Evaluation of satellite based indices for gross primary production estimates in a sparse savanna in the Sudan

One of the more frequently applied methods for integrating controls on primary production through satellite data is the Light Use Efficiency (LUE) approach. Satellite indices such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and the Shortwave Infrared Water Stress Index (SIWSI) have previously shown promise as predictors of primary production in several different environments. In this study, we evaluate NDVI, EVI and SIWSI derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor against in-situ measurements from central Sudan in order to asses their applicability in LUE-based primary production modeling within a water limited environment. Results show a strong correlation between vegetation indices and gross primary production (GPP), demonstrating the significance of vegetation indices for deriving information on primary production with relatively high accuracy at similar areas. Evaluation of SIWSI however, reveal that the fraction of vegetation apparently is to low for the index to provide accurate information on canopy water content, indicating that the use of SIWSI as a predictor of water stress in satellite data-driven primary production modeling in similar semi-arid ecosystems is limited

Modelling the spatial distribution of DEM Error

Assessment of a DEM’s quality is usually undertaken by deriving a measure of DEM accuracy – how close the DEM’s elevation values are to the true elevation. Measures such as Root Mean Squared Error and standard deviation of the error are frequently used. These measures summarise elevation errors in a DEM as a single value. A more detailed description of DEM accuracy would allow better understanding of DEM quality and the consequent uncertainty associated with using DEMs in analytical applications. The research presented addresses the limitations of using a single root mean squared error (RMSE) value to represent the uncertainty associated with a DEM by developing a new technique for creating a spatially distributed model of DEM quality – an accuracy surface. The technique is based on the hypothesis that the distribution and scale of elevation error within a DEM are at least partly related to morphometric characteristics of the terrain. The technique involves generating a set of terrain parameters to characterise terrain morphometry and developing regression models to define the relationship between DEM error and morphometric character. The regression models form the basis for creating standard deviation surfaces to represent DEM accuracy. The hypothesis is shown to be true and reliable accuracy surfaces are successfully created. These accuracy surfaces provide more detailed information about DEM accuracy than a single global estimate of RMSE

Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data

Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide (CO2) exchange in peatland ecosystems at large scales under these changing conditions. In this study, we developed empirical models of the CO2 balance (net ecosystem exchange, NEE), gross primary production (GPP), and ecosystem respiration (ER) that could be used for upscaling CO2 fluxes with remotely sensed data. Two to three years of eddy covariance (EC) data from five peatlands in Sweden and Finland were compared to modelled NEE, GPP and ER based on vegetation indices from 10 m resolution Sentinel-2 MSI and land surface temperature from 1 km resolution MODIS data. To ensure a precise match between the EC data and the Sentinel-2 observations, a footprint model was applied to derive footprint-weighted daily means of the vegetation indices. Average model parameters for all sites were acquired with a leave-one-out-cross-validation procedure. Both the GPP and the ER models gave high agreement with the EC-derived fluxes (R-2 = 0.70 and 0.56, NRMSE = 14% and 15%, respectively). The performance of the NEE model was weaker (average R-2 = 0.36 and NRMSE = 13%). Our findings demonstrate that using optical and thermal satellite sensor data is a feasible method for upscaling the GPP and ER of northern boreal peatlands, although further studies are needed to investigate the sources of the unexplained spatial and temporal variation of the CO2 fluxes.Peer reviewe

Self Organizing Networks for 3GPP LTE

Network elements and their parameters in mobile wireless networks, are largely manually configured. This has been somewhat sufficient; but with the growing data traffic compensated by new and emerging technologies with corresponding larger networks, there is an obvious need to redefine the network operations to achieve optimum performance. A manual configuration approach requires specialized expertise for device deployments,configurations, re-setting network parameters and general management of the network. This process is cost-intensive, time-consuming and prone to errors. Adoption of this approach in the evolved wireless technologies results in poor network performance. Therefore, the introduction of advanced mobile wireless networks has highlighted the need and essence for automation within the network. Self Organizing Networks (SON) developed by 3GPP, using automation, ensures operational efficiency and next generation simplified network management for a mobile wireless network. The introduction of SON in LTE therefore brings about optimum network performance and higher end user Quality of Experience. This paper highlights the SON techniques relevant within an LTE network, a brief description of SON architecture alternatives and then some information on the evolution of SON activities as LTE evolves towards LTE-A

People's Perception of Domestic Service Robots: Same Household, Same Opinion?

The study presented in this paper examined people’s perception of domestic service robots by means of an ethnographic study. We investigated initial reactions of nine households who lived with a Roomba vacuum cleaner robot over a two week period. To explore people’s attitude and how it changed over time, we used a recurring questionnaire that was filled at three different times, integrated in 18 semi-structured qualitative interviews. Our findings suggest that being part of a specific household has an impact how each individual household member perceives the robot. We interpret that, even though individual experiences with the robot might differ from one other, a household shares a specific opinion about the robot. Moreover our findings also indicate that how people perceived Roomba did not change drastically over the two week period

Avoiding the uncanny valley : robot appearance, personality and consistency of behavior in an attention-seeking home scenario for a robot companion

“The original publication is available at www.springerlink.com”. Copyright Springer. DOI: 10.1007/s10514-007-9058-3This article presents the results of video-based Human Robot Interaction (HRI) trials which investigated people’s perceptions of different robot appearances and associated attention-seeking features and behaviors displayed by robots with different appearance and behaviors. The HRI trials studied the participants’ preferences for various features of robot appearance and behavior, as well as their personality attributions towards the robots compared to their own personalities. Overall, participants tended to prefer robots with more human-like appearance and attributes. However, systematic individual differences in the dynamic appearance ratings are not consistent with a universal effect. Introverts and participants with lower emotional stability tended to prefer the mechanical looking appearance to a greater degree than other participants. It is also shown that it is possible to rate individual elements of a particular robot’s behavior and then assess the contribution, or otherwise, of that element to the overall perception of the robot by people. Relating participants’ dynamic appearance ratings of individual robots to independent static appearance ratings provided evidence that could be taken to support a portion of the left hand side of Mori’s theoretically proposed ‘uncanny valley’ diagram. Suggestions for future work are outlined.Peer reviewe

Environmental zonation across the Australian arid region based on long-term vegetation dynamics

Zonation of landscapes is generally based on broad scale biophysical data, field surveys, imagery and expert knowledge. Such zonation represents a static view of the environment and does not reflect dynamics and function. Arid environments are however often highly dynamic, and spatial and temporal patterns may be expressed over long periods of time. These dynamics need to be understood for management. Our aim is to understand the dynamics and functional response of vegetation in the Australian arid zone, and use this to inform and potentially improve the currently employed stratification. Principal component analysis of 25 years of satellite imagery identified underlying factors influencing patterns of arid vegetation growth, and regions of similar long-term response. Dominant factors of variation were identified as the spatial distribution of total vegetation growth, seasonality of growth, magnitude of seasonal variability in growth, and regularity of variation in growth. Additional variation resulted from episodic vegetation growth of limited spatial extent and duration. Classes expressing these functional components were compared with the existing biogeographical regions, revealing agreement in some instances, and in other cases adding information previously not available. The study demonstrates a new approach to Australian landscape zonation that has potential for much wider application.E.F. Lawley, M.M. Lewis, B. Ostendor