*3.2. Characteristics of Heavy Industrial Heat Source Distribution at the State Level*

The heavy industrial heat source characteristics for the 36 states and administrative regions of India were analyzed using the NWH and NFHWH values. The boundaries of the administrative divisions of these states contained land regions only, meaning that no NWH and NFHWH values for sources at sea were included.

Figure 10a shows the NWH for the states with the 20 highest values. These included Jharkhand, Chhattisgarh, and Odisha, followed by Gujarat and West Bengal. Furthermore, the NWH in Jharkhand state accounted for nearly 13.8% of the total for Indian mainland sources. The sum of the NWH in Jharkhand, Chhattisgarh, Odisha, and Gujarat accounted for 43.38% of the total; the total NWH for the top 20 states accounted for 98.37%. In addition, the NWH for Jharkhand, which is one of the richest mineral zones in the world and boasts 40% and 29% of India's mineral and coal reserves, respectively, increased continuously during the period 2012 to 2018 [34]. Chhattisgarh's heavy industry also developed due to its rich natural resources, policy incentives, and good infrastructure [35].

The NFHWH values indicate that there has been a reduction in the total amount of heavy industrial production in the administrative areas studied (Figure 10b). The largest number of fire hotspots was in Jharkhand, the same as the NWH shown in Figure 10a. However, the order of the five highest NFHWH values in 2018 was different: the order was now, Jharkhand, Odisha, Chhattisgarh, West Bengal, and then Gujarat. Moreover, the average NFHWH in Jharkhand between 2012 and 2018 accounted for nearly 29.05% of the Indian mainland total; the total NWH in Jharkhand, Odisha, and Chhattisgarh accounted for 58.36%; and the total NWH for the top 20 states accounted for 99.78%. In addition, NFHWH values in most states increased continuously after 2012, in line with India's economic development.

**Figure 10.** Changes in heavy industrial heat sources at the state level. (**a**) NWH for the top 20 states between 2012 and 2018. (**b**) NFHWH for the top 20 states between 2012 and 2018.

The distributions of *Slope*\_*NWH* and *Slope*\_*NFHWH* values were mapped to illustrate the changing trends for each statistical area during the seven-year period studied (Figure 11). The largest positive value of *Slope*\_*NWH* was in Jharkhand, followed by Chhattisgarh and Gujarat, indicating that the number of heavy industry heat sources in these three states increased quickly. The smallest negative *Slope*\_*NWH* value was found in Haryana, followed by Tripura and Andhra Pradesh. However, these negative values were all very small −0.49, −0.23, and −0.14, respectively. This means that the downward trends here were very slow. In addition, for 18 of the large states in mainland India, the values were positive and only four states had negative *Slope*\_*NWH* values. Therefore, it can be concluded that the total number of heavy industry heat sources in India increased, as confirmed by Figure 7.

**Figure 11.** Changes in heavy industry heat sources at the state level from 2012 to 2018 (including Jammu and Kashmir state). (**a**) The *Slope*\_*NWH* values for different states. (**b**) The *Slope*\_*NFHWH* values for different states.

The *Slope*\_*NFHWH* values are displayed in Figure 10b. These values reflect the scale of production associated with working heavy industry heat sources. The largest positive value of *Slope*\_*NFHWH* was the value for Odisha, followed by Chhattisgarh and Jharkhand. The *Slope*\_*NFHWH* value in Odisha was 1452.63 (the corresponding value of *Slope*\_*NWH* was only 1.11), which was double the value in Chhattisgarh. This means that the average scale of working heavy industry heat sources in Odisha increased. The smallest negative *Slope*\_*NWH* values were found in Haryana, followed by Gujarat and Arunachal Pradesh, showing that *Slope*\_*NFHWH* in Gujarat was the second most negative whereas it *Slope*\_*NWH* value was the third highest positive value. This shows that the average scale of the working heavy industry heat sources in this state was declining. It should be noted that this trend related only to the heavy industry heat sources in mainland Gujarat. In addition, there were 19 large states on the Indian mainland for which the *Slope*\_*NFHWH* values were positive and only nine states with negative values. It can be concluded that, overall, the scale of heavy industry heat sources in all of India increased, as supported by the details shown in Figure 6.

The distribution of heavy industry heat sources was then mapped to examine the heat source characteristics for the 36 state administrative regions of India in 2018 (Figure 12). The NWH values for 2018 (Figure 12a) indicate that Jharkhand, Chhattisgarh, and Odisha have relatively large numbers of heavy enterprises, with Gujarat following. In terms of NFHWH values (Figure 12b), Chhattisgarh is followed by Jharkhand and Odisha. In addition, both NWH and NFHWH values are highest in east-central India, followed by central India; in contrast, most of the northwest and south of the country have a small number of heavy industry heat sources and fire hotspots caused by heavy enterprises.

**Figure 12.** *Cont.*

**Figure 12.** Distribution of heavy industry heat sources at the state level (2018) (including Jammu and Kashmir state). (**a**) The NWH values for different states (2018). (**b**) The NFHWH values for different states (2018).

#### **4. Conclusions**

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.


(3) The largest values of NWH and NFHWH were in Jharkhand, Chhattisgarh, and Odisha. The two largest values of *Slope*\_*NWH* were in Jharkhand and Chhattisgarh. The smallest negative values of *Slope*\_*NWH* and *Slope*\_*NFHWH* were in Haryana. In addition, the *Slope*\_*NFHWH* value for mainland Gujarat was the second most negative value, whereas it's *Slope*\_*NWH* 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.

**Author Contributions:** F.C. and J.Y. conceived and designed the experiments; Y.M. built the experimental platform, and prepared and processed the remote sensing data; C.M. designed and performed the experiments, analyzed the data and wrote the paper; J.L. and Z.N. supervised the research, and also gave comments and revised the manuscript.

**Funding:** Supported by Open Fund of State Key Laboratory of Remote Sensing Science (Grant No. OFSLRSS201908); **Youth Innovation Promotion Association of the Chinese Academy of Sciences (No. Y6YR0300QM)**; National Natural Science Funds for Key Projects of China (Grant No.61731022); Hainan Provincial Natural Science Foundation of China (Grant No.618QN303).

**Acknowledgments:** The VNP14IMG was downloaded from the FIRMS website. The authors thank the editors and the three anonymous reviewers for their valuable comments that helped to improve our manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**


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