Fire risk evaluation using multicriteria analysis—a case study

Forest fires are one of the major causes of ecological disturbance and environmental concerns in tropical deciduous forests of south India. In this study, we use fuzzy set theory integrated with decision-making algorithm in a Geographic Information Systems (GIS) framework to map forest fire risk. Fuzzy set theory implements classes or groupings of data with boundaries that are not sharply defined (i.e., fuzzy) and consists of a rule base, membership functions, and an inference procedure. We used satellite remote sensing datasets in conjunction with topographic, vegetation, climate, and socioeconomic datasets to infer the causative factors of fires. Spatial-level data on these biophysical and socioeconomic parameters have been aggregated at the district level and have been organized in a GIS framework. A participatory multicriteria decision-making approach involving Analytical Hierarchy Process has been designed to arrive at a decision matrix that identified the important causative factors of fires. These expert judgments were then integrated using spatial fuzzy decision-making algorithm to map the forest fire risk. Results from this study were quite useful in identifying potential “hotspots” of fire risk, where forest fire protection measures can be taken in advance. Further, this study also demonstrates the potential of multicriteria analysis integrated with GIS as an effective tool in assessing “where and when” forest fires will most likely occur.     

Spatio-temporal analysis of fire events in India: implications for environmental conservation

Information on fires in different geographic regions of India is relatively scarce. This study quantifies spatial and temporal patterns in fire occurrences covering different states and districts in India. Two important scientific questions are answered in this study: (1) how are the fire events distributed across different geographical regions? (2) are there any specific districts where fire events clustered across space and time? To address these questions, Along Track Scanning Radiometer (ATSR) derived satellite fire counts from 1997–2006 were used and the datasets were analysed using spatial scan statistic. Spatial scan statistic provides a test statistic for most likely ‘hotspot’ spatial clusters, based on the likelihood ratio test and Monte Carlo simulation. Results from geographical analysis based on state boundaries suggested Maharastra state had the highest number of fires followed by Madhya Pradesh, Chattisgarh, Orissa, etc., during the 10-year period. Among the several districts, the spatial scan statistic identified the most likely cluster of fire events in Dausa, Karauli, Sawai Madhopur, Bharatpur and Alwar in addition to several other secondary clusters, with high statistical significance. These results are based on a large sample of cases, and they provide convincing evidence of spatial clustering of fire events in the Indian region. Results relating to hotspot areas of fire risk can guide policy makers towards the best management strategies for avoiding damages to forests, human life and personal property in the ‘hotspot’ districts.     

MODIS derived fire characteristics and aerosol optical depth variations during the agricultural residue burning season, north India

Agricultural residue burning is one of the major causes of greenhouse gas emissions and aerosols in the Indo-Ganges region. In this study, we characterize the fire intensity, seasonality, variability, fire radiative energy (FRE) and aerosol optical depth (AOD) variations during the agricultural residue burning season using MODIS data. Fire counts exhibited significant bi-modal activity, with peak occurrences during April-May and October-November corresponding to wheat and rice residue burning episodes. The FRE variations coincided with the amount of residues burnt. The mean AOD (2003-2008) was 0.60 with 0.87 (+1σ) and 0.32 (−1σ). The increased AOD during the winter coincided well with the fire counts during rice residue burning season. In contrast, the AOD-fire signal was weak during the summer wheat residue burning and attributed to dust and fossil fuel combustion. Our results highlight the need for ‘full accounting of GHG’s and aerosols’, for addressing the air quality in the study area.     

Spatial Distribution of Forest Fires and Controlling Factors in Andhra Pradesh, India Using Spot Satellite Datasets

Fires are one of the major causes of forest disturbance and destruction in several dry deciduous forests of southern India. In this study, we use remote sensing data sets in conjunction with topographic, vegetation, climate and socioeconomic factors for determining the potential causes of forest fires in Andhra Pradesh, India. Spatial patterns in fire characteristics were analyzed using SPOT satellite remote sensing datasets. We then used nineteen different metrics in concurrence with fire count datasets in a robust statistical framework to arrive at a predictive model that best explained the variation in fire counts across diverse geographical and climatic gradients. Results suggested that, of all the states in India, fires in Andhra Pradesh constituted nearly 13.53% of total fires. District wise estimates of fire counts for Andhra Pradesh suggested that, Adilabad, Cuddapah, Kurnool, Prakasham and Mehbubnagar had relatively highest number of fires compared to others. Results from statistical analysis suggested that of the nineteen parameters, population density, demand of metabolic energy (DME), compound topographic index, slope, aspect, average temperature of the warmest quarter (ATWQ) along with literacy rate explained 61.1% of total variation in fire datasets. Among these, DME and literacy rate were found to be negative predictors of forest fires. In overall, this study represents the first statewide effort that evaluated the causative factors of fire at district level using biophysical and socioeconomic datasets. Results from this study identify important biophysical and socioeconomic factors for assessing ‘forest fire danger’ in the study area. Our results also identify potential ‘hotspots’ of fire risk, where fire protection measures can be taken in advance. Further this study also demonstrate the usefulness of best-subset regression approach integrated with GIS, as an effective method to assess ‘where and when’ forest fires will most likely occur.     

Contemporary forest loss in Myanmar: Effect of democratic transition and subsequent timber bans on landscape structure and composition

This study addresses the effect of political transition and subsequent timber bans on forest loss in Myanmar, in the context of identified drivers. Cook’s Distance (CD) was applied to remotely sensed time-series forest loss dataset to measure the effect of the events. Forest loss derived fragmentation metrics were linked to drivers at a landscape scale. Results show that at the national level, the political transition in 2011 had maximum effect (CD 0.935) on forest loss while the timber bans decreased forest loss by 612.04 km² and 213.15 km² in 2015 and 2017 (CD 0.146 and 0.035), respectively. The effect of the events varied for different States/Regions. The dominant drivers of change shifted from plantations in 2011 to infrastructure development in 2015. This study demonstrates the effects of policy on forest loss at various scales and can inform decision-makers for forest conservation, planning and development of mitigation measures.