| 1. |
- Andersson, John Åke, et al.
(författare)
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Sweden's Economic Relationships with Uganda
- 2016
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This explorative study aims to map commercial and other economic relations between Sweden and Uganda during the years 2000-2014. In addition, we will discuss whether and how these relations may be related to Swedish bilateral aid.
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| 2. |
- Ehlers, Sarah, et al.
(författare)
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Data assimilation in stand-level forest inventories
- 2013
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Ingår i: Canadian Journal of Forest Research. - : Canadian Science Publishing. - 0045-5067 .- 1208-6037. ; 43, s. 1104-1113
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Tidskriftsartikel (refereegranskat)abstract
- The development of remote sensing methods through research and large-scale application nowadays makes it possible to obtain stand-level estimates of forest variables at short intervals and at low cost. This offers substantial possibilities to forestry practitioners, but it also poses challenges regarding how cost-efficient data acquisition strategies should be developed. For example, should cheap but low-quality data be acquired and discarded whenever new data become available or should investments be made in high-quality data that are continuously updated to last over a longer period of time? We suggest that the solution could be to establish data assimilation (DA) procedures linked to forest inventories to make appropriate use of data from several sources. With DA, old information is updated through growth forecasts and when new information becomes available it is assimilated with the old information; the different sources of information are made use of to the extent motivated by their accuracy. In this study we made a general assessment of the usefulness of DA in connection with stand-level forest inventories and we compared two different methodological approaches, the extended Kalman filter and the Bayesian method. Not surprisingly, the relative advantage of DA was found to be largest for cases where low-precision estimates of growing stock volume were obtained at short intervals and forecasts were made with accurate growth prediction models. The methodological comparison revealed a tendency of the extended Kalman filter to underestimate the variance of the estimates.
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| 3. |
- Ekström, Magnus, 1966-, et al.
(författare)
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Estimating density from presence/absence data in clustered populations
- 2020
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Ingår i: Methods in Ecology and Evolution. - : John Wiley & Sons. - 2041-210X. ; 11:3, s. 390-402
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Tidskriftsartikel (refereegranskat)abstract
- Inventories of plant populations are fundamental in ecological research and monitoring, but such surveys are often prone to field assessment errors. Presence/absence (P/A) sampling may have advantages over plant cover assessments for reducing such errors. However, the linking between P/A data and plant density depends on model assumptions for plant spatial distributions. Previous studies have shown, for example, how that plant density can be estimated under Poisson model assumptions on the plant locations. In this study, new methods are developed and evaluated for linking P/A data with plant density assuming that plants occur in clustered spatial patterns. New theory was derived for estimating plant density under Neyman-Scott-type cluster models such as the Matern and Thomas cluster processes. Suggested estimators, corresponding confidence intervals and a proposed goodness-of-fit test were evaluated in a Monte Carlo simulation study assuming a Matern cluster process. Furthermore, the estimators were applied to plant data from environmental monitoring in Sweden to demonstrate their empirical application. The simulation study showed that our methods work well for large enough sample sizes. The judgment of what is' large enough' is often difficult, but simulations indicate that a sample size is large enough when the sampling distributions of the parameter estimators are symmetric or mildly skewed. Bootstrap may be used to check whether this is true. The empirical results suggest that the derived methodology may be useful for estimating density of plants such as Leucanthemum vulgare and Scorzonera humilis. By developing estimators of plant density from P/A data under realistic model assumptions about plants' spatial distributions, P/A sampling will become a more useful tool for inventories of plant populations. Our new theory is an important step in this direction.
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| 4. |
- Ekström, Magnus, 1966-, et al.
(författare)
-
Estimating density from presence/absence data in clustered populations
- 2021
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- 1. Inventories of plant populations are fundamental in ecological research and monitoring, butsuch surveys are often prone to field assessment errors. Presence/ absence (P/A) samplingmay have advantages over plant cover assessments for reducing such errors. However, thelinking between P/A data and plant density depends on model assumptions for plant spatialdistributions. Previous studies have shown, for example, how that plant density can beestimated under Poisson model assumptions on the plant locations. In this study, newmethods are developed and evaluated for linking P/A data with plant density assuming thatplants occur in clustered spatial patterns.2. New theory was derived for estimating plant density under Neyman–Scott-type cluster models such as the Matérn and Thomas cluster processes. Suggested estimators, correspondingconfidence intervals and a proposed goodness-of-fit test were evaluated in a Monte Carlosimulation study assuming a Matérn cluster process. Furthermore, the estimators were applied to plant data from environmental monitoring in Sweden to demonstrate their empiricalapplication.3. The simulation study showed that our methods work well for large enough sample sizes.The judgment of what is ’large enough’ is often difficult, but simulations indicate that asample size is large enough when the sampling distributions of the parameter estimators aresymmetric or mildly skewed. Bootstrap may be used to check whether this is true. Theempirical results suggest that the derived methodology may be useful for estimating densityof plants such as Leucanthemum vulgare and Scorzonera humilis.4. By developing estimators of plant density from P/A data under realistic model assumptions about plants’ spatial distributions, P/A sampling will become a more useful tool forinventories of plant populations. Our new theory is an important step in this direction.
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| 5. |
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| 6. |
- Ekström, Magnus, et al.
(författare)
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Logistic regression for clustered data from environmental monitoring programs
- 2018
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Ingår i: Ecological Informatics. - : Elsevier. - 1574-9541 .- 1878-0512. ; 43, s. 165-173
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Tidskriftsartikel (refereegranskat)abstract
- Large-scale surveys, such as national forest inventories and vegetation monitoring programs, usually have complex sampling designs that include geographical stratification and units organized in clusters. When models are developed using data from such programs, a key question is whether or not to utilize design information when analyzing the relationship between a response variable and a set of covariates. Standard statistical regression methods often fail to account for complex sampling designs, which may lead to severely biased estimators of model coefficients. Furthermore, ignoring that data are spatially correlated within clusters may underestimate the standard errors of regression coefficient estimates, with a risk for drawing wrong conclusions. We first review general approaches that account for complex sampling designs, e.g. methods using probability weighting, and stress the need to explore the effects of the sampling design when applying logistic regression models. We then use Monte Carlo simulation to compare the performance of the standard logistic regression model with two approaches to model correlated binary responses, i.e. cluster-specific and population-averaged logistic regression models. As an example, we analyze the occurrence of epiphytic hair lichens in the genus Bryoria; an indicator of forest ecosystem integrity. Based on data from the National Forest Inventory (NFI) for the period 1993-2014 we generated a data set on hair lichen occurrence on > 100,000 Picea abies trees distributed throughout Sweden. The NFI data included ten covariates representing forest structure and climate variables potentially affecting lichen occurrence. Our analyses show the importance of taking complex sampling designs and correlated binary responses into account in logistic regression modeling to avoid the risk of obtaining notably biased parameter estimators and standard errors, and erroneous interpretations about factors affecting e.g. hair lichen occurrence. We recommend comparisons of unweighted and weighted logistic regression analyses as an essential step in development of models based on data from large-scale surveys.
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| 7. |
- Esseen, Per-Anders, et al.
(författare)
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Broad-scale distribution of epiphytic hair lichens correlates more with climate and nitrogen deposition than with forest structure
- 2016
-
Ingår i: Canadian Journal of Forest Research. - : Canadian Science Publishing. - 0045-5067 .- 1208-6037. ; 46:11, s. 1348-1358
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Tidskriftsartikel (refereegranskat)abstract
- Hair lichens are strongly influenced by forest structure at local scales, but their broad-scale distributions are less understood. We compared the occurrence and length of Alectoria sarmentosa (Ach.) Ach., Bryoria spp., and Usnea spp. in the lower canopy of > 5000 Picea abies (L.) Karst. trees within the National Forest Inventory across all productive forest in Sweden. We used logistic regression to analyse how climate, nitrogen deposition, and forest variables influence lichen occurrence. Distributions overlapped, but the distribution of Bryoria was more northern and that of Usnea was more southern, with Alectoria's distribution being intermediate. Lichen length increased towards northern regions, indicating better conditions for biomass accumulation. Logistic regression models had the highest pseudo R2 value for Bryoria, followed by Alectoria. Temperature and nitrogen deposition had higher explanatory power than precipitation and forest variables. Multiple logistic regressions suggest that lichen genera respond differently to increases in several variables. Warmingdecreased the odds for Bryoria occurrence at all temperatures. Corresponding odds for Alectoria and Usnea decreased in warmer climates, but in colder climates, they increased. Nitrogen addition decreased the odds for Alectoria and Usnea occurrence under high deposition, but under low deposition, the odds increased. Our analyses suggest major shifts in the broad-scale distribution of hair lichens with changes in climate, nitrogen deposition, and forest management.
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| 8. |
- Esseen, Per-Anders, et al.
(författare)
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Multiple drivers of large-scale lichen decline in boreal forest canopies
- 2022
-
Ingår i: Global Change Biology. - : John Wiley & Sons. - 1354-1013 .- 1365-2486. ; 28:10, s. 3293-3309
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Tidskriftsartikel (refereegranskat)abstract
- Thin, hair-like lichens (Alectoria, Bryoria, Usnea) form conspicuous epiphyte communities across the boreal biome. These poikilohydric organisms provide important ecosystem functions and are useful indicators of global change. We analyse how environmental drivers influence changes in occurrence and length of these lichens on Norway spruce (Picea abies) over 10 years in managed forests in Sweden using data from >6000 trees. Alectoria and Usnea showed strong declines in southern-central regions, whereas Bryoria declined in northern regions. Overall, relative loss rates across the country ranged from 1.7% per year in Alectoria to 0.5% in Bryoria. These losses contrasted with increased length of Bryoria and Usnea in some regions. Occurrence trajectories (extinction, colonization, presence, absence) on remeasured trees correlated best with temperature, rain, nitrogen deposition, and stand age in multinomial logistic regression models. Our analysis strongly suggests that industrial forestry, in combination with nitrogen, is the main driver of lichen declines. Logging of forests with long continuity of tree cover, short rotation cycles, substrate limitation and low light in dense forests are harmful for lichens. Nitrogen deposition has decreased but is apparently still sufficiently high to prevent recovery. Warming correlated with occurrence trajectories of Alectoria and Bryoria, likely by altering hydration regimes and increasing respiration during autumn/winter. The large-scale lichen decline on an important host has cascading effects on biodiversity and function of boreal forest canopies. Forest management must apply a broad spectrum of methods, including uneven-aged continuous cover forestry and retention of large patches, to secure the ecosystem functions of these important canopy components under future climates. Our findings highlight interactions among drivers of lichen decline (forestry, nitrogen, climate), functional traits (dispersal, lichen colour, sensitivity to nitrogen, water storage), and population processes (extinction/colonization).
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| 9. |
- Grafström, Anton, et al.
(författare)
-
Combinaison d’échantillons probabilistes indépendants
- 2019
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Ingår i: Survey Methodology. - : Statistics Canada. - 0714-0045 .- 1492-0921. ; 45:2, s. 371-387
-
Tidskriftsartikel (refereegranskat)abstract
- Dans divers domaines, il est de plus en plus important de fusionner les sources d’information disponibles pouraméliorer les estimations des caractéristiques de la population. En présence de plusieurs échantillonsprobabilistes indépendants d’une population finie, nous examinons plusieurs solutions d’estimateur combiné dutotal de la population, basé soit sur une combinaison linéaire d’estimateurs distincts, soit sur une méthode paréchantillon combiné. L’estimateur en combinaison linéaire fondé sur des variances estimées est susceptible d’êtrebiaisé, car les estimateurs distincts du total de la population peuvent être fortement corrélés à leurs estimateursde la variance respectifs. Nous illustrons la possibilité d’utiliser un échantillon combiné pour estimer les variancesdes estimateurs distincts, ce qui donne des estimateurs de la variance groupés généraux. Ces estimateurs de lavariance groupés utilisent tous les renseignements disponibles et peuvent réduire considérablement le biais d’unecombinaison linéaire d’estimateurs distincts.
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| 10. |
- Grafström, Anton, et al.
(författare)
-
Combinaison d’échantillons probabilistes indépendants
- 2019
-
Ingår i: Survey Methodology. - 0714-0045. ; 45, s. 371-387
-
Tidskriftsartikel (refereegranskat)abstract
- Dans divers domaines, il est de plus en plus important de fusionner les sources d’information disponibles pour améliorer les estimations des caractéristiques de la population. En présence de plusieurs échantillons probabilistes indépendants d’une population finie, nous examinons plusieurs solutions d’estimateur combiné du total de la population, basé soit sur une combinaison linéaire d’estimateurs distincts, soit sur une méthode par échantillon combiné. L’estimateur en combinaison linéaire fondé sur des variances estimées est susceptible d’être biaisé, car les estimateurs distincts du total de la population peuvent être fortement corrélés à leurs estimateurs de la variance respectifs. Nous illustrons la possibilité d’utiliser un échantillon combiné pour estimer les variances des estimateurs distincts, ce qui donne des estimateurs de la variance groupés généraux. Ces estimateurs de la variance groupés utilisent tous les renseignements disponibles et peuvent réduire considérablement le biais d’une combinaison linéaire d’estimateurs distincts.
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| 11. |
- Grafström, Anton, et al.
(författare)
-
On combining independent probability samples
- 2019
-
Ingår i: Survey Methodology. - : Statistics Canada. - 0714-0045 .- 1492-0921. ; 45:2, s. 349-364
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Tidskriftsartikel (refereegranskat)abstract
- Merging available sources of information is becoming increasingly important for improving estimates of population characteristics in a variety of fields. In presence of several independent probability samples from a finite population we investigate options for a combined estimator of the population total, based on either a linear combination of the separate estimators or on the combined sample approach. A linear combination estimator based on estimated variances can be biased as the separate estimators of the population total can be highly correlated to their respective variance estimators. We illustrate the possibility to use the combined sample to estimate the variances of the separate estimators, which results in general pooled variance estimators. These pooled variance estimators use all available information and have potential to significantly reduce bias of a linear combination of separate estimators.
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| 12. |
- Jonsson, Bengt-Gunnar, et al.
(författare)
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Dead wood availability in managed Swedish forests - Policy outcomes and implications for biodiversity
- 2016
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Ingår i: Forest Ecology and Management. - : Elsevier BV. - 0378-1127 .- 1872-7042. ; 376, s. 174-182
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Tidskriftsartikel (refereegranskat)abstract
- Dead wood is a critical resource for forest biodiversity and widely used as an indicator for sustainable forest management. Based on data from the Swedish National Forest Inventory we provide baseline information and analyze trends in volume and distribution of dead wood in Swedish managed forests during 15 years. The data are based on ≈30,000 sample plots inventoried during three periods (1994-1998; 2003-2007 and 2008-2012). The forest policy has since 1994 emphasized the need to increase the amount of dead wood in Swedish forests. The average volume of dead wood in Sweden has increased by 25% (from 6.1 to 7.6 m3 ha-1) since the mid-1990s, but patterns differed among regions and tree species. The volume of conifer dead wood (mainly from Picea abies) has increased in the southern part of the country, but remained stable or decreased in the northern part. Heterogeneity of dead wood types was low in terms of species, diameter and decay classes, potentially negatively impacting on biodiversity. Overall, we found only minor effects of the current forest policy since most of the increase can be attributed to storm events creating a pulse of hard dead wood. Therefore, the implementation of established policy instruments (e.g. legislation and voluntary certification schemes) need to be revisited. In addition to the retention of dead trees during forestry operations, policy makers should consider calling for more large-scale targeted creation of dead trees and management methods with longer rotation cycles. © 2016 The Authors.
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| 13. |
- Jonsson, Bengt-Gunnar, 1963-, et al.
(författare)
-
Rapid changes in ground vegetation of mature boreal forests : an analysis of Swedish national forest inventory data
- 2021
-
Ingår i: Forests. - : MDPI. - 1999-4907. ; 12:4
-
Tidskriftsartikel (refereegranskat)abstract
- The boreal forest floor vegetation is critical for ecosystem functioning and an important part of forest biodiversity. Given the ongoing global change, knowledge on broad-scale changes in the composition and abundance of different plant species and species groups is hence important for both forest conservation and management. Here, we analyse permanent plot data from the National Forest Inventory (NFI) on changes in the vegetation over a 10-year period in four regions of Sweden. To limit the direct and relatively well-known effects of forest management and associated succession, we only included mature forest stands not influenced by forestry during the 10 years between inventories, and focused on vegetation change mainly related to other factors. Results show strong decrease among many species and species groups. This includes dominant species such as Vaccinimum myrtillus and Deschampsia flexuosa as well as several forest herbs. The only species increasing are some mosses in the southern regions. Our data do not allow for a causal interpretation of the observed patterns. However, the changes probably result from latent succession in combination with climate change and nitrogen deposition, and with time lags complicating the interpretation of their relative importance. Regardless of the cause, the observed changes are on a magnitude that suggest impacts on ecosystem functioning and hence highlight the need for more experimental work.
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| 14. |
- Lindgren, Nils, et al.
(författare)
-
Data assimilation in stand level forest inventory – first results
- 2015
-
Ingår i: Natural resources and bioeconomy studies. - 2342-7639. ; 29, s. 37-37
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)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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| 15. |
- Lindgren, Nils, et al.
(författare)
-
Improved Prediction of Forest Variables Using Data Assimilation of Interferometric Synthetic Aperture Radar Data
- 2017
-
Ingår i: Canadian Journal of Remote Sensing. - : Informa UK Limited. - 0703-8992 .- 1712-7971. ; 43, s. 374-383
-
Tidskriftsartikel (refereegranskat)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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| 16. |
- Moberg, Christina, et al.
(författare)
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De unga gör helt rätt när de stämmer staten
- 2022
-
Ingår i: Aftonbladet. - 1103-9000.
-
Tidskriftsartikel (populärvet., debatt m.m.)abstract
- 1 620 forskare och lärare i forskarvärlden: Vi ställer oss bakom Auroras klimatkrav
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| 17. |
- Mold, Jeff E., et al.
(författare)
-
Divergent clonal differentiation trajectories establish CD8(+) memory T cell heterogeneity during acute viral infections in humans
- 2021
-
Ingår i: Cell Reports. - : Elsevier BV. - 2211-1247. ; 35:8
-
Tidskriftsartikel (refereegranskat)abstract
- The CD8(+) T cell response to an antigen is composed of many T cell clones with unique T cell receptors, together forming a heterogeneous repertoire of effector and memory cells. How individual T cell clones contribute to this heterogeneity throughout immune responses remains largely unknown. In this study, we longitudinally track human CD8(+) T cell clones expanding in response to yellow fever virus (YFV) vaccination at the single-cell level. We observed a drop in clonal diversity in blood from the acute to memory phase, suggesting that clonal selection shapes the circulating memory repertoire. Clones in the memory phase display biased differentiation trajectories along a gradient from stem cell to terminally differentiated effector memory fates. In secondary responses, YFV- and influenza-specific CD8(+) T cell clones are poised to recapitulate skewed differentiation trajectories. Collectively, we show that the sum of distinct clonal phenotypes results in the multifaceted human T cell response to acute viral infections.
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| 18. |
- Nyström, Mattias, et al.
(författare)
-
Assimilating remote sensing data with forest growth models
- 2015
-
Konferensbidrag (refereegranskat)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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| 19. |
- Nyström, Mattias, et al.
(författare)
-
Data assimilation : a prototype system to assimilate forest stand information
- 2016
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The purpose of this report is to describe a data assimilation prototype program(Appendix A) developed to estimate forest stand data. The program was developed and tested on data col-lected on the forest estate Remningstorp in southern Sweden. Data assimilation can be used to sequentially combine remote sensing based estimates of forest variables with predictions from growth models. The assimilation routine implemented was the extended Kalman Filter. The program supports two different ways to assimilate the forest data: (1) pixel-wise and (2)stand-wise. In the pixel-wise way, raster cells are used as assimilation unit and can beaggregated to a stand for evaluation. In the stand-wise way, the whole stand is assimilatedas one unit. The two methods has pros and cons. The pixel-wise way is simple to use as nostand-delineation is needed, but might be subject to boundary effects and noise due to geo-metric errors. Using the developed code, it has been shown in three case studies that thecombination of time series of remote sensing data and forest growth functions provides bet-ter estimates of forest variables than only using forecasting, or only using the latest remotesensing data. This opens up for a new way to keep forest stand registers up to date.
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| 20. |
- Nyström, Mattias, et al.
(författare)
-
Data assimilation in forest inventory: first empirical results
- 2015
-
Ingår i: Forests. - : MDPI AG. - 1999-4907. ; 6, s. 4540-4557
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Tidskriftsartikel (refereegranskat)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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| 21. |
- Nyström, Mattias, et al.
(författare)
-
Data assimilation in forest inventory, first empirical results using ALS data
- 2015
-
Konferensbidrag (refereegranskat)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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| 22. |
- Roberge, Cornelia, et al.
(författare)
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Forest damage inventory using the local pivotal sampling method
- 2017
-
Ingår i: Canadian Journal of Forest Research. - : Canadian Science Publishing. - 0045-5067 .- 1208-6037. ; 47, s. 357-365
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Tidskriftsartikel (refereegranskat)abstract
- Specially designed forest damage inventories, directed to areas with potential or suspected damage, are performed in many countries. In this study, we evaluate a new approach for damage inventories in which auxiliary data are used for the sample selection with the recently introduced local pivotal sampling design. With this design, a sample that is well spread in the space of the auxiliary variables is obtained. We applied Monte Carlo sampling simulation to evaluate whether this sampling design leads to more precise estimates compared with commonly applied baseline methods. The evaluations were performed using different damage scenarios and different simulated relationships between the auxiliary data and the actual damages. The local pivotal method was found to be more efficient than simple random sampling in all scenarios, and depending on the allocation of the sample and the properties of the auxiliary data, it sometimes outperformed two-phase sampling for stratification. Thus, the local pivotal method may be a valuable tool to cost-efficiently assess the magnitude of forest damage once outbreaks have been detected in a forest region.
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| 23. |
- Saarela, Svetlana, et al.
(författare)
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A new prediction-based variance estimator for two-stage model-assisted surveys of forest resources
- 2017
-
Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 192, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- Forest resource assessments utilizing remotely sensed auxiliary data are becoming increasingly important due to their ability to provide precise estimates of forest parameters at low cost. In presenting results from such surveys, it is important to provide not only estimates of the target parameters, but also their confidence intervals, which provide the range of values wherein the true value is located with a certain level of confidence. If such an interval is narrow the point estimates from the survey can be considered very reliable. In estimating the confidence interval the variance of an estimator must first be estimated. Unbiasedness, i.e. that an estimator on average coincides with the true value, is an important property also for variance estimators. Another important property is that the variance estimator itself has low variance, not least in cases when the variance estimates obtained with the estimator may not be strictly positive. One such important case is when two-stage designs are used to first allocate sample clusters in the form of strips from which auxiliary data, such as metrics derived from airborne laser scanning, are obtained; field data are then derived from sample plots beneath each sample strip in a second stage. In this article we compare two variance estimators for such surveys. The first estimator is a standard estimator suggested in reference textbooks on model-assisted sampling. The second estimator is proposed by the authors, and utilizes the auxiliary data to a larger extent. Through Monte Carlo simulation we show that both variance estimators are approximately unbiased, but that the new estimator is more stable (i.e., has lower empirical variance) and provides empirical confidence interval coverage rates that coincide more closely with the nominal coverage rates. (C) 2017 Elsevier Inc. All rights reserved.
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| 24. |
- Saarela, Svetlana, et al.
(författare)
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Effects of positional errors in model-assisted and model-based estimation of growing stock volume
- 2016
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 172, s. 101-108
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Tidskriftsartikel (refereegranskat)abstract
- Positional errors may cause problems when field and remotely sensed data are combined in connection with forest surveys. In this study we evaluated the effects of such errors on statistical estimates of growing stock volume using model-assisted and model-based estimation. With model-assisted estimation, positional errors affect the model parameter estimates for the models that are used as part of the estimation framework. In addition, positional errors affect the estimators, since the deviations between model predictions and field measurements are often larger than they would have been without positional errors. Using model-based estimation positional errors affect the model parameter estimates and thus the estimators. We compared the effects of positional errors in model-assisted and model-based estimation through Monte Carlo sampling simulation in a simulated study area resembling the forest conditions in Kuortane, western Finland. The forest population was created using a copula modelling approach based on field, Landsat and LiDAR data. We found that positional errors led to slightly biased estimators, and estimators with larger variances compared to the cases where data were perfectly geo-located. The relative increase of the variances of the estimators was of equal magnitude for model-assisted and model-based estimation, when models were developed and applied to data with geopositional errors. Further, the variance estimators were always more precise for the model-based estimators compared to the model-assisted estimators. When the models were developed based on perfectly geo-located data but applied to data with positional errors, model-based estimation was superior to model-assisted estimation. (C) 2015 Elsevier Inc. All rights reserved.
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| 25. |
- Saarela, Svetlana, et al.
(författare)
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Effects of sample size and model form on the accuracy of model-based estimators of growing stock volume
- 2015
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Ingår i: Canadian Journal of Forest Research. - : Canadian Science Publishing. - 0045-5067 .- 1208-6037. ; 45, s. 1524-1534
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Tidskriftsartikel (refereegranskat)abstract
- In this study, we investigate the use of model-based inference in forest surveys in which auxiliary data are available as a probability sample. We evaluate the effects of model form and sample size on estimators of growing stock volume, based on different types of remotely sensed auxiliary data. The study was performed through Monte Carlo sampling simulation using a two-phase sampling design within a simulated study area resembling the conditions in mid-western Finland. We show that the choice of model has a minor to moderate effect on the precision of model-based estimators. Similarly, the choice of estimator of the variance-covariance matrix of model parameter estimates, which is at the core of uncertainty assessment in model-based inference, was also found to have a minor to moderate effect on the precision of model-based estimators. Regarding sample sizes, the model error contribution to the total variance remains the same regardless of the sample size of the first phase (i.e., the size of the sample of auxiliary data); to reduce the model-error contribution, there is a need to increase the sample size of the second phase (i.e., the size of the sample of field plots for developing regression models). As a baseline for comparisons, model-assisted estimators were applied and found to be about equally precise as the model-based estimators, in accordance with the theory for the case when models are estimated from the sample data.
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| 26. |
- Saarela, Svetlana, et al.
(författare)
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Hierarchical model-based inference for forest inventory utilizing three sources of information
- 2016
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Ingår i: Annals of Forest Science. - : Springer Science and Business Media LLC. - 1286-4560 .- 1297-966X. ; 73, s. 895-910
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Tidskriftsartikel (refereegranskat)abstract
- The study presents novel model-based estimators for growing stock volume and its uncertainty estimation, combining a sparse sample of field plots, a sample of laser data, and wall-to-wall Landsat data. On the basis of our detailed simulation, we show that when the uncertainty of estimating mean growing stock volume on the basis of an intermediate ALS model is not accounted for, the estimated variance of the estimator can be biased by as much as a factor of three or more, depending on the sample size at the various stages of the design.This study concerns model-based inference for estimating growing stock volume in large-area forest inventories, combining wall-to-wall Landsat data, a sample of laser data, and a sparse subsample of field data.We develop and evaluate novel estimators and variance estimators for the population mean volume, taking into account the uncertainty in two model steps.Estimators and variance estimators were derived for two main methodological approaches and evaluated through Monte Carlo simulation. The first approach is known as two-stage least squares regression, where Landsat data were used to predict laser predictor variables, thus emulating the use of wall-to-wall laser data. In the second approach laser data were used to predict field-recorded volumes, which were subsequently used as response variables in modeling the relationship between Landsat and field data.a (TM) The estimators and variance estimators are shown to be at least approximately unbiased. Under certain assumptions the two methods provide identical results with regard to estimators and similar results with regard to estimated variances.We show that ignoring the uncertainty due to one of the models leads to substantial underestimation of the variance, when two models are involved in the estimation procedure.
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| 27. |
- Saarela, Svetlana, et al.
(författare)
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Kriging prediction of stand-level forest information using mobile laser scanning data adjusted for nondetection
- 2017
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Ingår i: Canadian Journal of Forest Research. - : Canadian Science Publishing. - 0045-5067 .- 1208-6037. ; 47, s. 1257-1265
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Tidskriftsartikel (refereegranskat)abstract
- This study presents an approach for predicting stand-level forest attributes utilizing mobile laser scanning data collected as a nonprobability sample. Firstly, recordings of stem density were made at point locations every 10th metre along a subjectively chosen mobile laser scanning track in a forest stand. Secondly, kriging was applied to predict stem density values for the centre point of all grid cells in a 5 m x 5 m lattice across the stand. Thirdly, due to nondetectability issues, a correction term was computed based on distance sampling theory. Lastly, the mean stem density at stand level was predicted as the mean of the point-level predictions multiplied with the correction factor, and the corresponding variance was estimated. Many factors contribute to the uncertainty of the stand-level prediction; in the variance estimator, we accounted for the uncertainties due to kriging prediction and due to estimating a detectability model from the laser scanning data. The results from our new approach were found to correspond fairly well to estimates obtained using field measurements from an independent set of 54 circular sample plots. The predicted number of stems in the stand based on the proposed methodology was 1366 with a 12.9% relative standard error. The corresponding estimate based on the field plots was 1677 with a 7.5% relative standard error.
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| 28. |
- Saarela, Svetlana, et al.
(författare)
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Model-assisted estimation of growing stock volume using different combinations of LiDAR and Landsat data as auxiliary information
- 2015
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 158, s. 431-440
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Tidskriftsartikel (refereegranskat)abstract
- Airborne Light Detection and Ranging (LiDAR) and Landsat data were evaluated as auxiliary information with the intent to increase the precision of growing stock volume estimates in field-based forest inventories. The aim of the study was to efficiently utilize both wall-to-wall Landsat data and a sample of LiDAR data using model-assisted estimation. Variables derived from the Landsat 7 ETM + satellite image were spectral values of blue, green, red, near infra-red (IR), and two shortwave IR (SWIR) bands. From the LiDAR data twenty-six height and density based metrics were extracted. Field plots were measured according to a design similar to the 10th Finnish National Forest Inventory, although with an increased number of plots per area unit. The study was performed in a 30000 ha area of Kuortane, Western Finland. Three regression models based on different combinations of auxiliary data were developed, analysed, and applied in the model-assisted estimators. Our results show that adding auxiliary Landsat and LiDAR data improves estimates of growing stock volume. Very precise results were obtained for the case where wall-to-wall Landsat data, LiDAR strip samples, and field plots were combined; for simple random sampling of LiDAR strips the relative standard errors (RSE) were in the range of 1-4%, depending on the size of the sample. With only LiDAR and field data the RSEs ranged from 4% to 25%. We also showed that probability-proportional-to-size sampling of LiDAR strips (utilizing predicted volume from Landsat data as the size variable) led to more precise results than simple random sampling. (C) 2014 Elsevier Inc. All rights reserved.
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| 29. |
- Ståhl, Göran, et al.
(författare)
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Informative plot sizes in presence-absence sampling of forest floor vegetation
- 2017
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Ingår i: Methods in Ecology and Evolution. - Hoboken : British Ecological Society. - 2041-210X. ; 8:10, s. 1284-1291
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Tidskriftsartikel (refereegranskat)abstract
- 1. Plant communities are attracting increased interest in connection with forest and landscape inventories due to society’s interest in ecosystem services. However, the acquisition of accurate information about plant communities poses several methodological challenges. Here, we investigate the use of presence-absence sampling with the aim to monitor state and change in plant density. We study what plot sizes are informative, i.e. the estimators should have as high precision as possible.2. Plant occurrences were modelled through different Poisson processes and tests were developed for assessing the plausibility of the model assumptions. Optimum plot sizes were determined by minimizing the variance of the estimators. While state estimators of similar kind as ours have been proposed in previous studies, our tests and change estimation procedures are new.3. We found that the most informative plot size for state estimation is 1.6 divided by the plant density, i.e. if the true density is 1 plant per square metre the optimum plot size is 1.6 square metres. This is in accordance with previous findings. More importantly, the most informative plot size for change estimation was smaller and depended on the change patterns. We provide theoretical results as well as some empirical results based on data from the Swedish National Forest Inventory.4. Use of too small or too large plots resulted in poor precision of the density (and density change) estimators. As a consequence, a range of different plot sizes would be required for jointly monitoring both common and rareplants using presence-absence sampling in monitoring programmes.
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| 30. |
- Ståhl, Göran, et al.
(författare)
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Presence-absence sampling for estimating plant density using survey data with variable plot size
- 2020
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Ingår i: Methods in Ecology and Evolution. - : John Wiley & Sons. - 2041-210X. ; 11:4, s. 580-590
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Tidskriftsartikel (refereegranskat)abstract
- Presence–absence sampling is an important method for monitoring state and change of both individual plant species and communities. With this method, only the presence or absence of the target species is recorded on plots and thus the method is straightforward to apply and less prone to surveyor judgement compared to other vegetation monitoring methods. However, in the basic setting, all plots must be equally large or otherwise it is unclear how data should be analysed. In this study, we propose and evaluate five different methods for estimating plant density based on presence–absence registrations from surveys with variable plot sizes.Using artificial plant population data as well as empirical data from the Swedish National Forest Inventory, we evaluated the performance of the proposed methods. The main analysis was conducted through sampling simulation in artificial populations, whereby bias and variance of density estimators for the different methods were quantified and compared.Both for state and change estimation of plant density, we found that the best method to handle variable plot size was to perform generalized least squares regression, using plot size as an independent variable. Methods where plots smaller than a certain threshold were excluded or their registrations recalculated were, however, almost as good. Using all registrations as if they were obtained from plots with the nominal plot size resulted in substantial bias.Our findings are important for plant population studies in a wide range of environmental monitoring programmes. In these programmes, plots are typically randomly laid out and may be located across boundaries between different land‐use or land‐cover classes, resulting in subplots of variable size. Such splitting of plots is common when large plots are used, for example, with the 100 m2 plots used in the Swedish National Forest Inventory. Our methods overcome problems to estimate plant density from presence–absence data observed in plots that vary in size.
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| 31. |
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