Over the few past decades, India has become one of the world’s fastest-growing major economies and is now considered a newly industrialized country [1
]. The amount of heavy industry, which is an important component of basic industry and provides technical equipment, power, and raw materials for all sectors of the national economy, has also soared in India [2
]. This industry effectively supports the economic development of the country. However, this growth has been accompanied by a large increase in greenhouse gas emissions and other air pollutants from heavy industrial production [3
]. Therefore, real-time maps of the layout of heavy industrial development are becoming important for studies of Indian economic development and air pollution issues [2
Many scholars and nonprofit organizations or institutions have focused their attention on the global distribution of one or more energy types or industries. The British Petroleum (BP) company [5
] and the International Energy Agency (IEA) [6
] provide regular, annual reports of energy (coal, oil, gas, etc.) prospects. The Global Power Emissions Database (GPED) [7
] was formed from individual power-generating units for 2010 [3
]. In addition, the India Coal-Fired Power Plant Database (ICPD) [8
] is also available for India. These databases include a large amount of information that can be used for mining and strategic development in India. However, traditional statistical methods usually involve a lot of human error; in addition, the real-time distribution of heavy industry in India is not available.
Satellite images, which can be considered to be objective, true data, have become the most effective way to monitor the dynamics of Land-Cover (LC) and Land-Use (LU) (also referred to as LULC) [9
]. Heat sources, such as the combustion of fossil fuels in cement plants and steelworks and the flaring of petroleum gas in oil fields [2
], are also vital for most heavy industries. Therefore, thermal anomaly products derived from remote sensing data provide new ways of revealing the objective and real-time distribution of heavy industry in India. Recently, it has been widely and well-used in the detection of global-scale self-ignition fire point data [12
]. Also, the night-time thermal anomaly product from the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) Visible Infrared Imaging Radiometer (VIIRS) has been successfully applied in studies of volcanic activity [17
] and oil exploitation [18
]. NPP VIIRS night-time fire data (resolution 750 m) were used to identify industrial heat sources considering their time, space, and temperature information [11
]. Also, better active global fire-points product named NPP VIIRS active fire product (VNP14IMG), with 375-m resolution and covering day- and night-time thermal anomaly, was provided by Schroeder et al. [20
] and Giglio et al. [21
]. It effectively provided an improved response for fires with small areas. Then, Ma et al. [2
] proposed a heavy industry heat source detection model based on an improved adaptive K-means algorithm using long-term VNP14IMG data. This produced good results for mainland China; however, due to the complexity of the Indian geographical coverage, the precision was not so good when this was applied to India.
In addition, large and heavy equipment and facilities (such as heavy equipment, large machine tools and large buildings) are also important characteristics of heavy industry. So, the use of lighting is also common and necessary in those areas. Night-time light (NTL) data, especially the VIIRS day/night band (DNB) data, can provide the day and night distribution of lights for the whole world [22
]. Therefore, in this study, NTL data were used to modify Ma’s model [2
]. The new heavy industry heat source detection model for revealing spatiotemporal patterns in and the development of heavy industry in India based on an improved adaptive K-means using VNP14IMG and NTL was then developed. As part of this study, VNP14IMG and NTL data were acquired and preprocessed. We adopted an improved adaptive K-means algorithm using long-term VNP14IMG data to construct heat-source objects. Then, many hot features, including geometric, statistical, and heat source attribute features, were extracted for each heat-source object. In addition, the initial heavy industry heat sources were discriminated from other heat-source objects using a threshold recognition model based on hot features. Finally, maximum night-time light data were used to delineate the final heavy industry heat sources.
The remainder of this article is organized as follows. Section 2
describes the study area, data sources, main data preprocessing steps, and methodology. Section 3
shows the experimental results that were obtained using the VNP14IMG and NTL data and discusses and assesses the distribution of heavy industrial heat sources in India. Conclusions are drawn in Section 4
, and recommendations for future research are given.
2. Materials and Methods
2.1. Study Area
India is a country in South Asia, lying to the north of the equator between 6°44′ N and 35°30′ N and 68°7′ E and 97°25′ E. It is surrounded by the Indian Ocean, the Arabian Sea, and the Bay of Bengal. Since market-based economic reforms began in 1991, India has emerged as a global player with one of the fastest-growing major economies and is now considered a newly industrialized country [24
]. It is also the world’s second-most populous country (with more than 1.3 billion people) as well as being the most populous democracy in the world. India is a federal republic governed under a parliamentary system and comprises 29 states and seven union territories, giving a total of 36 entities (as shown in Figure 1
). It should be noted, however, that Jammu and Kashmir state, marked by the red dashed line, lies within the disputed Kashmir region.
2.2. Data Sources
2.2.1. VIIRS Active Fire/hotspot Data
In this study, the VNP14IMG data were selected as input data for the evaluation of the distribution of heavy industrial heat sources in India. This product is based on reprocessed nominal-resolution Collection 1 data from the NASA Land Science Investigator Processing System (Land-SIPS) [20
]. Using the MOD14/MYD14 algorithm, several modifications were implemented to accommodate the unique characteristics associated with the VIIRS 375-m data [25
]. The newly improved 375-m data, compared to the traditional coarser-resolution (≥ v1 km) fire products, provide a greater response for fires that cover relatively small areas and improved mapping of large fire perimeters. So, it is well suited to support fire management as well as to meet other scientific applications’ needs. VNP14IMG data (19 January 2012 to now) can be freely obtained from the Fire Information for Resource Management System (FIRMS) [26
]. Three million nine hundred ninety-eight thousand four hundred sixty-five observed Indian fire hotspots, ranging from 19 January 2012 to 31 December 2018, were used in this paper, and their spatial density is shown in Figure 2
VIIRS Nightfire product (VNF), using Day/Night Band (DNB), near-infrared (M7 and M8), short-wave infrared (M10), and mid-wave infrared (M12 and M13) to detect subpixel heat sources, has been used in gas [27
] and industrial heat sources detection [11
]. So, VNF data were downloaded from the Earth’s Observation Group (EOG) [26
]. Their spatial distribution maps from VNF data and VNP14IMG data were made to compare in the study area (Figure 3
) on 01/01/2018. It showed that VNP14IMG data were quite abundant in India. The fire/hotspot number of VNP14IMG was more tban five times than VNF. Its spatial distribution range was also bigger than the VNF data. So, VNP14IMG data were used lastly to detect heavy industries.
2.2.2. NPP−VIIRS Night-time Light Data
NPP−VIIRS night-time light (NTL) data were also used in this study. Compared with the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) data, the night-time light data had a higher spatial resolution (15 arc-seconds, about 750 m) and a wider radiometric detection range [22
]. These data can be obtained from NOAA’s National Centers for Environmental Information (NOAA/NCEI) website [28
]. However, as it is a preliminary product, these data are not filtered to remove detected light associated with gas flares, fires, volcanoes, or aurorae, and the dataset has not been processed to remove background noise [29
]. In addition, the VIIRS annual night-time light data are being discontinued by NOAA, and only annual data from 2015 and 2016 are supported [30
]. Therefore, the ‘Flint’ annual data were also obtained from the Chinese Academy of Sciences [31
]. These data are not affected by fires, volcanoes, and background noise as they have been through statistical cleaning and average noise reduction preprocessing. Therefore, these annual products can be considered as the surface light, and ‘Flint’ version Beta 1 [32
] was used in this study. This ‘Flint’ imagery consists of 15 arc-second grids, spanning the range −180 to 180 degrees longitude and from −65 to 75 degrees latitude. The digital pixel numbers (DN) range from 0–255. The ‘Flint’ India light data for India in 2018 is shown below as Figure 4
2.2.3. Auxiliary Data
Indian national, state, and taluk boundaries were acquired from the Global Administrative Areas (GADM) provided by the Center for Spatial Sciences at the University of California, Davis [33
]. The latest version (version 3.6, released on 6 May 2018) was used. The coordinate reference system based on the WGS84 datum was adopted for the boundary files. In order to support the verification of heavy industry heat sources in India, high-resolution images from Google Earth were also utilized in this paper.
2.3. Data Preprocessing
The size of the long-term time series of active fire/hotspot data was huge, and the ‘Flint’ data consisted of global data; therefore, some preprocessing work was necessary for this study. In order to obtain information about heavy industry heat sources in India, the VNP14IMG and NTL data were processed, as shown below (Figure 5
). This processing consisted of two main parts: data preprocessing and a heavy industry heat source detection model.
2.3.1. NPP-VIIRS Active Fire/Hotspot Data Preprocessing
For the same reason in a previous paper [2
], the long-term time series of VNP14IMG products was also needed to be divided. It was almost impossible to divide one area of heavy industry into two or more administrative taluks in India. So, according to the taluk-level administrative boundaries, the 3,998,465 fire hotspots were then divided according to the taluk-level administrative boundaries.
2.3.2. Preprocessing of NPP-VIIRS Night-time Light Data
For most heavy industrial production activities, the use of lighting is also necessary. Therefore, superimposed light data can be used to verify industrial heat sources and filter out false ones. Also, due to economic problems or policy decisions, including regional plans and environmental protection policies, only a small fraction of large, heavy enterprises worked continuously between 2012 and 2018: most enterprises operated for only a few years or months. Thus, some preprocessing of the data was needed. The main processing step was as follows.
Step 1: The annual and global ‘Flint’ night-time light data were clipped according to the Indian national boundary to obtain annual Indian ‘Flint’ night-time light data.
Step 2: The annual Indian ‘Flint’ night-time light data were re-sampled from 750m to 375 m in order to maintain the same spatial resolution as the NP14IMG products.
Step 3: Maximum night-time light data were produced by selecting the maximum value from the annual Indian night-time light data for 2012 to 2018.
2.4. Heavy Industry Heat Source Detection Model
In this study, we propose an Indian heavy industry heat source detection model that uses VNP14IMG and NTL data. This model consists of six parts: constructing the heat source object detection model using real-time VNP14IMG data, extracting the hot features of the heat source objects, detecting the initial heavy industrial heat sources based on an empirical threshold, calculating the mean night-time light value for each heavy industrial heat source object, detecting the final heavy industrial heat sources based on the empirical threshold for the mean night-time light, and, finally, assessing the results. Details of the model are described in this section.
Step 1: Static and persistent industrial heat sources in the VNP14IMG time series were found to be concentrated around the hot centers due to the stability of their positions and temporal consistency. The heat source object detection model that used long-order VNP14IMG data based on an improved adaptive K-means algorithm was then implemented [2
Step 2: Extraction of the hot features of heat source objects. In this study, geometric, statistical, and heat source attribute features were used. The central point of the heat source, as well as the width and the height of the max-circumscribed rectangle, were used as the geometric features. For the statistical feature extraction, the number of fires/hotspots, the density of fires/hotspots per unit area, the initial and final detection times of the heat source object, and the mean and variance of the time interval sorted by date were adopted. For the heat source attributes, the minimum, maximum, mean, and variance attribute information of the VIIRS I-4 band brightness temperature (bright_ti4), the I-5 band brightness temperature (bright_ti4), scan direction pixel size (scan), track direction pixel size (track), and fire point radiation Power (FRP) were extracted for each heat source object.
Step 3: Heavy industrial heat source objects are static and persistent, whereas biomass fires are usually sparsely distributed. The initial heavy industrial heat source identification was based on an empirical threshold [2
]. Subsequently, the initial heavy industry heat sources were identified from heat-source objects.
Step 4: Once the initial vector data of the initial heavy industry heat sources had been registered to the raster data of the max night-time light data using the same WGS84 projection, the mean night-time light value was calculated for each initial heavy industrial heat source object.
Step 5: The final detection of the heavy industry heat sources was carried out by applying the empirical threshold algorithm to the mean night-time light data.
Step 6: Assessment of results. The number of working heavy industry heat sources (NWH), the total number of fire hotspots for each working heavy industry heat source area (NFHWH), as well as
], were used to analyze the distribution of the heavy industry heat sources in different statistical areas for different years.
India has now emerged as a global player with one of the fastest-growing major economies and is considered a newly industrialized country. Its heavy industry has grown rapidly in the past few decades. This has exacerbated pressures on the Indian environment and has also had a great impact on the world economy. The NASA’s Land-SIPS VIIRS 375-m active fire product (VNP14IMG) and NPP-VIIRS night-time light data (NTL) can objectively reveal the spatiotemporal patterns of heavy industrial development in the study area. We, therefore, proposed a heavy industry heat source detection model that uses VNP14IMG and NTL. The spatial distribution and trends for heavy industry heat sources were analyzed for India at the national and state levels. The results suggest that the model is an accurate and effective means of monitoring heat sources produced by heavy industry. The accuracy of this detection model was higher than 92.7%. The following conclusions can be drawn from this study.
Overall, heavy industry heat sources were found to be mainly concentrated in the north-east Assam state, ease central Jharkhand, north Chhattisgarh, and Odisha, and the coastal areas of Gujarat and Maharashtra. It is also interesting to note that a large number of heavy industrial heat sources were found concentrated around a line between Kolkata on the Eastern Indian Ocean and Mumbai on the Western Indian Ocean.
The total NWH and NFHWH values for India increased throughout the period studied, especially in the case of the NFHWH. These trends were similar to those for the GDP and total population of India (Figure 7
) between 2012 and 2017.
The largest values of NWH and NFHWH were in Jharkhand, Chhattisgarh, and Odisha. The two largest values of were in Jharkhand and Chhattisgarh. The smallest negative values of and were in Haryana. In addition, the value for mainland Gujarat was the second most negative value, whereas it’s was the third highest positive one.
The results of this study suggest that real-time VIIRS active fire/hotspot data and NPP-VIIRS night-time light data can successfully be used for monitoring Indian heavy industrial economic development. This could be beneficial for Indian policy-makers and heavy industry regulation. Future studies should focus on distinguishing biomass fires/hotspots from other fires/hotspots, which would allow the monitoring of biomass burning related to agriculture and forest fires. Finally, we plan to add much more fire data from different satellite sensors in order to improve temporary and spatial resolutions.