First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems

The importance of semi-arid ecosystems in the global carbon cycle as sinks for CO2 emissions has recently been highlighted. Africa is a carbon sink and nearly half its area comprises arid and semi-arid ecosystems. However, there are uncertainties regarding CO2 fluxes for semi-arid ecosystems in Africa, particularly savannas and dry tropical woodlands. In order to improve on existing remote-sensing based methods for estimating carbon uptake across semi-arid Africa we applied and tested the recently developed plant phenology index (PPI). We developed a PPI-based model estimating gross primary productivity (GPP) that accounts for canopy water stress, and compared it against three other Earth observation-based GPP models: the temperature and greenness model, the greenness and radiation model and a light use efficiency model. The models were evaluated against in situ data from four semi-arid sites in Africa with varying tree canopy cover (3 to 65 percent). Evaluation results from the four GPP models showed reasonable agreement with in situ GPP measured from eddy covariance flux towers (EC GPP) based on coefficient of variation, root-mean-square error, and Bayesian information criterion. The PPI-based GPP model was able to capture the magnitude of EC GPP better than the other tested models. The results of this study show that a PPI-based GPP model is a promising tool for the estimation of GPP in the semi-arid ecosystems of Africa.Comment: Accepted manuscript; 12 pages, 4 tables, 9 figure

Towards operational remote sensing of forest carbon balance across Northern Europe

Monthly averages of ecosystem respiration (ER), gross primary production (GPP) and net ecosystem exchange (NEE) over Scandinavian forest sites were estimated using regression models driven by air temperature (AT), absorbed photosynthetically active radiation (APAR) and vegetation indices. The models were constructed and evaluated using satellite data from Terra/MODIS and measured data collected at seven flux tower sites in northern Europe. Data used for model construction was excluded from the evaluation. Relationships between ground measured variables and the independent variables were investigated. <br><br> It was found that the enhanced vegetation index (EVI) at 250 m resolution was highly noisy for the coniferous sites, and hence, 1 km EVI was used for the analysis. Linear relationships between EVI and the biophysical variables were found: correlation coefficients between EVI and GPP, NEE, and AT ranged from 0.90 to 0.79 for the deciduous data, and from 0.85 to 0.67 for the coniferous data. Due to saturation, there were no linear relationships between normalized difference vegetation index (NDVI) and the ground measured parameters found at any site. APAR correlated better with the parameters in question than the vegetation indices. Modeled GPP and ER were in good agreement with measured values, with more than 90% of the variation in measured GPP and ER being explained by the coniferous models. The site-specific respiration rate at 10°C (<i>R</i><sub>10</sub>) was needed for describing the ER variation between sites. Even though monthly NEE was modeled with less accuracy than GPP, 61% and 75% (dec. and con., respectively) of the variation in the measured time series was explained by the model. These results are important for moving towards operational remote sensing of forest carbon balance across Northern Europe

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

Using keystroke logging to understand writers’ processes on a reading-into-writing test

Background Integrated reading-into-writing tasks are increasingly used in large-scale language proficiency tests. Such tasks are said to possess higher authenticity as they reflect real-life writing conditions better than independent, writing-only tasks. However, to effectively define the reading-into-writing construct, more empirical evidence regarding how writers compose from sources both in real-life and under test conditions is urgently needed. Most previous process studies used think aloud or questionnaire to collect evidence. These methods rely on participants’ perceptions of their processes, as well as their ability to report them. Findings This paper reports on a small-scale experimental study to explore writers’ processes on a reading-into-writing test by employing keystroke logging. Two L2 postgraduates completed an argumentative essay on computer. Their text production processes were captured by a keystroke logging programme. Students were also interviewed to provide additional information. Keystroke logging like most computing tools provides a range of measures. The study examined the students’ reading-into-writing processes by analysing a selection of the keystroke logging measures in conjunction with students’ final texts and interview protocols. Conclusions The results suggest that the nature of the writers’ reading-into-writing processes might have a major influence on the writer’s final performance. Recommendations for future process studies are provided

Increased Vegetation in Mountainous Headwaters Amplifies Water Stress During Dry Periods

The dynamics of blue and green water partitioning under vegetation and climate change, as well as their different interactions during wet and dry periods, are poorly understood in the literature. We analyzed the impact of vegetation changes on blue water generation in a central Spanish Pyrenees basin undergoing intense afforestation. We found that vegetation change is a key driver of large decreases in blue water availability. The effect of vegetation increase is amplified during dry years, and mainly during the dry season, with streamflow reductions of more than 50%. This pattern can be attributed primarily to increased plant water consumption. Our findings highlight the importance of vegetation changes in reinforcing the decrease in water resource availability. With aridity expected to rise in southern Europe over the next few decades, interactions between climate and land management practices appear to be amplifying future hydrological drought risk in the region.This work was supported by projects CGL2017-82216-R, PCI2019-103631, and PID2019-108589RA-I00 financed by the Spanish Commission of Science and Technology and FEDER; CROSSDRO project financed by AXIS (Assess-ment of Cross(X)-sectoral climate Impacts and pathways for Sustainable transformation), JPI-Climate co-funded call of the European Commission and INDECIS which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462). Dhais Peña-Angulo received a “Juan de la Cierva” postdoctoral contract (FJCI-2017-33652 Spanish Ministry of Economy and Competitiveness, MEC). Miquel Tomas-Burguera received a “Juan de la Cierva” postdoctoral contract (FJCI-2019-039261-I Spanish Ministry of Science and Innovation). C. Azorin-Molina and S. Grainger. acknowledge funding from the Irish Environmental Protection Agency grant 2019-CCRP-MS.60. C. Juez acknowl-edges funding from the H2020-MSCA-IF-2018 programme (Marie Sklodows-ka-Curie Actions) of the European Union under REA grant agreement, number 834329-SEDILAND

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

Introducing a New Algorithm for Classification of Etiology in Studies on Pediatric Pneumonia: Protocol for the Trial of Respiratory Infections in Children for Enhanced Diagnostics Study

Background: There is a need to better distinguish viral infections from antibiotic-requiring bacterial infections in children presenting with clinical community-acquired pneumonia (CAP) to assist health care workers in decision making and to improve the rational use of antibiotics.Objective: The overall aim of the Trial of Respiratory infections in children for ENhanced Diagnostics (TREND) study is to improve the differential diagnosis of bacterial and viral etiologies in children aged below 5 years with clinical CAP, by evaluating myxovims resistance protein A (MxA) as a biomarker for viral CAP and by evaluating an existing (multianalyte point-of-care antigen detection test system [mariPOC respi] ArcDia International Oy Ltd.) and a potential future point-of-care test for respiratory pathogens.Methods: Children aged 1 to 59 months with clinical CAP as well as healthy, hospital-based, asymptomatic controls will be included at a pediatric emergency hospital in Stockholm, Sweden. Blood (analyzed for MxA and C-reactive protein) and nasopharyngeal samples (analyzed with real-time polymerase chain reaction as the gold standard and antigen-based mariPOC respi test as well as saved for future analyses of a novel recombinase polymerase amplification-based point-of-care test for respiratory pathogens) will be collected. A newly developed algorithm for the classification of CAP etiology will be used as the reference standard.Results: A pilot study was performed from June to August 2017. The enrollment of study subjects started in November 2017. Results are expected by the end of 2019.Conclusions: The findings from the TREND study can be an important step to improve the management of children with clinical CAP