- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Wildfire Risk Assessment Using Multi-Source Remote Sense Derived Variables
摘要： This study focuses on the forest fire risk assessing using entirely remote sensing derived variables. These variables include Fuel moisture content (FMC), Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Elevation and Slope. The Difference and Anomaly of FMC in time series are also calculated since FMC is one of the critical factors in assessing the wildfire risk. The logistic regression model is used to integrate all the variables in the fire occurred and none-occurred areas to derive the Fire Risk Index (FRI). A case study of the above methodology is applied to assess the FRI in Yunnan Province in China. The result shows that the AUC is to 0.8 for grassland and 0.81 for woodland, which indicates the good performance of the presented methodology in assessing forest fire risk.
关键词： forest fire risk,logistic regression model,Forest fire
更新于2025-09-23 15:23:52
-
[IEEE 2018 3rd International Conference for Convergence in Technology (I2CT) - Pune (2018.4.6-2018.4.8)] 2018 3rd International Conference for Convergence in Technology (I2CT) - Fire Detection in a Varying Topography Using Landsat-8 for Nainital Region, India
摘要： Forest fires are the most frequent phenomenon during the summer season in India, and especially in the hilly terrains of Uttarakhand forests. Remote sensing sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Visible Infrared Imaging Radiometer Suite (VIIRS) with coarse spatial resolution on board different satellites were used to detect the forest fires across the world. Landsat-8 Operational Land Imager (OLI) data has the better spatial resolution (30m) as compared with the MODIS and VIIRS, therefore useful to detect the smaller fires. Nainital district in Uttarakhand state was severely affected by the massive forest fire events occurred during April-May, 2016. The main objective of the study is to identify the potential of Landsat-8 data in detecting the forest fire for varying topographic region like Nainital. Landsat-8 data acquired on 28th April 2016 and 1st May 2016 has been used in this study. The results obtained from Landsat-8 data are compared with the MODIS fire products and showed an improvement in the detection of small fires.
关键词： MODIS,fire algorithm,Landsat-8 OLI,Forest fire,VIIRS
更新于2025-09-04 15:30:14
-
Deriving forest fire probability maps from the fusion of visible/infrared satellite data and geospatial data mining
摘要： Information on fire probability is of vital importance to environmental and ecological studies as well as to fire management. This study aimed at comparing two forest fire probability mapping techniques, one based primarily on freely distributed EO (Earth observation) data from Landsat imagery, and another one based purely on GIS modeling. The Normalized Burn Ratio (NBR) computed from Landsat data was used to detect the high fire severity and probability area based on the NBR difference between pre- and post-fire conditions. The GIS-based modeling was based on a multi criterion evaluation technique, into which other attributes like anthropogenic and natural sources were also incorporated. The ability of both techniques to map forest fire probability was evaluated for a region in India, for which suitable ancillary data had been previously acquired to support a rigorous validation. Subsequently, a conceptual framework for the prediction of high fire probability zones in an area based on a newly introduced herein data fusion technique was constructed. Overall, the EO-based technique was found to be the most suitable option, since it required less computational time and resources in comparison to the GIS-based modeling approach. Furthermore, the fusion approach offered an appropriate path for developing a forest fire probability identification model for long-term pragmatic conservation of forests. The potential fusion of these two modeling approaches may provide information that can be useful to forest fire mitigation policy makers, and assist at conservation and resilience practices.
关键词： Multi criteria evaluation,India,Geographical information systems,Normalize burn ratio,Data fusion,Forest fire,Remote sensing
更新于2025-09-04 15:30:14
-
Impact of Land Cover Change Induced by a Fire Event on the Surface Energy Fluxes Derived from Remote Sensing
摘要： Forest fires affect the natural cycle of the vegetation, and the structure and functioning of ecosystems. As a consequence of defoliation and vegetation mortality, surface energy flux patterns can suffer variations. Remote sensing techniques together with surface energy balance modeling offer the opportunity to explore these changes. In this paper we focus on a Mediterranean forest ecosystem. A fire event occurred in 2001 in Almodóvar del Pinar (Spain) affecting a pine and shrub area. A two-source energy balance approach was applied to a set of Landsat 5-TM and Landsat 7-EMT+ images to estimate the surface fluxes in the area. Three post-fire periods were analyzed, six, seven, nine, and 11 years after the fire event. Results showed the regeneration of the shrub area in 6–7 years, in contrast to the pine area, where an important decrease in evapotranspiration, around 1 mm·day?1, remained. Differences in evapotranspiration were mitigated nine and 11 years after the fire in the pine area, whereas significant deviations in the rest of the terms of the energy balance equation were still observed. The combined effect of changes in the vegetation structure and surface variables, such as land surface temperature, albedo, or vegetation coverage, is responsible for these variations in the surface energy flux patterns.
关键词： surface energy fluxes,Landsat,forest fire,land cover change,evapotranspiration
更新于2025-09-04 15:30:14