For successful application of remote sensing and data integration in regional mineral exploration, it is crucial that, prior to regional application, relations between geological setting, mineralizing processes, related anomalies, and available remote sensing data are thoroughly studied in a calibration area with known mineralization that is representative for the region of interest. Recognizing features that are diagnostic for mineralization and its setting using Landsat The- A data integration study is presented for a 20-km x 20-km calibration area in Spain with known granite-related mineralization. The contact metamorphic setting is diagnostic for most mineral deposits. Therefore, TM and airborne data are used to map features predictive for contact metamorphic rocks. A concept of spatial reasoning is presented that reduces the damage caused by these problems: (1) The data manipulation is very straightforward and aimed at recognizing specific features known to be diagnostic for the setting in which mineralization is likely to occur. (2) Every step in the data manipulation is described in a quantitative way. (3) The nature of the pixels.surrounding a classified pixel is considered when deciding whether the classification is correct. (4) Weights are assigned to the degree of uncertainty within an interpretation. (5) integrating the interpreted and weighed data into a probability map highlights zones that are confirmed by all data sets, and thus are most reliably classified. This concept enables users of a careful and systematic analysis of the multiple spatial data sets to detect diagnostic features that are predictive for granite-related mineralization in this region.
The objective of this study was to use satellite imagery combined with field-based spectral analysis to assess the impacts of mining-related activities on vegetation around the smelter town of Karabash, South Ural Mountains of Russia. Time series analysis of normalized difference vegetation index (NDVI) and fraction of absorbed photosynthetically active radiation (FAPAR) images derived from Système Pour l'Observation de la Terre (SPOT)-VEGETATION was combined with the analysis of vegetation stress indices calculated from 140 in situ spectral measurements. Correlation analyses have revealed that vegetation stress affects vegetation density and resilience, and that it impedes a gradual increase in photosynthetic activity in the most affected areas ranging up to 10 km from the smelter. The prolongation of the growing season of healthier vegetation at greater distances, showing higher vegetation density, lower variation, and a more positive trend over time, can possibly be related to climate change. Although land cover shows a concentric pattern around Karabash, the analysis revealed that both spectral and time series-derived indices are defined more by the distance to the Karabash smelter and vegetation stress, rather than by the land cover class.
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