<p>Anthropogenic factors and climate change induced severe forest fires that are reoccurring globally. Because of the large spatial scale, frequent occurrence, and danger involved with the forest fires, remote sensing-based approaches are best suited to study this phenomenon. However, there are few studies addressing the temporal effects of the various factors associated with the &#160;forest fire. In this work, by using Analytical Hierarchy Process (AHP), a multi-criteria decision support system and geostatistical methods namely Getis-Ord Gi* statstic and Mann Kendall trend test, we have developed a framework to understand the temporal dynamics of forest fire hazard and associated factors by demarcating the hotspots of forest fire using freely available datasets . The proposed framework has been applied on the Similipal Biosphere Reserve (SBR), Odisha, India. With an area of 5569 km<sup>2</sup>, the SBR is the sixth largest biosphere reserve in India, comprising of a national park, bird sancturary, tiger reserve, and elephant corridor. Due to its biodiversity and importance in terms of rich and endemic species of flora and fauna, SBR was brought into the umbrella of world network of biosphere reserve under the Man and Biosphere (MAB) programme of UNESCO in the year 2008. Although being a biosphere of international importance, the SBR annually experiences nearly 12 km<sup>2</sup> of fire damage.Through this work, the most significant clusters of forest fire hotspots have been demarcated. We have used factors related to topographical, climatic, and physical characteristics of forest to generate forest fire hazard index at annual scale for 28 years (1988 &#8211; 2018) using AHP method. The geostatistical methods were applied on the generated annual fire hazard index data to demarcate the zones of emerging hotspots of forest fire. The results indicate that temporally, the trend of forest fire hazard in buffer zone of the area (Similipal Sanctuary) is decreasing, whereas in core area (Similipal National Park), it is increasing and corelates with the temporal trend of vegetation density in the whole area. However, vegetation density and land surface temperature in the core area does not changes significantly with time. The emerging hotspot analysis shows that most of the region (32% of the total area) is exhibiting an oscillating behaviour with respect to the fire hazard over the studied time-period of 28 years, with more than 50% of the years showing increasing trends of fire hazard. A total of 186 km<sup>2 </sup>of the region is persistently a hotspot of fire hazard in studied time-period. Overall, 11% of the study area is either under persistent fire hazard or showing increasing trend of fire hazard. However, in the central part of the SNP, the fire hazard is decreasing with time. The same region also observes intense rain, and this could be a factor for the observed decrement in the fire hazard. The results can be used for mitigating the fire hazard of the SBR, alsothe proposed framework can be applied globally to any region with dense vegetation for fire hazard spatiotemporal assessments.</p>
<p>The concept of geomorphic connectivity is being widely used since last two decades to understand and explain the various earth surface processes and dynamics. Its applicability to understand inter- and cross-scale process-response systems is now well established. In the present work, we have evaluated the applicability of the geomorphic connectivity framework (Singh et al., 2020, ESPL) for managing and mitigating various geological hazards. For an effective hazard mitigation and management planning, we need to know (a) source of hazard, (b) hazard propagation pathways, (c) probable affected areas, and (d) identification of escape routes/pathways. The connectivity concept can be effectively utilised to satisfy aforementioned requirements. For example, sediment and hydrological connectivity can be used to evaluate the potential pathways, identify sources and affected areas, and to assess return periods of fluvial-related hazards such as debris flow and riverine flash floods. Similarly, the potential sites of landslide, stream congestions (and hence, flash flood)- can be identified by evaluating the channel-slope sediment connectivity and longitudinal hydrological connectivity. The concept of landscape connectivity can play a pivotal role in understanding the forest fire probabilities by evaluating the connectivity between various fire-prone patches of forests, fuel, and the spatial positions of fire-breaking landscape patches. Based on connectivity concepts, the potential paths of forest fire propagation can be demarcated in advance and can play a crucial role in forest fire mitigation. Other than identifying the risk-prone zones with respect to various hazards, connectivity concept can also be used to plan evacuation routes as well. Therefore, we propose that the geomorphic connectivity framework can be a robust tool to manage and mitigate various geological hazards.</p>
<p>Landslides problems are one of the major natural hazards in the mountainous region. Every year due to the increase in anthropogenic factors and changing climate, the problem of landslides is increasing, which leads to huge loss of property and life. Landslide is a common and regular phenomenon in most of the northeastern states of India. &#160;However, in recent past years, Manipur has experienced several landslides including mudslides during the rainy season. Manipur is a geologically young and geodynamically active area with many streams flowing parallel to fault lines. As a first step toward hazard management, a landslide susceptibility map is the prime necessity of the region. In this study, we have prepared a landslide hazard map of the state using freely available earth observations datasets and multi-criteria decision making technique, i.e., Analytic Hierarchy Process (AHP). For this purpose, lithology, rainfall, slope, aspect, relative relief, Topographic Wetness Index, and distance from road, river and fault were used as the parameters in AHP based on the understanding of their influence towards landslide in that region. The hazard map is classified into four hazard zones: Very High, High, Moderate, and Low. About 40% of the state falls under very high and high hazard zone, and the hilly regions such as Senapati and Chandel district are more susceptible to the landslide. Among the factors, slope and rainfall have a more significant contribution towards landslide hazard. It is also observed that areas nearer to NH-39 that lies in the fault zones i.e., Mao is also susceptible to high hazard. The landslide susceptibility map gives an first-hand impression for future land use planning and hazard mitigation purpose.</p>
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