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Peat fires cause major environmental problems in Central Kalimantan Province, Indonesia and threaten human health and effect the social-economic sector. The lack of peat fire detection systems is one factor that causing these reoccurring fires. Therefore, in this study, we develop an Android mobile platform application and a web-based application to support the citizen-volunteers who want to contribute wildfires reports, and the decision-makers who wish to collect, visualize, and evaluate these wildfires reports. In this paper, the global navigation satellite system (GNSS) and a global position system (GPS) sensor from a smartphone’s camera, is a useful tool to show the potential fire and smoke’s close-range location. The exchangeable image (EXIF) file image and GPS metadata captured by a mobile phone can store and supply raw observation to our devices and sent it to the data center through global internet communication. This work’s results are the proposed application easy-to-use to monitoring potential peat fire by location and data activity. This paper focuses on developing an application for the mobile platform for peat fire reporting and a web-based application to collect peat fire location for decision-makers. Our main objective is to detect the potential and spread of fire in peatlands as early as possible by utilizing community reports using smartphones.

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In the regional scale of the province in Kalimantan, the spread case appeared in West Kalimantan and East Kalimantan on 18th March 2020, Central Kalimantan on 20th March 2020, South Kalimantan on 22nd March 2020 and North Kalimantan on 29th March 2020. In this case the Covid-19 epidemic was caused by coronavirus disease 2019 (Covid-19). The prediction of the Covid-19 is currently sought. We use the SIR Model to perform basic reproductive value calculations (R0). This model is the mathematic language, interpreted as the number of nativity of a new case due to a person infected with Covid-19 into a fully healthy and potential population for illness or infection by the Covid-19. Using the linear regression, we estimate the value of R0. The value of R0 in Kalimantan region is West Kalimantan (R0 = 1.15), East Kalimantan (R0 = 1.17), Central Kalimantan (R0 = 1.09), South Kalimantan (R0 = 1.24), and North Kalimantan (R0 = 1.20). According to the SIR Model, the highest R0 value is in South Kalimantan, followed by North Kalimantan, East Kalimantan, West Kalimantan, and Central Kalimantan.