Fabio Maselli

An atmospheric correction method for the automatic retrieval of surface reflectances from TM images

Abstract Most of the atmospheric correction methods proposed in the literature are not easily applicable in reaJ cases. The most sophisticated models frequently require inputs which are not commonly available, whilst traditional simple dark object subtraction techniques do not generally give real reflectance values. In the present work an atmospheric correction method applicable to Landsat-TM data is described, which requires only inputs that are commonly available and the presence in the imaged scenes of some dark surfaces in TM bands 1 (blue) and 3 (red). The method consists of an inversion algorithm based on a simplified radiative transfer model in which the characteristics of atmospheric aerosols are estimated by the use of the path radiance in two TM bands rather than a priori assumed. On the basis of this information, which is crucial for determining the atmospheric properties, the retrieval of real reflectances from TM images is possible. The method can be applied to all TM scenes in which some dar...

Use of probability entropy for the estimation and graphical representation of the accuracy of maximum likelihood classifications

Abstract A method is proposed for statistically evaluating the accuracy levels of maximum likelihood classifications and representing them graphically. Based on the concept that the heterogeneity of maximum likelihood membership probabilities can be taken as an indicator of the confidence for the classification, such a parameter is estimated for all pixels as relative probability entropy and represented in a separate channel. After a brief presentation of the statistical basis of the methodology, this is applied to a conventional and two modified maximum likelihood classifications in a case study using Landsat TM scenes. The results demonstrate the efficiency of the approach and, particularly, its usefulness for operational applications.

Integration of High and Low Resolution NDVI Data for Monitoring Vegetation in Mediterranean Environments

Abstract The integration of the useful features of high and low spatial and temporal resolution satellite data is a major issue in remote sensing studies. The current work presents the development and testing of a procedure based on classification and regression analysis techniques for generating an NDVI data set with the spatial resolution of Landsat TM images and the temporal resolution of NOAA AVHRR maximum-value composites. The procedure begins with a classification of the high resolution TM data which yields land use references. These are degraded to low spatial resolution in order to produce abundance images comparable with the AVHRR data. Linear regressions are then applied between the AVHRR NDVI data and the abundance images to estimate the profiles of the pure classes, which are then merged to the high spatial resolution classification outputs to generate an integrated data set. Experiments carried out in an area of Tuscany (Central Italy) intercomparing different strategies for each methodological step (hard and fuzzy classification, mean and Gaussian degradation, uni- and multivariate regression) identified an optimum methodology composed of fuzzy classification, mean degradation, and multivariate regression procedures.

Selection of optimum bands from TM scenes through mutual information analysis

Abstract The selection of optimum band subsets from remotely sensed for visual interpretation or automatic processing is an interesting task which will assume growing importance with the availability of highly multispectral data from future sensors. The usual methods for the mathematical evaluation of the best combination of channels are based on parametric statistical procedures such as eigenvector analysis and calculation of separability measurements. These procedures are not easy to be interpreted or computationally expensive and are not suited for evaluating the probabilistic information which can be exploited by non-parametric processes. For this kind of application, a method based on mutual information analysis is put forward in the present paper. Mutual information analysis is a statistical procedure which, using the concept of system entropy, is capable of mathematically evaluating the probabilistic information common to different variables. When applied to remotely sensed scenes superimposed on ground references related to some theme (for example vegetation types), information analysis can indicate which channels express more information about that theme. The method was applied to some Landsat TM scenes from three Italian areas about which ground references were available. Some mixed parametric-non-parametric classifications were then performed to test the results of the analyses. From these tests the subsets identified were demonstrated to be significantly more informative than standard subsets, which testifies to the efficiency of the procedure.

Definition of spatially variable spectral endmembers by locally calibrated multivariate regression analyses

Linear regression procedures can be applied to derive spectral endmembers using satellite images and superimposed abundance estimates of known components. A common problem, however, is represented by the spatial variability of the spectral endmembers to estimate, which may be caused by variations in several environmental factors (topography, water availability, soil type, etc.). This problem is currently addressed by a modified multivariate regression procedure that can define spatially variable spectral endmembers. The procedure is based on a local calibration of the regression statistics (mean vectors and variance/covariance matrices), which is obtained by weighting the values of the training pixels according to their distance from each pixel examined. The locally found regression statistics are then used to extrapolate pure class spectral endmembers, which are therefore different for each image pixel. An experiment was carried out using multitemporal NOAA-AVHRR NDVI profiles and class abundance estimates of Tuscany region in central Italy. The results show that the spatially variable spectral endmembers are far more accurate than conventional fixed endmembers to recompose the original NDVI imagery. Finally, it is discussed how these spatially variable pure class NDVI values can serve for data integration and as input for agro-meteorological applications and ecosystem simulation modeling.

Fuzzy classification of spatially degraded Thematic Mapper data for the estimation of sub-pixel components

Fuzzy classification procedures are hypothesized to provide more class membership information than hard methods as well as specific insights about sub-pixel components. This hypothesis is investigated in the present work using ancillary and spatially degraded Thematic Mapper (TM) data of a wide valley in central Italy. Hard and fuzzy modified maximum likelihood classifications (MLC) were applied to the data set. Fuzzy membership grades were found to be related to class cover proportions for each degraded pixel. Also, fuzzy probabilities were able to provide more precise estimates of class distribution, as measured by Kappa coefficients of agreement, with respect to hard attributions. Further analysis showed that this improvement was chiefly due to a better characterization of mixed, uncertainly attributable pixels. These results are of great interest for definition of the cases for which the fuzzy approach is appropriate.

Use of NOAA-AVHRR NDVI images for the estimation of dynamic fire risk in Mediterranean areas

Abstract Wildfires are a major cause of land degradation in the Mediterranean region due to their frequent recurrence in the same areas. The evaluation of fire risk is therefore of high practical importance, particularly during the summer arid season, when fires are most frequent and harmful. Recent studies have demonstrated that the evaluation of dynamic fire risk can be carried out by the use of remotely sensed images, and specifically of NOAA-AVHRR Normalized Difference Vegetation Index (NDVI) data. This use relies on the sensitivity of the index to vegetation dryness, which is a major predisposing factor for fire occurrence. Several problems, however, remain linked to the spatial variability of the risk in environmentally heterogeneous areas, which requires the application of suitable processing techniques to the low-resolution imagery. The current work reports on the development and testing of different methodologies for estimating dynamic fire risk by the use of NOAA-AVHRR data. The investigation was conducted in Tuscany (Central Italy) using a large archive of fires that occurred in the region and NOAA-AVHRR NDVI data of 16 years (1985–2000). Relying on previous methodological achievements of our group and other research groups, several procedures were tested to extract information related to fire risk from the remotely sensed images. These trials led to define an optimum method which is based on the identification of pixels where the accordance between interyear variations in fire probabilities and NDVI values is maximum. The accuracy of the risk estimates from this optimum method was finally evaluated by a leave-one-out cross-validation strategy. In this way, the performance of the methodology was assessed, together with its potential for operational fire risk monitoring and forecasting in Mediterranean areas.

Use of error matrices to improve area estimates with maximum likelihood classification procedures

Abstract The maximum likelihood classifier is by far the most widespread among supervised classification methods. This procedure offers numerous advantages, but it has considerable shortcomings in the presence of strongly irregular spectral distributions, mainly related to bias in area estimates. Since these cases are quite common, some methods have already been proposed to correct biased area estimates from maximum likelihood classifications, but they are often not generally applicable or statistically stable. In this article a method is put forward to correct maximum likelihood assignment probabilities by means of a transition matrix; this matrix is derived through a simple mathematical algorithm from a contingency table of a previous classification compared to reference pixels. The purpose is clearly to attain a diagonalization of the final error sources to better estimate area extents and, above all, to achieve higher global discrimination accuracy. As different environmental situations may cause wide variability in the results of such a procedure, this was tested in three case studies using TM data acquired over areas with different landscapes. The results, evaluated by means of suitable statistics, significantly support that the method has general validity and applicability.

Estimation of Mediterranean forest attributes by the application of k¿NN procedures to multitemporal Landsat ETM+ images

Routine applications of nonparametric estimation methods to satellite data for assisting the creation of forest inventories in Northern European countries are stimulating interest in the possible extension of these methods to more complex Mediterranean areas. This is the subject of the current work, which presents an experiment based on the integration of remotely sensed images and sample field measurements aimed at producing forest attribute maps in central Italy. Testing was carried out in an area where 370 geocoded field plots, sampled on a single‐stage cluster design, were collected to characterize wood and non‐wood forest attributes. These ground data served to apply various k‐Nearest Neighbour (k‐NN) estimation procedures to multitemporal Landsat 7 ETM+ images in order to map major forest attributes (basal area and simulated leaf area index, LAI). More specifically, the investigation focused on evaluating the effects of using satellite images from different periods of the growing season and spectral metrics of increasing complexity. The results achieved by the examined methods are finally discussed in order to provide guidelines for possible operational utilization.

Use of NOAA-AVHRR NDVI data for environmental monitoring and crop forecasting in the Sahel. Preliminary results

Abstract Several studies have shown that the NDVI calculated from NOAA-AVHRR data is related to annual rainfall and primary productivity in Sahelian areas. Such correlations, however, are affected by several environmental factors and have been tested only with data accumulated during rainy seasons, which is not ideal for the prediction of crop yield. In the present study a methodology of NOAA AVHRR data processing is presented which utilizes NDVI computed only in the first part of some rainy seasons and statistically takes into account the geographical variability in land resources and atmospheric conditions. From the first results of the application of the methodology in Niger, its potential has been shown both for environmental monitoring, and, more specifically, for crop yield assessment and forecasting.