An application of the perpendicular moisture index for the prediction of fire hazard

Various factors contribute to forest fire hazard, and among them vegetation moisture is the one that dictates susceptibility to fire ignition and propagation. The scientific community has developed a number of spectral indices based on remote sensing measurements in the optical domain for the assessment of vegetation equivalent water thickness (EWT), which is defined as the mass of liquid water per unit of leaf surface. However, fire models rely on the live fuel moisture content (LFMC) as a measure of vegetation moisture. LFMC is defined as the ratio of the mass of the liquid water in a fresh leaf over the mass of oven dry leaf, and spectral indices proposed so far fail in capturing LFMC variability. Recently, the perpendicular moisture index (PMI), based on MODIS, was pro-posed to overcome this limitation and provide a direct measure of LFMC. The aim of this research was to understand the potential and limitations of the PMI in predicting fire hazard, towards its ap-plication in a practical context. To this purpose, a data set of more than 7,700 fires recorded in Campania (13,595 km2), Italy, between 2000 and 2008 was compared with PMI derived from MODIS images. Results show that there is no relationship between PMI and fire size, whereas a linear correlation was found between the spectral index and fire rate of spread.Geoscience & Remote SensingCivil Engineering and Geoscience

Remote sensing estimation of vegetation moisture for the prediction of fire hazard (poster)

Geoscience & Remote SensingCivil Engineering and Geoscience

Thermal infrared observations of heterogeneous soil-vegetation systems

Remote SensingAerospace Engineerin

Fire risk assessment: The role of hyperspectral remote sensing

The increasing demand for effective forest fire prevention instruments has faced operational and future Earth observation instruments with the challenge of producing updated and reliable maps of vegetation moisture. Various empirical band-ratio indexes have been proposed so far, based on multispectral remote sensing data, that have been found to be related to vegetation moisture expressed in terms of equivalent water thickness (EWT), which is defined as the weight of liquid water per unit leaf area. More sophisticated retrieval methodologies can be adopted when hyperspectral data are available, e.g. based on spectral curve fitting in selected water absorption bands or radiative transfer model inversion, allowing for better estimates of EWT. Problems arise with the evaluation of fuel moisture content (FMC), which is the percentage weight of water per unit of oven-dried leaf weight, due to its weak signal in vegetation spectrum. FMC is essential in fire models, and it is not interchangeable with EWT. Basing on simulated vegetation spectra, this study aims at demonstrating that hyperspectral images of vegetated areas can be effectively used to evaluate FMC with accuracies not achievable with multispectral data. To this purpose, radiative transfer models PROSPECT and SAILH have been used to simulate canopy reflectance. Vegetation spectra have then been convolved to hyperspectral data basing on the design specifications of a formerly planned ASI-CSA hyperspectral mission (JHM configuration C), similar to those of the forthcoming PRISMA. For comparison against multispectral instruments, measurements from the Operational Land Imager (OLI) have also been simulated. Two retrieval methods have been tested, based on spectral indexes and on partial least squares (PLS) regression. The latter methodology is particularly suited to analyse high-dimensional data. Results confirm that spectral indexes are good predictors of vegetation moisture expressed as EWT, but their performance in evaluating FMC is poor. By using PLS regression on hyperspectral data, a linear model can be built that accurately predicts FMC. No such result is achievable from OLI simulated data.Remote SensingAerospace Engineerin

Automatic registration of iPhone images to laser point clouds of urban structures using shape features

Fusion of 3D airborne laser (LIDAR) data and terrestrial optical imagery can be applied in 3D urban modeling and model up-dating. The most challenging aspect of the fusion procedure is registering the terrestrial optical images on the LIDAR point clouds. In this article, we propose an approach for registering these two different data from different sensor sources. As we use iPhone camera images which are taken in front of the interested urban structure by the application user and the high resolution LIDAR point clouds of the acquired by an airborne laser sensor. After finding the photo capturing position and orientation from the iPhone photograph metafile, we automatically select the area of interest in the point cloud and transform it into a range image which has only grayscale intensity levels according to the distance from the image acquisition position. We benefit from local features for registering the iPhone image to the generated range image. In this article, we have applied the registration process based on local feature extraction and graph matching. Finally, the registration result is used for facade texture mapping on the 3D building surface mesh which is generated from the LIDAR point cloud. Our experimental results indicate possible usage of the proposed algorithm framework for 3D urban map updating and enhancing purposes.Geoscience and Remote SensingCivil Engineering and Geoscience

Traffic monitoring using helicopters; a system for the acquisition and analysis of image sequences to model longitudinal driving behavior

Aerospace Engineerin

Predicting forest fires burned area and rate of spread from pre-fire multispectral satellite measurements

Operational forest fire danger rating systems rely on the recent evolution of meteorological variables to estimate dead fuel condition. Further combining the latter with meteorological and environmental variables, they predict fire occurrence and spread. In this study we retrieved live fuel condition from MODIS multispectral measurements in the near infrared and shortwave infrared. Next, we combined these retrievals with an extensive dataset on actual forest fires in Campania (13,595 km2), Italy, to determine how live fuel condition affects the probability distribution functions of fire characteristics. Accordingly, the specific objective of this study was to develop and evaluate a new approach to estimate the probability distribution functions of fire burned area, duration and rate of spread as a function of the Perpendicular Moisture Index (PMI), whose value decreases with decreasing live fuel moisture content (LFMC). To this purpose, available fire data was intersected with MODIS 8-day composited reflectance data so to associate each fire event with the corresponding pre-fire PMI observation. Fires were then grouped in ten decile bins of PMI, and the conditional probability distribution functions of burned area, fire duration and rate of spread were determined in each bin. Distributions of burned area and rate of spread vary across PMI decile bins, while no significant difference was observed for fire duration. Further testing this result with a likelihood ratio test confirmed that PMI is a covariate of burned area and rate of spread, but not of fire duration. We defined an extreme event as a fire whose burned area (respectively rate of spread) exceeds the 95th percentile of the frequency distribution of all observed fire events. The probability distribution functions in the ten decile bins of PMI were combined to obtain a conditional probability distribution function, which was then used to predict the probability of extreme fires, as defined. It was found that the probability of extreme events steadily increases with decreasing PMI. Overall, at the end of the dry season the probability of extreme events is about the double than at the beginning. These results may be used to produce frequently (e.g. daily) updated maps of the probability of extreme events given a PMI map retrieved from e.g. MODIS reflectance data.Optical and Laser Remote Sensin

Modeling and reconstruction of time series of passive microwave data by Discrete Fourier Transform guided filtering and Harmonic Analysis

Daily time series of microwave radiometer data obtained in one-orbit direction are full of observation gaps due to satellite configuration and errors from spatial sampling. Such time series carry information about the surface signal including surface emittance and vegetation attenuation, and the atmospheric signal including atmosphere emittance and atmospheric attenuation. To extract the surface signal from this noisy time series, the Time Series Analysis Procedure (TSAP) was developed, based on the properties of the Discrete Fourier Transform (DFT). TSAP includes two stages: (1) identify the spectral features of observation gaps and errors and remove them with a modified boxcar filter; and (2) identify the spectral features of the surface signal and reconstruct it with the Harmonic Analysis of Time Series (HANTS) algorithm. Polarization Difference Brightness Temperature (PDBT) at 37 GHz data were used to illustrate the problems, to explain the implementation of TSAP and to validate this method, due to the PDBT sensitivity to the water content both at the land surface and in the atmosphere. We carried out a case study on a limited heterogeneous crop land and lake area, where the power spectrum of the PDBT time series showed that the harmonic components associated with observation gaps and errors have periods ≤8 days. After applying the modified boxcar filter with a length of 10 days, the RMSD between raw and filtered time series was above 11 K, mainly related to the power reduction in the frequency range associated with observation gaps and errors. Noise reduction is beneficial when applying PDBT observations to monitor wet areas and open water, since the PDBT range between dryland and open water is about 20 K. The spectral features of the atmospheric signal can be revealed by time series analysis of rain-gauge data, since the PDBT at 37 GHz is mainly attenuated by hydrometeors that yield precipitation. Thus, the spectral features of the surface signal were identified in the PDBT time series with the help of the rain-gauge data. HANTS reconstructed the upper envelope of the signal, i.e., correcting for atmospheric influence, while retaining the spectral features of the surface signal. To evaluate the impact of TSAP on retrieval accuracy, the fraction of Water Saturated Surface (WSS) in the region of Poyang Lake was retrieved with 37 GHz observations. The retrievals were evaluated against estimations of the lake area obtained with MODerate-resolution Imaging Spectroradiometer (MODIS) and Advanced Synthetic Aperture Radar (ASAR) data. The Relative RMSE on WSS was 39.5% with unfiltered data and 23% after applying TSAP, i.e., using the estimated surface signal only.Optical and Laser Remote Sensin

Remote sensing evaluation of fire hazard: Towards operational tools for improving the security of citizens and protecting the environment

Forest fires are a threat for both the environment and the security of citizens. This is particularly relevant in the Mediterranean, where the population density is high, and long dry summers drive vegetation into fireprone conditions. Policy makers underline the key role of prevention over damage reparation, and indeed efforts are conducted at regional, national and international level to develop tools supporting fire managers’ activities. The preventive allocation of intervention resources is based on fire hazard maps; these should be updated frequently throughout the fire season. In this framework satellite remote sensing can play a key role, providing daily measurements in the optical domain for the determination of vegetation water content, a key parameter for the prediction of fire hazard. This paper outlines current practices adopted in Mediterranean Europe, analyses how earth observation data are used, and underlines areas of improvement. Strengths and weaknesses of algorithms for the retrieval of vegetation water content are discussed. Examples are provided with the production of remote sensing maps of vegetation moisture in three different areas of the Mediterranean: Campania (Italy), Tuscany (Italy) and Provence-Alpes-Côte d'Azur (France). Finally, a brief review describes current and forthcoming Earth Observation missions with the potential to support fire prevention activities.Geoscience and EngineeringCivil Engineering and Geoscience

Stima della pericolosita d'incendio in Campania mediante serie temporali di misure MODIS nell'infrarosso termico

Geoscience & Remote SensingCivil Engineering and Geoscience