Tree Line Change Detection Using Historical Hexagon Mapping ...

Tree Line Change Detection Using Historical Hexagon Mapping Camera Imagery and Google Earth Data Thomas A. Groen1 and Hailemariam G. Fanta

Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, PO Box 217, 7500 AE, Enschede, The Netherlands

Georgi Hinkov and Ivaylo Velichkov

Department of Silviculture, Forest Research Institute–Sofia, 132 St. Kliment Ohridski Blvd., 1756 Sofia, Bulgaria

Iris Van Duren

Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, PO Box 217, 7500 AE, Enschede, The Netherlands

Tzvetan Zlatanov

Department of Silviculture, Forest Research Institute–Sofia, 132 St. Kliment Ohridski Blvd., 1756 Sofia, Bulgaria

Abstract: Monitoring the response of tree lines to climatic change requires long time series. Therefore ground-based studies, initially designed for other purposes, are used, causing a bias in the sampling design. Using historical satellite data might overcome this bias. This study explores the usability of historical spy-satellite imagery from the United States Hexagon missions to detect changes in tree lines. We find that both vertical and horizontal errors are within acceptable boundaries (± 18.0 m in horizontal direction and 5.5 m in vertical direction) to detect change. This opens opportunities to explore tree line changes globally with a more robust sampling strategy.

INTRODUCTION Climate change is an issue of growing concern (Solomon et al., 2007), and many studies focus on monitoring the impacts of climate change on ecosystems. Typical ecosystems for which clear effects of climatic change are expected include tree lines (Körner, 1998; Harsch et al., 2009; Feeley et al., 2010). Tree lines are the high-­altitude limits of forests, also referred to as timberlines or forest lines. The interpretation of the word “line,” however, needs specification. Natural boundaries often represent 1

Corresponding author; email: [email protected]

933 GIScience & Remote Sensing, 2012, 49, No. 6, p. 933–943. http://dx.doi.org/10.2747/1548-1603.49.6.933 Copyright © 2012 by Bellwether Publishing, Ltd. All rights reserved.

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transition zones, making localization of tree lines in principle inexact and therefore subject to pre-defined conventions (Armand, 1992). However, often it does represent a steep gradient that appears as a straight line when viewed from a great distance, such as from aerial photography or remote sensing imagery (Körner and Paulsen, 2004). Tree lines are associated with a common temperature threshold (Körner and Paulsen, 2004), suggesting that above the tree line temperature limits tree growth. Therefore, it is expected that with contemporary climate change, where for most places in the world an increase in mean temperature is predicted (Solomon et al., 2007), tree lines will shift to higher altitudes and latitudes (Harsch et al., 2009 and references therein). To test whether this is indeed the case, long-term monitoring is needed because trends in climate variables are measured over long time scales. Besides, responses of tree lines are expected to be slow, depending on the system under study. This implies that most of these studies rely on observations that started before climate change became an issue, although observations on short time frames have been reported (Feeley et al., 2010). This has resulted in useful studies which assess the potential effects of climate change on tree lines (Harsch et al., 2009), showing that indeed in general tree lines are shifting upwards, as a result of increases in winter temperatures. When relying on existing observations, an important drawback that cannot be corrected for is the potential bias in the selection of study sites. Harsch et al. (2009) found an overall trend of tree line advance (i.e., moving to higher altitudes) from 1900 onward in a study, combining 166 sites distributed all over the world. They could, however, not exclude the possibility that a bias was present in their research, as the majority of the studies they could use were located in Europe and North America. Ideally, sites should be chosen on the basis of a random sampling design. With the advance of remote sensing, using sensors from satellite platforms makes it possible to apply systematic and standardized monitoring over large areas (Hansen and DeFries, 2004; Hansen et al., 2008; Stow et al., 2004). The archive of remote sensing images that has been created in recent decades makes it possible to distribute observations over the globe using a randomized sampling design. However, with respect to the monitoring of tree lines related to climatic change, there are two requirements: (1) a time span long enough to allow such ecosystems to respond to climate change; and (2) a sensor that has a spatial resolution that makes it possible to detect positional shifts of tree lines. Older remote sensing imagery available (such as Landsat TM Imagery) has a ground resolution of 30 m, which is too coarse to monitor tree line changes. Currently, higher spatial resolution imagery is available, such as the Ikonos (82 cm ground resolution) or GEOEYE-1 (40 cm ground resolution). But the period they cover (launched in 1999 and 2008, respectively) is too short to detect changes in tree lines. In 1995, however, spy satellite imagery recorded during the period 1960–1972 by the American Corona satellite program and its successor (the Hexagon program) was declassified, making it available to the public for civilian uses (Dashora et al., 2007). This database consists of mainly panchromatic imagery, but often with remarkably high spatial resolution (up to 6 m ground resolution). In combination with new sources of information, such as GEOEYE-1 imagery or the content that is available from Google Earth (with accuracies up to ~ 1 m; www.googleearth.com), it can be used to investigate tree line dynamics over periods longer than five decades.



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However, to make effective use of these possibilities, an essential first step is to quantify the accuracy with which tree lines can be recognized on these images. The ability to trace changes in the position of a tree line depend heavily on the rate of change, the period between observations, and the accuracy with which the tree line position can be established. The accuracy of establishing the tree line position is influenced by errors originating from several sources, including from geo-referencing of the images used, the ground resolution of these images, delineation errors, and the accuracy of ground observations. Besides, tree line change normally refers to shifts in the vertical position of a tree line, while from airborne-derived imagery a horizontal shift is derived. To convert the horizontal shift into a vertical shift, a digital elevation model (DEM) is needed. DEMs are globally available from the shuttle radar topography mission (SRTM; http://www2.jpl.nasa.gov/srtm/) with a 30 m ground resolution. The conversion from a horizontal position to a vertical position adds an additional uncertainty. This also means that on steep slopes, errors in establishing the horizontal position of the tree line will have a larger effect on the determination of its vertical position than on more gentle slopes. This study attempts to assess with what accuracy the horizontal and vertical position of tree lines can be detected on historical Hexagon imagery in comparison with contemporary imagery derived from Google Earth. We wanted to find out whether the total error that results from the various sources would be sufficiently small to detect changes in tree line position. For this goal the tree line position in an area with known changes (Osogovo Mountain in Bulgaria) was identified on a Hexagon image from 1976 and a Google Earth–derived image from 2005. This was compared against ground truthing data that was collected in autumn 2010. Furthermore, we assessed how these cumulative errors affect the assessment of the vertical position of the tree line. METHODS Study Area The study was performed on Osogovo Mountain (42°09′00″ N–22°30′00″ E; Fig. 1), which is a mountain and ski resort area located along the border between Bulgaria and the Republic of Macedonia. It is about 110 km long and 50 km wide, with the highest peak being the Ruen at 2251 m. This study mainly focuses on the upper part of the mountain where the current tree line is located and covers an area of about 113 km2 inside the territory of Bulgaria. The area falls under the European broadleaved tree species region, but the dominant species of trees on this mountain changes with altitude. From 1200 to 1700 meters above sea level the mountain is dominated by Fagus sylvatica, Abies alba, and Pinus sylvestris, while between 1700 and 2000 meters it is covered predominantly with P. sylvestris. Around 4500 years ago the high mountain slopes and flat ridges were covered by coniferous forests (Lazarova et al., 2009), but the tree line was artificially lowered around 2000–3000 years ago (Velichkov, 2004; Filpovich et al., 2006) mainly through use of fire, as shepherds began to burn large areas to clear new pasture to replace stands of coniferous forest. Following the initial clearance, the area was grazed for many centuries until 1950. After the regime change in Bulgaria to socialism in 1947, natural areas were nationalized, and the western

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Fig. 1. Location of the study area, the position of the delineated tree lines for 1976 (yellow) and 2005 (red), and the position of the transects where field observations were made (green dots). The white square indicates the region where the photo displayed in Figure 2 was taken.

part of the mountain near the border with the Republic of Macedonia was fenced and restricted from any human or livestock intervention until 1990. During those decades (1950–1990), livestock grazing only occurred on the eastern part of the mountain, which was accessible for free grazing. Discouraged by the limited access to the area,



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people started to migrate to cities and towns, causing a decline in the number of livestock grazing on the mountain meadows. The number of animals decreased even ­further after the 1985 market crisis and currently there are only few people living on the mountain with few animals grazing in the area. In addition to reduced grazing pressure, the exodus led to a lower incidence of management fires. The changes in land use have caused an upward shift in the position of the tree line between the 1970s and the present on some parts of the mountain. In these areas, mainly P. sylvestris is encroaching relatively rapidly onto areas previously covered with grass. Osogovo Mountain constitutes an ideal area for testing the possible accuracy in tree line change detection from historical imagery. The tree line in the area is of a very abrupt nature, which facilitates a first test of tree line detection. Also, there is local knowledge of locations where the tree line has not moved and of locations where the tree line has shifted significantly in a vertical direction. This makes it possible to assess the accuracy of tree line detection from historical imagery in areas where the tree line has not moved, while the potential to detect shifts can be investigated as well. Imagery For the study, images from two sources were used. With the software Google Earth Pro we downloaded a high-resolution image from August 10, 2005. Secondly, we used a Hexagon image that was recorded in July 1976. Both images were georeferenced on the basis of a fully georeferenced ASTER 14 Image, Russian staff maps of the area, and in situ–collected GPS tracings of the local road network. On each resulting georeferenced image, the tree line was manually delineated following the tonal and textural patterns of the edges of the forest. In addition to the aforementioned two images, an ASTER 14–derived DEM was used to derive altitudes for unvisited points as well as aspect and slope. Fieldwork Based on the tree line visible on the most recent (i.e., Google) image, coordinates were selected randomly to position transects along the altitudinal gradient. In the field, these locations were found using a hand-held GPS. Data collection took place in September and early October 2010. Each transect starting point was at 50 meters horizontal distance downslope inside the forest adjoining the starting point and then aligned upwards until there would be no more change in land cover detected. Along the transect line, changes in land cover, canopy density, and slope were recorded at 10-meter intervals. Every 50 meters dominant tree height also was measured. In places where the tree line position was very clear and abrupt, which is a common case in this study area (Fig. 2), coordinates and altitude were recorded to locate the actual tree line position. In total, 37 transects were recorded. Of these, 14 transects coincided with sections of the tree line that had not shifted since 1976 (Velichkov, 2004). Analyses The accuracy of the tree line delineation was established in both horizontal and vertical directions. To determine the error for the horizontal direction, the shortest

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Fig. 2. Oblique photo displaying the sharp boundary of the tree line in the field. The photo was taken in the area indicated with the white square in Figure 1.

distance between the delineated tree line on the Hexagon and Google images and the GPS-based tree line positions was measured as an indicator of delineation uncertainty. To indicate the direction of the errors, field-based positions that fell on the forest side from the digitized line were considered as positive differences and those on the meadow side were considered as negative differences. The error determined in this way for each location x (εline,x) is the accumulation of delineation errors, georeferencing errors, and errors as a result of the ground resolution of the used imagery. On top of this, there could be an error due to inaccuracy of the position determination using a GPS (εgps). Therefore, we combined εline and εgps for each location using equation 1 H , x =

2

2

 gps +  line , x

(1)

To determine εgps, in 12 transects the estimated precision error (EPE, in m) was recorded and averaged. As the Garmin EPE readout (we used a Garmin eTrex) is generally accepted to indicate that there is an equal probability that the error is greater or less than the indicated EPE (i.e., a z-value of 0.5), EPE had to be multiplied by 1.48 to calculate the standard deviation from this (Mehaffey, 2011). Subsequently the average and standard deviation for εH,x was calculated. To test whether a systematic error existed, a t-test was used to assess whether the error was significantly different from 0. The standard deviation of all the εH,x was used as the total horizontal error εH. This

H ,1976

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quantity was compared with the hypothesised maximum error of two times the pixel width. These comparisons were made for both the 1976 (εH,1976) and the 2005 (εH,2005) based tree line delineations. A similar approach was used to determine the error in the vertical position of the tree line. For this, the locations on the delineated tree lines closest to the field-based tree line positions were used to derive altitude and slope values for these locations from the ASTER 14 DEM. These values were compared against altitude and slope values that were derived from the same ASTER DEM at the true (i.e., field-based) positions of the tree line. This yielded indicators for the vertical error for 1976 (εV,1976) and 2005 (εV,2005). Because the values for εV were derived from the horizontal positions, it could be that the slope had an effect on the accuracy as well, as with steeper slopes, a horizontal displacement will result in a larger vertical change. Therefore, we tested whether vertical differences (εH,x) were correlated with slope using a regression analysis. The shift in tree line position for the period 1976–2005 was calculated and compared with εH,total which was calculated from 2

2

 H ,1976 +  H ,2005

(2)

To do so, 756 transects perpendicular to the isoclines were created along the Hexagonbased 1976 and the Google-based 2005 tree line delineations (Fig. 3). The distance between the points where these created transects intersected the tree lines was used as an indicator for the shift in tree line position. Using a t-test, it was assessed whether the average calculated shift was larger than the accumulated horizontal error (εH,total). Similarly, the vertical shift was calculated by determining the altitude at the intersections of the 756 transects with the delineated tree lines from the ASTER DEM. The differences between pairs of intersections (with the 1976 and 2005 tree lines) were calculated and a t-test was applied as well to test whether the average vertical shift was larger than the accumulated vertical error (εV,total). This error was calculated in a similar fashion to how εH,total was calculated using Equation 2. All statistical analyses were performed using R software (version 12.2.1; R Development Core Team, 2010). RESULTS The georeferencing of the Hexagon data (1976) yielded an image with a ground resolution of 4 m while for the Google Earth (2005) image a ground resolution of 1.8 m was realized. Figure 1 shows an overview of the digitized lines in the area as well as the distribution of the field-based transects. There were 37 field points to compare with the 2005 image and 14 points to compare with the 1976 image. The average εH for the 1976 and 2005 imagery   H ,1976 ,  H ,2005  were –4.56 m and –0.20 m, respectively, and = 0.32 = , P both did not significantly differ from 0  P   = 0;df = = 13    = 0;df = 36  H ,1976

H ,2005

0.86), but did significantly differ from two pixel widths (either slope upwards or downwards) except for slope downwards for the 1976 image = P 0.22; P   H ,1976  – 8;df = 13 

 – 8;df = 13 

  H ,1976  8;

0.22; = P 0.007; = P 0.004 = ; P 0.002    H ,1976  8;df = 13 

  H ,2005  – 3.6;df = 36 

  H ,2005  3.6;df = 36 

6;df = 36 

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Fig. 3. Example of an area where the tree line shifted since 1976, with the delineated tree line of 1976 indicated in yellow, the delineated tree line of 2005 in red, and the randomly generated perpendicular lines used to calculate the positional shift in black. Example (A) has the 1976 Hexagon image as background and (B) has the 2005 Google image as background.

0.004 = ; P 0.002  , suggesting a downslope bias for the 1976 image–   H ,2005  3.6;df = 36 

derived tree line. The estimates for εH,1976 and εH,2005 (i.e., the standard deviations of the differences) were 16.4 m and 7.37 m, respectively. Similarly for the vertical differences, the average εV for the 1976 and 2005 imagery   V ,1976 ,  V ,2005  were 0.07 m and –0.38 m, respectively, and both did not



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­significantly differ from 0 P = 0.96 = , P 0.39 .   V ,1976 = 0= ;df 13 

  V ,2005 = 0= ;df 36 

The estimates for εV,1976 and εV,2005 were 4.8 m and 2.7 m, respectively. There was no significant relationship between the vertical errors and the steepness of the areas, despite a wide range of slopes (from 2.9–36°). Subsequently we tested whether observed vertical and horizontal shifts between 1976 and 2005 were significantly different from the accumulated error that resulted from delineation errors for both years as described above. The accumulated errors in both horizontal and vertical directions were 18.0 m and 5.51, respectively. The tree line had shifted horizontally on average by 21.7 m, but with large variation (standard deviation of 62 m), and the average vertical shift was 4.7 m with a standard deviation of 19 m. We found that the horizontal shift was marginally larger than the accumulated error  P = 0.047  but that the vertical shift was not  Hor.Shift   H ,total ,df = 764 

P

 Ver.Shift   V ,total ,df = 764 

= 0.876  . However, when we compared the vertical

shifts in the upper quartile (i.e., between the 75% and 100% quantiles) then the shifts in both horizontal and vertical directions were significantly larger than the cumulative errors  P  0.001 ; P  0.001  .  Hor.Shift   H ,total ,df = 190 

 Ver.Shift   V ,total ,df = 164 

DISCUSSION Tree lines move with rates that can be as little as 4–5 m meter per decennium (e.g., Shiyatov, 2000), so an estimation error of a few meters will already affect accurate estimations of rates of change. Therefore developing methods that can cover sufficiently long enough periods, and at the same time minimize the error in determining these shifts, are an essential step toward making accurate analyses possible. Because the tree line on Osovogo Mountain has been artificially lowered in the past due to land use by people (Velichkov, 2004; Filpovich et al., 2006), it is expected that with a reduction in human influence the tree line will shift upwards. Climate change may accelerate that process. This study attempted to assess whether possible shifts in tree lines can be detected making use of the available historical imagery from declassified high-resolution Hexagon imagery to detect these shifts given the increased time span that can be covered when tapping into this resource. We found that although the accumulated errors are quite high (18.0 m in the horizontal direction and 5.5 m in the vertical) it may well be possible to detect shifts. In the analyzed case study, we could not conclude a significant shift for the entire tree line. However, as this is an area with considerable human influence, shifts in tree line position are not evenly distributed along the tree line (Fig. 3), with some sections of the area showing hardly any tree line shift whereas others do so quite substantially. An analysis of only the areas that did exhibit an upward shift (approximated by the upper quartiles of the horizontal and vertical shifts of the 765 randomly distributed perpendicular lines) showed that for these areas, the changes in tree line position were definitely larger than the accumulated error. This suggests that for areas with a uniform change in tree line position, the procedure outlined here affords a way to assess the magnitude of tree line shift. The proposed approach paves the way for a more rigorous and systematic analy­ sis of tree line change on a global level, allowing for standardized and repeatable

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measurements from remotely sensed imagery. This would build on previous work by Harsch et al. (2009) by correcting for the possible bias that was present in their dataset. In the current light of anthropogenic climate change and the potential of tipping points (Lenton et al., 2008) that have largely unknown effects on land-atmosphere interactions, exact quantification of land cover processes is important. Tree lines are also suggested as potential ecosystems that exhibit sudden shifts to climate change rather than more gradual responses (Danby and Hik, 2007) as a result of multiple interacting factors such as species composition and human impacts such as burning (Bader et al. 2007). Although the “tipping point” behavior of tree lines is thus far still subject to debate (e.g., Lloyd, 2005; Danby and Hik 2007), shifts in tree lines are also expected to have repercussions for biodiversity of the often unique, endemic upper tree line ecosystems (Moen et al., 2004), making tree line change monitoring relevant also from a biodiversity viewpoint. This study has attempted to contribute by assessing the potential of tapping into an available source of global information for tree line monitoring that has thus far been underutilized within this field of research. ACKNOWLEDGMENTS We would like to thank Job Duim and Benno Masselink with assisting in fieldwork preparation, and Eduard Westinga and Kees de Bie for initial map preparation and advice on geo-referencing. Special thanks also to David Rossiter for fruitful discussions on the statistical analysis and useful tricks on using R. REFERENCES Armand, A. D., 1992, “Sharp and Gradual Mountain Timberlines as a Result of Species Interaction,” in Landscape Boundaries. Consequences for Biotic Diversity and Ecological Flows, Hansen A. J. and F. Di Castri (Eds.), Berlin, Germany: Springer, Ecological Studies No. 92, 360–378 . Bader, M. Y., Van Geloof, I., and M. Rietkerk, 2007, “High Solar Radiation Hinders Tree Regeneration above the Alpine Treeline in Northern Ecuador,” Plant Ecology, 191:33–45. Dashora, A., Lohani, B., and J. N. Malik, 2007, “A Repository of Earth Resource Information – CORONA Satellite Programme,” Current Science, 92:926–932. Danby, R. K. and D. S. Hik, 2007, “Variability, Contingency and Rapid Change in Recent Subarctic Alpine Tree Line Dynamics,” Journal of Ecology, 95:352–363. Feeley, K. J., Silman, M. R., Bush, M. B., Farfan, W., Cabrera, K. C., Malhi, Y., Meir, P., Revilla, N. S., Quisiyupanqui, M. N. R., and S. Saatchi, 2010, “Upslope Migration of Andean Trees,” Journal of Biogeography [doi:10.1111/j.1365248 2699.2010.02444.x]. Filpovich, L., Popov, G., and I. Velichkov, 2006, “Changes of the Forest Vegetation in Osogovo Mountain during the last 5000 Years,” Forest Science, 3:5–12 (in Bulgarian). Harsch, M. A., Hulme, P. E., McGlone, M. S., and R. P. Duncan, 2009, “Are Treelines Advancing? A Global Meta-analysis of Treeline Response to Climate Warming,” Ecology Letters, 12:1040–1049.



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Hansen, M. C. and R. S. DeFries, 2004, “Detecting Long-Term Global Forest Change Using Continuous Fields of Tree Cover Maps from 8-km Advanced Very High Resolution Radiometer (AVHRR) Data for the Years 1982–99,” Ecosystems, 7:695–716. Hansen, M. C., Roy, D. P., Lindquist, E., Adusei, B., Justice, C. O., and A. Altstatt, 2008, “A Method for Integrating MODIS and Landsat Data for Systematic Monitoring of Forest Cover and Change in the Congo Basin,” Remote Sensing of Environment, 112:2495–2513. Körner, C., 1998, “A Re-assessment of High Elevation Treeline Positions and Their Explanation,” Oecologia, 115:445–459. Körner, C. and J. Paulsen, 2004, “A Worldwide Study of High Altitude Treeline Temperatures,” Journal of Biogeography, 31:713–732. Lazarova, M., Tonkov, S., Snowball, I., and E. Marinova, 2009, “Peat-Bog Begbunar (Osogovo Mountains, Southwest Bulgaria): Four Millennia of Vegetation History,” Grana, 48:147–149. Lenton, T. M., Held, H., Kriegler, E., Hall, J. W., Lucht, W., Rahmstorf, S., and H. J. Schellnhuber, 2008, “Tipping Elements in the Earth’s Climate System,” Proceedings of the National Academy of Schiences of the United Stated of America (PNAS), 12:1786–1793 [doi: 10.1073/pnas.0705414105]. Lloyd, A. H., 2005 “Ecological Histories from Alaskan Tree Lines Provide Insight into Future Change,” Ecology, 86:1687–1695. Mehaffey, J., 2011, “Error Measures” [http://gpsinformation.net/main/errors.htm], accessed October 25, 2011. Moen, J., Aune, K., Edenius, L., and A. Angerbjörn, 2004, “Potential Effects of Climate Change on Treeline Position in the Swedish Mountains,” Ecology and Society, 9:16 [http://www.ecologyandsociety.org/vol9/iss1/art16/]. R Development Core Team, 2010. R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing. Shiyatov, S., 2003, “Rates of Change in the Upper Treeline Ecotone in the Polar Ural Mountains,” PAGES News, 11:8–10. Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. J. (Eds.), 2007, Climate Change 2007: The Physical Science Basis, Cambridge, UK: Cambridge University Press. Stow, D. A., Hope, A., McGuire, D., Verbyla, D., Gamon, J., Huemmrich, F., Houston, S. et al., 2004, “Remote Sensing of Vegetation and Land-cover Change in Arctic Tundra Ecosystems,” Remote Sensing of Environment, 89:281–308. Velichkov, I., 2004, Successional Processes in the Natural Mixed Beech-Coniferous Forests of Osogovo Mountain. Ph.D. thesis, Department of Silviculture, Forest Research Institute, Sofia, 178 p. (in Bulgarian).