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Modelling forest lines and forest distribution patterns with remote sensing data in a mountainous region of semi-arid Central Asia

Modelling forest lines and forest distribution patterns with remote sensing data in a mountainous... Biogeosciences, 12, 2893–2905, 2015 www.biogeosciences.net/12/2893/2015/ doi:10.5194/bg-12-2893-2015 © Author(s) 2015. CC Attribution 3.0 License. Modeling forest lines and forest distribution patterns with remote-sensing data in a mountainous region of semiarid central Asia 1 2 1 M. Klinge , J. Böhner , and S. Erasmi Institute of Geography, University of Göttingen, Goldschmidtstr. 5, 37077 Göttingen, Germany Institute of Geography, University of Hamburg, Bundesstraße 55, 20146 Hamburg, Germany Correspondence to: M. Klinge (mklinge1@gwdg.de) Received: 17 July 2014 – Published in Biogeosciences Discuss.: 13 October 2014 Revised: 19 April 2015 – Accepted: 21 April 2015 – Published: 20 May 2015 Abstract. Satellite images and digital elevation models pro- 1 Introduction vide an excellent database to analyze forest distribution pat- terns and forest limits in the mountain regions of semiarid The latitudinal and elevational variation in distinct plant central Asia on the regional scale. For the investigation area associations and geomorphologic landscape units has been in the northern Tien Shan, a strong relationship between for- used for a long time to deduce regional environmental and est distribution and climate conditions could be found. Addi- climatical conditions in geosciences (e.g., von Humboldt, tionally areas of potential human impact on forested areas are 1845–1862; Troll, 1973a, b; Hövermann, 1985). Image clas- identified at lower elevations near the edge of the mountains sification and GIS modeling of remote-sensing data are stan- based on an analysis of the differences in climatic precondi- dard methods to map landscape elements and their distribu- tions and the present occurrence of forest stands. tion in remote areas, which are poorly accessible due to lo- The distribution of spruce (Picea schrenkiana) forests is gistic or political difficulties. Satellite analysis based on auto- hydrologically limited by a minimum annual precipitation of mated image processing offers a quick and useful alternative 250 mm and thermally by a minimum monthly mean tem- to field mapping or manual digitalization from aerial images perature of 5 C during the growing season. While the ac- (Mayer and Bussemer, 2001). While satellite images such as tual lower forest limit increases from 1600 m a.s.l. (above Landsat data provide excellent information to delineate the sea level) in the northwest to 2600 m a.s.l. in the south- spatial forest distribution (Hansen et al., 2013), SRTM (Shut- east, the upper forest limit rises in the same direction from tle Radar Topography Mission) data can be used to examine 1800 m a.s.l. to 2900 m a.s.l.. In accordance with the main relief-dependent distribution patterns with a digital terrain wind directions, the steepest gradient of both forest lines and model (DTM). The combination of these two data sets en- the greatest local vertical extent of the forest belt of 500 to ables high-resolution mapping on a regional to local scale. 600 m to a maximum of 900 m occur at the northern and The geoecologic and climatic environmental settings con- western mountain fronts. trol the natural distribution of forest stands (Holtmeier, 2000; The forests in the investigation area are strongly restricted Körner, 2012; Miehe et al., 2003). In addition, the actual sit- to north-facing slopes, which is a common feature in semi- uation can strongly be influenced by human activities such arid central Asia. Based on the presumption that variations in as logging, fire clearing and animal grazing, which decreases local climate conditions are a function of topography, the po- the potential natural forest area (PFA). This often makes it tential forest extent was analyzed with regard to the param- difficult to differentiate between natural factors and human eters slope, aspect, solar radiation input and elevation. All impact on the distribution of timbered areas. In general, hu- four parameters showed a strong relationship to forest distri- man activity has reduced the forest area since prehistorical bution, yielding a total potential forest area that is 3.5 times times so that the actual forest area (AFA) pattern mostly larger than the present forest remains of 502 km . represents the minimum of the potential environmental dis- Published by Copernicus Publications on behalf of the European Geosciences Union. 2894 M. Klinge et al.: Modeling forest lines and forest distribution patterns tribution range. However, due to the possibility of anthro- value for the mean air temperature during the growing sea- pogenic forest management and afforestation during the last son, which is defined as the period of monthly mean tem- centuries, forests may occur at sites less favorable for natural peratures above 5 C (Dai et al., 2013; Körner, 2012; Körner tree growth. and Paulsen, 2004). Based on the strong correlation between Due to the highly continental, cold and semiarid climate soil and air temperatures, Körner and Paulsen (2004) state a in central Asia, tree growth is mostly determined by topog- global range of 5.5 to 7.5 C for minimum mean air tempera- raphy parameters. Forest stands beyond sites with more fa- tures during the growing season. Paulsen and Körner (2014) vorable conditions regarding groundwater are predominantly developed a climate-based model for treeline prediction by limited to north-facing slopes in the mountains with an up- defining the growing season as days with a mean temperature per and lower forest limit (Dulamsuren et al., 2014; Hilbig, above 0.9 C and a mean temperature of more than 6.4 C 1995; Klinge et al., 2003; Treter, 1996, 2000). during that time. For the upper forest line between 2750 and Different definitions have been used for tree and forest 2920 m a.s.l. in the Tien Shan in Kyrgyzstan Körner (2012) lines (Körner, 2012; Körner and Paulsen, 2004). The tree- found a mean temperature of 6.5 C during the 155 days of line ecotone covers three main boundary lines at the upper the growing season (late April until late September). limit of forest distribution. The highest is the tree species The forest expansion into dry regions is controlled by pre- line, where tree seedlings occur but no adult trees. The tree- cipitation and soil water supply (Dulamsuren et al., 2010, line is the maximum elevation where patches of forest can 2014; Kastner, 2000; Klinge et al., 2003). Between the more exist at topographically favorable places. In our investigation humid mountain regions and the arid basins of central Asia, a we refer to the forest line, which is defined as the limit of lower limit of forest distribution occurs, which is termed the closed forest at the upper (timberline) and lower boundary of lower forest line. According to Walter and Breckle (1994) forest distribution. this forest distribution boundary coincides with an annual For the region of the northern Tien Shan in China, Dai precipitation of at least 300 mm, while Holdridge (1947) pro- et al. (2013) state an upper forest line beginning with poses 250 mm and Miehe et al. (2003) found Juniperus trees 2900 m a.s.l. in the west, which decreases eastward down to in southern Tibet growing in regions with an annual precipi- 2500 m a.s.l. and then rises again to 2900 m a.s.l. in the east. tation of between 200 and 250 mm. Dulamsuren et al. (2010) In the northwestern Tien Shan, Fickert (1998) reports an up- state an annual precipitation between 230 and 400 mm at per forest line of 2900 and 2850 m a.s.l. and a lower forest lower elevations for larch forests in northern and central line of 2400 and 2500 m a.s.l., respectively for the Sailijski- Mongolia. In western Mongolia Dulamsuren et al. (2014) Alatau and Kungeij-Alatau. In the Altai Mountains, Klinge et found coniferous forests existing at an annual precipitation al. (2003) found upper forest lines increasing eastward from of around 120 mm, which are explained by soil water bene- 1800 to 2600 m a.s.l. and, concurrently, lower forest lines in- fits due to the occurrence of permafrost ice in the soil. creasing from 1000 to 2200 m a.s.l., while the vertical exten- Everywhere in mountainous areas of the semiarid inner- sion of the forest belt varies between 400 and 1200 m. Asian forest steppe coniferous forests are restricted to north- Tree growth in high mountains is generally restricted by facing slopes. While the north-facing slopes are dominated temperature conditions (Haase et al., 1964; Holtmeier, 2000; by larch trees (Larix sibirica) in Mongolia, spruce trees Jobbagy and Jackson, 2000; Körner, 2012). The upper for- (Picea schrenkiana) occur in the Tien Shan (Dai et al., 2013; est line is a thermally determined distribution boundary that Fickert, 1998; Liu et al., 2013; Wang 2005, 2006). Thus, is generally defined by the mean July temperature (Walter the restriction of conifers to north-facing slopes in the inner- and Breckle, 1994) or the warmest-month isotherm of 10 C. Asian forest steppe is not bound to certain tree species but According to Körner (2012) and Körner and Paulsen (2004), rather to the environmental settings. this parameter is not appropriate for all parts of the world. The semiarid climate conditions generate an overall de- This can be seen in Fickert (1998), who shows that the upper ficiency of moisture which considerably influences the ele- forest line in the northern Tien Shan coincides well with the vational forest distribution and may even control the upper 10 C July isotherm, while further south in the northwest- forest limit (Liang et al., 2012; Liu et al., 2013; Miehe et al., ern Himalaya and northwestern Karakorum, it is connected 2008). A specific relief position is combined with particular to the July isotherms of 16 and 12 C, respectively. For the climate conditions such as temperature, precipitation, evapo- eastern side of the northern Tien Shan, Dai et al. (2013) re- ration and insulation, which are similar at comparable sites in port a mean temperature of the warmest month of 10.5 C the surroundings. For this reason the relief parameters eleva- at the mean position of the alpine forest line. Moreover, the tion, aspect, slope angle and solar radiation input can be used mean annual air temperature (MAAT) is weakly correlated to define topoclimatic conditions in mountain regions (Miehe to the forest line because it includes temperatures from the et al., 2003). However, to identify potential forest sites based nongrowing season, which play a minor role in tree growth on those definitions, the geologic and soil properties have to (Jobbagy and Jackson, 2000; Körner, 2012). be comparable. A suitable way of describing the temperature environment The impact of human activity on vegetation and especially at the upper forest line is by using a minimum threshold on the forest since prehistorical times is a permanent question Biogeosciences, 12, 2893–2905, 2015 www.biogeosciences.net/12/2893/2015/ M. Klinge et al.: Modeling forest lines and forest distribution patterns 2895 2 Study area The investigation area detailed, Uzynkara Ridge, also known as the Ketmen Mountain range, the Ketmen Mountain range, is located in the northernmost part of the Tien Shan in cen- tral Asia on the border between Kazakhstan and China (79– 0  0 81 E/42 45 –43 45 N) (Fig. 1). This closed mountain sys- tem was chosen for investigation because it provides excel- lent topographic preconditions to clearly indicate the lower and upper boundaries of forest distribution between the mid- dle and central part of Asia. It fills a knowledge gap about forest lines between regions of the Tien Shan in the south and east, and the Altai mountains in the north (Fickert, 1998; Dai et al., 2013; Klinge et al., 2003). The main cities in the region are Shonzy and Kegen. The complete mountain Figure 1. Map overview showing the investigation area detailed range is part of the catchment area (CA) of the Ili River (trapezoid) in central Asia. in the north. While the northern mountain side is directly drained to the Ili River, the Kegen River in the southern intermountain basin first flows westward and then, turning that needs to be investigated to clarify the environmental sig- into the Sharyn River, flows in a northerly direction, and nificance of any actual forest line (Miehe and Miehe, 2000). the Tekes River in the southernmost part runs eastward into Dulamsuren et al. (2014) found a considerable anthropo- Chinese territory. The mountain system is structured by two zoogenic influence on the actual lower forest line in the Mon- main ridges – a northern front range (NFR) and a southern golian Altai. For northern Mongolia, Schlütz et al. (2008) mountain range (SMR) – which converge in the east and en- showed that the present vegetation pattern in the mountain close an intermountain basin in the west. The highest peak taiga where steppes occur on south-facing slopes is caused is the Nebesnaja, reaching 3652 m a.s.l. A high mountain by climate conditions and relief and does not originate from plateau at  3400 m a.s.l. drops southward, while the north- human activities. facing slopes are cut steeply by Pleistocene cirques. Today, Human impact on natural forests in Kazakhstan goes back no glaciation but permafrost occurs in the uppermost areas. to prehistoric times, with nomadism and animal grazing as The edge of the mountains is tectonically clearly distin- a way of life adapted to the natural environmental condi- guished from the alluvial fans and fanglomerates at approx- tions of the steppe (Karger, 1965; Giese, 1981, 1983). During imately 1500 m a.s.l. in the north and at 2000 m a.s.l. in the summertime the alpine meadows and mountain steppes in the southern intermountain basin, following west to east trend- upper mountains were regularly used as pastures for the live- ing fault lines. The mountains mainly consist of metamor- stock. Even during Soviet times in Kazakhstan the nomadic phic and volcanic Carboniferous and Devonian rocks, includ- movements were generally adopted by the sovkhoz system. ing several Palaeozoic granite bodies. Permian, Silurian and Even today the alpine pastures are still in use. Extensive an- Jurassic rocks are also distributed locally. imal grazing prevents the rejuvenation of trees, and nomads The MAAT in Almaty (848 m a.s.l.) is 8.7 C and in may expand the grassland by setting fire to it. Karakol (1744 m a.s.l.), which is situated south of the Spatial models, which are able to predict the climatically investigation area, 6.3 C (Fickert, 1998). According to induced forest distribution and especially the upper forest Medeu (2010) the mean air temperature is between 8 and line on a global scale by exclusively using spatial climate 10 C in January and between 20 and 24 C in July. In data, already exist (Paulsen and Körner, 2014). However, wintertime the Siberian anticyclone produces weather con- a clear method to empirically distinguish the actual forest ditions with cold air masses in the basins and warmer air distribution and its elevational limits for small areas cov- temperatures above the inversion layer between 1000 and ering a single mountain system and to simultaneously in- 1550 m a.s.l. (Giese, 1973). vestigate the potential human impact is lacking. In this in- The majority of precipitation in Kazakhstan comes with vestigation we introduce a procedure to solve this problem air masses from the west and southwest. In the mountains of based on medium-resolution remote-sensing data. In addi- the northern Tien Shan, mainly convective rainfall occurs in tion, spatially explicit climate data and tree-growth-limiting spring and autumn. Additionally, cold air masses from north- climate parameters serve to differentiate potential human im- ern directions bring precipitation to the northern Tien Shan pact from natural conditions in the forest distribution. in summertime (Böhner, 2006; Lydolph, 1977). According to Giese (1973) the annual precipitation in the basins of the foreland lies between 100 and 300 mm; in the lower moun- tains and in the intermountain basin, it is between 300 and www.biogeosciences.net/12/2893/2015/ Biogeosciences, 12, 2893–2905, 2015 2896 M. Klinge et al.: Modeling forest lines and forest distribution patterns Figure 2. Workflow of DEM (digital elevation model) and satellite image processing to determine the spatial forest distribution patterns in semiarid mountain systems of central Asia on a high-resolution scale. 400 mm. In the mountains it increases to more than 800 mm. tremula) and in the northeastern part birch trees (Betula pen- The precipitation maxima occur in May and June, with a mi- dula) also occur. On the southern slopes shrub areas also ex- nor, secondary maximum in September. ist. The foreland, basins and treeless mountain areas are The soils are distributed according to the climate condi- covered by steppe vegetation with forb and bunch grass tions and the vegetation zones (Medeu, 2010). In the fore- (Medeu, 2010). In the drier regions to the north, it changes land, desert soils occur. In the lower front ranges and in the to grassland, sagebrush desert, saltwort and sedge vegeta- intermountain basin, mountain steppe soils of castanozem tion. The forest belt mainly consists of spruce trees (Picea and chernozem type are distributed. In the forest belt dark schrenkiana). In the westernmost part, aspen trees (Populus chernozems, which are locally bleached and podzolized, oc- Biogeosciences, 12, 2893–2905, 2015 www.biogeosciences.net/12/2893/2015/ M. Klinge et al.: Modeling forest lines and forest distribution patterns 2897 Figure 3. Spatial distribution of actual forested area (AFA) and potential forest area (PFA) in the mountainous region of the northernmost Tien Shan shown above a true-color composite of a Landsat 7 satellite image from the 13 September 2000. Table 1. Confusion matrix showing the accuracy report of the supervised maximum likelihood classification (area in ha). Reference Classification data Sum Producer’s Omission data Forest No forest reference accuracy error Forest 2696.7 333.6 3030.3 0.890 0.110 No forest 14.7 76 426.2 76 440.9 0.9998 Sum classification 2711.4 76 759.8 Total Sum of Overall User’s accuracy 0.995 0.995 test data accuracy Commission error 0.0054 79 471.2 0.996 cur in forests, and pheaozem soils exist at meadow steppe 3 Methods sites. At high elevations, alpine and subalpine soils occur in mountain meadows and meadow steppes. A schematic workflow of the GIS analysis procedure with Arable land in eastern Kazakhstan is located at the foot input data, intermediate data and output data is presented in of the mountain ranges on the alluvial fans in the basins and Fig. 2. The analysis is divided into two main processes: the on the foothills at a lower elevation. In this transition zone first is working on the relief parameters to estimate the PFA between the pediments and mountain ranges, the soils are and the second conducts the delineation of the upper and improved by a certain amount of Pleistocene loess (Giese, lower forest lines. The forest lines are defined as the distribu- 1983; Karger, 1965; Machalett et al., 2006). After leaving tion boundaries of closed forest stands with areas larger than the mountains, the water from the rivers is used for irriga- 0.5 ha, disregarding single trees, which may represent special tion cultivation on the pediments. In the foothills agricul- environmental places or remnants of former forests. Trees ture is supported by sufficient rainfall; this is the so-called near the rivers in the valley bottoms were excluded from the bogar cultivation (Giese, 1983). According to these require- examination because these are sites which have more favor- ments the settlements are located along the main valleys at able conditions regarding groundwater and which are mostly the mountain boundary. Around the settlements, there is sig- occupied by deciduous trees. nificant woodcutting for construction and fuel. The determination of the AFA in the investigation area was achieved based on a supervised maximum likelihood classi- fication from multispectral satellite images (visible light and infrared channels) of Landsat 7/ETMC from the 13 Septem- www.biogeosciences.net/12/2893/2015/ Biogeosciences, 12, 2893–2905, 2015 2898 M. Klinge et al.: Modeling forest lines and forest distribution patterns of up to 11 % mainly occurs at the edges of closed forest, where the classification depends on the quantity of trees in one Landsat pixel. The relief parameters elevation, aspect, slope gradient and total solar radiation input were derived from a DTM based on SRTM data (Rabus et al., 2003), which was converted to UTM zone 44 N with a spatial resolution of 90 90 m. The polygons of the delineated forest stands were intersected with the relief parameters in order to investigate the relief- dependent spatial distribution of forest sites in the study area. In addition, the statistics of all relief parameters were com- puted for the total study area (TSA) to indicate the poten- tial impact of topography on the spatial distribution of for- est stands (Fig. 4). The PFA was then identified based on the assumption of confidence ranges for all four relief pa- rameters, which were found responsible for forest distribu- tion. This range was defined by the standard deviation (95 % confidence interval) from the single-frequency distribution of the relief parameters aspect, slope gradient and sum of solar radiation input during the mean growing season (March to November). While these parameters are not systematically influenced by human impact, the vertical distribution may have been changed by forest clearing at the lower and up- per boundary. Therefore 99 % of the frequency distribution of the elevation parameter was chosen in this case. Baseline climate data sets for central Asia, comprising monthly radiation, temperature and precipitation data at a horizontal resolution of 0.5 arc seconds (approximately 1200 m in longitudinal and 850 m in latitudinal direction), are provided by Böhner (2006). The regular-grid climate layers were estimated using an empirical modeling ap- proach, which basically integrates statistical downscaling of coarse-resolution atmospheric fields (NCAR/NCEP-CDAS reanalyses series – National Center for Atmospheric Re- search/National Center for Environmental Prediction – cli- mate data assimilation system; Kalnay et al., 1996) and GIS- based surface parameterization techniques, to sufficiently ac- Figure 4. Frequency distribution of relief parameters in relation to count for the topographic heterogeneity of the target area. actual forest area (AFA, green; in the diagrams of aspect, slope gra- A comprehensive description of data bases and modeling dient and solar radiation input, the light green areas represent the standard deviation of 95 range excluded from PFA delineation) and techniques is given in Böhner (2006) and Böhner and An- total study area (TSA, brown). tonic (2009). The suitability and precision of the model- ing approach is discussed in Gerlitz et al. (2013, 2014) and Soria-Auza et al. (2010). ber 2000 (Fig. 3). Aerial photos provided as imagery and The frequency distribution of selected climate parameters Bing basemaps by ESRI were used to detect forest area refer- related to the AFA and the TSA shown in Fig. 5 was cal- ence sites for the training and validation of the classifier. Two culated in the same way as described above. In contrast to classes were built and manually digitized for the classifica- the high resolution of the SRTM data, the climate data has tion and validation process. One class represents the forest a resolution about 10 times lower, which leads to a gener- areas and the other class includes different kinds of no-forest alization and coarser-scale of relief positions where climatic landscape. Depending on the ground resolution of 30 30 m, differences between slope aspects inside the valleys are av- one pixel covers several individual trees so that small clear- eraged. The climate data related to the forest stands are ana- ings and aisles may have been disregarded. The confusion lyzed by the climatic limitation values for forest development matrix in Table 1 shows a producer’s accuracy of 89 % for to detect potential human impact on the forest distribution forest areas, where  6 % of the forest area were used for patterns when obvious discrepancies occur. validation. This possible underestimation of the forest area Biogeosciences, 12, 2893–2905, 2015 www.biogeosciences.net/12/2893/2015/ M. Klinge et al.: Modeling forest lines and forest distribution patterns 2899 Figure 5. Frequency distribution of climate parameters for actual forest area (AFA: columns and left axes, in km ) and the total study area (TSA: graph and right axes, in km ). Table 2. Statistical values of the relief parameters related to the forest distribution. Parameter Unit Maximum Total 95 % of the distribution distribution distribution value range range Elevation m a.s.l. 2500 1575–2900 1925–2775 Aspect degree horizontal 315 (NW) 0–360 260–70 Slope gradient degree vertical 28 0–62 12–38 Solar radiation input kWh m 1075 450–1550 800–1325 To outline the actual forest lines, it is initially necessary of sections on the left and on the right side of a valley. Hav- to segment the relief into small CAs, which represent small ing different aspects in one segment is expedient to receive a side valleys or slope niches divided by convex ridges. This is general forest line value for one valley section. done by computing the surficial hydrology regime from the After combining the catchment polygons with the forest DTM. The size of a CA is given by the threshold value for the polygons, it is possible to determine the maximum and min- stream definition function, which assigns the minimum num- imum elevation values for forests inside a single CA. The ber of cells that must discharge into a specific cell to start a calculated values are spatially allocated as points to the po- depth contour. In this study a value of 200 was found practi- sition of those pixels for which forest line values have been cal for the lower forest line and a value of 100 was suitable determined. To eliminate the preconditions on the lower for- for the upper forest line. The single CAs generally consist est line given by the elevation limits of the relief, only those www.biogeosciences.net/12/2893/2015/ Biogeosciences, 12, 2893–2905, 2015 2900 M. Klinge et al.: Modeling forest lines and forest distribution patterns Figure 6. The lower forest line and the catchment areas providing lower forest line values in the investigation area. Figure 7. The upper forest line and the catchment areas providing upper forest line values in the investigation area. minimum values of forest stands were chosen which are more than 100 m below the total maximum elevation of the catch- than 50 m higher than the total minimum value of the CA. ment. Finally, the forest lines were calculated from the re- The distance between the highest forest stands and the crest maining points by a Natural Neighbor interpolation method. line above has a special influence on the upper forest line; this is called the “summit syndrome” by Körner (2012). Near the summits the local climate conditions strongly suppress tree growth by stronger wind, reduced temperature and snow drift. To receive a reasonable value for the climatic upper for- est line and to eliminate preconditions by relief height, only those maximum forest values were chosen which lie more Biogeosciences, 12, 2893–2905, 2015 www.biogeosciences.net/12/2893/2015/ M. Klinge et al.: Modeling forest lines and forest distribution patterns 2901 Figure 8. Distribution of AFA in relation to the hydrological climatic environment. Figure 9. Distribution of AFA and PFA and the July isotherms. 4 Results Less than 5 % of the forests occur on southern slopes (SE– S–SW). The curve of the parameter aspect has a steeper left 4.1 Relief parameterization slope and a maximum value in the northwestern direction (315 ), which is strongly related to the diurnal air temper- The total AFA in the investigation area is 502 km (Fig. 3). ature trend caused by insolation and heating processes on Frequency distributions of relief parameters for the AFA are different slope directions. This underlines the fact that the shown in Fig. 4. The slope gradient and solar radiation of strong relation of forest distribution to slope aspect is caused the forest stands show a normal distribution. The values with by natural environmental conditions. The logistic problems the maximum distribution are 28 for slope gradient and of access in the relief influence how forest is transformed by 1075 kWh m for the sum of solar radiation input (Table 2). www.biogeosciences.net/12/2893/2015/ Biogeosciences, 12, 2893–2905, 2015 2902 M. Klinge et al.: Modeling forest lines and forest distribution patterns Figure 10. Distribution of AFA and the 5 C monthly isotherms during the growing season Table 3. Comparison between of the modeled area values of single relief parameter classes and of the combination of all four relief parame- ters. (AFA: actual forest area; PFA: potential forest area.) Relief parameter: Elevation Solar radiation input Slope gradient Slope aspect All four parameters 2 2 2 2 2 Site classification km % FA % TMA km % FA % TMA km % FA % TMA km % FA % TMA km % FA % TMA AP AP AP AP AP (3) PFA without AFA 5358.7 91.4 65.9 4791.2 90.5 59.0 4279.9 89.5 52.7 3850.5 88.5 47.4 1323.6 72.5 16.3 (1) PFA with AFA 502.0 8.6 6.2 480.5 9.1 5.9 483.8 10.1 6.0 474.2 10.9 5.8 446.7 24.5 5.5 (4) No PFA with AFA 0.3 0.004 0.003 21.5 0.4 0.3 18.0 0.4 0.2 27.9 0.6 0.3 55.4 3.0 0.7 (2) No PFA without AFA 2265.2 27.9 2832.9 34.9 3344.4 41.2 3773.6 46.4 6300.7 77.5 Sum of all classifications which 2767.2 34.1 3313.4 40.8 3828.3 47.1 4247.8 52.3 6747.4 83.0 represent the actual situation % FA : percentage of the total actual and potential forest area. % TMA: percentage of total mountain area with 8126 km . AP nomads and woodcutters, which reduces the pure signal of single relief parameter against the combination of all four re- elevation in the data. Climate controls environmental condi- lief parameters (Table 3). Comparing the modeled PFA and tions in a coarser regional scale and creates sharper eleva- the AFA, four different classes can be built: (1) PFA with tional boundaries. The curve of the parameter elevation has AFA and (2) no PFA without AFA represent the mapped sit- a shallow left and a steep right slope, which indicates hu- uation; (3) PFA without AFA and (4) no PFA with AFA rep- man impact on forest distribution at the lower boundary. The resent the differences between modeling and mapping. To re- lower forest lines start at 1575 m a.s.l. and the upper forest ceive a statistical background for the evaluation of the mod- lines exceed 2900 m a.s.l. so that the maximal vertical dis- eling quality of the delineated PFA, it is once related to the tance of the forest limits for the entire investigation area is sum (FA ) of AFA and the PFA and twice referred to the AP 1325 m (Table 2). total mountain area (TMA) of 8126 km , while the boundary The TSA represents the complete area of the elevation belt between the mountains and the pediments of the foreland is between the forest lines from 1500 and 2900 m a.s.l.. Except generally defined by the changeover line of the slope gradient for the slope gradient diagram, the flat slope positions < 5 at 2.5 . From all four single relief parameters the modeling were additionally excluded from the TSA. The resulting TSA based on the slope aspect coincides best with the actual situa- is approximately 4975 km . In regard to the independent fre- tion, but, in any case, the combination of all four parameters quency distribution curves of the relief parameters in Fig. 4 obviously enhances the prediction accuracy (Table 3). The between forest stands and the TSA, no statistical influence PFA calculated from all four relief parameters is 1825 km of the main topographic pattern on the forest distribution is and therefore 3.5 times larger than the AFA. Figure 3 shows detectable. the spatial differences between the AFA and PFA. In relation From a statistical point of view, all four relief parameters to the AFA the PFA generally extends to the lower and upper control the forest distribution. Therefore, it is necessary to elevations. check the modeling accuracy of the PFA received from one Biogeosciences, 12, 2893–2905, 2015 www.biogeosciences.net/12/2893/2015/ M. Klinge et al.: Modeling forest lines and forest distribution patterns 2903 4.2 Forest line patterns line increases to 2000 m a.s.l., 400 m higher than in the west- ern part of the NFR. Here, the lower forest line occurs with Figure 6 shows the lower forest line in the investigation area a precipitation of 700 mm and at a positive pWB of 150 starting at 1600 m a.s.l. in the northwest and increasing to to 300 mm a , while the PFA extends more into the lower 2600 m a.s.l. in the southeast. Values for the lower forest slope positions; this corresponds to the mean values of the re- line are mostly derived from the lower CAs but there are gions described above. This is an indication for a nonnatural also many CAs at the higher elevations of the NFR, where distribution and points to greater human influence on forests the forest stands do not reach the valley bottom. This phe- in this region. nomenon may be caused by the local relief of tree-free flat The forest distribution related to mean air temperature in valley bottoms, which would be a climate rather than a to- July ranges between 7 and 17 C, with a maximum of around pographic signal. However, regarding the lower forest line 11 to 12 C (Fig. 5). Comparing the AFA and PFA with the in the second mountain range southeast of the intermountain July isotherms (Fig. 9) shows that the upper AFA is mainly basin and behind the NFR, it remains at a higher elevation bordered by the 10 C July isotherm but also extends to the around 2400 m a.s.l.. Here the high lower forest line position 8 C July isotherm in many places. The upper PFA is gen- is obviously caused by the drier conditions of the rain shadow erally aligned to the 8 C July isotherm. Figure 10 shows position, which may also be true for the upper valleys in the the distribution of the monthly mean air temperature 5 C NFR. isotherm during the growing season and the AFA. Except at The upper forest line distribution and the area above the the westernmost part, the 5 C isotherm is above the AFA forest line are shown in Fig. 7. In the NFR the upper forest between June and September, and the upper AFA bound- line at the edge of the mountains starts at 1800 m a.s.l. in the ary coincides well with the 5 C isotherm of September. The west and increases to 2200 m a.s.l. in the east, maintaining PFA at the upper forest boundary extends up to the position a vertical distance of 200 m to the lower forest line. From of 5 C isotherm in June; the growing season obviously be- the edge of the mountains in the north to the crest line, the comes very short at these highly elevated places. As shown upper forest line rises to 2800 m a.s.l., and, crossing the in- in Figs. 9 and 10, the PFA at the upper limit is overestimated termountain basin, it lies at an elevation between 2400 and and the upper AFA boundary generally has a natural limita- 2800 m a.s.l. in the SMR. The local vertical distance of the tion. forest belt reaches its maximum value of more than 900 m on the northern side of the NFR. On the southern side and in the SMR, the forest belt is very narrow, with vertical distances 5 Discussion and conclusions between 50 and 400 m. It was shown that the AFA and the forest lines coincide well 4.3 Climate environmental conditions with the local climate conditions. At the lower limit, forests are restricted to a minimum annual precipitation of 250 mm. The environmental conditions were analyzed in terms of The upper forest line coincides with the 10 C July isotherm frequency distribution of climate parameters for the AFA in most places and to the minimum monthly mean tempera- (Fig. 5) and were mapped together with the AFA (Figs. 8– ture of 5 C for the period between June and September. In 10). The diagrams in Fig. 5 show the differences between the more humid parts of the investigation area at the western AFA and TSA for all climate parameters except for the and northern slopes of the NFR both forest lines have a steep MAAT, which was already excluded as a significant forest gradient and the forest belt has its greatest vertical extension limitation parameter. between 500 and 600 m and locally up to 900 m. This agrees The lowest value class of forest stands for annual precip- well with the findings of increasing vertical forest extension itation is 250 mm, while the highest potential evapotranspi- concurrent with increasing humidity and vice versa by Fick- ration is up to 1100 mm a . One third of the AFA lies in ert (1998), Dai et al. (2013) and Klinge et al. (2003) in the areas with a negative potential water balance (pWB, i.e., the surrounding regions. Besides temperature, rainfall influences difference between annual precipitation and potential evap- the upper forest line because clouds reduce the air tempera- otranspiration; cf. Fig. 5) but with a precipitation amount of ture by shadowing and by the reflection of solar insulation. between 300 and 700 mm a (Fig. 8). These areas are sit- This explains the steep gradient of the upper forest line at the uated at the westernmost edges and on the southern slopes windward side of the mountain ridges. of the mountain ranges. While the westernmost sites are ex- The comparison of the AFA with climate data reveals a posed to the westerlies, which transport most of the humidity, strong relation between the distribution patterns at the up- the southern slopes lie in the rain shadow but at a higher el- per boundary, but divergences occur at the lower boundary. evation, and therefore the lower forest line is around 600 m This indicates human impact on the forests at the edge of the higher than on the northern side of the NFR. mountains, modifying the lower forest line, while the upper In the eastern part of the northern side of the NFR, the forest line represents the natural condition. Accordingly the AFA belt is very small and, concurrently, the lower forest PFA derived from relief parameters at lower elevations in- www.biogeosciences.net/12/2893/2015/ Biogeosciences, 12, 2893–2905, 2015 2904 M. Klinge et al.: Modeling forest lines and forest distribution patterns dicates additional area for more potential natural forest. The Dulamsuren, C., Khishigjargal, M., Leuschner, C., and Hauck, M.: Response of tree-ring width to climate warming and selective PFA at the upper boundary is overestimated by highest forest logging in larch forests of the Mongolian Altai, J. Plant Ecol., stands occurring at few places with favorable climatic con- 7, 24–38, 2014. ditions because we used the total vertical distance of forest Fickert, T.: Vergleichende Beobachtungen zu Solifluktions- und distribution as a relief parameter instead of the standard vari- Frostmustererscheinungen im Westteil Hochasiens, Erlanger Ge- ation, presuming that extensive logging may also occur in the ogr. Arb., 60, 150 pp., 1998. alpine meadow pastures. GIS analysis combined with multi- Gerlitz, L., Bechtel, B., Zaksek, K., Kawohl, T., and Böhner, J.: spectral satellite images and DTM is well suited to determine SAGA GIS based processing of spatial high resolution tempera- forest lines and potential forest areas for semiarid regions on ture data, Proceedings of the 27th EnviroInfo-Conference, 2013, a local to regional scale. For forest line delineation it is neces- 693–702, 2013. sary to eliminate elevation values which are restricted by the Gerlitz, L., Conrad, O., Thomas, A., and Böhner, J.: Assessment of relief conditions and do not represent climatic limitations. Warming Patterns for the Tibetan Plateau and its adjacent Low- lands based on an elevation and bias corrected ERA-Interim Data The DTM-derived relief parameters slope aspect, gradient Set, Climate Res., 58, 235–246, 2014. and solar radiation serve well as indicators of the climatic Giese, E.: Wetterwirksamkeit atmosphärischer Zustände und environment in the investigation area and help to transfer en- Prozesse in Sowjet-Mittelasien, Westfälische Geogr. Stud., 37, vironmental settings to other places in the broader study area. 395–410, 1973. Human impact is recognized by the evaluation of the parame- Giese, E.: Seßhaftwerden von Nomaden, Erfahrungen über die ter elevation. 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Modelling forest lines and forest distribution patterns with remote sensing data in a mountainous region of semi-arid Central Asia

Oct 13, 2014

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Abstract

Biogeosciences, 12, 2893–2905, 2015 www.biogeosciences.net/12/2893/2015/ doi:10.5194/bg-12-2893-2015 © Author(s) 2015. CC Attribution 3.0 License. Modeling forest lines and forest distribution patterns with remote-sensing data in a mountainous region of semiarid central Asia 1 2 1 M. Klinge , J. Böhner , and S. Erasmi Institute of Geography, University of Göttingen, Goldschmidtstr. 5, 37077 Göttingen, Germany Institute of Geography, University of Hamburg, Bundesstraße 55, 20146 Hamburg, Germany Correspondence to: M. Klinge (mklinge1@gwdg.de) Received: 17 July 2014 – Published in Biogeosciences Discuss.: 13 October 2014 Revised: 19 April 2015 – Accepted: 21 April 2015 – Published: 20 May 2015 Abstract. Satellite images and digital elevation models pro- 1 Introduction vide an excellent database to analyze forest distribution pat- terns and forest limits in the mountain regions of semiarid The latitudinal and elevational variation in distinct plant central Asia on the regional scale. For the investigation area associations and geomorphologic landscape units has been in the northern Tien Shan, a strong relationship between for- used for a long time to deduce regional environmental and est distribution and climate conditions could be found. Addi- climatical conditions in geosciences (e.g., von Humboldt, tionally areas of potential human impact on forested areas are 1845–1862; Troll, 1973a, b; Hövermann, 1985). Image clas- identified at lower elevations near the edge of the mountains sification and GIS modeling of remote-sensing data are stan- based on an analysis of the differences in climatic precondi- dard methods to map landscape elements and their distribu- tions and the present occurrence of forest stands. tion in remote areas, which are poorly accessible due to lo- The distribution of spruce (Picea schrenkiana) forests is gistic or political difficulties. Satellite analysis based on auto- hydrologically limited by a minimum annual precipitation of mated image processing offers a quick and useful alternative 250 mm and thermally by a minimum monthly mean tem- to field mapping or manual digitalization from aerial images perature of 5 C during the growing season. While the ac- (Mayer and Bussemer, 2001). While satellite images such as tual lower forest limit increases from 1600 m a.s.l. (above Landsat data provide excellent information to delineate the sea level) in the northwest to 2600 m a.s.l. in the south- spatial forest distribution (Hansen et al., 2013), SRTM (Shut- east, the upper forest limit rises in the same direction from tle Radar Topography Mission) data can be used to examine 1800 m a.s.l. to 2900 m a.s.l.. In accordance with the main relief-dependent distribution patterns with a digital terrain wind directions, the steepest gradient of both forest lines and model (DTM). The combination of these two data sets en- the greatest local vertical extent of the forest belt of 500 to ables high-resolution mapping on a regional to local scale. 600 m to a maximum of 900 m occur at the northern and The geoecologic and climatic environmental settings con- western mountain fronts. trol the natural distribution of forest stands (Holtmeier, 2000; The forests in the investigation area are strongly restricted Körner, 2012; Miehe et al., 2003). In addition, the actual sit- to north-facing slopes, which is a common feature in semi- uation can strongly be influenced by human activities such arid central Asia. Based on the presumption that variations in as logging, fire clearing and animal grazing, which decreases local climate conditions are a function of topography, the po- the potential natural forest area (PFA). This often makes it tential forest extent was analyzed with regard to the param- difficult to differentiate between natural factors and human eters slope, aspect, solar radiation input and elevation. All impact on the distribution of timbered areas. In general, hu- four parameters showed a strong relationship to forest distri- man activity has reduced the forest area since prehistorical bution, yielding a total potential forest area that is 3.5 times times so that the actual forest area (AFA) pattern mostly larger than the present forest remains of 502 km . represents the minimum of the potential environmental dis- Published by Copernicus Publications on behalf of the European Geosciences Union. 2894 M. Klinge et al.: Modeling forest lines and forest distribution patterns tribution range. However, due to the possibility of anthro- value for the mean air temperature during the growing sea- pogenic forest management and afforestation during the last son, which is defined as the period of monthly mean tem- centuries, forests may occur at sites less favorable for natural peratures above 5 C (Dai et al., 2013; Körner, 2012; Körner tree growth. and Paulsen, 2004). Based on the strong correlation between Due to the highly continental, cold and semiarid climate soil and air temperatures, Körner and Paulsen (2004) state a in central Asia, tree growth is mostly determined by topog- global range of 5.5 to 7.5 C for minimum mean air tempera- raphy parameters. Forest stands beyond sites with more fa- tures during the growing season. Paulsen and Körner (2014) vorable conditions regarding groundwater are predominantly developed a climate-based model for treeline prediction by limited to north-facing slopes in the mountains with an up- defining the growing season as days with a mean temperature per and lower forest limit (Dulamsuren et al., 2014; Hilbig, above 0.9 C and a mean temperature of more than 6.4 C 1995; Klinge et al., 2003; Treter, 1996, 2000). during that time. For the upper forest line between 2750 and Different definitions have been used for tree and forest 2920 m a.s.l. in the Tien Shan in Kyrgyzstan Körner (2012) lines (Körner, 2012; Körner and Paulsen, 2004). The tree- found a mean temperature of 6.5 C during the 155 days of line ecotone covers three main boundary lines at the upper the growing season (late April until late September). limit of forest distribution. The highest is the tree species The forest expansion into dry regions is controlled by pre- line, where tree seedlings occur but no adult trees. The tree- cipitation and soil water supply (Dulamsuren et al., 2010, line is the maximum elevation where patches of forest can 2014; Kastner, 2000; Klinge et al., 2003). Between the more exist at topographically favorable places. In our investigation humid mountain regions and the arid basins of central Asia, a we refer to the forest line, which is defined as the limit of lower limit of forest distribution occurs, which is termed the closed forest at the upper (timberline) and lower boundary of lower forest line. According to Walter and Breckle (1994) forest distribution. this forest distribution boundary coincides with an annual For the region of the northern Tien Shan in China, Dai precipitation of at least 300 mm, while Holdridge (1947) pro- et al. (2013) state an upper forest line beginning with poses 250 mm and Miehe et al. (2003) found Juniperus trees 2900 m a.s.l. in the west, which decreases eastward down to in southern Tibet growing in regions with an annual precipi- 2500 m a.s.l. and then rises again to 2900 m a.s.l. in the east. tation of between 200 and 250 mm. Dulamsuren et al. (2010) In the northwestern Tien Shan, Fickert (1998) reports an up- state an annual precipitation between 230 and 400 mm at per forest line of 2900 and 2850 m a.s.l. and a lower forest lower elevations for larch forests in northern and central line of 2400 and 2500 m a.s.l., respectively for the Sailijski- Mongolia. In western Mongolia Dulamsuren et al. (2014) Alatau and Kungeij-Alatau. In the Altai Mountains, Klinge et found coniferous forests existing at an annual precipitation al. (2003) found upper forest lines increasing eastward from of around 120 mm, which are explained by soil water bene- 1800 to 2600 m a.s.l. and, concurrently, lower forest lines in- fits due to the occurrence of permafrost ice in the soil. creasing from 1000 to 2200 m a.s.l., while the vertical exten- Everywhere in mountainous areas of the semiarid inner- sion of the forest belt varies between 400 and 1200 m. Asian forest steppe coniferous forests are restricted to north- Tree growth in high mountains is generally restricted by facing slopes. While the north-facing slopes are dominated temperature conditions (Haase et al., 1964; Holtmeier, 2000; by larch trees (Larix sibirica) in Mongolia, spruce trees Jobbagy and Jackson, 2000; Körner, 2012). The upper for- (Picea schrenkiana) occur in the Tien Shan (Dai et al., 2013; est line is a thermally determined distribution boundary that Fickert, 1998; Liu et al., 2013; Wang 2005, 2006). Thus, is generally defined by the mean July temperature (Walter the restriction of conifers to north-facing slopes in the inner- and Breckle, 1994) or the warmest-month isotherm of 10 C. Asian forest steppe is not bound to certain tree species but According to Körner (2012) and Körner and Paulsen (2004), rather to the environmental settings. this parameter is not appropriate for all parts of the world. The semiarid climate conditions generate an overall de- This can be seen in Fickert (1998), who shows that the upper ficiency of moisture which considerably influences the ele- forest line in the northern Tien Shan coincides well with the vational forest distribution and may even control the upper 10 C July isotherm, while further south in the northwest- forest limit (Liang et al., 2012; Liu et al., 2013; Miehe et al., ern Himalaya and northwestern Karakorum, it is connected 2008). A specific relief position is combined with particular to the July isotherms of 16 and 12 C, respectively. For the climate conditions such as temperature, precipitation, evapo- eastern side of the northern Tien Shan, Dai et al. (2013) re- ration and insulation, which are similar at comparable sites in port a mean temperature of the warmest month of 10.5 C the surroundings. For this reason the relief parameters eleva- at the mean position of the alpine forest line. Moreover, the tion, aspect, slope angle and solar radiation input can be used mean annual air temperature (MAAT) is weakly correlated to define topoclimatic conditions in mountain regions (Miehe to the forest line because it includes temperatures from the et al., 2003). However, to identify potential forest sites based nongrowing season, which play a minor role in tree growth on those definitions, the geologic and soil properties have to (Jobbagy and Jackson, 2000; Körner, 2012). be comparable. A suitable way of describing the temperature environment The impact of human activity on vegetation and especially at the upper forest line is by using a minimum threshold on the forest since prehistorical times is a permanent question Biogeosciences, 12, 2893–2905, 2015 www.biogeosciences.net/12/2893/2015/ M. Klinge et al.: Modeling forest lines and forest distribution patterns 2895 2 Study area The investigation area detailed, Uzynkara Ridge, also known as the Ketmen Mountain range, the Ketmen Mountain range, is located in the northernmost part of the Tien Shan in cen- tral Asia on the border between Kazakhstan and China (79– 0  0 81 E/42 45 –43 45 N) (Fig. 1). This closed mountain sys- tem was chosen for investigation because it provides excel- lent topographic preconditions to clearly indicate the lower and upper boundaries of forest distribution between the mid- dle and central part of Asia. It fills a knowledge gap about forest lines between regions of the Tien Shan in the south and east, and the Altai mountains in the north (Fickert, 1998; Dai et al., 2013; Klinge et al., 2003). The main cities in the region are Shonzy and Kegen. The complete mountain Figure 1. Map overview showing the investigation area detailed range is part of the catchment area (CA) of the Ili River (trapezoid) in central Asia. in the north. While the northern mountain side is directly drained to the Ili River, the Kegen River in the southern intermountain basin first flows westward and then, turning that needs to be investigated to clarify the environmental sig- into the Sharyn River, flows in a northerly direction, and nificance of any actual forest line (Miehe and Miehe, 2000). the Tekes River in the southernmost part runs eastward into Dulamsuren et al. (2014) found a considerable anthropo- Chinese territory. The mountain system is structured by two zoogenic influence on the actual lower forest line in the Mon- main ridges – a northern front range (NFR) and a southern golian Altai. For northern Mongolia, Schlütz et al. (2008) mountain range (SMR) – which converge in the east and en- showed that the present vegetation pattern in the mountain close an intermountain basin in the west. The highest peak taiga where steppes occur on south-facing slopes is caused is the Nebesnaja, reaching 3652 m a.s.l. A high mountain by climate conditions and relief and does not originate from plateau at  3400 m a.s.l. drops southward, while the north- human activities. facing slopes are cut steeply by Pleistocene cirques. Today, Human impact on natural forests in Kazakhstan goes back no glaciation but permafrost occurs in the uppermost areas. to prehistoric times, with nomadism and animal grazing as The edge of the mountains is tectonically clearly distin- a way of life adapted to the natural environmental condi- guished from the alluvial fans and fanglomerates at approx- tions of the steppe (Karger, 1965; Giese, 1981, 1983). During imately 1500 m a.s.l. in the north and at 2000 m a.s.l. in the summertime the alpine meadows and mountain steppes in the southern intermountain basin, following west to east trend- upper mountains were regularly used as pastures for the live- ing fault lines. The mountains mainly consist of metamor- stock. Even during Soviet times in Kazakhstan the nomadic phic and volcanic Carboniferous and Devonian rocks, includ- movements were generally adopted by the sovkhoz system. ing several Palaeozoic granite bodies. Permian, Silurian and Even today the alpine pastures are still in use. Extensive an- Jurassic rocks are also distributed locally. imal grazing prevents the rejuvenation of trees, and nomads The MAAT in Almaty (848 m a.s.l.) is 8.7 C and in may expand the grassland by setting fire to it. Karakol (1744 m a.s.l.), which is situated south of the Spatial models, which are able to predict the climatically investigation area, 6.3 C (Fickert, 1998). According to induced forest distribution and especially the upper forest Medeu (2010) the mean air temperature is between 8 and line on a global scale by exclusively using spatial climate 10 C in January and between 20 and 24 C in July. In data, already exist (Paulsen and Körner, 2014). However, wintertime the Siberian anticyclone produces weather con- a clear method to empirically distinguish the actual forest ditions with cold air masses in the basins and warmer air distribution and its elevational limits for small areas cov- temperatures above the inversion layer between 1000 and ering a single mountain system and to simultaneously in- 1550 m a.s.l. (Giese, 1973). vestigate the potential human impact is lacking. In this in- The majority of precipitation in Kazakhstan comes with vestigation we introduce a procedure to solve this problem air masses from the west and southwest. In the mountains of based on medium-resolution remote-sensing data. In addi- the northern Tien Shan, mainly convective rainfall occurs in tion, spatially explicit climate data and tree-growth-limiting spring and autumn. Additionally, cold air masses from north- climate parameters serve to differentiate potential human im- ern directions bring precipitation to the northern Tien Shan pact from natural conditions in the forest distribution. in summertime (Böhner, 2006; Lydolph, 1977). According to Giese (1973) the annual precipitation in the basins of the foreland lies between 100 and 300 mm; in the lower moun- tains and in the intermountain basin, it is between 300 and www.biogeosciences.net/12/2893/2015/ Biogeosciences, 12, 2893–2905, 2015 2896 M. Klinge et al.: Modeling forest lines and forest distribution patterns Figure 2. Workflow of DEM (digital elevation model) and satellite image processing to determine the spatial forest distribution patterns in semiarid mountain systems of central Asia on a high-resolution scale. 400 mm. In the mountains it increases to more than 800 mm. tremula) and in the northeastern part birch trees (Betula pen- The precipitation maxima occur in May and June, with a mi- dula) also occur. On the southern slopes shrub areas also ex- nor, secondary maximum in September. ist. The foreland, basins and treeless mountain areas are The soils are distributed according to the climate condi- covered by steppe vegetation with forb and bunch grass tions and the vegetation zones (Medeu, 2010). In the fore- (Medeu, 2010). In the drier regions to the north, it changes land, desert soils occur. In the lower front ranges and in the to grassland, sagebrush desert, saltwort and sedge vegeta- intermountain basin, mountain steppe soils of castanozem tion. The forest belt mainly consists of spruce trees (Picea and chernozem type are distributed. In the forest belt dark schrenkiana). In the westernmost part, aspen trees (Populus chernozems, which are locally bleached and podzolized, oc- Biogeosciences, 12, 2893–2905, 2015 www.biogeosciences.net/12/2893/2015/ M. Klinge et al.: Modeling forest lines and forest distribution patterns 2897 Figure 3. Spatial distribution of actual forested area (AFA) and potential forest area (PFA) in the mountainous region of the northernmost Tien Shan shown above a true-color composite of a Landsat 7 satellite image from the 13 September 2000. Table 1. Confusion matrix showing the accuracy report of the supervised maximum likelihood classification (area in ha). Reference Classification data Sum Producer’s Omission data Forest No forest reference accuracy error Forest 2696.7 333.6 3030.3 0.890 0.110 No forest 14.7 76 426.2 76 440.9 0.9998 Sum classification 2711.4 76 759.8 Total Sum of Overall User’s accuracy 0.995 0.995 test data accuracy Commission error 0.0054 79 471.2 0.996 cur in forests, and pheaozem soils exist at meadow steppe 3 Methods sites. At high elevations, alpine and subalpine soils occur in mountain meadows and meadow steppes. A schematic workflow of the GIS analysis procedure with Arable land in eastern Kazakhstan is located at the foot input data, intermediate data and output data is presented in of the mountain ranges on the alluvial fans in the basins and Fig. 2. The analysis is divided into two main processes: the on the foothills at a lower elevation. In this transition zone first is working on the relief parameters to estimate the PFA between the pediments and mountain ranges, the soils are and the second conducts the delineation of the upper and improved by a certain amount of Pleistocene loess (Giese, lower forest lines. The forest lines are defined as the distribu- 1983; Karger, 1965; Machalett et al., 2006). After leaving tion boundaries of closed forest stands with areas larger than the mountains, the water from the rivers is used for irriga- 0.5 ha, disregarding single trees, which may represent special tion cultivation on the pediments. In the foothills agricul- environmental places or remnants of former forests. Trees ture is supported by sufficient rainfall; this is the so-called near the rivers in the valley bottoms were excluded from the bogar cultivation (Giese, 1983). According to these require- examination because these are sites which have more favor- ments the settlements are located along the main valleys at able conditions regarding groundwater and which are mostly the mountain boundary. Around the settlements, there is sig- occupied by deciduous trees. nificant woodcutting for construction and fuel. The determination of the AFA in the investigation area was achieved based on a supervised maximum likelihood classi- fication from multispectral satellite images (visible light and infrared channels) of Landsat 7/ETMC from the 13 Septem- www.biogeosciences.net/12/2893/2015/ Biogeosciences, 12, 2893–2905, 2015 2898 M. Klinge et al.: Modeling forest lines and forest distribution patterns of up to 11 % mainly occurs at the edges of closed forest, where the classification depends on the quantity of trees in one Landsat pixel. The relief parameters elevation, aspect, slope gradient and total solar radiation input were derived from a DTM based on SRTM data (Rabus et al., 2003), which was converted to UTM zone 44 N with a spatial resolution of 90 90 m. The polygons of the delineated forest stands were intersected with the relief parameters in order to investigate the relief- dependent spatial distribution of forest sites in the study area. In addition, the statistics of all relief parameters were com- puted for the total study area (TSA) to indicate the poten- tial impact of topography on the spatial distribution of for- est stands (Fig. 4). The PFA was then identified based on the assumption of confidence ranges for all four relief pa- rameters, which were found responsible for forest distribu- tion. This range was defined by the standard deviation (95 % confidence interval) from the single-frequency distribution of the relief parameters aspect, slope gradient and sum of solar radiation input during the mean growing season (March to November). While these parameters are not systematically influenced by human impact, the vertical distribution may have been changed by forest clearing at the lower and up- per boundary. Therefore 99 % of the frequency distribution of the elevation parameter was chosen in this case. Baseline climate data sets for central Asia, comprising monthly radiation, temperature and precipitation data at a horizontal resolution of 0.5 arc seconds (approximately 1200 m in longitudinal and 850 m in latitudinal direction), are provided by Böhner (2006). The regular-grid climate layers were estimated using an empirical modeling ap- proach, which basically integrates statistical downscaling of coarse-resolution atmospheric fields (NCAR/NCEP-CDAS reanalyses series – National Center for Atmospheric Re- search/National Center for Environmental Prediction – cli- mate data assimilation system; Kalnay et al., 1996) and GIS- based surface parameterization techniques, to sufficiently ac- Figure 4. Frequency distribution of relief parameters in relation to count for the topographic heterogeneity of the target area. actual forest area (AFA, green; in the diagrams of aspect, slope gra- A comprehensive description of data bases and modeling dient and solar radiation input, the light green areas represent the standard deviation of 95 range excluded from PFA delineation) and techniques is given in Böhner (2006) and Böhner and An- total study area (TSA, brown). tonic (2009). The suitability and precision of the model- ing approach is discussed in Gerlitz et al. (2013, 2014) and Soria-Auza et al. (2010). ber 2000 (Fig. 3). Aerial photos provided as imagery and The frequency distribution of selected climate parameters Bing basemaps by ESRI were used to detect forest area refer- related to the AFA and the TSA shown in Fig. 5 was cal- ence sites for the training and validation of the classifier. Two culated in the same way as described above. In contrast to classes were built and manually digitized for the classifica- the high resolution of the SRTM data, the climate data has tion and validation process. One class represents the forest a resolution about 10 times lower, which leads to a gener- areas and the other class includes different kinds of no-forest alization and coarser-scale of relief positions where climatic landscape. Depending on the ground resolution of 30 30 m, differences between slope aspects inside the valleys are av- one pixel covers several individual trees so that small clear- eraged. The climate data related to the forest stands are ana- ings and aisles may have been disregarded. The confusion lyzed by the climatic limitation values for forest development matrix in Table 1 shows a producer’s accuracy of 89 % for to detect potential human impact on the forest distribution forest areas, where  6 % of the forest area were used for patterns when obvious discrepancies occur. validation. This possible underestimation of the forest area Biogeosciences, 12, 2893–2905, 2015 www.biogeosciences.net/12/2893/2015/ M. Klinge et al.: Modeling forest lines and forest distribution patterns 2899 Figure 5. Frequency distribution of climate parameters for actual forest area (AFA: columns and left axes, in km ) and the total study area (TSA: graph and right axes, in km ). Table 2. Statistical values of the relief parameters related to the forest distribution. Parameter Unit Maximum Total 95 % of the distribution distribution distribution value range range Elevation m a.s.l. 2500 1575–2900 1925–2775 Aspect degree horizontal 315 (NW) 0–360 260–70 Slope gradient degree vertical 28 0–62 12–38 Solar radiation input kWh m 1075 450–1550 800–1325 To outline the actual forest lines, it is initially necessary of sections on the left and on the right side of a valley. Hav- to segment the relief into small CAs, which represent small ing different aspects in one segment is expedient to receive a side valleys or slope niches divided by convex ridges. This is general forest line value for one valley section. done by computing the surficial hydrology regime from the After combining the catchment polygons with the forest DTM. The size of a CA is given by the threshold value for the polygons, it is possible to determine the maximum and min- stream definition function, which assigns the minimum num- imum elevation values for forests inside a single CA. The ber of cells that must discharge into a specific cell to start a calculated values are spatially allocated as points to the po- depth contour. In this study a value of 200 was found practi- sition of those pixels for which forest line values have been cal for the lower forest line and a value of 100 was suitable determined. To eliminate the preconditions on the lower for- for the upper forest line. The single CAs generally consist est line given by the elevation limits of the relief, only those www.biogeosciences.net/12/2893/2015/ Biogeosciences, 12, 2893–2905, 2015 2900 M. Klinge et al.: Modeling forest lines and forest distribution patterns Figure 6. The lower forest line and the catchment areas providing lower forest line values in the investigation area. Figure 7. The upper forest line and the catchment areas providing upper forest line values in the investigation area. minimum values of forest stands were chosen which are more than 100 m below the total maximum elevation of the catch- than 50 m higher than the total minimum value of the CA. ment. Finally, the forest lines were calculated from the re- The distance between the highest forest stands and the crest maining points by a Natural Neighbor interpolation method. line above has a special influence on the upper forest line; this is called the “summit syndrome” by Körner (2012). Near the summits the local climate conditions strongly suppress tree growth by stronger wind, reduced temperature and snow drift. To receive a reasonable value for the climatic upper for- est line and to eliminate preconditions by relief height, only those maximum forest values were chosen which lie more Biogeosciences, 12, 2893–2905, 2015 www.biogeosciences.net/12/2893/2015/ M. Klinge et al.: Modeling forest lines and forest distribution patterns 2901 Figure 8. Distribution of AFA in relation to the hydrological climatic environment. Figure 9. Distribution of AFA and PFA and the July isotherms. 4 Results Less than 5 % of the forests occur on southern slopes (SE– S–SW). The curve of the parameter aspect has a steeper left 4.1 Relief parameterization slope and a maximum value in the northwestern direction (315 ), which is strongly related to the diurnal air temper- The total AFA in the investigation area is 502 km (Fig. 3). ature trend caused by insolation and heating processes on Frequency distributions of relief parameters for the AFA are different slope directions. This underlines the fact that the shown in Fig. 4. The slope gradient and solar radiation of strong relation of forest distribution to slope aspect is caused the forest stands show a normal distribution. The values with by natural environmental conditions. The logistic problems the maximum distribution are 28 for slope gradient and of access in the relief influence how forest is transformed by 1075 kWh m for the sum of solar radiation input (Table 2). www.biogeosciences.net/12/2893/2015/ Biogeosciences, 12, 2893–2905, 2015 2902 M. Klinge et al.: Modeling forest lines and forest distribution patterns Figure 10. Distribution of AFA and the 5 C monthly isotherms during the growing season Table 3. Comparison between of the modeled area values of single relief parameter classes and of the combination of all four relief parame- ters. (AFA: actual forest area; PFA: potential forest area.) Relief parameter: Elevation Solar radiation input Slope gradient Slope aspect All four parameters 2 2 2 2 2 Site classification km % FA % TMA km % FA % TMA km % FA % TMA km % FA % TMA km % FA % TMA AP AP AP AP AP (3) PFA without AFA 5358.7 91.4 65.9 4791.2 90.5 59.0 4279.9 89.5 52.7 3850.5 88.5 47.4 1323.6 72.5 16.3 (1) PFA with AFA 502.0 8.6 6.2 480.5 9.1 5.9 483.8 10.1 6.0 474.2 10.9 5.8 446.7 24.5 5.5 (4) No PFA with AFA 0.3 0.004 0.003 21.5 0.4 0.3 18.0 0.4 0.2 27.9 0.6 0.3 55.4 3.0 0.7 (2) No PFA without AFA 2265.2 27.9 2832.9 34.9 3344.4 41.2 3773.6 46.4 6300.7 77.5 Sum of all classifications which 2767.2 34.1 3313.4 40.8 3828.3 47.1 4247.8 52.3 6747.4 83.0 represent the actual situation % FA : percentage of the total actual and potential forest area. % TMA: percentage of total mountain area with 8126 km . AP nomads and woodcutters, which reduces the pure signal of single relief parameter against the combination of all four re- elevation in the data. Climate controls environmental condi- lief parameters (Table 3). Comparing the modeled PFA and tions in a coarser regional scale and creates sharper eleva- the AFA, four different classes can be built: (1) PFA with tional boundaries. The curve of the parameter elevation has AFA and (2) no PFA without AFA represent the mapped sit- a shallow left and a steep right slope, which indicates hu- uation; (3) PFA without AFA and (4) no PFA with AFA rep- man impact on forest distribution at the lower boundary. The resent the differences between modeling and mapping. To re- lower forest lines start at 1575 m a.s.l. and the upper forest ceive a statistical background for the evaluation of the mod- lines exceed 2900 m a.s.l. so that the maximal vertical dis- eling quality of the delineated PFA, it is once related to the tance of the forest limits for the entire investigation area is sum (FA ) of AFA and the PFA and twice referred to the AP 1325 m (Table 2). total mountain area (TMA) of 8126 km , while the boundary The TSA represents the complete area of the elevation belt between the mountains and the pediments of the foreland is between the forest lines from 1500 and 2900 m a.s.l.. Except generally defined by the changeover line of the slope gradient for the slope gradient diagram, the flat slope positions < 5 at 2.5 . From all four single relief parameters the modeling were additionally excluded from the TSA. The resulting TSA based on the slope aspect coincides best with the actual situa- is approximately 4975 km . In regard to the independent fre- tion, but, in any case, the combination of all four parameters quency distribution curves of the relief parameters in Fig. 4 obviously enhances the prediction accuracy (Table 3). The between forest stands and the TSA, no statistical influence PFA calculated from all four relief parameters is 1825 km of the main topographic pattern on the forest distribution is and therefore 3.5 times larger than the AFA. Figure 3 shows detectable. the spatial differences between the AFA and PFA. In relation From a statistical point of view, all four relief parameters to the AFA the PFA generally extends to the lower and upper control the forest distribution. Therefore, it is necessary to elevations. check the modeling accuracy of the PFA received from one Biogeosciences, 12, 2893–2905, 2015 www.biogeosciences.net/12/2893/2015/ M. Klinge et al.: Modeling forest lines and forest distribution patterns 2903 4.2 Forest line patterns line increases to 2000 m a.s.l., 400 m higher than in the west- ern part of the NFR. Here, the lower forest line occurs with Figure 6 shows the lower forest line in the investigation area a precipitation of 700 mm and at a positive pWB of 150 starting at 1600 m a.s.l. in the northwest and increasing to to 300 mm a , while the PFA extends more into the lower 2600 m a.s.l. in the southeast. Values for the lower forest slope positions; this corresponds to the mean values of the re- line are mostly derived from the lower CAs but there are gions described above. This is an indication for a nonnatural also many CAs at the higher elevations of the NFR, where distribution and points to greater human influence on forests the forest stands do not reach the valley bottom. This phe- in this region. nomenon may be caused by the local relief of tree-free flat The forest distribution related to mean air temperature in valley bottoms, which would be a climate rather than a to- July ranges between 7 and 17 C, with a maximum of around pographic signal. However, regarding the lower forest line 11 to 12 C (Fig. 5). Comparing the AFA and PFA with the in the second mountain range southeast of the intermountain July isotherms (Fig. 9) shows that the upper AFA is mainly basin and behind the NFR, it remains at a higher elevation bordered by the 10 C July isotherm but also extends to the around 2400 m a.s.l.. Here the high lower forest line position 8 C July isotherm in many places. The upper PFA is gen- is obviously caused by the drier conditions of the rain shadow erally aligned to the 8 C July isotherm. Figure 10 shows position, which may also be true for the upper valleys in the the distribution of the monthly mean air temperature 5 C NFR. isotherm during the growing season and the AFA. Except at The upper forest line distribution and the area above the the westernmost part, the 5 C isotherm is above the AFA forest line are shown in Fig. 7. In the NFR the upper forest between June and September, and the upper AFA bound- line at the edge of the mountains starts at 1800 m a.s.l. in the ary coincides well with the 5 C isotherm of September. The west and increases to 2200 m a.s.l. in the east, maintaining PFA at the upper forest boundary extends up to the position a vertical distance of 200 m to the lower forest line. From of 5 C isotherm in June; the growing season obviously be- the edge of the mountains in the north to the crest line, the comes very short at these highly elevated places. As shown upper forest line rises to 2800 m a.s.l., and, crossing the in- in Figs. 9 and 10, the PFA at the upper limit is overestimated termountain basin, it lies at an elevation between 2400 and and the upper AFA boundary generally has a natural limita- 2800 m a.s.l. in the SMR. The local vertical distance of the tion. forest belt reaches its maximum value of more than 900 m on the northern side of the NFR. On the southern side and in the SMR, the forest belt is very narrow, with vertical distances 5 Discussion and conclusions between 50 and 400 m. It was shown that the AFA and the forest lines coincide well 4.3 Climate environmental conditions with the local climate conditions. At the lower limit, forests are restricted to a minimum annual precipitation of 250 mm. The environmental conditions were analyzed in terms of The upper forest line coincides with the 10 C July isotherm frequency distribution of climate parameters for the AFA in most places and to the minimum monthly mean tempera- (Fig. 5) and were mapped together with the AFA (Figs. 8– ture of 5 C for the period between June and September. In 10). The diagrams in Fig. 5 show the differences between the more humid parts of the investigation area at the western AFA and TSA for all climate parameters except for the and northern slopes of the NFR both forest lines have a steep MAAT, which was already excluded as a significant forest gradient and the forest belt has its greatest vertical extension limitation parameter. between 500 and 600 m and locally up to 900 m. This agrees The lowest value class of forest stands for annual precip- well with the findings of increasing vertical forest extension itation is 250 mm, while the highest potential evapotranspi- concurrent with increasing humidity and vice versa by Fick- ration is up to 1100 mm a . One third of the AFA lies in ert (1998), Dai et al. (2013) and Klinge et al. (2003) in the areas with a negative potential water balance (pWB, i.e., the surrounding regions. Besides temperature, rainfall influences difference between annual precipitation and potential evap- the upper forest line because clouds reduce the air tempera- otranspiration; cf. Fig. 5) but with a precipitation amount of ture by shadowing and by the reflection of solar insulation. between 300 and 700 mm a (Fig. 8). These areas are sit- This explains the steep gradient of the upper forest line at the uated at the westernmost edges and on the southern slopes windward side of the mountain ridges. of the mountain ranges. While the westernmost sites are ex- The comparison of the AFA with climate data reveals a posed to the westerlies, which transport most of the humidity, strong relation between the distribution patterns at the up- the southern slopes lie in the rain shadow but at a higher el- per boundary, but divergences occur at the lower boundary. evation, and therefore the lower forest line is around 600 m This indicates human impact on the forests at the edge of the higher than on the northern side of the NFR. mountains, modifying the lower forest line, while the upper In the eastern part of the northern side of the NFR, the forest line represents the natural condition. Accordingly the AFA belt is very small and, concurrently, the lower forest PFA derived from relief parameters at lower elevations in- www.biogeosciences.net/12/2893/2015/ Biogeosciences, 12, 2893–2905, 2015 2904 M. Klinge et al.: Modeling forest lines and forest distribution patterns dicates additional area for more potential natural forest. The Dulamsuren, C., Khishigjargal, M., Leuschner, C., and Hauck, M.: Response of tree-ring width to climate warming and selective PFA at the upper boundary is overestimated by highest forest logging in larch forests of the Mongolian Altai, J. Plant Ecol., stands occurring at few places with favorable climatic con- 7, 24–38, 2014. ditions because we used the total vertical distance of forest Fickert, T.: Vergleichende Beobachtungen zu Solifluktions- und distribution as a relief parameter instead of the standard vari- Frostmustererscheinungen im Westteil Hochasiens, Erlanger Ge- ation, presuming that extensive logging may also occur in the ogr. Arb., 60, 150 pp., 1998. alpine meadow pastures. GIS analysis combined with multi- Gerlitz, L., Bechtel, B., Zaksek, K., Kawohl, T., and Böhner, J.: spectral satellite images and DTM is well suited to determine SAGA GIS based processing of spatial high resolution tempera- forest lines and potential forest areas for semiarid regions on ture data, Proceedings of the 27th EnviroInfo-Conference, 2013, a local to regional scale. For forest line delineation it is neces- 693–702, 2013. sary to eliminate elevation values which are restricted by the Gerlitz, L., Conrad, O., Thomas, A., and Böhner, J.: Assessment of relief conditions and do not represent climatic limitations. Warming Patterns for the Tibetan Plateau and its adjacent Low- lands based on an elevation and bias corrected ERA-Interim Data The DTM-derived relief parameters slope aspect, gradient Set, Climate Res., 58, 235–246, 2014. and solar radiation serve well as indicators of the climatic Giese, E.: Wetterwirksamkeit atmosphärischer Zustände und environment in the investigation area and help to transfer en- Prozesse in Sowjet-Mittelasien, Westfälische Geogr. Stud., 37, vironmental settings to other places in the broader study area. 395–410, 1973. Human impact is recognized by the evaluation of the parame- Giese, E.: Seßhaftwerden von Nomaden, Erfahrungen über die ter elevation. Therefore, a forest line evaluation with respect Dynamik traditioneller sozialer Einrichtungen (am Beispiel des to the general climatic conditions has to be performed be- kasachischen Volkes), in: Die Nomaden in Geschichte und fore the parameter elevation is incorporated into the spatial Gegenwart, Beiträge zu einem internationalen Nomadismus- delineation process of the PFA. In conclusion, the proposed Symposium am 11 und 12 Dezember 1975 im Museum für Völk- workflow is a helpful method for the evaluation of the poten- erkunde Leipzig, Berlin, 175–197, 1981. tial forest distribution and the delineation of human impact. Giese, E.: Nomaden in Kasachstan – Ihre Seßhaftwerdung und Einordnung in das Kolchos- und Sowchossystem, Geogr. Rund- It can be used to indicate local climate variability, for land- schau, 11, 575–588, 1983. scape analysis and for effective reforestation planning. 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