Fire is a worldwide phenomenon that appears in the geological record soon after the appearance of terrestrial plants. Fire influences global ecosystem patterns and processes, including vegetation distribution and structure, the carbon cycle, and climate. Although humans and fire have always coexisted, our capacity to manage fire remains imperfect and may become more difficult in the future as climate change alters fire regimes. This risk is difficult to assess, however, because fires are still poorly represented in global models. Here, we discuss some of the most important issues involved in developing a better understanding of the role of fire in the Earth system.

/pdf/fire-in-the-earth-system-481yvwaoo1.pdf

Fire in the Earth System

Digital Coding of Waveforms

This paper compares two general and formal solutions to the problem of fusion of multispectral images with high-resolution panchromatic observations. The former exploits the undecimated discrete wavelet transform, which is an octave bandpass representation achieved from a conventional discrete wavelet transform by omitting all decimators and upsampling the wavelet filter bank. The latter relies on the generalized Laplacian pyramid, which is another oversampled structure obtained by recursively subtracting from an image an expanded decimated lowpass version. Both the methods selectively perform spatial-frequencies spectrum substitution from an image to another. In both schemes, context dependency is exploited by thresholding the local correlation coefficient between the images to be merged, to avoid injection of spatial details that are not likely to occur in the target image. Unlike other multiscale fusion schemes, both the present decompositions are not critically subsampled, thus avoiding possible impairments in the fused images, due to missing cancellation of aliasing terms. Results are presented and discussed on SPOT data.

Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis

https://www.agro.uba.ar/users/garbulsk/pdfs/Garbulsky%20et%20al2011_RemSensEnv.pdf

The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis

This article aims at explaining the ARSIS concept. By fusing two sets of images A and B, one with a high spatial resolution, the other with a low spatial resolution and different spectral bands, the ARSIS concept permits to synthesise the dataset B at the resolution of A that is as close as possible to reality. It is based on the assumption that the missing information is linked to the high frequencies in the sets A and B. It searches a relationship between the high frequencies in the multispectral set B and the set A and models this relationship. The general problem for the synthesis is presented first. The general properties of the fused product are given. Then, the ARSIS concept is discussed. The general scheme for the implementation of a method belonging to this concept is presented. Then, this article intends to help practitioners and researchers to better understand this concept through practical details about implementations. Two Multiscale Models are described as well as two Inter-Band Structure Models (IBSM). They are applied to an Ikonos image as an illustration case. The fused products are assessed by means of a known protocol comprising a series of qualitative and quantitative tests. The products are found of satisfactory quality. This case illustrates the differences existing between the various models, their advantages and limits. Tracks for future improvements are discussed.

https://hal.archives-ouvertes.fr/hal-00356163/document

Image fusion—the ARSIS concept and some successful implementation schemes

The definition of noise models suitable for hyperspectral data is slightly different depending on whether whiskbroom
or push-broom are dealt with. Focussing on the latter type (e.g., VIRS-200) the noise is intrinsically non-stationary
in the raw digital counts. After calibration, i.e. removing the variability effects due to different gains and offsets
of detectors, the noise will exhibit stationary statistics, at least spatially. Hence, separable 3D processes correlated
across track (x), along track (y) and in the wavelength (?), modelled as auto-regressive with GG statistics have
been found to be adequate. Estimation of model parameters from the true data is accomplished through robust techniques
relying on linear regressions calculated on scatter-plots of local statistics. An original procedure was devised
to detect areas within the scatter-plot corresponding to statistically homogeneous pixels. Results on VIRS-200 data
show that the noise is heavy-tailed (tails longer than those of a Gaussian PDF) and somewhat correlated along and
across track by slightly different extents. Spectral correlation has been investigated as well and found to depend both
on the sparseness (spectral sampling) and on the wavelength values of the bands that have been selected.

/pdf/noise-modelling-and-estimation-of-hyperspectral-data-from-njjz7s0s0w.pdf

Noise modelling and estimation of hyperspectral data from airborne imaging spectrometers

This work focuses on estimating the information conveyed to a user by hyperspectral image data. The goal is establishing the extent to which an increase in spectral resolution enhances the amount of usable information. Indeed, a tradeoff exists between spatial and spectral resolution due to physical constraints of multi-band sensors imaging with a prefixed SNR. After describing an original method developed for the automatic estimation of variance and correlation of the noise introduced by hyperspectral imagers, lossless interband data compression is exploited to measure the useful information content of hyperspectral data. In fact, the bit rate achieved by the reversible compression process takes into account both the contribution of the "observation" noise (i.e., information regarded as statistical uncertainty, but whose relevance to a user is null) and the intrinsic information of radiance sampled and digitized through an ideally noise-free process. An entropic model of the decorrelated image source is defined and, once the parameters of the noise, assumed to be Gaussian and stationary, have been measured, such a model is inverted to yield an estimate of the information content of the noise-free source from the code rate. Results are reported and discussed on both simulated and AVIRIS data.

http://www.ifac.cnr.it/corsari/papers/Information-theoretic%20assessment.pdf

Information-theoretic assessment of sampled hyperspectral imagers

Recent investigations have induced relevant advancements of imaging interferometry, which is becoming a viable option for Earth remote sensing. Various research programs have chosen the Sagnac configuration for new imaging interferometers. Due to the growing diffusion of this technique, we have developed a self-contained theory for describing the signal produced by triangular FTSs and its optimal processing. We investigate the relevant disadvantages of multiplexing, and compare dispersive with FTS instruments. The paper addresses some methods for correcting the phase error, and the non-unitary transformation performed by a Sagnac interferometer. The effect of noise on spectral estimations is discussed.

Theoretical aspects of Fourier Transform Spectrometry and common path triangular interferometers.

Presents a general and formal solution to the problem of multisensor image data fusion and applies its results to a specific case study concerning multi-spectral observations: Landsat TM (30 m resolution) and MOMS-2P (18 m). The proposed scheme relies on the generalized Laplacian pyramid, which is a non-dyadic band-pass analysis structure unconstrained from the ground scales of the imaged data: for a p/q>1 ratio only one low-pass filter with cut-off at 1/p of the spatial frequency content is needed. Filter design is easy and noncritical for performances. Thus, the pyramid method is simple to be designed and generalized to images having whatsoever ground resolution, as in most applications involving new-generation sensors.

Multispectral fusion of multisensor image data by the generalized Laplacian pyramid

We focus on reliably estimating the information conveyed to a user by multispectral image data. The goal is establishing the extent to which an increase in spectral resolution can increase the amount of us- able information. As a matter of fact, a trade-off exists between spatial and spectral resolution, due to physical constraints of sensors imaging with a prefixed SNR. After describing some methods developed for au- tomatically estimating the variance of the noise introduced by multispec- tral imagers, lossless data compression is exploited to measure the use- ful information content of the multispectral data. In fact, the bit rate achieved by the reversible compression process takes into account both the contribution of the ''observation'' noise, i.e., information regarded as statistical uncertainty, whose relevance is null to a user, and the intrinsic information of hypothetically noise free multispectral data. An entropic model of the image source is defined and, once the standard deviation of the noise, assumed to be white and Gaussian, has been preliminarily estimated, such a model is inverted to yield an estimate of the informa- tion content of the noise-free source from the code rate. Results of both noise and information assessment are reported and discussed on syn- thetic noisy images and on Landsat thematic mapper (TM) data. © 2002

http://www.ifac.cnr.it/corsari/papers/Estimating%20noise%20and.pdf

Ivan Pippi

Papers

Noise modelling and estimation of hyperspectral data from airborne imaging spectrometers

Information-theoretic assessment of sampled hyperspectral imagers

Theoretical aspects of Fourier Transform Spectrometry and common path triangular interferometers.

Multispectral fusion of multisensor image data by the generalized Laplacian pyramid

Estimating noise and information of multispectral imagery