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  • Author: Allan Sims x
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Abstract

Assessment of tree mortality provides deeper understanding of forest structure and functioning. This enables evaluation of stand sustainability and provides information on stand productivity, diversity and health condition. Tree mortality can be assessed by spatiotemporal patterns as well as by studying the processes and causes of mortality. Tree mortality is caused by specific disturbance agents or by the complex effect of various disturbances. The purpose of this study is to examine tree mortality in Estonian forests, determine the causes of tree death, and estimate how different management regimes affect tree mortality and its causes. This study is based on 360 sample plots measured in 2003-2005 and re-measured in 2008-2010. The sample plots were divided into recently managed (RM, 146 plots) and low intensity/unmanaged (LU, 214 plots) plots based on forest management regime. In total, 53,990 trees were measured, of which 20,020 were located on RM and 33,970 on LU plots. The tree mortality for 5-year period was 3.4% on RM plots and 8.0% on LU plots. The main cause of tree mortality in RM stands was insect damage, which attributes to 29.8% of tree mortality, whereas in LU stands the main cause was tree competition, which attributes to 45.4% of tree mortality. The analysis of tree mortality indicated that an increase in relative tree diameter in both stand types contributes to an increase in mortality due to insect, wind or fungal damage and diseases. Opposite results were received with respect to competition - the smaller the tree relative diameter, the more probable it is that a tree will die because of competition with neighbouring trees. The analysis of game damage and other causes of tree mortality showed that these were not dependent on the relative diameter of trees. The analysis of the overall probability of tree mortality revealed that relatively smaller trees have a higher probability of mortality than larger trees.

Abstract

The abandonment of agricultural land is an actual problem in Estonia due to significant impact on landscape ecology and structure. Abandoned agricultural fields are usually converting into forest. Mapping of agricultural land use is a strategic interest of each country. Airborne laser scanning (ALS) is used in many countries for topographical mapping and the laser pulse return positions are promising datasets for mapping the abandonment of agricultural land. We used ALS data based woody plant canopy cover estimates made at certain reference height unachievable for field crops to map abandoned agricultural land in nine test sites in Tartumaa, Estonia. The maximum height of trees in test sites ranged from 6.5 m to 13.4 m. The lidar pulse returns based canopy cover estimate was assessed 1) by using ortophoto based digitized maps of tree canopy, 2) repeated measurements made with plant canopy analyzer LAI-2000 and 3) by using allometric crown radius models and repeated tree measurements from sample plots. The interpretation of canopy boundaries and separation of small spaces between tree crowns from ortophotos is a challenging task for an operator. The relationship between ALS based canopy cover and ortophoto based canopy cover was linear in all test sites except when ALS data from beginning of June were used. It the beginning of June foliage is not fully developed on trees. An increase in the woody canopy cover was detected from repeated LAI-2000 measurements and also from repeated tree measurements-based simulated crowns. The impact of reference height change from 2.0 m to 1.3 m on canopy cover estimations was not significant and much smaller compared to the tree growth induced increase in canopy cover, indicating that similar errors originating from e.g. digital elevation model are not problematic for the proposed method in practical applications.

Abstract

Landsat-8 OLI and Sentinel-2 MSI images from years 2015 and 2016, a 1:10,000 digital soil map and a large number of reference samples were used with a random forest machine learning implementation in GRASS GIS to construct a tree species map for the entire territory of Estonia (42,755 km2). Class probabilities for seven main tree species, an extra class for other species and probability of the forest cover not conforming to the forest definition were assigned for each pixel. Validation of dominant species distribution by area showed very strong correlation at county level both in state forests (R2 = 0.98) and in private forests (R2 = 0.93). Validation of tree species composition using harvester measurement data from 2,045 regeneration felling areas showed also very strong correlation (R2 = 0.75) with the measured values of the proportion of coniferous trees. There was some tendency to underestimate the proportion of more common species and overestimation was found for the species with smaller proportion in the mixture. The accuracy for the proportion of deciduous species that were present in a smaller number of reference observations was substantially smaller. Validation of the results by using data from 659 large sample plots from the database of the Estonian Network of Forest Research Plots and 3,002 small sample plots from the National Forest Inventory (NFI) data base confirmed the findings based on harvester data. The NFI data revealed also a decrease of estimation error with the increase of forest age. Cohen’s kappa index of agreement for main species for NFI sample plots with main species proportion equal to or greater than 75% decreased from 0.69 to 0.66 when observations with forests younger than 20 years were included in the comparison. Overall, the constructed map provides valuable data about tree species composition for the forests where no up to date inventory data are available or for the projects that require continuous cover of tree species data of known quality over the entire Estonia.

Abstract

Forest management has become a more complex issue than it has ever been before. Foresters need to fulfill the demands of several interest groups, often which are conflicting. Finding the balance between different management objectives can be facilitated with the use of decision support systems. Since no decision support systems have been developed in Estonia, the aim of this study is to assess the applicability of the Finnish stand growth simulator MOTTI in Estonia. The evaluation focuses on the basal area growth models; the data used originates from the Estonian network of permanent forest growth plots. Tree-level bias models were constructed for all major tree species in order to assess model performance. Also, bias was examined visually with the use of residual plots. Results show that bias levels and variables which contribute to bias differ by species. Based on the fit statistics of the bias models, Common aspen shows the highest bias level whereas the growth of Gray alder seems to be predicted most accurately. Although model performance is decent for a model that is used outside of its application limits, calibration should still be considered as a prerequisite to implement the MOTTI system in Estonian forestry practice.

Abstract.

Nearest neighbour techniques are useful for constructing maps of forest inventory variables based on sample plot and auxiliary data from remote sensing. The most problematic issue of the nearest neighbour technique is possible systematic bias in the estimated values. In this study a 15 by 15 km test site in nemoral multilayer mixed forests was established in Laeva, Estonia. A set of 444 circular sample plots was used as reference set. Airborne lidar data and Landsat-8 Operational Land Imager image were used to construct five different feature variable sets consisting of original variables and alternatively principal components. The response variables were wood volume of first tree layer, wood volume of the second tree layer and main species code. A special test was carried out where a substantial amount of Silver birch dominated plots were removed from the reference set. The wood volume prediction validation was carried out on 89 forest growth sample plots and on 2290 forest stands. Species composition prediction was validated on 986 forest stands. As in many previous studies the results confirmed superiority of airborne lidar variables over spectral variables for wood volume estimation. The first three principal components of airborne lidar variables and first five principal components of all possible original feature variables contained over 99% of the information and performed well in imputations. The imputed wood volume at small values was overestimated and underestimated at large values regardless of used reference set. The feature variable sets containing spectral data performed better for species composition imputation. There was a forest age dependent discrepancy in predicted species proportions: birch and spruce proportions were underestimated in young stands and overestimated in older stands while proportion of aspen had exactly the opposite errors. The lack of fit depended slightly on the feature variable sets. The birch dominated plot partial removal from the reference set changed the predicted proportion of species but did not remove the forest age dependent lack of fit. The result can be important for the studies in which bootstrap samples are used to estimate error statistics for nearest neighbour technique based forest inventory variable maps.

Abstract

Forest research has long traditions in Estonia that can be traced back to the 19th century. Data from long-term forest experiments are available since 1921. The first studies mainly focused on silvicultural treatments and application of such data for understanding and modeling ecological processes was limited. The Department of Forest Management of the Estonian University of Life Sciences started to develop the Estonian Network of Forest Research Plots (ENFRP) in 1995. Since then, plots have been continuously re-measured with 5-year interval. Approximately 100-150 permanent sample plots were measured annually. In 2014, the long-term research network consisted of 729 permanent sample plots, of which 699 have been re-measured at least once, 667 plots - twice and 367 plots - three times. The total number of trees recorded in the network database amounts to 130,479. The plots are systematically distributed throughout the country. Detailed dendrometric measurements including tree spatial distribution are part of the survey protocol. Initially the network was set up to produce suitable data for development of individual tree growth models for Estonia. The significance of the network for the Estonian forest research is continuously increasing and nowadays ENFRP is recognized as an important national research infrastructure.