Potential of mapping forest damage from remotely sensed data

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

Predicting bilberry and cowberry yields using airborne laser scanning and other auxiliary data combined with National Forest Inventory field plot data

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

Updating of forest stand data by using recent digital photogrammetry in combination with older airborne laser scanning data

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%

Lessons learned from assessing the cover and yield of bilberry and lingonberry using the national forest inventories in Finland and Sweden

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

Usability of citizen science observations together with airborne laser scanning data in determining the habitat preferences of forest birds

Citizens' field observations are increasingly stored in accessible databases, which makes it possible to use them in research. Citizen science (CS) complements the field work that must necessarily be carried out to gain an understanding of any of bird species' ecology. However, CS data holds multiple biases (e.g. presence only data, location error of bird observations, spatial data coverage) that should be paid attention before using the data in scientific research. The use of Airborne Laser Scanning (ALS) enables investigating forest bird species' habitat preferences in detail and over large areas. In this study the breeding time habitat preferences of 25 forest bird species were investigated by coupling CS observations together with nine forest structure parameters that were computed using ALS data and field plot measurements. Habitat preferences were derived by comparing surroundings of presence-only observations against the full landscape. Also, in order to account for bird observation location errors, we analysed several buffering alternatives. The results correspond well with the known ecology of the selected forest bird species. The size of a bird species' territory as well as some behavioural traits affecting detectability (song volume, mobility etc.) seemed to determine which bird species' CS data could be analysed with this approach. Especially the habitats of specialised species with small or medium sized territories differed from the whole forest landscape in the light of several forest structure parameters. Further research is needed to tackle issues related to the behaviour of the observers (e.g. birdwatchers' preference for roads) and characteristics of the observed species (e.g. preference for edge habitats), which may be the reasons for few unexpected results. Our study shows that coupling CS data with ALS yield meaningful results that can be presented with distribution figures easy to understand and, more importantly, that can cover areas larger than what is normally possible by means of purpose-designed research projects. However, the use of CS data requires an understanding of the process of data collection by volunteers. Some of the biases in the data call for further thinking in terms of how the data is collected and analysed.Peer reviewe

Predicting bilberry yields using ALS and other auxiliary data combined with NFI field plots

10410123FORMASAcademy of Finlan

Mustikan ja puolukan peittävyyksien ja marjasatojen arviointi valtakunnan metsien inventointiaineistojen avulla Suomessa ja Ruotsissa

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