Semantic Override of Low-level Features in Image Viewing – Both Initially and Overall

Guidance of eye-movements in image viewing is believed to be controlled by stimulus driven factors as well as viewer dependent higher level factors such as task and memory. It is currently debated what proportions these factors contribute to gaze guidance, and also how they vary over time after image onset. Overall, the unanimity regarding these issues is surprisingly low and there are results supporting both types of factors as being dominant in eye-movement control under certain conditions. We investigate how low, and high level factors influence eye guidance by manipulating contrast statistics on images from three different semantic categories and measure how this affects fixation selection. Our results show that the degree to which contrast manipulations affect fixation selection heavily depends on an image’s semantic content, and how this content is distributed over the image. Over the three image categories, we found no systematic differences between contrast and edge density at fixated location compared to control locations, neither during the initial fixation nor over the whole time course of viewing. These results suggest that cognitive factors easily can override low-level factors in fixation selection, even when the viewing task is neutral

Data Assimilation of Growing Stock Volume Using a Sequence of Remote Sensing Data from Different Sensors

Airborne Laser Scanning (ALS) has implied a disruptive transformation of how data are gathered for forest management planning in Nordic countries. We show in this study that the accuracy of ALS predictions of growing stock volume can be maintained and even improved over time if they are forecasted and assimilated with more frequent but less accurate remote sensing data sources like satellite images, digital photogrammetry, and InSAR. We obtained these results by introducing important methodological adaptations to data assimilation compared to previous forestry studies in Sweden. On a test site in the southwest of Sweden (58 degrees 27 ' N, 13 degrees 39 ' E), we evaluated the performance of the extended Kalman filter and a proposed modified filter that accounts for error correlations. We also applied classical calibration to the remote sensing predictions. We evaluated the developed methods using a dataset with nine different acquisitions of remotely sensed data from a mix of sensors over four years, starting and ending with ALS-based predictions of growing stock volume. The results showed that the modified filter and the calibrated predictions performed better than the standard extended Kalman filter and that at the endpoint the prediction based on data assimilation implied an improved accuracy (25.0% RMSE), compared to a new ALS-based prediction (27.5% RMSE)

A vector-based, multidimensional scanpath similarity measure

Jarodzka, H., Holmqvist, K., & Nyström, M. (2010). A vector-based, multidimensional scanpath similarity measure. In C. Morimoto & H. Instance (Eds.), Proceedings of the 2010 Symposium on Eye Tracking Research & Applications ETRA ’10 (pp. 211-218). New York, NY: ACM.A great need exists in many fields of eye-tracking research for a robust and general method for scanpath comparisons. Current mea sures either quantize scanpaths in space (string editing measures like the Levenshtein distance) or in time (measures based on attention maps). This paper proposes a new pairwise scanpath similarity measure. Unlike previous measures that either use AOI sequences or forgo temporal order, the new measure defines scanpaths as a series of geometric vectors and compares temporally aligned scanpaths across several dimensions: shape, fixation position, length, direction, and fixation duration. This approach offers more multifaceted insights to how similar two scanpaths are. Eight fictitious scanpath pairs are tested to elucidate the strengths of the new measure, both in itself and compared to two of the currently most popular measures - the Levenshtein distance and attention map corre- lation

Sampling frequency and eye-tracking measures: how speed affects durations, latencies, and more

We use simulations to investigate the effect of sampling frequency on common dependent variables in eye-tracking. We identify two large groups of measures that behave differently, but consistently. The effect of sampling frequency on these two groups of measures are explored and simulations are performed to estimate how much data are required to overcome the uncertainty of a limited sampling frequency. Both simulated and real data are used to estimate the temporal uncertainty of data produced by low sampling frequencies. The aim is to provide easy-to-use heuristics for researchers using eye-tracking. For example, we show how to compensate the uncertainty of a low sampling frequency with more data and postexperiment adjustments of measures. These findings have implications primarily for researchers using naturalistic setups where sampling frequencies typically are low

Modelling potential yield capacity in conifers using Swedish long-term experiments

Information on forest site productivity is a key component to assess the carbon sequestration potential of boreal forests. While site index (SI) is commonly used to indicate forest site productivity, expressions of SI in the form of yield capacity (potential maximum mean annual volume increment) is desirable since volume yield is central to the economic and ecological analyses of a given species and site. This paper assessed the functional relationship between SI and yield capacity on the basis of yield plot data from long-term experiments measured over several decades for Norway spruce (Picea abies), Scots pine (Pinus sylvestris), Lodgepole pine (Pinus contorta) and Larch (Larix decidua and Larix sibirica) in Sweden. Component models of total basal area and volume yield were also developed. SI was determined by existing height development functions using top height and age, whereas functions for stand-level (m2 ha- 1) basal area development were constructed based on age, SI and initial stand density using difference equations and nonlinear mixed-effects models. The relation between volume yield (m3 ha- 1) and top height was adjusted with total basal area production through nonlinear mixed-effects models. Species-specific parametric regression models were used to construct functional relationships between SI and yield capacity. The root mean square errors of the species-specific models ranged from 2 to 6% and 10-18% of the average values for the basal area and volume equations, respectively. For the yield capacity functions, the explained variations (R2) were within 80-96%. We compared our yield capacity functions to earlier functions of the species and significant differences were observed in both lower and higher SI classes, especially, for Scots pine and Norway spruce. The new functions give better prediction of yield capacity in current growing conditions; hence, they could later be used for comparing tree species' production under similar site and management regimes in Sweden

Importance of Calibration for Improving the Efficiency of Data Assimilation for Predicting Forest Characteristics

Data assimilation (DA) is often used for merging observations to improve the predictions of the current and future states of characteristics of interest. In forest inventory, DA has so far found limited use, although dense time series of remotely sensed (RS) data have become available for estimating forest characteristics. A problem in forest inventory applications based on RS data is that errors from subsequent predictions tend to be strongly correlated, which limits the efficiency of DA. One reason for such a correlation is that model-based predictions, using techniques such as parametric or non-parametric regression, are normally biased conditional on the actual ground conditions, although they are unbiased conditional on the RS predictor variables. A typical case is that predictions are shifted towards the mean, i.e., small true values are overestimated, and large true values are underestimated. In this study, we evaluated if the classical calibration of RS-based predictions could remove this type of bias and improve DA results. Through a simulation study, we mimicked growing stock volume predictions from two different sensors: one from a metric strongly correlated with growing stock volume, mimicking airborne laser scanning, and one from a metric slightly less correlated with growing stock volume, mimicking data obtained from 3D digital photogrammetry. Consistent with previous findings, in areas such as chemistry, we found that classical calibration made the predictions approximately unbiased. Further, in most cases, calibration improved the DA results, evaluated in terms of the root mean square error of predicted volumes, evaluated at the end of a series of ten RS-based predictions

Mapping site index in coniferous forests using bi-temporal airborne laser scanning data and field data from the Swedish national forest inventory

Recent advancements in remote sensing of forests have demonstrated the capabilities of three-dimensional data acquired by airborne laser scanning (ALS) and, consequently, have become an integral part of enhanced forest inventories in Northern Europe. In Sweden, the first national laser scanning revolutionised forest management planning through low-cost production of large-scale and spatially explicit maps of forest attributes such as basal area, volume, and biomass, compared to the earlier practice based on field survey data. A second scanning at the national level was launched in 2019, and it provides conditions for the estimation of height growth and site index. Accurate and up-to-date information about site productivity is relevant for planning silvicultural treatments and for the prognosis of forest status and development over time. In this study, we explored the potential of bi-temporal ALS data and other auxiliary information to predict and map site productivity by site index according to site properties (SIS) of Norway spruce (Picea abies (L.) Karst) and Scots pine (Pinus sylvestris L.) in even aged stands in Sweden. We linked ground survey data of SIS from more than 11,500 plots of the Swedish National Forest Inventory (NFI) to bi-temporal ALS data to predict and map site index using an area-based method and two regression modelling strategies: (1) a multiple linear regression (MLR) model with an ordinary least-squares parameter estimation method, and (2) a non-parametric random forests (RF) model optimised for hyper parameter tuning. For model development, permanent plots were used, whereas the validation was done on the temporary plots of the Swedish NFI and an independent stand-level dataset. Species-specific models were developed, and the root mean square error (RMSE) metric was used to quantify the residual variability around model predictions. For both species, the MLR model gave precise and accurate estimates of SIS. The RMSE for SIS predictions was in the range of 1.96 - 2.11 m, and the relative RMSE was less than 10 % (7.68 - 9.49 %) of the reference mean value. Final predictors of site index include metrics of 90th percentile height and annual increment in the 95th percentile height, altitude, distance to coast, and soil moisture. Country-wide maps of SIS and the corresponding pixel-level prediction errors at a spatial resolution of 12.5 m grid cells were produced for the two species. Independent validations show the site index maps are suitable for use in operational forest management planning in Sweden

Preparation and self-assembly of amphiphilic polylysine dendrons

Polylysine dendrons with lipid tails prepared by divergent solid-phase synthesis showed self-assembling properties in aqueous solutions.</p