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Detecting Treeline Dynamics in Response to Climate Warming Using Arekhi et al. Forest Ecosystems (2018) 5:23 https://doi.org/10.1186/s40663-018-0141-3 RESEARCH Open Access Detecting treeline dynamics in response to climate warming using forest stand maps and Landsat data in a temperate forest Maliheh Arekhi1* , Ahmet Yesil2, Ulas Yunus Ozkan2 and Fusun Balik Sanli3 Abstract Background: Treeline dynamics have inevitable impacts on the forest treeline structure and composition. The present research sought to estimate treeline movement and structural shifts in response to recent warming in Cehennemdere, Turkey. After implementing an atmospheric correction, the geo-shifting of images was performed to match images together for a per pixel trend analysis. We developedanewapproachbasedontheNDVI,LST(landsurface temperature) data, air temperature data, and forest stand maps for a 43-year period. The forest treeline border was mapped on the forest stand maps for 1970, 1992, 2002, and 2013 to identify shifts in the treeline altitudes, and then profile statistics were calculated for each period. Twenty sample plots (10 × 10 pixels) were selected to estimate the NDVI and LST shifts across the forest timberline using per-pixel trend analysis and non-parametric Spearman’s correlation analysis. In addition, the spatial and temporal shifts in treeline tree species were computed within the selected plots for four time periods on the forest stand maps to determine the pioneer tree species. Results: A statistically significant increasing trend in all climate variables was observed, with the highest slope in the monthly average mean July temperature (tau = 0.62, ρ < 0.00). The resultant forest stand maps showed a geographical expansion of the treeline in both the highest altitudes (22 m–45 m) and the lowest altitudes (20 m–105 m) from 1970 to 2013. The per pixel trend analysis indicated an increasing trend in the NDVI and LST values within the selected plots. Moreover, increases in the LST were highly correlated with increases in the NDVI between 1984 and 2017 (r = 0.75, ρ <0.05).Cedrus libani and Juniperus communis spp. were two pioneer tree species that expanded and grew consistently on open lands, primarily on rocks and soil-covered areas, from 1970 to 2013. Conclusion: The present study illustrated that forest treeline dynamics and treeline structural changes can be detected using two data sources. Additionally, the results will have a significant contribution to and implication for treeline movement studies and forest landscape change investigations attempting to project climate change impacts on tree species in response to climate warming. The results will assist forest managers in establishing some developmental adaptation strategies for forest treeline ecotones. Keywords: NDVI, Geoshift, LST, Timberline, Mann-Kendall, Landsat, Climate warming Background higher altitudes is mainly related to thermal variations Do forest treelines respond to global warming? The and other factors (Zinnert et al. 2011, Du et al. 2017, vegetation of treelines and alpine ecotones is among the Gaire et al. 2017, Holtmeier and Broll 2017, Skowno most vulnerable and sensitive to the impact of global et al. 2017), such as enhanced atmospheric CO2 and ni- warming on earth (Masek 2001, Bharti et al. 2012, trogen deposition (Masek 2001, Grace et al. 2002, Salzer Weisberg et al. 2013, Gaire et al. 2017, Mishra and et al. 2009, Capers et al. 2013, Skowno et al. 2017). Mainali 2017). The movement of forest treelines towards Simultaneously, increases in these factors usually result in the migration of alpine plants to upper altitudes. * Correspondence: [email protected] Moreover, downward shifts in treelines and decreases in 1Institutes of Graduate Studies in Science and Engineering, Forest Engineering, Istanbul University, 34452 Istanbul, Turkey treeline stability have been reported due to harsh envir- Full list of author information is available at the end of the article onmental conditions (Zhang et al. 2009, Du et al. 2017, © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Arekhi et al. Forest Ecosystems (2018) 5:23 Page 2 of 14 Gaire et al. 2017) and human disturbances (Jump and demonstrates trends in historical shifts that can be detected Penuelas 2005, Li et al. 2016b). Indeed, these shifts prob- by remote sensing techniques (Singh et al. 2011,Raynolds ably have a remarkable influence on plants that are a et al. 2013,Roparsetal.2015, Morley et al. 2017). carbon sink major source (Hanberry and Hansen 2015, Combining forest stand maps with long-term Li et al. 2016a). In fact, the reliable assessment of forest Landsat data provides a good opportunity for detect- treeline dynamics is essential to conserve the ecological ing and monitoring historical shifts in the forest condition of alpine ecotone biodiversity for sustainable structure, timberline border, and treeline movement management and forest conservation strategies (Grace (Zinnert et al. 2011,Solár2013). Additionally, an- et al. 2002, Holtmeier and Broll 2010, Mishra and Mainali other aspect of remote sensing data accessibility is 2017,Skownoetal.2017). that its data include LST, which provides an unpre- Various methods have developed and used in various cedented opportunity to investigate and compare for- parts of the world to determine woody plant encroach- est dynamics (van Leeuwen et al., 2011)among ment in response to global warming, including methods certain interval times. The importance of LST in cli- using dendroecological techniques (Danby 2007, Salzer mate change analyses has been proven (Cristóbal et al. 2009, Du et al. 2017, Gaire et al. 2017, Wang et al. et al. 2008,Brabynetal.2014)(Shuman and Comiso 2017b), aerial photograph assessments (Lutz et al. 2013, 2002,Parastatidisetal.2017). However, LST anomal- Solár 2013, Ropars et al. 2015), field plot resurveys ies are poorly investigated, and the spatial and tem- (Grabherr et al. 1994, Holtmeier and Broll 2005, Danby poral monitoring of LST can be used in treeline and Hik, 2007a, Elmendorf et al. 2012), satellite images monitoring studies. The Mediterranean region is rec- (Masek 2001, Holtmeier and Broll 2005, Fraser et al. ognized as an area impacted by global warming in 2011, Zinnert et al. 2011, Patrick Shawn Sawyer 2014, Turkey, and it is facing increasing summer tempera- Mishra and Mainali 2017, Skowno et al. 2017), repeated tures and winter precipitation (Solomon et al. 2007, photographic evidence (Kullman 1993), palynological Talu et al. 2010,Parolly2015). Moreover, evidence data investigations (Grace et al. 2002), seedling recruit- of the upward movement of treelines has been re- ment studies (Miller et al. 2017), and model scenarios ported in the eastern Karadeniz region (Black Sea), (Masek 2001, Trivedi et al. 2008). Furthermore, the and treelines may move approximately 200–400-m number of successful investigations that have integrated according to climate change predictions (Terzioglu Salih remote sensing images and historical maps is increasing 2015). Some climate change scenarios have indicated that (Wallentin et al. 2008, Zinnert et al. 2011, Weisberg the mountain flora in the western and central Taurus et al. 2013). Simultaneously, remote sensing data provide mountain regions (Parolly 2015) and the eastern Mediter- both fine- and coarse-resolution data (Fraser et al. 2011, ranean region are threatened (Ozturk et al. 2015). Con- 2012). Moreover, Landsat satellite data are the most ef- ventionally, a treeline vertical movement of approximately fective data sets and cover large areas (Arekhi et al. 640 m is expected to occur with a 4.5 °C warming rate per 2017, Chen et al. 2017). Landsat images, which have century and 33 m of movement per 1 °C increase. been recorded since 1972 and are freely available for a Holtmeier and Broll 2017 stated, “82% of coarse scale (re- period of more than 45 years with high spatial-temporal gional) variation in treeline elevation is associated with resolution and a 16-day revisit time (Masek 2001, Wang thermal conditions”. However, there is evidence that accel- et al. 2017a), represent one of the most valuable data erated glacier melt in sensitive, snow-covered mountain sources for determining woody plant encroachment. areas due to increasing temperature impacts treeline Likewise, they provide a great resource for depicting structure and composition (MacDonald et al. 2008,Capers timberline dynamics and treeline movements with vari- et al. 2013, Wilmers et al. 2013, Holtmeier and Broll ous methods and vegetation indices (Holtmeier and 2017). However, there are a few studies investigating the Broll 2005, 2017, Zinnert et al. 2011, Bharti et al. 2012, case of forest treelines in the Mediterranean region, and Elmendorf et al. 2012, Gaire et al. 2017, Wang et al. there is a gap in knowledge about forest treeline dynamics 2017a). The NDVI is the most common vegetation and structural shifts. index, and it is well known as a good representative of Thepresentstudyobjectiveswere1)toquantify plant biomass. Additionally, the NDVI is directly indica- treeline movement rates from 1970 to 2013 by the tive of plant photosynthetic activity (Zinnert et al. 2011, combination of NDVI maps with forest stand maps; Du et al. 2016, Li et al. 2016a, Mishra and Mainali 2) to explore whether the increasing trends in LST 2017). Therefore, monitoring of changes in the NDVI and NDVI coincided with treeline advancement from values across the forest timberline border provides a 1984 to 2017; 3) to determine promising tree species quantitative estimate of the tree biomass and density in the timberline border in four times (1970, 1992, over time (Singh et al. 2011, Bharti et al. 2012, Sawyer 2002, 2013) by overlaying forest stand maps on and Stephen 2012, Iverson and McKenzie 2013), which NDVI maps.
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