| 1. |
- Bohlin, Jonas, et al.
(författare)
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Mapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory
- 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
- Exploring the possibility to produce nation-wide forest attribute maps using stereophotogrammetry of aerial images, the national terrain model and data from the National Forest Inventory (NFI). The study areas are four image acquisition blocks in mid-and south Sweden. Regression models were developed and applied to 12.5 m x 12.5 m raster cells for each block and validation was done with an independent dataset of forest stands. Model performance was compared for eight different forest types separately and the accuracies between forest types clearly differs for both image-and LiDAR methods, but between methods the difference in accuracy is small at plot level. At stand level, the root mean square error in percent of the mean (RMSE%) were ranging: from 7.7% to 10.5% for mean height; from 12.0% to 17.8% for mean diameter; from 21.8% to 22.8% for stem volume; and from 17.7% to 21.1% for basal area. This study clearly shows that aerial images from the national image program together with field sample plots from the NFI can be used for large area forest attribute mapping.
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| 2. |
- Bohlin, Inka, et al.
(författare)
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Quantifying post-fire fallen trees using multi-temporal lidar
- 2017
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 0303-2434 .- 1569-8432. ; 63, s. 186-195
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Tidskriftsartikel (refereegranskat)abstract
- Massive tree-felling due to root damage is a common fire effect on burnt areas in Scandinavia, but has so far not been analyzed in detail. Here we explore if pre- and post-fire lidar data can be used to estimate the proportion of fallen trees. The study was carried out within a large (14,000 ha) area in central Sweden burnt in August 2014, where we had access to airborne lidar data from both 2011 and 2015. Three data-sets of predictor variables were tested: POST (post-fire lidar metrics), D1F (difference between post- and pre-fire lidar metrics) and combination of those two (POST_DIF). Fractional logistic regression was used to predict the proportion of fallen trees. Training data consisted of 61 plots, where the number of fallen and standing trees was calculated both in the field and with interpretation of drone images. The accuracy of the best model was tested based on 100 randomly selected validation plots with a size of 25 x 25 m.Our results showed that multi-temporal lidar together with field-collected training data can be used for quantifying post-fire tree felling over large areas. Several height-, density- and intensity metrics correlated with the proportion of fallen trees. The best model combined metrics from both datasets (POST DIF), resulting in a RMSE of 0.11. Results were slightly poorer in the validation plots with RMSE of 0.18 using pixel size of 12.5 m and RMSE of 0.15 using pixel size of 6.25 m. Our model performed least well for stands that had been exposed to high-intensity crown fire. This was likely due to the low amount of echoes from the standing black tree skeletons. Wall-to-wall maps produced with this model can be used for landscape level analysis of fire effects and to explore the relationship between fallen trees and forest structure, soil type, fire intensity or topography.
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| 3. |
- Lindberg, Eva, et al.
(författare)
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Potential of mapping forest damage from remotely sensed data
- 2022
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Remote sensing is an efficient tool for mapping, monitoring, and assessing forest damage and the risk of damage. This report presents ongoing research on those topics with preliminary results as well as research planned by the Department of Forest Resource Management, SLU in Umeå, in the near future. The damage types include spruce bark beetle attacks, storm damage, and forest fire. The report also outlines proposed continued research in the area and possible collaborations within and outside SLU.
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| 4. |
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| 5. |
- Bohlin, Inka, et al.
(författare)
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Lessons learned from assessing the cover and yield of bilberry and lingonberry using the national forest inventories in Finland and Sweden
- 2021
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Ingår i: Silva Fennica. - : Finnish Society of Forest Science. - 0037-5330 .- 2242-4075. ; 55
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Tidskriftsartikel (refereegranskat)abstract
- Bilberry (Vaccinium myrtillus L.) and lingonberry (V. vitis-idaea L.) can be a part of healthy diet and are important for many animals. Two approaches are described to assessing their vegetation cover and berry yield via national forest inventory (NFI) observations. The aim was to provide estimates and predictions of the abundance and yield of the species at regional and national levels in Finland and Sweden. In Finland, the model-based predictions are used in evaluating the impacts of cutting intensity on forest berries needed in forest-related decision making. In Sweden, seasonal inventory-based estimates are used to evaluate the annual national and regional berry yields, and in a forecasting system aimed at large public and berry enterprises. Based on the NFI sample plots measured between 2014 and 2018, the total annual yields are estimated to be 208 Mkg of bilberry and 246 Mkg of lingonberry on productive forest land (increment at least 1 m3 ha–1 year–1) in Finland, and 336 and 382 Mkg respectively in Sweden (average of NFI inventories in 2015–2019). The predicted development of berry yields is related to the intensity of cuttings in alternative forest management scenarios: lower removals favoured bilberry, and higher removals lingonberry. The model-based method describes the effects of stand development and management on berry yields, whereas the inventory-based method can calibrate seasonal estimates through field observations. In providing spatially and timely more accurate information concerning seasonal berry yields, an assessment of berry yields should involve the elements of both inventory-based and model-based approaches described in this study.
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| 6. |
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| 7. |
- Bohlin, Inka, et al.
(författare)
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Predicting bilberry and cowberry yields using airborne laser scanning and other auxiliary data combined with National Forest Inventory field plot data
- 2021
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Ingår i: Forest Ecology and Management. - : Elsevier BV. - 0378-1127 .- 1872-7042. ; 502
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Tidskriftsartikel (refereegranskat)abstract
- The increasing availability of wall-to-wall remote sensing datasets in combination with accurate field data enables the mapping of different ecosystem services more accurately and over larger areas than before. The provision of wild berries is an essential ecosystem service, and berries are the most used non-wood forest products in Nordic countries. The aim of the study was to 1) develop general prediction models for bilberry and cowberry yield based on metrics derived from airborne laser scanning (ALS) data and other existing wall-to-wall data and 2) to identify laser-based structural features of forests that can be linked to locations of the highest berry yields. We used the indirect approach where the correlation between forest structure described by the ALS data and the berry yields are utilized. Berry data collected in the Swedish National Forest Inventory (NFI) 2007–2016 were used for training the models and ALS data from 2009 to 2014 from the national ALS campaign of Sweden. Berry yields were modelled using generalised linear mixed models (GLMMs), and forest structural differences were demonstrated in histograms of presence/absence data. The ALS-based canopy cover was an important variable both in bilberry and cowberry models. Other significant variables were ALS-based height variance, shrub cover, height above sea level, slope, soil wetness and terrain ruggedness, satellite-based species-specific volume and percentage, seasonality of temperature and precipitation and annual precipitation, inventory year, soil type and land use class. In addition, the time difference between the inventory day and the Julian day when berries were expected to be ripe showed a 1.5% decrease for bilberry and a 1.1% decrease for cowberry yield per day during the season. The highest bilberry yield was identified in forests with a canopy cover of 50% and the highest cowberry yield in forests with a canopy cover close to zero. The canopy height of 15 m reflected the highest bilberry yield, whereas a canopy height close to 0 m resulted in the highest cowberry yield. The shrub cover was close to zero both with highest bilberry and cowberry yields. This is the first study combining ALS metrics with other wall-to-wall variables and NFI field data to model bilberry and cowberry yields. Prediction models can be used to produce maps showing the most potential locations for berry picking. Further, the models may, in the future, be imported into forest planning systems to obtain stand-level prognoses of berry yield development under different forest management strategies.
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| 8. |
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| 9. |
- Bohlin, Jonas, et al.
(författare)
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A comparison of forest inventories based on aerial image matching and Airborne Laser Scanning data
- 2014
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Forest inventories are now commonly done by Airborne Laser Scanning (ALS), especially because many countries are collecting ALS data nation-wide to produce high quality elevation data. With an accurate digital elevation model, 3D data from aerial image matching could be a more cost-effective alternative to repeated ALS acquisitions for providing updated data to forest management planning in the future. This study aims at comparing the quality of forest inventory data obtained by aerial image matching and ALS. In the study area, a mixed boreal forest situated in central Sweden, aerial images from the national acquisition program with a ground sampling distance of 0.5 m and ALS data with a point density of 1.5-2 pulses per m2 from the national ALS production were available. The aerial images were matched with three different algorithms to assess possible differences in forest information content. Two hundred field plots, located within the study area, were utilized for non-parametric prediction of forest variables using random forest. Accuracy assessment was made by leave-one-out cross-validation at plot level. The results show similar accuracy of ALS and image matching-based predictions, with ALS slightly superior. Accuracy, in terms of root mean square errors in percent of surveyed plot mean, of ALS were: 6.4% for tree height; 12.5% for tree diameter; 18.2% for basal area and 20.0% for stem volume, and of image matching: 9.5% for tree height; 15.3% for tree diameter; 21.8% for basal area and 24.8% for stem volume. Among the image matching algorithms used, SURE was found to estimate the forest variables with best accuracies. However, the other algorithms produced similar results. These results indicate that inventory data acquired by matching of aerial images have a large potential for operational use in forest management planning as a cost-effective alternative to new ALS campaigns.
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| 10. |
- Bohlin, Jonas, et al.
(författare)
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Deciduous forest mapping using change detection of multi-temporal canopy height models from aerial images acquired at leaf-on and leaf-off conditions
- 2016
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Ingår i: Scandinavian Journal of Forest Research. - : Informa UK Limited. - 0282-7581 .- 1651-1891. ; 31, s. 517-525
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Tidskriftsartikel (refereegranskat)abstract
- Discrimination of deciduous trees using spectral information from aerial images has only been partly successfully due to the complexity of the reflectance at different view angles, times of acquisition, phenology of the trees and inter-tree radiance. Therefore, the objective was to evaluate the accuracy of estimating the proportion of deciduous stem volume (P) utilizing change detection between canopy height models (CHMs) generated by digital photogrammetry from leaf-on and leaf-off aerial images instead of using spectral information. The study was conducted at a hemiboreal study area in Sweden. Using aerial images from three seasons, CHMs with a resolution of approximately 0.5 m were generated using semi-global matching. For training plots, metrics describing the change between leaf-on and leaf-off conditions were calculated and used to model the continuous variable P, using the Random Forest approach. Validated at sub-stands, the estimation accuracy of P in terms of root mean square error and bias was found to be 18% and −6%, respectively. The overall classification accuracy, using four equally wide classes, was 83% with a kappa value of 0.68. The validation plots in classes of high proportion of coniferous or deciduous stem volume were well classified, whereas the mixed forest classes showed lower classification accuracies.
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