Paleoalterações e cimentações nos depósitos continentais terciários de Portugal central: importância na interpretação de processos antigos

This paper describes the palaeoweathering, cementation, clay minerals association and other closely related characteristics of central Portugal allostratigraphic Tertiary units (SLD's), that can be used for palaeoclimatic interpretation and palaeoenvironmental reconstruction. Lateral and vertical changes in palaeosols are of value for improving our understanding of the autocyclic and allocyclic controls on sediment acumulation in an alluvial basin, but they can also have stratigraphic importance. In some cases it is concluded that the geomorphological setting may have been more decisive than climatic conditions to the production of the palaeoweathering. During late Palaeogene (SLD7-8), surface and near-surface silicification were developed on tectonically stable land surfaces of minimal local relief under a semi-arid climate; groundwater flow was responsible for some eodiagenesis calcareous accumulations, with the neoformation of palygorskite. Conditions during the Miocene (SLD9-11) were favourable for the smectization of the metamorphic basement and arenization of granites. Intense rubefaction associated with basement conversion into clay (illite and kaolinite), is ascribed to internal drainage during late Messinian-Zanclean (SLD12). During Piacenzian (SLD13) intense kaolinization and hydromorphism are typical, reflecting a more humid and hot temperate climate and important Atlantic fluvial drainage. Later on (Gelasian-early Pleistocene ?; SLD14). more cold and dry conditicns are interpreted, at the beginning of the fluvial incision sage. Silica cementation is identified in the upper Eocence-Oligocene ? (SLD18; the major period of silicification), middle to upper Miocene (SLD10)and upper Tortonian-Messinian (SLD11); these occurrences are compatible with either arid or semi-arid conditions and the establishment of a flat landscape upon which a silcrete was developed.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.