloaded, calculating the outbound throughput of the cargo that is loaded and shipped out of the port, and calculating the inbound and outbound throughputs of the transit cargo that enters the port by waterway and is shipped out of the port by waterway after unloading and loading processes. The cargo handling capacity is calculated after the completion of cargo loading and unloading and the handling of transfer formalities by the ship at the port. The weight of the cargoes, such as livestock, poultry, and light cargo, of which the actual weight cannot be obtained, is converted by the use of coefficients. Accurate estimation of the cargo handling capacity can provide a scientific basis for the future planning and construction of customized and elaborate and efficient coastal ports, which plays an important role in further understanding port development status, exploring the spatiotemporal dynamics of ports, discerning the new rules of port development, and improving port competitiveness.
Previously, statistical data was the predominant basis for research on cargo handling capacity. For example, when analyzing cargo distribution of Brazilian ports based on fuzzy logic and social networks, Sun and Tan collected the national statistical data on cargo handling capacity [4
]; Eddie et al. used the cargo handling capacity data released by the Statistics Department of Hong Kong Special Administrative Region to analyze the response of Hong Kong Port’s cargo handling capacity to factor cost differentials [5
]. In studying the impacts of exchange rate changes, global economic activities, and Baltic dry index (BDI) fluctuations on the cargo handling capacity of South Korean ports, Chang also utilized cargo handling capacity data from national statistical data [6
]. Dependence on statistical data not only requires a high amount of effort in collecting and sorting data, but also often suffers from the different statistical caliber of various sets of data, as well as the lack of statistical data in certain cases (e.g., war zones). Therefore, there is a need for stable and faster data sources for better port studies.
Field research indicates that in order to facilitate day- and nighttime operation, the ships berthing at coastal ports at night for loading and unloading cargo, as well as those in the cargo handling area, usually keep their lights on. The more ships there are that are loading and unloading cargoes, the larger the cargo volume and the higher the light intensity. Based on this feature of coastal ports, we envisaged the possibility of estimating the port throughput by observing the light intensity of ports at night. In terms of collecting nighttime light data, the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime light data is a good data source that is widely used to establish models and estimate socio-economic factors [7
]. Nighttime lights could be used as a proxy for some economic variables, especially in areas or times where other data are insufficient or unavailable [16
]. The DMSP is made up of military meteorological remote-sensing satellites launched by the United States and carries the OLS sensor that usually operates at night. The OLS sensor is four times more sensitive than conventional sensors, especially to visible light and near-infrared light [18
]. The OLS sensor is designed to collect low-light imaging data to detect moonlight reflected by the clouds. However, it was quickly discovered that light radiation associated with human activities can also be observed when clouds are not present [16
]. For instance, data on light sources such as city lights, gas flares, firelight, and vessel lights can be collected by DMSP-OLS sensors [22
]. As a result, many scholars have applied nighttime light data to the study of fishing boats, cargo volumes, ports, etc. For example, Elvidge et al. used nighttime lights to detect and trace ships giving off strong light signals [24
]. Shi et al. used NPP-VIIRS data to estimate freight volume [28
]. Li et al. used the DMSP-OLS nighttime light imagery to provide comprehensive scores for port economics of major cities in the Yangtze River Basin [29
]. Although these studies are carried out around seaports and offshore areas, quantitative remote sensing studies on cargo handling capacity have not been reported. This paper attempted to fill this void by using DMSP-OLS nighttime light data to estimate the cargo handling capacity of seaports.
Panel data analysis is a regression analysis method based on spatio-temporal data, which is commonly used in trade and logistics research. For example, Chu used panel data to study logistics and economic growth [30
]. Lakew et al. used panel data to analyze air cargo costs in the United States, airport traffic and airport delays in the aviation industry [31
]. Guo et al. studied the relationship among carbon emissions, GDP, and logistics by using a panel data model and a combination of statistics and econometrics theory [32
]. Considering data as a panel, not just a cross-sectional or time series data, has multiple advantages. Panel data have more information, more variability, more degrees of freedom, and other properties. The panel data can be adjusted dynamically, and the appropriate model can be generated [33
]. In recent years, panel data have also been gradually applied in the remote sensing field. For instance, Shi et al. performed the panel data analysis of spatiotemporal emission dynamics of Chinese CO2
based on DMSP-OLS stable night light data and proved that panel data analysis could provide a series of regression models to model CO2
emissions efficiently across spatial and temporal dimensions without any ancillary data [36
]. Therefore, based on the predecessors’ studies, the cargo throughputs of the coastal ports in China are estimated using the panel model and DMSP-OLS night light data.
The cargo transportation activities were carried out in the sea area near the port, without any respite, all day and night. In order to ensure normal nighttime operation similar to daytime operation, the sea area at the port is lit up at night. Using this feature, we attempted to estimate the CHC of coastal ports by observing the nighttime light data of the ports. We collected DMSP-OLS NAL data of 28 coastal ports in China from 2001 to 2010 as sample data to establish the panel model and verified it with data from 2011 to 2013. Finally, it was found that there is a long-term stable regression relationship between CHC and NAL. The fitting coefficients (R2) of regression equations were all higher than 0.9, i.e., the NAL data can be used to accurately estimate the CHC of coastal ports. The results have certain application values for the study of coastal port development at the regional scale, and compensate for the lack of data on CHC for some ports. Especially in areas where data are not available or data collection is inconvenient, the method is obviously an effective way to estimate the CHC data. The application of panel data model also makes the conclusions obtained in this paper have a broad significance, not limited to the research area.
In existing studies, DMSP-OLS stable nighttime light data is widely used in the socio-economic field, as it filters out a large number of temporary and unstable lights, including the lights of ships, which are very important for nighttime operation of ports. Hence, this paper used DMSP-OLS NAL data that retains such information as the data source. This data is seldom used in existing studies. Hence, to a certain degree, this study also extended the usage and values of NAL data.
Only DMSP-OLS nighttime lights data prior to 2013 has been released. Hence, the research period of this paper ends at 2013, which affects the relevance of the research. However, with innovations in technology, the update speed of future data can be improved, and the available sources of nighttime lights data are also increasing. In October 2011, the first Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on board the Suomi National Polar Orbiting Partnership (S-NPP) satellite was launched. NPP/VIIRS has a higher spatial resolution and a wider radiation range than traditional DMSP/OLS data, so it may have advantages in simulating economic parameters. However, NPP/VIIRS presently only has six years of data, which is too few to build a panel model and verify accuracy. When we obtain enough samples, the data will be used to estimate the cargo handling capacity of coastal ports in future.
The predicted values of the RBAPG are generally lower than the real values. Combined with the actual situation, we speculated that the brightness of the image detected by the sensor would be influenced by air quality. Thus, AQI data of coastal cities were collected and processed, and the guesswork was confirmed. Air quality also has an impact on the authenticity of light values in night-time images, especially in areas where air quality is not perfect. AQIs can be added to later studies to perfect the panel formula, to correct images, to make the light values closer to the ground real situation, and reduce interferenceWe assume that if the AOI data were integrated into the model in future, better fitting effect might be obtained. At present, few studies have tried to estimate port cargo throughputs using night image values. This research has both methodological and practical significance.