Mario Chica-Olmo

Computing geostatistical image texture for remotely sensed data classification

Abstract Most classical mathematical algorithms for image classification do not usually consider the spectral dependence existing between a pixel and its neighbours, i.e., spatial autocorrelation. Thus, it would be advisable for discrimination of landcover classes to add to the radiometric bands of the sensor complementary information related to the textural features of an image, which can be analysed from the autocorrelation spatial structure of the digital numbers. In this way, the results obtained from pixel-by-pixel classifiers simultaneously taking into account both radiometric and texture information could be improved. This improvement would arise from the hypothesis that a pixel is not independent of its neighbours and, furthermore, that its dependence can be quantified and incorporated into the classifier. In this paper we present a methodology based on computing a set of univariate and multivariate textural measures of spatial variability based on several variogram estimators. Madogram and direct variogram for the univariate case, and cross and pseudo-cross variograms for the multivariate one, have been proposed. These measures are calculated for a specific lag of distance in a neighbourhood using a moving window on the two most representative principal components of the radiometric bands, enabling us to quantify the spatial variability of radiometric data at a local level. A computer program has been written to create a multiband image texture as output file that can be used within the classification process as additional information. An application of this methodology to lithological discrimination is presented using a Landsat-5 TM image.

The Fourier Integral Method: An efficient spectral method for simulation of random fields

The Fourier Integral Method (FIM) of spectral simulation, adapted to generate realizations of a random function in one, two, or three dimensions, is shown to be an efficient technique of non-conditional geostatistical simulation. The main contribution is the use of the fast Fourier transform for both numerical calculus of the density spectral function and as generator of random finite multidimensional sequences with imposed covariance. Results obtained with the FIM are compared with those obtained by other classic methods: Shinozuka and Jan Method in 1D and Turning Bands Method in 2D and 3D, the points for and against different methodologies are discussed. Moreover, with the FIM the simulation of nested structures, one of which can be a nugget effect and the simulation of both zonal and geometric anisotropy is straightforward. All steps taken to implement the FIM methodology are discussed.

Downscaling Cokriging for Super-Resolution Mapping of Continua in Remotely Sensed Images

The main aim of this paper is to show the implementation and application of downscaling cokriging for super-resolution image mapping. By super-resolution, we mean increasing the spatial resolution of satellite sensor images where the pixel size to be predicted is smaller than the pixel size of the empirical image with the finest spatial resolution. It is assumed that coregistered images with different spatial and spectral resolutions of the same scene are available. The main advantages of cokriging are that it takes into account the correlation and cross correlation of images, it accounts for the different supports (i.e., pixel sizes), it can explicitly take into account the point spread function of the sensor, and it has the property of prediction coherence. In addition, ancillary images (topographic maps, thematic maps, etc.) as well as sparse experimental data could be included in the process. The main problem is that super-resolution cokriging requires several covariances and cross covariances, some of which are not empirically accessible (i.e., from the pixel values of the images). In the adopted solution, the fundamental concept is that of covariances and cross-covariance models with point support. Once the set of point-support models is estimated using linear systems theory, any pixel-support covariance and cross covariance can be easily obtained by regularization. We show the performance of the method using Landsat Enhanced Thematic Mapper Plus images.

Downscaling Landsat 7 ETM+ thermal imagery using land surface temperature and NDVI images

Thermal infrared (TIR) satellite images and derived land surface temperature (LST) are variables of great interest in many remote sensing applications. However, the TIR band has a spatial resolution which is coarser than the other multispectral bands for a given satellite sensor (visible, near and shortwave infrared bands); therefore, the spatial resolution of the retrieved LST from available satellite-borne sensors is not accurate enough to be used in certain applications. The application of a method is shown here for obtaining LST images with enhanced spatial resolution using the LST at a coarser resolution and the Normalized Difference Vegetation Index (NDVI) of the same scene using Downscaling Cokriging (DCK). A LST image with perfect coherence was obtained by applying this method to a Landsat 7 ETM+ image. This implies that, if the downscaled LST image is degraded to its original resolution, the degraded image obtained is identical to the original. Hence high spatial resolution LST images were obtained without altering the original radiometry with the inclusion of artefacts. Moreover, the performance of DCK was compared with global and local TSHARP methods. The RMSE of the sharpened images were 0.85, 0.92 and 1.1 K, respectively.

Artificial neural networks as a tool for mineral potential mapping with GIS

A back-propagation artificial neural network (ANN) model is proposed to discriminate zones of high mineral potential in the Rodalquilar gold field, south-east Spain, using remote sensing and mineral exploration data stored in a GIS database. A neural network model with three hidden units was selected by means of the k -fold cross-validation method. The trained network estimated a gold potential map efficiently, indicating that both previously known and unknown potentially mineralized areas can be detected. These initial results suggest that ANN can be an effective tool for mineral exploration spatial data modelling.

Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area, Southern Spain

Mineral exploration activities require robust predictive models that result in accurate mapping of the probability that mineral deposits can be found at a certain location. Random forest (RF) is a powerful machine data-driven predictive method that is unknown in mineral potential mapping. In this paper, performance of RF regression for the likelihood of gold deposits in the Rodalquilar mining district is explored. The RF model was developed using a comprehensive exploration GIS database composed of: gravimetric and magnetic survey, a lithogeochemical survey of 59 elements, lithology and fracture maps, a Landsat 5 Thematic Mapper image and gold occurrence locations. The results of this study indicate that the use of RF for the integration of large multisource data sets used in mineral exploration and for prediction of mineral deposit occurrences offers several advantages over existing methods. Key advantages of RF include: (1) the simplicity of parameter setting; (2) an internal unbiased estimate of the prediction error; (3) the ability to handle complex data of different statistical distributions, responding to nonlinear relationships between variables; (4) the capability to use categorical predictors; and (5) the capability to determine variable importance. Additionally, variables that RF identified as most important coincide with well-known geologic expectations. To validate and assess the effectiveness of the RF method, gold prospectivity maps are also prepared using the logistic regression (LR) method. Statistical measures of map quality indicate that the RF method performs better than LR, with mean square errors equal to 0.12 and 0.19, respectively. The efficiency of RF is also better, achieving an optimum success rate when half of the area predicted by LR is considered.

Categorical Indicator Kriging for assessing the risk of groundwater nitrate pollution: The case of Vega de Granada aquifer (SE Spain)

Groundwater nitrate pollution associated with agricultural activity is an important environmental problem in the management of this natural resource, as acknowledged by the European Water Framework Directive. Therefore, specific measures aimed to control the risk of water pollution by nitrates must be implemented to minimise its impact on the environment and potential risk to human health. The spatial probability distribution of nitrate contents exceeding a threshold or limit value, established within the quality standard, will be helpful to managers and decision-makers. A methodology based on non-parametric and non-linear methods of Indicator Kriging was used in the elaboration of a nitrate pollution categorical map for the aquifer of Vega de Granada (SE Spain). The map has been obtained from the local estimation of the probability that a nitrate content in an unsampled location belongs to one of the three categories established by the European Water Framework Directive: CL. 1 good quality [Min - 37.5 ppm], CL. 2 intermediate quality [37.5-50 ppm] and CL. 3 poor quality [50 ppm - Max]. The obtained results show that the areas exceeding nitrate concentrations of 50 ppm, poor quality waters, occupy more than 50% of the aquifer area. A great proportion of the areas municipalities are located in these poor quality water areas. The intermediate quality and good quality areas correspond to 21% and 28%, respectively, but with the highest population density. These results are coherent with the experimental data, which show an average nitrate concentration value of 72 ppm, significantly higher than the quality standard limit of 50 ppm. Consequently, the results suggest the importance of planning actions in order to control and monitor aquifer nitrate pollution.

Development of a Decision Support System based on remote sensing and GIS techniques for gold-rich area identification in SE Spain

Remote sensing techniques and spatial data analysis through Geographic Information Systems (GIS) have been jointly applied in a mineral exploration context to identify gold-rich potential areas in SE Spain. Results confirm the usefulness of this integrated methodological approach as an effective tool to assess mineral potential in the studied region. Satellite and airborne image analysis have offered valuable thematic information referring both to lithology and altered zone mapping from photointerpretation and digital classification. For this goal, SPOT panchromatic and Landsat TM images were merged in order to obtain high resolution image documents for photointerpretation purposes, and the Feature Principal Component Selection technique was applied to highlight hydrothermal alteration zones characterized by hydroxyl-bearing minerals. Remote sensing results were integrated, in conjunction with existing maps and data from mineral exploration surveys, into the GIS as vector or raster layers. The GIS implementation stage consisted of the creation of a relational database, including a complete set of georeferenced data, and the elaboration of a specific user interface for spatial analysis using GIS facilities in order to establish a Decision Support System (DSS). The system was used to simulate different mineral exploration scenarios, calculating and mapping a Mineral Potential Index that was interpreted in a practical sense in terms of surface reduction and economic costs.

CYSTRATI: a computer program for spectral analysis of stratigraphic successions

Abstract The spectral analysis of stratigraphic successions represents an objective approach for the detection of periodic components that give the succession its cyclical form, as for example, in the periodicity attributed to climatic changes and related to the Milankovitch cycles or with the decennial cyclicity of solar activity. We present the CYSTRATI program, written in ANSI standard FORTRAN 77, which includes five of the most widely established techniques for spectral analysis in the earth sciences: Blackman-Tukeys classic approach; classic periodogram approach; maximum entropy approach; Thompson multitaper approach for continuous variables; and finally Walshs spectral analysis that lets us investigate cyclicity rhythms for binary variables (for example the alternation of limestone-marl). Another important characteristic is the programs versatility in providing different ways of processing experimental data (windowing, tapering, smoothing) in order to better estimate the power spectrum. Obviously any one-dimensional (1-D) succession can be analyzed with CYSTRATI, but for stratigraphic successions the possibility of adopting a previous, more exhaustive treatment has been included, a treatment in which the lithology and the type of contact between the strata can be included. The estimated results of the power spectrum in two different temporal successions will be provided, the first corresponding to lacustrine varves and the second to the Wolfer number of sunspots: such results can be used to check the programs implementation.

Geostatistics with the Matern semivariogram model: A library of computer programs for inference, kriging and simulation

In many modern applications of geostatistics in the earth sciences, the empirical information is abundant and with complete spatial coverage (e.g. satellite sensor images). In these cases, a critical characteristic of spatial variability is the continuity of the random field that better models the natural phenomenon of interest. Such continuity describes the smoothness of the process at very short distances and is related to the behaviour of the semivariogram near the origin. For this reason, a semivariogram model that is flexible enough to describe the spatial continuity is very convenient for applications. A model that provides such flexibility is the Matern model that controls continuity with a shape parameter. The shape parameter must be larger than zero; a value larger than 1 implies a random field that is m-times mean square differentiable if the shape parameter is larger than m. A package of computer programs is provided for performing the different steps of a geostatistical study using the Matern model and the performance and implementation are illustrated by an example.