36 resultados para Album cover
Resumo:
In this paper we compare the performance of two image classification paradigms (object- and pixel-based) for creating a land cover map of Asmara, the capital of Eritrea and its surrounding areas using a Landsat ETM+ imagery acquired in January 2000. The image classification methods used were maximum likelihood for the pixel-based approach and Bhattacharyya distance for the object-oriented approach available in, respectively, ArcGIS and SPRING software packages. Advantages and limitations of both approaches are presented and discussed. Classifications outputs were assessed using overall accuracy and Kappa indices. Pixel- and object-based classification methods result in an overall accuracy of 78% and 85%, respectively. The Kappa coefficient for pixel- and object-based approaches was 0.74 and 0.82, respectively. Although pixel-based approach is the most commonly used method, assessment and visual interpretation of the results clearly reveal that the object-oriented approach has advantages for this specific case-study.
Resumo:
Due to highly erodible volcanic soils and a harsh climate, livestock grazing in Iceland has led to serious soil erosion on about 40% of the country's surface. Over the last 100 years, various revegetation and restoration measures were taken on large areas distributed all over Iceland in an attempt to counteract this problem. The present research aimed to develop models for estimating percent vegetation cover (VC) and aboveground biomass (AGB) based on satellite data, as this would make it possible to assess and monitor the effectiveness of restoration measures over large areas at a fairly low cost. Models were developed based on 203 vegetation cover samples and 114 aboveground biomass samples distributed over five SPOT satellite datasets. All satellite datasets were atmospherically corrected, and digital numbers were converted into ground reflectance. Then a selection of vegetation indices (VIs) was calculated, followed by simple and multiple linear regression analysis of the relations between the field data and the calculated VIs. Best results were achieved using multiple linear regression models for both %VC and AGB. The model calibration and validation results showed that R2 and RMSE values for most VIs do not vary very much. For percent VC, R2 values range between 0.789 and 0.822, leading to RMSEs ranging between 15.89% and 16.72%. For AGB, R2 values for low-biomass areas (AGB < 800 g/m2) range between 0.607 and 0.650, leading to RMSEs ranging between 126.08 g/m2 and 136.38 g/m2. The AGB model developed for all areas, including those with high biomass coverage (AGB > 800 g/m2), achieved R2 values between 0.487 and 0.510, resulting in RMSEs ranging from 234 g/m2 to 259.20 g/m2. The models predicting percent VC generally overestimate observed low percent VC and slightly underestimate observed high percent VC. The estimation models for AGB behave in a similar way, but over- and underestimation are much more pronounced. These results show that it is possible to estimate percent VC with high accuracy based on various VIs derived from SPOT satellite data. AGB of restoration areas with low-biomass values of up to 800 g/m2 can likewise be estimated with high accuracy based on various VIs derived from SPOT satellite data, whereas in the case of high biomass coverage, estimation accuracy decreases with increasing biomass values. Accordingly, percent VC can be estimated with high accuracy anywhere in Iceland, whereas AGB is much more difficult to estimate, particularly for areas with high-AGB variability.
Resumo:
Seasonal snow cover is of great environmental and socio-economic importance for the European Alps. Therefore a high priority has been assigned to quantifying its temporal and spatial variability. Complementary to land-based monitoring networks, optical satellite observations can be used to derive spatially comprehensive information on snow cover extent. For understanding long-term changes in alpine snow cover extent, the data acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensors mounted onboard the National Oceanic and Atmospheric Association (NOAA) and Meteorological Operational satellite (MetOp) platforms offer a unique source of information. In this paper, we present the first space-borne 1 km snow extent climatology for the Alpine region derived from AVHRR data over the period 1985–2011. The objective of this study is twofold: first, to generate a new set of cloud-free satellite snow products using a specific cloud gap-filling technique and second, to examine the spatiotemporal distribution of snow cover in the European Alps over the last 27 yr from the satellite perspective. For this purpose, snow parameters such as snow onset day, snow cover duration (SCD), melt-out date and the snow cover area percentage (SCA) were employed to analyze spatiotemporal variability of snow cover over the course of three decades. On the regional scale, significant trends were found toward a shorter SCD at lower elevations in the south-east and south-west. However, our results do not show any significant trends in the monthly mean SCA over the last 27 yr. This is in agreement with other research findings and may indicate a deceleration of the decreasing snow trend in the Alpine region. Furthermore, such data may provide spatially and temporally homogeneous snow information for comprehensive use in related research fields (i.e., hydrologic and economic applications) or can serve as a reference for climate models.
Resumo:
The delineation of shifting cultivation landscapes using remote sensing in mountainous regions is challenging. On the one hand, there are difficulties related to the distinction of forest and fallow forest classes as occurring in a shifting cultivation landscape in mountainous regions. On the other hand, the dynamic nature of the shifting cultivation system poses problems to the delineation of landscapes where shifting cultivation occurs. We present a two-step approach based on an object-oriented classification of Advanced Land Observing Satellite, Advanced Visible and Near-Infrared Spectrometer (ALOS AVNIR) and Panchromatic Remote-sensing Instrument for Stereo Mapping (ALOS PRISM) data and landscape metrics. When including texture measures in the object-oriented classification, the accuracy of forest and fallow forest classes could be increased substantially. Based on such a classification, landscape metrics in the form of land cover class ratios enabled the identification of crop-fallow rotation characteristics of the shifting cultivation land use practice. By classifying and combining these landscape metrics, shifting cultivation landscapes could be delineated using a single land cover dataset.
Resumo:
Viscum album L. lipophilic extract (VALE) contains pharmacologically active pentacyclic triterpenes that are known to exhibit immunomodulatory, antitumor, and wound healing activity. Preliminary clinical observations indicate that VALE was able to influence cutaneous wound healing in vivo. The objective of this study was to investigate wound closure related properties of VALE in vitro. As measured in a wound healing assay, VALE and its predominant triterpene oleanolic acid (OA) significantly and dose dependently promoted the migration of NIH/3T3 fibroblasts in vitro, thereby leading to an enhanced wound closure. Compared to the negative control, maximal stimulation by 26.1% and 26.2%, respectively, was attained with 10 μg/mL VALE and 1 μg/mL OA. Stimulation of proliferation in NIH/3T3 fibroblasts by VALE and OA could be excluded. At higher concentrations both substances affected proliferation and viability of NIH/3T3 fibroblasts and HaCat keratinocytes. In the toxic range of concentrations of VALE and OA, migration of NIH/3T3 fibroblasts was suppressed. The extent of the stimulatory effect on cell migration of VALE quite closely corresponded to the effect expected by the concentrations of OA contained in the crude extract VALE. These data support the casual observation that Viscum album L. lipophilic extract might modulate wound healing related processes in vivo.
Assimilation of point SWE data into a distributed snow cover model comparing two contrasting methods
Resumo:
Aim The global spread of woody plants into grasslands is predicted to increase over the coming century. While there is general agreement regarding the anthropogenic causes of this phenomenon, its ecological consequences are less certain. We analysed how woody vegetation of differing cover affects plant diversity (richness and evenness) and the surrogates of multiple ecosystem processes (multifunctionality) in global drylands, and how these change with aridity. Location Two hundred and twenty-four dryland sites from all continents except Antarctica, widely differing in their environmental conditions (from arid to dry-subhumid sites) and relative woody cover (from 0 to 100). Methods Using a standardized field survey, we measured the cover, richness and evenness of perennial vegetation. At each site, we measured 14 soil variables related to fertility and the build-up of nutrient pools. These variables are critical for maintaining ecosystem functioning in drylands. Results Species richness and ecosystem multifunctionality were strongly related to woody vegetation, with both variables peaking at a relative woody cover (RWC) of 41–60. This relationship shifted with aridity. We observed linear positive effects of RWC in dry-subhumid sites. These positive trends shifted to hump-shaped RWC–diversity and multifunctionality relationships under semi-arid environments. Finally, hump-shaped (richness, evenness) or linear negative (multifunctionality) effects of RWC were found under the most arid conditions. Main conclusions Plant diversity and multifunctionality peaked at intermediate levels of woody cover, although this relationship became increasingly positive in wetter environments. This comprehensive study accounts for multiple ecosystem attributes across a range of levels of woody cover and environmental conditions. Our results help us to reconcile contrasting views of woody encroachment found in the current literature and can be used to improve predictions of the likely effects of encroachment on biodiversity and ecosystem services.
Resumo:
We present quantitative reconstructions of regional vegetation cover in north-western Europe, western Europe north of the Alps, and eastern Europe for five time windows in the Holocene around 6k, 3k, 0.5k, 0.2k, and 0.05k calendar years before present (bp)] at a 1 degrees x1 degrees spatial scale with the objective of producing vegetation descriptions suitable for climate modelling. The REVEALS model was applied on 636 pollen records from lakes and bogs to reconstruct the past cover of 25 plant taxa grouped into 10 plant-functional types and three land-cover types evergreen trees, summer-green (deciduous) trees, and open land]. The model corrects for some of the biases in pollen percentages by using pollen productivity estimates and fall speeds of pollen, and by applying simple but robust models of pollen dispersal and deposition. The emerging patterns of tree migration and deforestation between 6k bp and modern time in the REVEALS estimates agree with our general understanding of the vegetation history of Europe based on pollen percentages. However, the degree of anthropogenic deforestation (i.e. cover of cultivated and grazing land) at 3k, 0.5k, and 0.2k bp is significantly higher than deduced from pollen percentages. This is also the case at 6k in some parts of Europe, in particular Britain and Ireland. Furthermore, the relationship between summer-green and evergreen trees, and between individual tree taxa, differs significantly when expressed as pollen percentages or as REVEALS estimates of tree cover. For instance, when Pinus is dominant over Picea as pollen percentages, Picea is dominant over Pinus as REVEALS estimates. These differences play a major role in the reconstruction of European landscapes and for the study of land cover-climate interactions, biodiversity and human resources.