Dominique Fasbender

Support-Based Implementation of Bayesian Data Fusion for Spatial Enhancement: Applications to ASTER Thermal Images

In this letter, a general Bayesian data fusion (BDF) approach is proposed and applied to the spatial enhancement of ASTER thermal images. This method fuses information coming from the visible or near-infrared bands (15 times 15 m pixels) with the thermal infrared bands (90 times 90 m pixels) by explicitly accounting for the change of support. By relying on linear multivariate regression assumptions, differences of support size for input images can be explicitly accounted for. Due to the use of locally varying variances, it also avoids producing artifacts on the fused images. Based on a set of ASTER images over the region of Lausanne, Switzerland, the advantages of this support-based approach are assessed and compared to the downscaling cokriging approach recently proposed in the literature. Results show that improvements are substantial with respect to both visual and quantitative criteria. Although the method is illustrated here with a specific case study, it is versatile enough to be applied to the spatial enhancement problem in general. It thus opens new avenues in the context of remotely sensed images.

Revisiting the concept of a symmetric index of agreement for continuous datasets

Quantifying how close two datasets are to each other is a common and necessary undertaking in scientific research. The Pearson product-moment correlation coefficient r is a widely used measure of the degree of linear dependence between two data series, but it gives no indication of how similar the values of these series are in magnitude. Although a number of indexes have been proposed to compare a dataset with a reference, only few are available to compare two datasets of equivalent (or unknown) reliability. After a brief review and numerical tests of the metrics designed to accomplish this task, this paper shows how an index proposed by Mielke can, with a minor modification, satisfy a series of desired properties, namely to be adimensional, bounded, symmetric, easy to compute and directly interpretable with respect to r. We thus show that this index can be considered as a natural extension to r that downregulates the value of r according to the bias between analysed datasets. The paper also proposes an effective way to disentangle the systematic and the unsystematic contribution to this agreement based on eigen decompositions. The use and value of the index is also illustrated on synthetic and real datasets.

Bayesian Data Fusion: Spatial and Temporal Applications

Because the characteristics of remotely sensed data vary greatly with the sensors, spectral and spatial resolutions are practically unique for each sensor. Therefore, there is a real need for a theoretical framework that aims at merging information from two or more different sources. In this paper, a new Bayesian data fusion (BDF) framework is used in order to tackle several classical remote sensing issues. This BDF framework is dedicated to spatial prediction, which draws new avenues for applications in remote sensing. An existing BDF method proposed for the pansharpening of IKONOS image is adapted in the case of SPOT 5 image. The BDF approach is then tested for the enhancement of the spatial resolution of coarse images with high-resolution images. In order to illustrate these methods, SPOT 5 and SPOT VEGETATION images were purchased at two different dates in die province of Ninh Thuan (Vietnam). Finally, prospective considerations are addressed for updating past high-resolution images with recent coarse images.

Updating Scarce High Resolution Images with Time Series of Coarser Images : a Bayesian Data Fusion Solution

As a consequence of the great variability between sensors, the characteristics of remotely sensed data widely differ with respect to spectral and spatial resolutions. Additionally to their respective technical characteristics and peculiarities, sensors also have different temporal frequencies of acquisition. Coarser sensors (e.g. SPOT VEGETATION or TERRA MODIS) have generally close to daily acquisition rates while high spatial resolution sensors (e.g. SPOT HRVIR or IKONOS) have lower acquisition rates. Cloud-free high resolution imagery may therefore not be available at the required period unlike coarser resolution images. On top of this, high resolution images are sometimes so highly priced that updating past high resolution images with recent coarse images can be cost effective. For these reasons, there is a real need for a sound theoretical framework that aims at merging information coming from two or more different sensors while taking explicitly into account the spatial resolution discrepancies between images. Typically, for cost effective applications, this could involve predicting a high resolution image by updating a past one with more recent but coarser images. It is a common fact that remote sensors have different spatial resolution. This change of resolution is thus a typical issue in remote sensing applications. Depending on users’ needs and the heterogenity of the study areas, different algorithms of fusion were proposed for the spatial enhancement of remotely sensed images. These include Brovey method (Pohl & van Genderen, 1998), Intensity-Hue-Saturation (IHS; Harrison & Jupp, 1990), Principal Component Analysis (PCA; Pohl & van Genderen, 1998), wavelet-based Multi-Resolution Analyses (MRA; Zhou et al., 1998; Garzelli & Nencini, 2005; Ranchin et al., 2003), High-Pass Filter (HPF; Chavez et al., 1991)

Evaluation of the Standardized Precipitation Index as an early predictor of seasonal vegetation production anomalies in the Sahel

ABSTRACT We analysed the performance and timeliness of the Standardized Precipitation Index (SPI) in anticipating deviations from mean seasonal vegetation productivity in the Sahel. Gridded rainfall estimates are used to compute the SPI for 1–6-month timescales, whereas the Z-score of the cumulative value of the Fraction of Absorbed Photosynthetically Active Radiation over the growing season (zCFAPAR) is used as a proxy of seasonal productivity. Results show that the strength of the link varies in space as a function of both the SPI timescale and the timing of the SPI calculation with respect to the vegetative season’s progress. For productivity forecasting, we propose an operational strategy to select per grid cell the SPI timescale and computation time with the highest correlation with zCFAPAR at different moments of the season. The linear relationship between SPI and zCFAPAR is significant for 32–66% of the study area, depending on the timing at which SPI is considered (at 0% and 75% of the seasonal progress, respectively). For these areas, the selected SPI explains on average about 40% of the variance of zCFAPAR and may thus assist in the earlier identification of agricultural drought.

Remote sensing monitoring of land restoration interventions in semi-arid environments using a before-after control-impact statistical design

Highlights • A rapid, standardised and objective assessment of the biophysical impact of restoration interventions is proposed.• The intervention impact is evaluated by a before–after control-impact sampling design.• The method provides a statistical test of the no-change hypothesis and the estimation of the relative magnitude of the change.• The method is applicable to NDVI and other remote sensing-derived variables.

Nonlinear spatial prediction with non-Gaussian data : a maximum entropy viewpoint

We propose to look here at the problem of nonlinear spatial prediction from a maximum entropy viewpoint, where the marginal probability distribution function (pdf) is assumed to belong to the parametric family of exponential polynomials of order p, i.e. the family of maximum entropy solutions under constraints for the p first moments. The general methodology for modeling this marginal pdf is given first, allowing afterwards an estimation of multivariate maximum entropy pdf’s that account at the same time for the marginal pdf and a specified covariance function.

Data Fusion in a Spatial Multivariate Framework: Trading off Hypotheses Against Information

Due to the exponential growth in the amount and diversity of data that one may expect to provide greater modeling and predictions opportunities, there is a real need for methods that aim at reconciling them inside a flexible and sound theoretical framework. In a geostatistical prediction context, beside more or less straighforward variations around univariate kriging (e.g. kriging with external drift), the most classical method (i.e. cokriging) is based on a multivariate random field approach of the problem, at the price of strong modeling hypotheses. However, there are expected practical situations where these hypotheses may be hard to fulfill or do not make sense from a modeling viewpoint.