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Many projects and research efforts implement geo-information (GI) workflows, ranging from very basic ones to complicated software processing chains. The creation of these workflows normally needs considerable expertise and sharing them is often hampered by undocumented and non-interoperable geoprocessing implementations. We believe that the visual representation of workflows can help in the creation, sharing and understanding of software processing of geodata. In our efforts we aim at bridging abstract and concrete workflow representations for the sake of easing the creation and sharing of simple geoprocessing logic within and across projects. We have implemented a first version of our workflow approach in one of our current projects.MARIS, the Mara Rangeland Information System, is being developed in the Mau Mara Serengeti Sustainable Water Initiative (MaMaSe). It is a web client that uses the Integrated Land and Water Information System (ILWIS), our open source Remote Sensing and GIS software. It aims to integrate historic, near real time and near future forecast of rainfall, biomass, carrying capacity and livestock market information for the sustainable management of rangelands by conservancies in the Maasai Mara in Kenya. More importantly it aims to show results of a carrying capacity model implemented in a comprehensive geoprocessing workflow. In this paper we briefly describe our software and show the workflow implementation strategy and discuss the innovative aspects of our approach as well as our project evaluation and the opportunities for further grounding of our software development. [ABSTRACT FROM AUTHOR]

This study aimed to map mine waste piles and iron oxide by-product minerals from an Earth Observing 1 (EO-1) Hyperion data set that covers an abandoned mine in southwest Spain. This was achieved by a procedure involving data pre-processing, atmospheric calibration, data post-processing, and image classification. In several steps, the noise and artefacts in the spectral and spatial domains of the EO-1 Hyperion data set were removed. These steps include the following: (1) angular shift, which was used to translate time sequential data into a spatial domain; (2) along-track de-striping to remove the vertical stripes from the data set; and (3) reducing the cross-track low-frequency spectral effect (smile). The Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm in combination with the radiance transfer code MODTRAN4 was applied for quantification and removal of the atmospheric affect and retrieval of the surface reflectance. The data set was post-processed (filtering, spectral polishing) in order to remove the negative values and noise that were produced as the a result of de-striping and atmospheric calibration. The Mahalanobis distance algorithm is used to differentiate the area covered by mine piles from other main land-use classes. The spatial variations of iron oxide and carbonate minerals within the mine area were mapped using the Spectral Feature Fitting (SFF) algorithm. The pre-processing of the data and atmospheric correction were vital and played a major role on the quality of the final output. The results indicate that the vertical stripes can be removed rather well by the local algorithm compared to the global method and that the FLAASH algorithm for atmospheric correction produces better results than the empirical line algorithm. The results also showed that the method developed for correcting angular shifts has the advantage of keeping the original pixel values since it does not require re-sampling. The classification results showed that the mine waste deposits can be easily mapped using available standard algorithms such as Mahalanobis Distance. The results obtained from the SFF method suggest that there is an abundance of different minerals such as alunite, copiapite, ferrihydrite, goethite, jarosite, and gypsum within the mine area. From a total number of 754 pixels that cover the mine area, 43 pixels were classified as sulphide and carbonate minerals and 711 pixels remained unclassified, showing no abundance of any dominant mineral within the area presented by these pixels. [ABSTRACT FROM AUTHOR]

Genetic variation between various plant species determines differences in their physio-chemical makeup and ultimately in their hyperspectral emissivity signatures. The hyperspectral emissivity signatures, on the one hand, account for the subtle physio-chemical changes in the vegetation, but on the other hand, highlight the problem of high dimensionality. The aim of this paper is to investigate the performance of genetic algorithms coupled with the spectral angle mapper (SAM) to identify a meaningful subset of wavebands sensitive enough to discriminate thirteen broadleaved vegetation species from the laboratory measured hyperspectral emissivities. The performance was evaluated using an overall classification accuracy and Jeffries Matusita distance. For the multiple plant species, the targeted bands based on genetic algorithms resulted in a high overall classification accuracy (90%). Concentrating on the pairwise comparison results, the selected wavebands based on genetic algorithms resulted in higher Jeffries Matusita (J-M) distances than randomly selected wavebands did. This study concludes that targeted wavebands from leaf emissivity spectra are able to discriminate vegetation species. [ABSTRACT FROM AUTHOR]