Carla Mora

Ground temperature regimes and geomorphological implications in a Mediterranean mountain (Serra da Estrela, Portugal)

Air and shallow ground temperatures from two monitoring sites at the Serra da Estrela (Portugal) are analysed. The Cantaro Gordo site is located at 1875 m ASL and the Fraga das Penas at 1640 m ASL. The climate of the study area is Mediterranean and very irregular, both on a yearly and monthly basis. This is particularly significant during winter, when differences in snow cover have direct influence on the ground thermal regimes and therefore on geocryological processes. To assess the significance of the ground thermal regimes for the geomorphological dynamics, bi-hourly records of temperature are studied at a daily basis. Eight types of daily regime were identified: isothermal unfrozen, non-isothermal unfrozen, surficial freeze-thaw, surficial freeze-thaw and subsurficial frost, surficial and subsurficial freeze-thaw, subsurficial frost, surficial and subsurficial frost and surficial frost but no daily rhythm. The occurrence of these regimes is analysed and their geomorphological significance is presented. Based on the altitudinal differences of the two monitoring sites, on the occurrence of the different regimes and on field observations, a conceptual model for the altitudinal and seasonal zoning of the daily thermal regimes of the ground is presented. This model was prepared for the Serra da Estrela, but it can be used in other Mediterranean or tropical areas if altitude and seasonal precipitation differences are taken in explanation. D 2002 Elsevier Science B.V. All rights reserved.

Land Cover Classification Using High‐Resolution Aerial Photography in Adventdalen, Svalbard

Abstract A methodology was tested for high‐resolution mapping of vegetation and detailed geoecological patterns in the Arctic Tundra, based on aerial imagery from an unmanned aerial vehicle (visible wavelength – RGB, 6 cm pixel resolution) and from an aircraft (visible and near infrared, 20 cm pixel resolution). The scenes were fused at 10 and 20 cm to evaluate their applicability for vegetation mapping in an alluvial fan in dventdalen, Svalbard. Ground‐truthing was used to create training and accuracy evaluation sets. Supervised classification tests were conducted with different band sets, including the original and derived ones, such as and principal component analysis bands. The fusion of all original bands at 10 cm resolution provided the best accuracies. The best classifier was systematically the maximum neighbourhood algorithm, with overall accuracies up to 84%. Mapped vegetation patterns reflect geoecological conditioning factors. The main limitation in the classification was differentiating between the classes graminea, moss and Salix, and moss, graminea and Salix, which showed spectral signature mixing. Silty‐clay surfaces are probably overestimated in the south part of the study area due to microscale shadowing effects. The results distinguished vegetation zones according to a general gradient of ecological limiting factors and show that + high‐resolution imagery are excellent tools for identifying the main vegetation groups within the lowland fan study site of dventdalen, but do not allow for detailed discrimination between species.

Evaluation of Envisat ASAR IMP imagery for snow mapping at varying spatial resolution (Deception Island, South Shetlands – Antarctica)

Abstract Advanced synthetic aperture radar image mode precision (ASAR IMP) scenes of Deception Island from December 2008 to September 2010 have been analysed to assess its potential for snow cover classification. Backscattering was checked against ground truth. Despite the good spatial resolution of the ASAR, its applicability for detecting snow cover, and especially wet snow, was only possible at much lower resolutions, since noise was found to be very high. Scenes with bare ground or with dry snow cover showed highest backscattering, with averages from −10 to −12 dB. Wet snow showed a shift towards lower values, peaking at −15 dB. A threshold of −13 to −14 dB was identified between dry/bare ground and wet snow scenes at Crater Lake. The backscatter difference to a reference snow-free scene usually provided better classification results, and a threshold ranging from −2 to −3 dB was found. Results show that, despite the relative ease of use of C-band ASAR, special care is necessary since the results show significant noise, and it should only be applied to large areas. Large seasonal patterns of snow melt were identified on Deception Island.

A synthetic map of the climatopes of the Serra da Estrela (Portugal)

Abstract Please click here to download the map associated with this article. The Serra da Estrela is part of the Iberian Central Cordillera and is the highest mountain in mainland Portugal (40° 20′N, 7° 35′W, 1993 m a.s.l.). The topoclimates are controlled by the NNE-SSW direction of the mountain range, asymmetric exposure to the prevailing westerly air masses, high relief and irregular morphology. Supported by a network of air temperature data loggers, the influence of the topography on the temperature patterns has been examined. A thorough analysis of climate data from meteorological stations, bibliographical references and the study of the terrain characteristics, including field work, remote sensing and GIS-based digital elevation model analyses, allowed the information to be synthesised in a map of the climatopes at the scale 1:75,000. The climatopes are based on the variation of climatic factors as a result of topography and land cover. Each climatope unit responds similarly to the forcing induced by the atmosphere boundary layer and represents a local climate. The map includes 18 types of climatope and contains a simple, easy-to-understand legend for the non-specialist in climatology, with the possibility for application in land planning.

Evaluation of the use of very high resolution aerial imagery for accurate ice-wedge polygon mapping (Adventdalen, Svalbard)

The main objective of this paper is to verify the accuracy of delineating and characterizing ice-wedge polygonal networks with features exclusively extracted from remotely sensed images of very high resolution. This kind of mapping plays a key role for quantifying ice-wedge degradation in warming permafrost. The evaluation of mapping a network is performed in this study with two sets of aerial images that are compared to ground reference data determined by fieldwork on the same network, located in Adventdalen, Svalbard (78°N). One aerial dataset is obtained from a photogrammetric survey with RGB+NIR imagery of 20cm/pixel, the other from an UAV (Unmanned Aerial Vehicle) survey that acquired RGB images of 6cm/pixel of spatial resolution. Besides evaluating the degree of matching between the delineations, the morphometric and topological features computed for the differently mapped versions of the network are also confronted, to have a more solid basis of comparison. The results obtained are similar enough to admit that remotely sensed images of very high resolution are an adequate support to provide extensive characterizations and classifications of this kind of patterned ground.

Accurate determination of surface reference data in digital photographs in ice-free surfaces of Maritime Antarctica.

The ice-free areas of Maritime Antarctica show complex mosaics of surface covers, with wide patches of diverse bare soils and rock, together with various vegetation communities dominated by lichens and mosses. The microscale variability is difficult to characterize and quantify, but is essential for ground-truthing and for defining classifiers for large areas using, for example high resolution satellite imagery, or even ultra-high resolution unmanned aerial vehicle (UAV) imagery. The main objective of this paper is to verify the ability and robustness of an automated approach to discriminate the variety of surface types in digital photographs acquired at ground level in ice-free regions of Maritime Antarctica. The proposed method is based on an object-based classification procedure built in two main steps: first, on the automated delineation of homogeneous regions (the objects) of the images through the watershed transform with adequate filtering to avoid an over-segmentation, and second, on labelling each identified object with a supervised decision classifier trained with samples of representative objects of ice-free surface types (bare rock, bare soil, moss and lichen formations). The method is evaluated with images acquired in summer campaigns in Fildes and Barton peninsulas (King George Island, South Shetlands). The best performances for the datasets of the two peninsulas are achieved with a SVM classifier with overall accuracies of about 92% and kappa values around 0.89. The excellent performances allow validating the adequacy of the approach for obtaining accurate surface reference data at the complete pixel scale (sub-metric) of current very high resolution (VHR) satellite images, instead of a common single point sampling.