Economic value of near real-time hotspot data products for Indonesian forest and land fire monitoring

Several hotspot detection studies in Indonesia were conducted between 2012 and 2024 using remote sensing satellite data. The results show that improved hotspot detection accuracy has helped improve the location of forest/land fires easier, faster, and more accurately, one of which is using the filtering and clustering method. This study focuses on measuring the economic benefits of using Near Real-Time (NRT) hotspot technology in Indonesia. The methodology used was a mixed method, explaining the hotspot value chain and measuring hotspot data/information using the DeLone and McLean information value model, ordinary least square model, and contingent valuation method (CVM). The respondents were users responsible for monitoring hotspots at the central and regional levels. The data test was normal and reliable, and the model test was unbiased, consistent, and had minimum variance. The results of the study show that the economic value generated from Remote Sensing (RS) applications is quite high, namely, 83.33% with a proportional value of IDR 5,733,622. The influencing factors are perceived system quality (reliability/economy, availability/response time, and adaptability/portability), user satisfaction, and net benefits (cost savings and decision making), with a confidence level of 95%. This study shows that NRT hotspot data are considered very useful by users. In addition, the research has practical implications for policy-making and environmental management, highlighting the significant role of NRT hotspot data in assisting environmental sustainability and green economy initiatives. This study emphasizes the importance of providing these data to stakeholders and institutions aiming to reduce forest fires and related impacts. • NRT hotspot data show high economic value for fire monitoring. • Information value reaches 83.33% with strong user benefits. • WTP averages IDR 5.73 million, indicating high perceived value. • System quality, satisfaction, and net benefit drive WTP. • Improved accuracy enhances efficiency and reduces monitoring costs.