Land managers need cost-effective methods for mapping and characterizing fire fuels quickly and accurately. The advent of sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite and gradient modeling for mapping fuel layers for fire behavior modeling within FARSITE and FLAMMAP. Empirical models, based upon field data and spectral information from an ASTER image, were employed to test the efficacy of ASTER for mapping and characterizing canopy closure and crown bulk density. Surface fuel models (NFFL 1-13) were mapped using a classification tree based upon three gradient layers; potential vegetation type, cover type, and structural stage.
The FWI-tel instantaneous is a system for computing aforest fire risk index; it elaborates instantaneous weathermaps to evaluate fire risk indicator on alpine regions.This system is based on the previsional Canadian FireWeather Index (FWI) adjusted for continental Europelatitudes and climatology and adapted to alpine regionsorography. FWI is a meteorological index, which uses asinput data meteorological forecasts (or analysis), satelliteremote sensed and weather radar data.In the current version of the system, air temperature andrelative humidity data come from MODIS and AIRSsensors, rain data come from MeteoSwiss weather radarsand wind data come from BOLAM Numerical WeatherPrediction models. In particular, relative humidity dataare characterized by a very coarse spatial resolutionwhich makes difficult over complex-orography regionsthe precise localization and evaluation of the fire dangerindex.Furthermore, MERIS relative humidity sensed data, areused as new input for…
read more
The present work describes a methodology based on Artificial Neural Networks (ANN) and multitemporal images from the MODIS/Terra-Aqua sensors in order to detect areas with high risk of forest fire in the Brazilian Amazon. The hypothesis of this work is that, due to the characteristics of land use and land cover change dynamics in the Amazon forest, the temporal spectral profile of forest areas preparing to be burned can be separated from other areas. A study case was carried out in three municipalities in the north region of Mato Grosso State, Brazilian Amazon. Feedforward ANNs, with different architectures, were trained with a backpropagation algorithm, taking as inputs the NDVI values calculated from MODIS images acquired during five different periods preceding the forest fire season. Samples were extracted from areas where forest fires were detected in 2005, and also from forest and agricultural areas. These samples were divided to train, to validate and to test the…
read moreESOA represents all European satellite operators. The Association works with policy-makers to ensure that satellite technology and services are taken into proper account in the delivery of public sector objectives so citizens all over the globe can benefit from them. The availability of satellite services depends on political support, a favourable regulatory environment, fair industrial policy and awareness.
Demo on the Space Application Matrix and geoinformation for disaster and risk management e.g. examples and best practices.