| 1. |
- Ståhl, Göran, et al.
(författare)
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Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time – and how this affects applications
- 2024
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Ingår i: Forest Ecosystems. - : Elsevier. - 2095-6355 .- 2197-5620. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Remotely sensed data are frequently used for predicting and mapping ecosystem characteristics, and spatially explicit wall-to-wall information is sometimes proposed as the best possible source of information for decision-making. However, wall-to-wall information typically relies on model-based prediction, and several features of model-based prediction should be understood before extensively relying on this type of information. One such feature is that model-based predictors can be considered both unbiased and biased at the same time, which has important implications in several areas of application. In this discussion paper, we first describe the conventional model-unbiasedness paradigm that underpins most prediction techniques using remotely sensed (or other) auxiliary data. From this point of view, model-based predictors are typically unbiased. Secondly, we show that for specific domains, identified based on their true values, the same model-based predictors can be considered biased, and sometimes severely so.We suggest distinguishing between conventional model-bias, defined in the statistical literature as the difference between the expected value of a predictor and the expected value of the quantity being predicted, and design-bias of model-based estimators, defined as the difference between the expected value of a model-based estimator and the true value of the quantity being predicted. We show that model-based estimators (or predictors) are typically design-biased, and that there is a trend in the design-bias from overestimating small true values to underestimating large true values. Further, we give examples of applications where this is important to acknowledge and to potentially make adjustments to correct for the design-bias trend. We argue that relying entirely on conventional model-unbiasedness may lead to mistakes in several areas of application that use predictions from remotely sensed data.
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| 2. |
- Nyström, Kenneth
(författare)
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Stem volume and above ground biomass models for Sitka spruce (Picea sitchensis (Bong.) Carr.) in Iceland
- 2023
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Ingår i: Icelandic Agricultural Sciences. - 1670-567X .- 2298-786X. ; 36, s. 69-80
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Tidskriftsartikel (refereegranskat)abstract
- The prediction of tree biomass and stem volume is necessary for monitoring and assessing of national forest biomass, carbon stock and sustainable forest management. Such predictions of biomass or carbon stocks are generated by using either allometric models or applying biomass expansion factors (BEFs), where the former is a better method. Volume models are generated by using allometric models. The data for development of the new volume and biomass models for Iceland presented in this paper were collected between years 2000-2021, from 48 locations in even aged stands that were planted between 1942-1983. Because of the young age of forest plantations in Iceland, existing biomass and volume models need to be updated regularly as trees get older and larger. The new models use the same independent variables as the previous ones for Iceland but use a wider approach and have a different form than the older ones. Using the previous Icelandic models outside their data range results in underestimation for both aboveground biomass and stem volume.
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| 3. |
- Appiah Mensah, Alex, et al.
(författare)
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Modelling potential yield capacity in conifers using Swedish long-term experiments
- 2022
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Ingår i: Forest Ecology and Management. - : Elsevier BV. - 0378-1127 .- 1872-7042. ; 512
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Lindgren, Nils, et al.
(författare)
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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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Ingår i: Canadian Journal of Remote Sensing. - : Informa UK Limited. - 0703-8992 .- 1712-7971. ; 48, s. 127-143
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Tidskriftsartikel (refereegranskat)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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| 5. |
- Lindgren, Nils, et al.
(författare)
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Importance of Calibration for Improving the Efficiency of Data Assimilation for Predicting Forest Characteristics
- 2022
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- Holmström, Emma, et al.
(författare)
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The millennium shift: Investigating the relationship between environment and growth trends of Norway spruce and Scots pine in northern Europe
- 2021
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Ingår i: Forest Ecology and Management. - : Elsevier BV. - 0378-1127 .- 1872-7042. ; 481
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Tidskriftsartikel (refereegranskat)abstract
- For boreal forests in colder climates, changes in environmental conditions are hypothesised to substantially affect ecosystem processes. In this study, trends of top height growth of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst) were analysed using permanent sample plot data from more than 300 long-term experimental sites distributed from temperate zones to the boreal forest conditions in Sweden. By regression analyses, the effects of temperature-sum and precipitation-sum on top height growth were assessed in the period 1986-2018. A significant upward temporal trend in height growth was observed for both species, with the trend more pronounced after the millennium shift. The magnitude of the annual relative height growth after the millennium was about 16.92% and 9.54% higher than expected, respectively for Scots pine and Norway spruce. A potential climate response on height growth was found for both species with temperature-sum positively correlated with top height growth. No significant effect of precipitation-sum on height growth was observed for either species. Our results suggest improved growing conditions and forest sites became more productive in response to increasing temperature in the northern temperate and boreal regions. The increasing growth trends may offer shorter rotation periods and increased forest value for Norway spruce and Scots pine, coupled with contributions of boreal forests to the emerging bio-economy and the regulation of global atmospheric carbon.
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| 7. |
- Lindgren, Nils, et al.
(författare)
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Updating of forest stand data by using recent digital photogrammetry in combination with older airborne laser scanning data
- 2021
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Ingår i: Scandinavian Journal of Forest Research. - : Informa UK Limited. - 0282-7581 .- 1651-1891. ; 36, s. 401-407
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Tidskriftsartikel (refereegranskat)abstract
- Accurate and up-to-date data about growing stock volume are essential for forest management planning. Airborne Laser Scanning (ALS) is known for producing accurate wall-to-wall predictions but the data are at present collected at long time intervals. Digital Photogrammetry (DP) is cheaper and often more frequently available but known to be less accurate. This study investigates the potential of using contemporary DP data together with older ALS data and compares this with the case when only old ALS data are trained with recent field data. Combining ALS data from 2010 to 2011 with DP data from 2015, both trained with National Forest Inventory (NFI) field plot data from 2015, improved predictions of growing stock volume. Validation using data from 100 stands inventoried in 2015 gave an RMSE of 24.3% utilizing both old ALS data and recent DP data, 26.0% for old ALS only and 24.9% for recent DP only. If information about management actions were assumed available, combining old ALS and recent DP gave RMSE of 23.0%, only ALS 23.3% and only DP 23.8%.
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| 8. |
- Wallerman, Jörgen, et al.
(författare)
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Nation-wide mapping of tree growth using repeated airborne laser scanning
- 2020
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Konferensbidrag (refereegranskat)abstract
- In this study, mapping of tree growth was performed using data from the two nation-wide acquisitions of airborne laser scanning in Sweden. Following the successful first national acquisition performed in 2009 - 2015, a new, repeated, scanning is now launched and ongoing. The first scanning provided new, accurate (in accuracy as well as in spatial resolution) data about the forest and quickly found wide-spread use in the forest industry. It outperformed previous methods and provided a new standard of data capture for forest management planning. The addition of a second scanning provide information also about changes, where forest tree growth is of high interest in the industry. This study presents the first results from large-scale assessment of growth for basal area-weighted mean tree height (H) and mean stem volume (V), using the bi-temporal scannings and sample-plot data from the National Forest Inventory. Growth was most accurately assessed by the direct change metrics of the scannings, although the accuracies were moderate. The accuracy of forecasts, i.e. only utilizing the predicted forest state at the first scanning, were similar for H but inferior for V, though.
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| 9. |
- Egbäck, Samuel, et al.
(författare)
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Effects of phenotypic selection on height-diameter ratio of Norway spruce and Scots pine in Sweden
- 2018
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Ingår i: Silva Fennica. - : Finnish Society of Forest Science. - 0037-5330 .- 2242-4075. ; 52
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Tidskriftsartikel (refereegranskat)abstract
- Genetically improved Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) are extensively used in operational Swedish forestry plantations. However, relatively little is known about the stein slenderness (height-diameter ratio) of genetically improved material. Thus, in this study we investigated effects of plus-tree selection on stem slenderness of Norway spruce and Scots pine in Sweden by evaluating both the plus-tree selection and a large number of progeny trials. Species-specific models for predicting the height-diameter ratio were estimated using regression and mixed model approach. Our results show that phenotypic plus-tree selection promoted less slender Norway spruce trees and more slender Scots pine trees compared to neighboring trees. Similar results were also found for the progeny trials which indicated that genetics played a prominent role in the phenotypic appearance. Compared to the progeny of neighboring trees, Norway spruce plus-tree progenies had a 5.3% lower height-diameter ratio, while Scots pine plus-tree progenies had a 1.5% greater height-diameter ratio. The narrow sense heritability for height-diameter ratio was 0.19 for Norway spruce and 0.11 for Scots pine, indicating that it is possible to modify the height-diameter ratio by breeding. Correlation coefficients between breeding values for height-diameter ratio and diameter were negative for Scots pine (-0.71) and Norway spruce (-0.85), indicating that selection for diameter only would result in less slender stems of both species. Similar correlations were also found between breeding values for height-diameter ratio and height of Scots pine (-0.34) and Norway spruce (-0.74).
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| 10. |
- Egbäck, Samuel, et al.
(författare)
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Modeling early height growth in trials of genetically improved Norway spruce and Scots pine in Southern Sweden
- 2017
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Ingår i: Silva Fennica. - : Finnish Society of Forest Science. - 0037-5330 .- 2242-4075. ; 51
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Tidskriftsartikel (refereegranskat)abstract
- Genetically improved Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) are used extensively in operational Swedish forestry plantations to increase production. Depending on the genetic status of the plant material, the current estimated genetic gain in growth is in the range 10-20% for these species and this is expected to increase further in the near future. However, growth models derived solely from data relating to genetically improved material in Sweden are still lacking. In this study we investigated whether an individual tree growth model based on data from unimproved material could be used to predict the height increment in young trials of genetically improved Norway spruce and Scots pine. Data from 11 genetic experiments with large genetic variation, ranging from offspring of plus-trees selected in the late 1940s to highly improved clonal materials selected from well performing provenances were used. The data set included initial heights at the age of 7-15 years and 5-year increments for almost 2000 genetic entries and more than 20 000 trees. The evaluation indicated that the model based on unimproved trees predicted height development relatively well for genetically improved Norway spruce and there was no need to incorporate a genetic component. However, for Scots pine, the model needed to be modified. A genetic component was developed based on the genetic difference recorded within each trial, using mixed linear models and methods from quantitative genetics. By incorporating the genetic component, the prediction errors were significantly reduced for Scots pine. This study provides the first step to incorporate genetic gains into Swedish growth models and forest management planning systems.
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