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Sensed Data (sensed + data)
Selected AbstractsMapping the geochemistry of the northern Rub' Al Khali using multispectral remote sensing techniquesEARTH SURFACE PROCESSES AND LANDFORMS, Issue 7 2001Kevin White Abstract Spatial variations in sand sea geochemistry relate to mixing of different sediment sources and to variations in weathering. Due to problems of accessibility, adequate spatial coverage cannot be achieved using field surveys alone. However, maps of geochemical composition produced from remotely sensed data can be calibrated against limited field data and the results extrapolated over large, inaccessible areas. This technique is applied to part of the Rub' Al Khali in the northern United Arab Emirates. Trend surface analysis of the results suggests that the sand sea at this location can be modelled as an east,west mixing zone of two spectral components: terrestrial reddened quartz sands and marine carbonate sands. Optical dating of these sediments suggests that dune emplacement occurred rapidly around 10 ka BP, when sea level was rising rapidly. The spatial distribution of mineralogical components suggests that this phase of dune emplacement resulted from coastal dune sands being driven inland during marine transgression, thereby becoming mixed with rubified terrestrial sands. Copyright © 2001 John Wiley & Sons, Ltd. [source] Modelling patterned ground distribution in Finnish Lapland: an integration of topographical, ground and remote sensing informationGEOGRAFISKA ANNALER SERIES A: PHYSICAL GEOGRAPHY, Issue 1 2006Jan Hjort Abstract New data technologies and modelling methods have gained more attention in the field of periglacial geomorphology during the last decade. In this paper we present a new modelling approach that integrates topographical, ground and remote sensing information in predictive geomorphological mapping using generalized additive modelling (GAM). First, we explored the roles of different environmental variable groups in determining the occurrence of non-sorted and sorted patterned ground in a fell region of 100 km2 at the resolution of 1 ha in northern Finland. Second, we compared the predictive accuracy of ground-topography- and remote-sensing-based models. The results indicate that non-sorted patterned ground is more common at lower altitudes where the ground moisture and vegetation abundance is relatively high, whereas sorted patterned ground is dominant at higher altitudes with relatively high slope angle and sparse vegetation cover. All modelling results were from good to excellent in model evaluation data using the area under the curve (AUC) values, derived from receiver operating characteristic (ROC) plots. Generally, models built with remotely sensed data were better than ground-topography-based models and combination of all environmental variables improved the predictive ability of the models. This paper confirms the potential utility of remote sensing information for modelling patterned ground distribution in subarctic landscapes. [source] A Geostatistical Analysis of Soil, Vegetation, and Image Data Characterizing Land Surface VariationGEOGRAPHICAL ANALYSIS, Issue 2 2007Sarah E. Rodgers The elucidation of spatial variation in the landscape can indicate potential wildlife habitats or breeding sites for vectors, such as ticks or mosquitoes, which cause a range of diseases. Information from remotely sensed data could aid the delineation of vegetation distribution on the ground in areas where local knowledge is limited. The data from digital images are often difficult to interpret because of pixel-to-pixel variation, that is, noise, and complex variation at more than one spatial scale. Landsat Thematic Mapper Plus (ETM+) and Satellite Pour l'Observation de La Terre (SPOT) image data were analyzed for an area close to Douna in Mali, West Africa. The variograms of the normalized difference vegetation index (NDVI) from both types of image data were nested. The parameters of the nested variogram function from the Landsat ETM+ data were used to design the sampling for a ground survey of soil and vegetation data. Variograms of the soil and vegetation data showed that their variation was anisotropic and their scales of variation were similar to those of NDVI from the SPOT data. The short- and long-range components of variation in the SPOT data were filtered out separately by factorial kriging. The map of the short-range component appears to represent the patterns of vegetation and associated shallow slopes and drainage channels of the tiger bush system. The map of the long-range component also appeared to relate to broader patterns in the tiger bush and to gentle undulations in the topography. The results suggest that the types of image data analyzed in this study could be used to identify areas with more moisture in semiarid regions that could support wildlife and also be potential vector breeding sites. [source] Regionalization of methane emissions in the Amazon Basin with microwave remote sensingGLOBAL CHANGE BIOLOGY, Issue 5 2004John M. Melack Abstract Wetlands of the Amazon River basin are globally significant sources of atmospheric methane. Satellite remote sensing (passive and active microwave) of the temporally varying extent of inundation and vegetation was combined with field measurements to calculate regional rates of methane emission for Amazonian wetlands. Monthly inundation areas for the fringing floodplains of the mainstem Solimões/Amazon River were derived from analysis of the 37 GHz polarization difference observed by the Scanning Multichannel Microwave Radiometer from 1979 to 1987. L-band synthetic aperture radar data (Japanese Earth Resources Satellite-1) were used to determine inundation and wetland vegetation for the Amazon basin (<500 m elevation) at high (May,June 1996) and low water (October 1995). An extensive set of measurements of methane emission is available from the literature for the fringing floodplains of the central Amazon, segregated into open water, flooded forest and floating macrophyte habitats. Uncertainties in the regional emission rates were determined by Monte Carlo error analyses that combined error estimates for the measurements of emission and for calculations of inundation and habitat areas. The mainstem Solimões/Amazon floodplain (54,70°W) emitted methane at a mean annual rate of 1.3 Tg C yr,1, with a standard deviation (SD) of the mean of 0.3 Tg C yr,1; 67% of this range in uncertainty is owed to the range in rates of methane emission and 33% is owed to uncertainty in the areal estimates of inundation and vegetative cover. Methane emission from a 1.77 million square kilometers area in the central basin had a mean of 6.8 Tg C yr,1 with a SD of 1.3 Tg C yr,1. If extrapolated to the whole basin below the 500 m contour, approximately 22 Tg C yr,1 is emitted; this mean flux has a greenhouse warming potential of about 0.5 Pg C as CO2. Improvement of these regional estimates will require many more field measurements of methane emission, further examination of remotely sensed data for types of wetlands not represented in the central basin, and process-based models of methane production and emission. [source] Use of multi-platform, multi-temporal remote-sensing data for calibration of a distributed hydrological model: an application in the Arno basin, ItalyHYDROLOGICAL PROCESSES, Issue 13 2006Lorenzo Campo Abstract Images from satellite platforms are a valid aid in order to obtain distributed information about hydrological surface states and parameters needed in calibration and validation of the water balance and flood forecasting. Remotely sensed data are easily available on large areas and with a frequency compatible with land cover changes. In this paper, remotely sensed images from different types of sensor have been utilized as a support to the calibration of the distributed hydrological model MOBIDIC, currently used in the experimental system of flood forecasting of the Arno River Basin Authority. Six radar images from ERS-2 synthetic aperture radar (SAR) sensors (three for summer 2002 and three for spring,summer 2003) have been utilized and a relationship between soil saturation indexes and backscatter coefficient from SAR images has been investigated. Analysis has been performed only on pixels with meagre or no vegetation cover, in order to legitimize the assumption that water content of the soil is the main variable that influences the backscatter coefficient. Such pixels have been obtained by considering vegetation indexes (NDVI) and land cover maps produced by optical sensors (Landsat-ETM). In order to calibrate the soil moisture model based on information provided by SAR images, an optimization algorithm has been utilized to minimize the regression error between saturation indexes from model and SAR data and error between measured and modelled discharge flows. Utilizing this procedure, model parameters that rule soil moisture fluxes have been calibrated, obtaining not only a good match with remotely sensed data, but also an enhancement of model performance in flow prediction with respect to a previous calibration with river discharge data only. Copyright © 2006 John Wiley & Sons, Ltd. [source] Spatial variability of above-ground net primary production in Uruguayan grasslands: a remote sensing approachAPPLIED VEGETATION SCIENCE, Issue 1 2010S. Baeza Abstract Question: How does above-ground net primary production (ANPP) differ (estimated from remotely sensed data) among vegetation units in sub-humid temperate grasslands? Location: Centre-north Uruguay. Methods: A vegetation map of the study area was generated from LANDSAT imagery and the landscape configuration described. The functional heterogeneity of mapping units was analysed in terms of the fraction of photosynthetically active radiation absorbed by green vegetation (fPAR), calculated from the normalized difference vegetation index (NDVI) images provided by the moderate resolution imaging spectroradiometer (MODIS) sensor. Finally, the ANPP of each grassland class was estimated using NDVI and climatic data. Results: Supervised classification presented a good overall accuracy and moderate to good average accuracy for grassland classes. Meso-xerophytic grasslands occupied 45% of the area, Meso-hydrophytic grasslands 43% and Lithophytic steppes 6%. The landscape was shaped by a matrix of large, unfragmented patches of Meso-xerophytic and Meso-hydrophytic grasslands. The region presented the lowest anthropic fragmentation degree reported for the Rio de la Plata grasslands. All grassland units showed bimodal annual fPAR seasonality, with spring and autumn peaks. Meso-hydrophytic grasslands showed a radiation interception 10% higher than the other units. On an annual basis, Meso-hydrophytic grasslands produced 3800 kg dry matter (DM) ha,1 yr,1 and Meso-xerophytic grasslands and Lithophytic steppes around 3400 kg·DM·ha,1·yr,1. Meso-xerophytic grasslands had the largest spatial variation during most of the year. The ANPP temporal variation was higher than the fPAR variability. Conclusions: Our results provide valuable information for grazing management (identifying spatial and temporal variations of ANPP) and grassland conservation (identifying the spatial distribution of vegetation units). [source] Assessing species density and abundance of tropical trees from remotely sensed data and geostatisticsAPPLIED VEGETATION SCIENCE, Issue 4 2009J. Luis Hernández-Stefanoni Abstract Question: What relationships exist between remotely sensed measurements and field observations of species density and abundance of tree species? Can these relationships and spatial interpolation approaches be used to improve the accuracy of prediction of species density and abundance of tree species? Location: Quintana Roo, Yucatan peninsula, Mexico. Methods: Spatial prediction of species density and abundance of species for three functional groups was performed using regression kriging, which considers the linear relationship between dependent and explanatory variables, as well as the spatial dependence of the observations. These relationships were explored using regression analysis with species density and abundance of species of three functional groups as dependent variables, and reflectance values of spectral bands, computed NDVI (normalized difference vegetation index), standard deviation of NDVI and texture measurements of Landsat 7 Thematic Mapper (TM) imagery as explanatory variables. Akaike information criterion was employed to select a set of candidate models and calculate model-averaged parameters. Variogram analysis was used to analyze the spatial structure of the residuals of the linear regressions. Results: Species density of trees was related to reflectance values of TM4, NDVI and spatial heterogeneity of land cover types, while the abundance of species in functional groups showed different patterns of association with remotely sensed data. Models that accounted for spatial autocorrelation improved the accuracy of estimates in all cases. Conclusions: Our approach can substantially increase the accuracy of the spatial estimates of species richness and abundance of tropical tree species and can help guide and evaluate tropical forest management and conservation. [source] | |||||||||||||