IDENTIFYING AREAS AFFECTED BY FIRES IN SUMATRA BASED ON TIME SERIES OF REMOTELY SENSED FIRE HOTSPOTS AND SPATIAL MODELING

Wildfires threaten the environment not only at local scales, but also at wider scales. Rapid monitoring system to detect active wildfires has been provided by satellite remote sensing technology, particularly through the advancement on thermal infrared sensors. However, satellite-based fire hotspots data, even at relatively high temporal resolution of less than one-day revisit period, such as time series of fire hotspots collected from TERRA and AQUA MODIS, do not tell exactly if they are fire ignitions or fire escapes, since other factors like wind, slope, and fuel biomass significantly drive the fire spread. Meanwhile, a number of biophysical fire simulation models have been developed, as tools to understand the roles of biophysical factors on the spread of wildfires.  Those models explicitly incorporate effects of slope, wind direction, wind speed, and vegetative fuel on the spreading rate of surface fire from the ignition points across a fuel bed, based on either field or laboratory experiments.  Nevertheless, none of those models have been implemented using real time fire data at relatively large extent areas. This study is aimed at incorporating spatially explicit time series data of weather (i.e. wind direction and wind speed), remotely sensed fuel biomass and remotely sensed fire hotspots, as well as incorporating more persistent biophysical factors (i.e. terrain), into an agent-based fire spread model, in order to identify fire ignitions within time series of remotely sensed fire hotspots

Identifying Areas Affected By Fires In Sumatra Based On TIME Series Of Remotely Sensed Fire Hotspots And Spatial Modeling

Wildfires threaten the environment not only at local scales, but also at wider scales. Rapid monitoring system to detect active wildfires has been provided by satellite remote sensing technology, particularly through the advancement on thermal infrared sensors. However, satellite-based fire hotspots data, even at relatively high temporal resolution of less than one-day revisit period, such as time series of fire hotspots collected from TERRA and AQUA MODIS, do not tell exactly if they are fire ignitions or fire escapes, since other factors like wind, slope, and fuel biomass significantly drive the fire spread. Meanwhile, a number of biophysical fire simulation models have been developed, as tools to understand the roles of biophysical factors on the spread of wildfires. Those models explicitly incorporate effects of slope, wind direction, wind speed, and vegetative fuel on the spreading rate of surface fire from the ignition points across a fuel bed, based on either field or laboratory experiments. Nevertheless, none of those models have been implemented using real time fire data at relatively large extent areas. This study is aimed at incorporating spatially explicit time series data of weather (i.e. wind direction and wind speed), remotely sensed fuel biomass and remotely sensed fire hotspots, as well as incorporating more persistent biophysical factors (i.e. terrain), into an agent-based fire spread model, in order to identify fire ignitions within time series of remotely sensed fire hotspots

Measuring Similarity of Deforestation Patterns in Time and Space across Differences in Resolution

This article demonstrated an easily applicable method for measuring the similarity between a pair of point patterns, which applies to spatial or temporal data sets. Such a measurement was performed using similarity-based pattern analysis as an alternative to conventional approaches, which typically utilize straightforward point-to-point matching. Using our approach, in each point data set, two geometric features (i.e., the distance and angle from the centroid) were calculated and represented as probability density functions (PDFs). The PDF similarity of each geometric feature was measured using nine metrics, with values ranging from zero (very contrasting) to one (exactly the same). The overall similarity was defined as the average of the distance and angle similarities. In terms of sensibility, the method was shown to be capable of measuring, at a human visual sensing level, two pairs of hypothetical patterns, presenting reasonable results. Meanwhile, in terms of the method′s sensitivity to both spatial and temporal displacements from the hypothetical origin, the method is also capable of consistently measuring the similarity of spatial and temporal patterns. The application of the method to assess both spatial and temporal pattern similarities between two deforestation data sets with different resolutions was also discussed