Objectives of the Project
Monitoring and management of forest fire is imperative in India where 50% of forest cover is prone to the fire. The present work aims for applying the geospatial technology towards forest fire characterization and evaluation of relationship with meteorological thematic layers, geographical features and socio-economy of the local area. Spatial analysis of forest fires in the state of Arunachal Pradesh was carried out based upon the decadal (2008-2016) forest fire count datasets, which was assessed for spatial variability over the known Himalayan biodiversity hotspot in diverse geographical and socio-economic gradients.
The study area was Arunachal Pradesh state which is also known for rich biodiversity and is a Himalayan biodiversity hotspot. The study has utilized the 9 (Nine) years forest fire data (point data of location forest fire) and analyzed it on GIS platform towards visualization and evaluation of spatial and temporal dimension of fire pattern with objectives of studying temporal variation and to analyze forest fire events and its distribution across different vegetation types, topographical gradients and socio-economic and demographic conditions. It was further aimed to perform statistical analysis by Cramer V coefficient methodology using meteorological datasets to analyze its correlation with forest fire incidences and to generate fire hotspots (areas with relative higher chances of forest fire occurrence), for effective monitoring and strategic resource allocation to provide insight into various issues which would help in solving problems for effective future planning and making strategies to minimize the forest losses. Thus, developing a forest fire predictive model for the state.
It also aimed to develop an efficient information dissemination system through user friendly Android based mobile App (eForestFire)and at the same time link fire data shared by the citizens on a web portal (http://220.127.116.11/), which will be subsequently used to upgrade predictive model on GIS platform. Following were the major objectives-
To integrate poverty, population density, forest cover, forest type, temperature, rainfall, slope, & elevation to find initial hotspot & its correlation with actual data obtained from Forest Survey of India. And, to correlate socio-economy, geographical features & climatic factors with fire incidences.
To develop predictive model by integrating real data (FSI) & initial GIS based hotspot in order to extract villages towards strategic allocation of Govt. resources for damage mitigation and to assess where exactly to place fire controlling measures such a fire lines, watch towers etc.
Study at the level of lowest administrative unit i.e. villages and list of villages to be extracted with extremely high risk areas for preferential allocation of Govt. resources & for early warning system.
To link predictive model, Android App and web portal so as to refine the prediction every time when data is shared by the citizens. The eForestFire is a Fire Reporting System, a scientific approach to ease governance by involving people meaningfully and to promote e-Governance up to the lowest level (village) for end user.
The study highlights a significant relationship of forest fire with biophysical and socio economic dimension and predicts list of 560 villages/settlements of high to extremely high forest fire risks. Due to lack of such study, there existed a kind of policy paralysis especially when it comes to the issue of control of forest fire disasters. Such study produces not only fire hotspots but also gives an early warning system to take preventive measures. The study has reinforced the relationship between forest fire incidences and poverty as 42.3% incidences occurred in high to very high poverty index areas. A strong correlation was established with metrological parameters such as relative humidity, precipitation, solar radiation and maximum temperature with forest fire incidences. This work has been published in SPRINGER Journal and may be seen at- https://link.springer.com/article/10.1007/s41324-018-0175-1
The spatial technology has tremendous potential in monitoring and assessment for forest fire incidences and can be potentially used towards strategic resource allocation. The fire points (MODIS) and various vegetation types (SPOT-4) were used in this analysis which has spatial resolution of 1 Km, which compromised the project strength. Now, it has been augmented with citizen centric inputs though mobile App and a web portal and therefore, the outcome have become much significant. Fire incidents can be checked at a very primitive stage which will crucially reduce the damage.
Arunachal Pradesh is Himalayan biodiversity hotspot and holds more than 60% tribal population, most of them belonging to low poverty indices. Majority of them practice shifting cultivation for their livelihood. With the help of the App, people of tribal state can report fire incidents of their nearby forests and can also be in direct touch with Divisional Forest Officers (DFOs). Further, it helps improving Fire Predictive Model that not only improves the prediction at village level, about higher chances of fire incidences but also greatly helps the Govt. agencies in tackling cases of forest fires, thereby, minimising the damage of life and property to a great extent. Further, the concept can be extended to the whole country. The App involves people in Forest Fire mitigation on multidimensional approach, which greatly enhances the existing machinery of Forests Department as maximum governance with limited resources. App is specifically designed to work in OFFLINE mode which will further boost the capability considering the internet connectivity issues of the remote Himalayan State.
The project will educate local masses in coming out of ill-practices of ‘Shifting Cultivation’ and will be useful for the people residing in forest-fringe areas as early warning system during the event of fire break. The cause of forest fire can be understood scientifically and things can be planned in accordance to minimize the forest fire losses. The project will bring people closer to the government by ensuring a meaningful people participation mechanism and they can approach or have access to the government officials anytime not only for fire incidence but for other issues as well.
Due to this timely intervention and efficient information dissemination, the fire incidences reported this year were around 31 % less in comparison of period before start of the project. In year 2017, a total of 6551 cases of fire were reported in the state while this year (up to 07.12.2019), fire incidences were limited to 4535 cases as reported by NASA's Fire Information for Resource Management System (https://firms.modaps.eosdis.nasa.gov/). Thus, we have been able to save tremendous loss to the bio-diversity, flora-fauna, human life and public property by this initiative of ‘Himalayan Forest Fire Predictive Modelling- eForestFire Arunachal Pradesh’