Assessing Landscape Fire Hazard by Multitemporal Automatic Classification of Landsat Time Series Using the Google Earth Engine in West-Central Spain (original) (raw)
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Computing fire danger and fire risk on a spatio-temporal scale is of crucial importance in fire management planning, and in the simulation of fire growth and development across a landscape. However, due to the complex nature of forests, fire risk and danger potential maps are considered one of the most difficult thematic layers to build up. Remote sensing and digital terrain data have been introduced for efficient discrete classification of fire risk and fire danger potential. In this study, two time-series data of Landsat imagery were used for determining spatio-temporal change of fire risk and danger potential in Korudag forest planning unit in northwestern Turkey. The method comprised the following two steps: (1) creation of indices of the factors influencing fire risk and danger; (2) evaluation of spatio-temporal changes in fire risk and danger of given areas using remote sensing as a quick and inexpensive means and determining the pace of forest cover change. Fire risk and danger potential indices were based on species composition, stand crown closure, stand development stage, insolation, slope and, proximity of agricultural lands to forest and distance from settlement areas. Using the indices generated, fire risk and danger maps were produced for the years 1987 and 2000. Spatio-temporal analyses were then realized based on the maps produced. Results obtained from the study OPEN ACCESS Sensors 2008, 8 3971
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011
LANDFIRE is a large interagency project designed to provide nationwide spatial data for fire management applications. As part of the effort, many 2000 vintage Landsat Thematic Mapper and Enhanced Thematic Mapper plus data sets were used in conjunction with a large volume of field information to generate detailed vegetation type and structure data sets for the entire United States. In order to keep these data sets current and relevant to resource managers, there was strong need to develop an approach for updating these products. We are using three different approaches for these purposes. These include: 1) updating using Landsat-derived historic and current fire burn information derived from the Monitoring Trends in Burn Severity project; 2) incorporating vegetation disturbance information derived from time series Landsat data analysis using the Vegetation Change Tracker; and 3) developing data products that capture subtle intra-state disturbance such as those related to insects and disease using either Landsat or the Moderate Resolution Imaging Spectroradiometer (MODIS). While no one single approach provides all of the land cover change and update information required, we believe that a combination of all three captures most of the disturbance conditions taking place that have relevance to the fire community.
The weight of fire as an environmental concern significantly increased in the second half of the past century, often as a consequence of dramatic land-use changes experienced in many countries. While a variety of prevention and restoration initiatives have been taken, the difficulty to monitor their effects over long periods and for large areas has been noted. This suggests the utilisation of remote sensing data, which may be employed to perform retrospective studies and evaluate the impact of past management actions. Mapping fires and characterising post-fire dynamics have been the target of numerous studies. For global to regional scale, these are often based on small-scale sensor systems, such as NOAA-AVHRR, SPOT-Vegetation or MODIS (e.g., Barbosa et al., 1999), while local studies requiring higher levels of detail make use of medium-resolution data, such as Landsat-TM or SPOT (e.g., Garcia-Haro et al. 2001). Concerning target variables, Elmore et al. (2000) have demonstrated limitations of using NDVI in semi-arid areas, and suggested to employ Spectral Mixture Analysis (SMA) to derive quantitative vegetation estimates. In the current study, a long time series has been procured for a test site in the Ayora region (Eastern Spain). Based on geometrically corrected data, full radiometric processing has been applied, making use of a modified 5S Code (Tanré et al., 1990), and incorporating a correction accounting for topography-induced illumination variations (Hill et al., 1995). Subsequently, SMA has been applied, using a 3 endmember model to derive quantitative estimates of proportional vegetation cover, soil and bedrock background, and a shade component accounting for micro-shading effects. Making use of these information layers, an interpretation framework has been developed to support the creation of vegetation cover maps, the identification of fire events and perimeters, and the quantitative and qualitative assessment of vegetation recovery following the fires.
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Galicia, in north-west Spain, is a region especially affected by devastating forest fires. The development of a fire danger prediction model adapted to this particular region is required. In this paper, we focus on changes in the condition of vegetation as an indicator of fire danger. The potential of the Enhanced Vegetation Index (EVI) together with period-of-year to monitor vegetation changes in Galicia is shown. The Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the Terra satellite, was chosen for this study. A 6-year dataset of EVI images, from the product MOD13Q1 (16-day composites), together with fire data in a 10 × 10-km grid basis, were used. Logistic regression was used to assess the relationship between the percentage of fire activity and EVI variations together with period-of-year. The results show the ability of the model obtained to discriminate different levels of fire occurrence danger, with an estimation error of ~5%. This remote sensing technique may...