| 1. |
- Lindgren, Nils, et al.
(author)
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Data assimilation in stand level forest inventory – first results
- 2015
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In: Natural resources and bioeconomy studies. - 2342-7639. ; 29, s. 37-37
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Conference paper (other academic/artistic)abstract
- Data assimilation in stand-level forest inventory – first results Nils Lindgren 1 , Mattias Nyström1 , Jörgen Wallerman 1 , Sarah Ehlers 1 , Anton Grafström1 , Anders Muszta 1 , Kenneth Nyström1 , Erik Willen 2 , Johan Fransson 1 , Jonas Bohlin 1 , Håkan Olsson 1 , Göran Ståhl 1 1Swedish University of Agricultural Sciences, Umeå, Sweden 2Skogforsk, Uppsala, Sweden As we are entering an era of increased supply of remote sensing data, we believe that data assimilation has a large potential for keeping forest stand registers up to date (Ehlers et al. 2013). Data assimilation combines forecasts of previous estimates with new observations of the current state in an optimal way based on the uncertainties in the forecast and the observations. These forecasting and updating steps can be repeated with new available observations to get improved estimations. In the present study, we use canopy height models obtained from matching of digital aerial photos over the test site Remningstorp in Sweden, acquired 2003, 2005, 2007, 2009, 2010 and 2012 and normalized with a DEM from airborne laser scanning. Stem volume was estimated for each data acquisition and stand, using regression functions based on field reference data from sample plots. Forecasting was done with growth functions constructed from National Forest Inventory plots. The remote sensing estimates for each time point were assimilated with the forecasts of the previous estimates, using extended Kalman filtering. Validation was done on 40 m radius sample plots dominated by Norway spruce. Early results for three stands show that the variances were lower when using assimilation of new estimates and there was less fluctuation compared to repeated remote sensing estimates. The results for the assimilated data at year 2011 were also consistently closer to the validation data measured in 2011 compared to the remote sensing estimates from year 2011.
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| 2. |
- Lindgren, Nils, et al.
(author)
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Data Assimilation of Growing Stock Volume Using a Sequence of Remote Sensing Data from Different Sensors
- 2022
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In: Canadian Journal of Remote Sensing. - : Informa UK Limited. - 0703-8992 .- 1712-7971. ; 48, s. 127-143
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Journal article (peer-reviewed)abstract
- 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).
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| 3. |
- Lindgren, Nils, et al.
(author)
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Improved Prediction of Forest Variables Using Data Assimilation of Interferometric Synthetic Aperture Radar Data
- 2017
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In: Canadian Journal of Remote Sensing. - : Informa UK Limited. - 0703-8992 .- 1712-7971. ; 43, s. 374-383
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Journal article (peer-reviewed)abstract
- The statistical framework of data assimilation provides methods for utilizing new data for obtaining up-to-date forest data: existing forest data are forecasted and combined with each new remote sensing data set. This new paradigm for updating forest database, well known from other fields of study, will provide a framework for utilizing all available remote sensing data in proportion to their quality to improve prediction. It also solves the problem that not all remote sensing data sets provide information for the entire area of interest, since areas with no remote sensing data can be forecasted until new remote sensing data become available. In this study, extended Kalman filtering was used for assimilating data from 19 TanDEM-X InSAR images on 137 sample plots, each of 10-meter radius at a test site in southern Sweden over a period of 4 years. At almost all time points data assimilation resulted in predictions closer to the reference value than predictions based on data from that single time point. For the study variables Lorey's mean height, basal area, and stem volume, the median reduction in root mean square error was 0.4 m, 0.9 m(2)/ha, and 15.3 m(3)/ha (2, 3, and 6 percentage points), respectively.
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| 4. |
- Nyström, Mattias, et al.
(author)
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Assimilating remote sensing data with forest growth models
- 2015
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Conference paper (peer-reviewed)abstract
- As we are entering an era of increased supply of remote sensing data, we believe that dataassimilation that combines growth forecasts of previous estimates with new observations of thecurrent state has a large potential for keeping forest stand registers up to date (Ehlers et al. 2013).The data assimilation will update a forest model e in an optimal way based on the uncertainties inthe forecast and the observations, each time new data becomes available. These forecasting andupdating steps can be repeated with new available observations to get improved estimations. In thisstudy we present the first practical results from data assimilation of mean tree height, basal area andgrowing stock. The remote sensing data used were canopy height models obtained from matching ofdigital aerial photos over the test site Remningstorp in Sweden. The photos were acquired 2003,2005, 2007, 2009, 2010 and 2012 and normalized with a DEM from airborne laser scanning.The procedure for the data assimilation was as follows: mean tree height, basal area and growingstock were predicted on 18 m × 18 m raster cells using the area based method. Ten meter radiussample plots were used as field calibration data. For each photo year, the field data were adjustedfor growth to have the same state year as each acquisition year of the photos. Growth models wereconstructed from National Forest Inventory plot data. Data assimilation could then be performed onraster cell level by initially start with the estimates from 2003 year´s photos. This prediction was thenforecasted to year 2005 by calculating the growth for the raster cell. This forecasted value is thenblended with the new remote sensing estimation collected 2005. The process was then repeated forthe following years where new measurements were available. In this study, extended Kalmanfiltering was used to blend the forecasted values with the new remote sensing measurements.Validation was done for 40 m radius field plots. Further, the results were also compared with twoalternative approaches: the first was to forecast the first remote sensing estimate to the endpointand the second was to use remote sensing data acquired at the endpoint only.The preliminary results for the eight forest stands show that the variances were lower when usingassimilation of new estimates and there were less fluctuation compared to only using remote sensingdata from the endpoint. However, the mean deviation from the measured value 2011 was lowerwhen only data from the endpoint were used. The assimilated values 2011 were consistently closerto the validation data compared to only forecasting the starting estimate from 2003 to 2011.
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| 5. |
- Nyström, Mattias, et al.
(author)
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Data assimilation in forest inventory: first empirical results
- 2015
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In: Forests. - : MDPI AG. - 1999-4907. ; 6, s. 4540-4557
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Journal article (peer-reviewed)abstract
- Data assimilation techniques were used to estimate forest stand data in 2011 bysequentially combining remote sensing based estimates of forest variables with predictions fromgrowth models. Estimates of stand data, based on canopy height models obtained from imagematching of digital aerial images at six different time-points between 2003 and 2011, served asinput to the data assimilation. The assimilation routines were built on the extended Kalman filter.The study was conducted in hemi-boreal forest at the Remningstorp test site in southern Sweden(lat. 13˝371 N; long. 58˝281 E). The assimilation results were compared with two other methodsused in practice for estimation of forest variables: the first was to use only the most recent estimateobtained from remotely sensed data (2011) and the second was to forecast the first estimate (2003)to the endpoint (2011). All three approaches were validated using nine 40 m radius validation plots,which were carefully measured in the field. The results showed that the data assimilation approachprovided better results than the two alternative methods. Data assimilation of remote sensing timeseries has been used previously for calibrating forest ecosystem models, but, to our knowledge,this is the first study with real data where data assimilation has been used for estimating forestinventory data. The study constitutes a starting point for the development of a framework usefulfor sequentially utilizing all types of remote sensing data in order to provide precise and up-to-dateestimates of forest stand parameters.
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| 6. |
- Nyström, Mattias, et al.
(author)
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Data assimilation in forest inventory, first empirical results using ALS data
- 2015
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Conference paper (peer-reviewed)abstract
- A first data assimilation case study using a time series of ALS for updating forest stand data is presented. Forest stand data are predicted from each ALS acquisition. Kalman filtering and growth models are then used to combine each new ALS based prediction with forecasts from the previous data acquisition.
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| 7. |
- Ahlström, Christer, et al.
(author)
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Fit-for-duty test for estimation of drivers sleepiness level: Eye movements improve the sleep/wake predictor
- 2013
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In: Transportation Research Part C. - : Elsevier. - 0968-090X .- 1879-2359. ; 26, s. 20-32
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Journal article (peer-reviewed)abstract
- Driver sleepiness contributes to a considerable proportion of road accidents, and a fit-for-duty test able to measure a drivers sleepiness level might improve traffic safety. The aim of this study was to develop a fit-for-duty test based on eye movement measurements and on the sleep/wake predictor model (SWP, which predicts the sleepiness level) and evaluate the ability to predict severe sleepiness during real road driving. Twenty-four drivers participated in an experimental study which took place partly in the laboratory, where the fit-for-duty data were acquired, and partly on the road, where the drivers sleepiness was assessed. A series of four measurements were conducted over a 24-h period during different stages of sleepiness. Two separate analyses were performed; a variance analysis and a feature selection followed by classification analysis. In the first analysis it was found that the SWP and several eye movement features involving anti-saccades, pro-saccades, smooth pursuit, pupillometry and fixation stability varied significantly with different stages of sleep deprivation. In the second analysis, a feature set was determined based on floating forward selection. The correlation coefficient between a linear combination of the acquired features and subjective sleepiness (Karolinska sleepiness scale, KSS) was found to be R = 0.73 and the correct classification rate of drivers who reached high levels of sleepiness (KSS andgt;= 8) in the subsequent driving session was 82.4% (sensitivity = 80.0%, specificity = 84.2% and AUC = 0.86). Future improvements of a fit-for-duty test should focus on how to account for individual differences and situational/contextual factors in the test, and whether it is possible to maintain high sensitive/specificity with a shorter test that can be used in a real-life environment, e.g. on professional drivers.
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| 8. |
- Al-Manasir, Nodar, et al.
(author)
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Effects of Temperature and pH on the Contraction and Aggregation of Microgels in Aqueous Suspensions
- 2009
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In: Journal of Physical Chemistry B. - : American Chemical Society (ACS). - 1520-6106 .- 1520-5207. ; 113:32, s. 11115-11123
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Journal article (peer-reviewed)abstract
- Chemically cross-linked poly(N-isopropylacrylamide) (PNIPAM) microgels and PNIPAM with different amounts of acrylic acid groups (PNIPAM-co-PAA) were synthesized and the temperature-induced aggregation behaviors of aqueous suspensions of these microgels were investigated mainly with the aid of dynamic light scattering (DLS) and turbidimetry. The DLS results show that the particles at all conditions shrink at temperatures up to approximately the lower critical solution temperature (LCST), but the relative contraction effect is larger for the microgels without acid groups or for microgels with added anionic surfactant (SDS). A significant depression of the cloud point is found in suspensions of PNIPAM with very low concentrations of SDS. The compression of the microgels cannot be traced from the turbidity results, but rather the values of the turbidity increase in this temperature interval. This phenomenon is discussed in the framework of a theoretical model. At temperatures above LCST, the size of the microgels without attached charged groups in a very dilute suspension is unaffected by temperature, while the charged particles (pH 7 and 11) continue to collapse with increasing temperature over the entire domain. In this temperature range, low-charged particles of higher concentration and particles containing acrylic acid groups at low pH (pH 2) aggregate, and macroscopic phase separation is approached at higher temperatures. This study demonstrates how the stabilization of microgels can be affected by factors such as polymer concentration, addition of ionic surfactant to particles without charged acid groups, amount of charged groups in the polymer, and pH.
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| 9. |
- Andersson, Richard, et al.
(author)
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One algorithm to rule them all? : An evaluation and discussion of ten eye movement event-detection algorithms
- 2017
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In: Behavior Research Methods. - : Springer Science and Business Media LLC. - 1554-3528. ; 49:2, s. 616-637
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Journal article (peer-reviewed)abstract
- Almost all eye-movement researchers use algorithms to parse raw data and detect distinct types of eye movement events, such as fixations, saccades, and pursuit, and then base their results on these. Surprisingly, these algorithms are rarely evaluated. We evaluated the classifications of ten eye-movement event detection algorithms, on data from an SMI HiSpeed 1250 system, and compared them to manual ratings of two human experts. The evaluation focused on fixations, saccades, and post-saccadic oscillations. The evaluation used both event duration parameters, and sample-by-sample comparisons to rank the algorithms. The resulting event durations varied substantially as a function of what algorithm was used. This evaluation differed from previous evaluations by considering a relatively large set of algorithms, multiple events, and data from both static and dynamic stimuli. The main conclusion is that current detectors of only fixations and saccades work reasonably well for static stimuli, but barely better than chance for dynamic stimuli. Differing results across evaluation methods make it difficult to select one winner for fixation detection. For saccade detection, however, the algorithm by Larsson, Nyström and Stridh (IEEE Transaction on Biomedical Engineering, 60(9):2484–2493,2013) outperforms all algorithms in data from both static and dynamic stimuli. The data also show how improperly selected algorithms applied to dynamic data misestimate fixation and saccade properties.
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| 10. |
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