Modelling forest lines and forest distribution patterns with remote sensing data in a 1 mountainous region of semi-arid Central Asia. 2

13 Satellite images and digital elevation models provide an excellent database to analyse forest


Introduction
The latitudinal and elevational variation of distinct plant associations and geomorphologic landscape units has been used for a long-time to deduce regional environmental and climatical conditions in geosciences (e.g.Humboldt, 1845Humboldt, -1862;;Troll, 1973a, b;Hövermann, 1985).Image classification and GIS-modelling of remote sensing data are standard methods to map landscape elements and their distribution in remote areas, which are poorly accessible due to logistic or political difficulties.
Satellite analysis based on automated image processing offers a quick and beneficial alternative to field mapping or manual digitalisation from aerial image (Mayer and Bussemer, 2001).While satellite images like Landsat data provide excellent information to delineate the spatial forest distribution (Hansen et al., 2013), SRTM (Shuttle Radar Topography Mission) data can be used to examine relief dependent distribution patterns with a digital terrain model (DTM).The combination of these two data sets enables high resolution mapping at regional to local scale.
The geoecologic and climatic environmental settings control the natural distribution of forest stands (Holtmeier, 2000;Körner, 2012;Miehe et al., 2003).In addition, the actual situation can strongly be influenced by human activities like logging, fire clearing and animal grazing, which decreases the potential natural forest area (PFA).This often makes it difficult to differentiate between natural factors and human impact on the distribution of timbered areas.In general, human activity has reduced the forest area since prehistorical times so that the actual forest area (AFA) pattern mostly represent the minimum of the potential environmental distribution range.However, due to the possibility of anthropogenic forest management and afforestation during the last centuries forests may occur at sites less favourable for natural tree growth.
Due to the highly continental, cold and semi-arid climate in Central Asia, tree growth is mostly determined by topography parameters.Forest stands beyond groundwater favoured sites are predominantly limited to north-facing slopes in the mountains with an upper and lower forest limit (Dulamsuren et al., 2014;Hilbig, 1995;Klinge et al., 2003;Treter, 1996Treter, , 2000)).
Different definitions have been used for tree-and forest lines (Körner, 2012;Körner and Paulsen, 2004).The treeline ecotone covers three main boundary lines at the upper limit of forest distribution.
The highest is the tree species line, where tree seedlings occur but no adult trees.The treeline is the maximum elevation where patches of forest can exist at topographic favoured places.In our investigation we refer to the forest line, which is defined as the limit of closed forest at the upper (timberline) and lower boundary of forest distribution.
For the region of northern Tien Shan in China, Dai et al. (2013) state an upper forest line beginning with 2,900 m asl in the west, which decreases eastward down to 2,500 m asl and then rises again to 2,900 m asl in the east.In the north-western Tien Shan Fickert (1998) reports an upper forest line of 2,900 and 2,850 m asl and a lower forest line of 2,400 m and 2,500 m asl, respectively for the Sailijski-Alatau and Kungeij-Alatau.In the Altai Mountains Klinge et al. (2003) found upper forest lines increasing eastward from 1,800 m asl to 2,600 m asl and lower forest lines concurrently increasing from 1,000 m asl to 2,200 m asl while the vertical extension of the forest belt varies between 400 and 1,200 m.
Tree growth in high mountains is generally restricted by temperature conditions (Haase et al., 1964;Holtmeier, 2000;Jobbagy and Jackson, 2000;Körner, 2012).The upper forest line is a thermally determined distribution boundary that is generally defined by the mean July temperature (Walter and Breckle, 1994) or the warmest month isotherm of 10 °C.According to Körner (2012) and Körner and Paulsen (2004) this parameter is not suitable in all parts of the world.This can be seen in Fickert (1998), who shows that the upper forest line in the northern Tien Shan coincides well with the 10 °C July-isotherm, while further south in NW-Himalaya and NW-Karakorum it is connected to the Julyisotherms of 16 °C and 12 °C, respectively.For the eastern side of the northern Tien Shan Dai et al. (2013) report a mean temperature of the warmest month of 10.5 °C at the mean position of the alpine forest line.Also the mean annual air temperature (MAAT) is weakly correlated to the forest line because it includes temperatures from the non-growing season which play a minor role for tree growth (Jobbagy and Jackson, 2000;Körner, 2012).
A suitable way to describe the temperature environment at the upper forest line is a minimum threshold value for the mean air temperature during the growing season which is defined as the period of monthly mean temperatures above 5 °C (Dai et al., 2013;Körner, 2012;Körner and Paulsen, 2004).
Based on the strong correlation between soil and air temperatures, Körner and Paulsen (2004) state a global range of 5.5 to 7.5 °C for minimum mean air temperature during the growing season.Paulsen and Körner (2014) developed a climate-based model for treeline prediction by defining the growing season as days with mean temperature above 0.9 °C and a mean temperature of more than 6.4 °C during that time.For the upper forest line between 2,750 and 2,920 m asl in the Tien Shan Mountains in Kyrgyzstan Körner (2012) found a mean temperature of 6.5 °C during the 155 days of the growing season (late April until late September).
The forest expansion into dry regions is controlled by precipitation and soil water supply (Dulamsuren et al., 2010(Dulamsuren et al., , 2014;;Kastner, 2000;Klinge et al., 2003).Between the more humid mountain regions and the arid basins of Central Asia a lower limit of forest distribution occurs which is termed the lower forest line.According to Walter and Breckle (1994) this forest distribution boundary coincides with an annual precipitation of at least 300 mm, while Holdridge (1947) proposes 250 mm and Miehe et al. (2003) found Juniperus trees in southern Tibet growing in regions with annual precipitation between 200 and 250 mm.Dulamsuren et al. (2010) state an annual precipitation between 230 and 400 mm at lower elevations for larch forests in northern and central Mongolia.In western Mongolia Dulamsuren et al. (2014) found coniferous forests existing at annual precipitation near 120 mm, which are explained by soil water benefits due to the occurrence of permafrost ice in the soil.
Everywhere in mountainous areas of the semi-arid Inner Asian forest-steppe coniferous forests are restricted to north facing slopes.While the north-facing slopes are dominated by larch trees (Larix sibirica) in Mongolia, spruce trees (Picea schrenkiana) occur in the Tien Shan Mountains (Dai et al., 2013;Fickert, 1998;Liu et al., 2013;Wang 2005Wang , 2006)).Thus the restriction of conifers to northfacing slopes in the Inner Asian forest-steppe is not bound to certain tree species but rather to the environmental settings.
The semi-arid climate conditions generate an overall deficiency of moisture which considerably influences the elevational forest distribution and may even control the upper forest limit (Liang et al., 2012;Liu et al., 2013;Miehe et al., 2008).A specific relief position is combined with particular climate conditions like temperature, precipitation, evaporation and insulation, which are similar at comparable sites in the surroundings.For this reason the relief parameters elevation, aspect, slope angle and solar radiation input can be used to define topoclimatic conditions in mountain regions (Miehe et al., 2003).However, to identify potential forest sites based on those definitions, the geologic and soil properties have to be comparable.
The impact of human activity on vegetation and especially on the forest since prehistorical times is a permanent question that needs to be proofed to clarify the environmental significance of any actual forest line (Miehe and Miehe, 2000).Dulamsuren et al. (2014) found a considerable anthropozoogenic influence on the actual lower forest line in the Mongolian Altai.For northern Mongolia Schlütz et al. (2008) showed that the present vegetation pattern in the mountain taiga where steppes occur on south-facing slopes is caused by climate conditions and relief, and is not originating from human activities.
Human impact on natural forests in Kazakhstan goes back to prehistoric times with nomadism and animal grazing as lifestyle adapted to the natural framework of the steppe (Karger, 1965;Giese, 1981Giese, , 1983)).During summertime the alpine meadows and mountain steppes in the upper mountains were regularly used as pastures for the livestock.Even during Soviet times in Kazakhstan the nomadic movements were generally adopted by the Sowchos-System.Even today the alpine pastures are still in use.Extensive animal grazing prohibits the rejuvenation of trees and nomads may expand the grassland by fire setting.
Spatial models, which are able to predict the climatically induced forest distribution and especially the upper forest line on a global scale by exclusively using spatial climate data already exist (Paulsen and Körner, 2014).However, a clear method to empirically distinguish the actual forest distribution and its elevational limits for small areas covering a single mountain system and to simultaneously proof the potential human impact is lacking.In this investigation we introduce a procedure to solve this problem based on medium resolution remote sensing data.In addition, spatially explicit climate data and tree growth limiting climate parameters serve to differentiate potential human impact from natural conditions in the forest distribution.

Study Area
The detailed investigation area Uzynkara ridge or Ketmen mountain range is located in the northernmost part of the Tien Shan Mountains in Central Asia at the border between Kazakhstan and China (79°-81°E / 42°45´-43°45´N) (Fig. 1).This closed mountain system was chosen for investigation because it provides excellent topographic preconditions to clearly indicate the lower and upper boundaries of forest distribution between the middle and central part of Asia.It is filling a gap of information about forest lines between regions of the Tien Shan mountains 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 range is part of the catchment area (CA) of the Ili river 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 turns as Sharyn river into a northern direction, and the Tekes river in the southernmost part runs eastward to Chinese territory.The mountain system is structured by two main ridges, a northern front range (NFR) and a southern mountain range (SMR), which converge in the east and enclose an intermountain basin in the west.The highest peak is the Nebesnaja reaching 3,652 m asl.A high mountain plateau in ~3,400 m asl is dropping southward, while the north facing slopes are cut steeply by Pleistocene cirques.Today, no glaciation but permafrost occurs in the uppermost areas.The MAAT in Almaty (848 m asl) is 8.7 °C and in Karakol (1,744 m asl) which is situated south of the investigation area 6.3 °C, respectively (Fickert, 1998).According to Medeu (2010) the mean air temperature is between -8 and -10 °C in January and between 20 and 24 °C in July.In wintertime the Siberian anticyclone produces weather conditions with cold air masses in the basins and warmer air temperatures above the inversion layer between 1,000 and 1,550 m asl (Giese, 1973).
The majority of precipitation in Kazakhstan comes along with air masses from western and southwestern directions.In the mountains of northern Tien Shan mainly convective rainfall occurs in spring and autumn.Additionally cold air masses from northern directions bring precipitation to the northern Tien Shan 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 mountains and in the intermountain basin it is between 300 and 400 mm.In the mountains it increases to more than 800 mm.The precipitation maxima occur in May, June, and with minor secondary maximum in September.
The foreland, basins, and treeless mountain areas are covered by steppe vegetation with forb and bunch grass (Medeu, 2010).To the drier regions in the north it changes to grassland, sagebrush desert, saltwort, and sedge vegetation.The forest belt mainly consists of spruce trees (Picea schrenkiana).In the westernmost part aspen trees (Populus tremula) and in the northeastern part birch trees (Betula pendula) additionally occur.On the southern slopes shrub areas exist also.
The soils are distributed according to the climate conditions and the vegetation zones (Medeu, 2010).
In the foreland desert soils occur.In the lower front ranges and in the intermountain basin mountain steppe soils of castanozem and chernozem type are distributed.In the forest belt dark chernozems which are locally bleached and podzolized occur under forest and pheaozem soils exist at meadow steppe sites.In the high elevations alpine and subalpine soils occur with mountain meadows and meadow steppes.
Arable land in eastern Kazakhstan is located in front of the mountain ranges on the alluvial fans in the basins and on the foothills in lower elevation.In this transition zone between the pediments and mountain ranges the soils are improved by a content of Pleistocene loess (Giese, 1983;Karger, 1965;Machalett et al., 2006).In front of the mountain border, the rivers spend the water for irrigation cultivation on the pediments.On the foothills agriculture is supported by sufficient rainfall as the socalled "Bogar"-Cultivation (Giese, 1983).According to these requirements the settlements are located along main valleys at the mountain boundary.Around the settlements wood-cutting is pronounced for construction and fuel.

Methods
A schematic workflow of the GIS-analysis procedure with input data, intermediate data, and output data is presented in Fig. 2. The analysis is divided into two main processes: The first is working on the relief parameters to estimate the PFA and the second conducts the delineation of the upper and lower forest lines.The forest lines are defined as the distribution boundaries of closed forest stands with areas larger than 0.5 ha, disregarding single trees which may represent special environmental places or remnants of former forests.Trees near the rivers of the valley bottoms were excluded from the examination because these are groundwater favoured sites which are mostly occupied by deciduous trees.
The determination of the AFA in the investigation area was achieved based on a supervised maximum likelihood classification from multispectral satellite images (visible light and infrared channels) of Landsat 7/ETM+ of the 13 th September 2000 (Fig. 3).Aerial photos provided as imagery and Bing basemaps by ESRI served for the detection of forest area reference sites for training and validation of the classifier.Two classes were built and manually digitised for the classification and validation process.One class represents the forest areas and the other class includes different kinds of no forest landscape.Depending on the ground resolution of 30 x 30 m one pixel covers the occurrence of several individual trees so that small clearings and aisles could have been disregarded.The confusion matrix in Table 1 shows a producer's accuracy of 89% for forest areas, where ~6% of the forest area were used for validation.This possible underestimation of the forest area of up to 11% mainly occurs at the borders of closed forest, where the classification depends on the quantity of trees inside of 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 North with a spatial resolution of 90 x 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 computed for the total study area (TSA) to indicate the potential impact of topography on the spatial distribution of forest stands (Fig. 4).The PFA was then identified based on the assumption of confidence ranges for all four relief parameters, which were found responsible for forest distribution.This range was defined by the standard deviation (95% confidence interval) from 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 upper 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 in a horizontal resolution of 0.5 arc seconds (approximately 1,200 m in longitudinal and 850 m in latitudinal direction) are provided by Böhner (2006).The regular-grid climate layers were estimated using an empirical modelling approach, which basically integrates statistical downscaling of coarse resolution atmospheric fields (NCAR / NCEP-CDAS reanalyses series) (National Center for Atmospheric Research / (National Center for Environmental Prediction -Climate data assimilation system, Kalnay et al., 1996) and GIS based surface parameterization techniques, to sufficiently account for the topographic heterogeneity of the target area.A comprehensive description of data bases and modelling techniques is given in Böhner (2006) and Böhner and Antonic (2009).
The suitability and precision of the modelling approach is discussed in Gerlitz et al. (2013Gerlitz et al. ( , 2014) ) and

Soria-Auza et al. (2010).
The frequency distribution of selected climate parameters related to the AFA and the TSA shown in Fig. 5 was calculated in the same way as described above.In contrast to the high resolution of the SRTM-data the climate data has a resolution about 10 times lower which leads to a generalisation and coarser scale of relief positions, where climatic differences between slope aspects inside the valleys are averaged.The climate data related to the forest stands is analysed by the climatic limitation values for forest development to detect potential human impact on the forest distribution patterns when obvious discrepancies occur.
To outline the actual forest lines it is initially necessary to segment the relief into small CAs, which represent small side-valleys or slope niches divided by convex ridges.This is done by computing the surficial hydrology regime from the DTM.The size of a CA is given by the threshold value for the stream definition function, which assigns the minimum number of cells to discharge into a specific cell to start a depth contour.In this study a value of 200 was found practical for the lower forest line and a value of 100 was suitable for the upper forest line.The single CAs generally consist of a part from the left and right side of a valley.Having different aspects in one segment is expedient to receive a general forest line value for one valley section.
After combining the catchment polygons with the forest polygons it is possible to determine the maximum and minimum elevation values for forests inside a single CA.The calculated values are spatially allocated as points to the position of those pixels which have the determined forest line value.
To eliminate the preconditions on the lower forest line given by the elevation limits of the relief only those minimum values of forest stands were chosen, which are more than 50 m higher as the total minimum value of the CA.The distance between the highest forest stands and the crest line above has a special influence on the upper forest line which 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 forest line and to eliminate preconditions by relief height, only those maximum forest values were chosen which lie more than 100 m below the total maximum elevation of the catchment.Finally, the forest lines were calculated from the remaining points by a natural neighbour interpolation method.

Relief parameterisation
The total AFA in the investigation area is 502 km 2 (Fig. 3).Frequency distributions of relief parameters for the AFA are shown in Fig. 4. The slope gradient and solar radiation of the forest stands show a normal distribution.The values with maximum distribution are 28° for slope gradient and 1,075 kWh/m 2 for the sum of solar radiation input (Table 2).Less than 5% of the forests exist on southern slopes (SE-S-SW).The curve of the parameter aspect has a steeper left slope and a maximum value in the north-western direction (315°), which is strongly related to the diurnal air temperature trend caused by insolation and heating processes on different slope directions.This underlines the fact that the strong relation of forest distribution to slope aspect is caused by natural environmental conditions.Nomads and woodcutters approach to forest transformation by logistic problems of access in the relief, which reduces the pure signal of elevation in the data.Climate controls environmental conditions in a coarser regional scale and is creating sharper elevational boundaries.The curve of the parameter elevation has a shallow left and a steep right slope, which indicates human impact on forest distribution at the lower boundary.The lower forest lines start at 1,575 m asl and the upper forest lines exceed 2,900 m asl so that the maximal vertical distance of the forest limits for the entire investigation area is 1,325 m (Table 2).
The TSA as equal positions for forests in the mountain area were inferred from the forest lines and represent the total elevation belt from 1,500 and 2,900 m asl.Except for the slope gradient diagram, the flat slope positions <5° were additionally excluded from the TSA.The resulting TSA area is approximately 4,975 km 2 .In regard to the independent frequency distribution curves of the relief parameters in Fig. 4 between forest stands and the total TSA no statistical influence of the main topographic pattern on the forest distribution is detectable.
From the statistical point of view all four relief parameters control the forest distribution.Therefore, it is necessary to check the modelling accuracy of the PFA received from one single relief parameter against the combination of all four relief parameters (Table 3).Comparing the modelled PFA and the AFA four different classes can be built: 1.) PFA with AFA and 2.) no PFA without AFA are representing the mapped situation, 3.) PFA without AFA, and 4.) no PFA with AFA are representing the differences between modelling and mapping.To receive a statistical background for the evaluation of the modelling quality of the delineated PFA, it is once related to the sum (FA AP ) of AFA and the PFA and twice referred to the total mountain area (TMA) of 8,126 km 2 , when the mountain boundary to the pediments of the foreland is generally defined by the changeover line of the slope gradient at 2.5°.From all four single relief parameters the modelling based on the slope aspect coincides best with the actual situation, but anyway the combination of all four parameters obviously enhances the prediction accuracy (Table 3).The PFA calculated from all 4 relief parameters is 1,825 km 2 and therefore 3.5 times larger than the AFA.Fig. 3 shows the spatial differences between the AFA and PFA.In relation to the AFA the PFA generally extends to the lower and upper elevations.

Forest line patterns
Fig. 6 shows the lower forest line in the investigation area starting at 1,600 m asl in the northwest and increasing to 2,600 m asl in the southeast.Values for the lower forest line mostly are derived from the lower CAs but there are also many CAs in the higher elevations of the NFR, where the forest stands do not reach the valley bottom.This phenomenon may be caused by the local relief of tree free flat valley bottoms, which would be rather a climate than a topographic signal.But regarding the lower forest line in the second mountain range southeast of the intermountain basin and behind the NFR the lower forest line remains at a higher elevation around 2,400 m asl.Here the high lower forest line position is obviously caused by the drier conditions of the rain shadow position, which may also be true for the upper valleys in the NFR.
The upper forest line distribution and the area above the forest line are shown in Fig. 7.In the NFR the upper forest line at the mountain border starts in 1,800 m asl in the west and increases to 2,200 m asl in the east maintaining a vertical distance of 200 m to the lower forest line.From the mountain border in the north to the crest line the upper forest line rises to 2,800 m asl and crossing the intermountain basin it lies in an elevation between 2,400 and 2,800 m asl in the SMR.The local vertical distance of the forest belt reaches its maximum value of more than 900 m at the northern side of the NFR.On the southern side and in the SMR the forest belt is very narrow with vertical distances between 50 and 400 m.

Climate environmental conditions
The environmental conditions were analysed in terms of frequency distribution of climate parameters for the AFA (Fig. 5) and were mapped together with the AFA (Fig. 8-10).The diagrams in Fig. 5 show the differences between AFA and TSA for all climate parameters except for the MAAT, which was already excluded as significant forest limitation parameter.
The lowest value class of forest stands for annual precipitation is 250 mm, while the highest potential evapotranspiration is up to 1,100 mm/a..One third of the AFA lies in areas with a negative potential water balance (pWB, i.e. the difference between annual precipitation and potential evapotranspiration, cf.Fig. 5) but with a precipitation amount between 300 and 700 mm/a (Fig. 8).These areas are specially situated at the westernmost edges and on the southern slopes of the mountain ranges.While the westernmost sites are exposed to the westerlies which transport most of the humidity, the southern slopes lie in the rain shadow but at a higher elevation and therefore the lower forest line is around 600 m higher than on the northern side of the NFR.
In the eastern part of the northern side of the NFR the AFA belt is very small and concurrently the lower forest line increases to 2,000 m asl, 400 m higher than in the western part of the NFR.Here the lower forest line occurs at precipitation of 700 mm and at positive pWB of 150 to 300 mm/a, while the PFA extends more into the lower slope positions which corresponds to the mean values of the regions described above.This is an indication for a non-natural distribution and points to greater human influence on forests in this region.
The forest distribution related to mean air temperature in July ranges between 7 and 17 °C, with a maximum around 11 to 12 °C (Fig. 5).Comparing the AFA and PFA with the July-isotherms (Fig. 9), shows that the upper AFA is mainly bordered by the 10 °C July-isotherm, but also extends to the 8 °C July-isotherm at many places.The upper PFA is generally aligned to the 8 °C July-isotherm.Fig. 10 shows the distribution of the monthly mean air temperature 5 °C isotherm during the growing season and the AFA.Except at the westernmost part the 5 °C isotherm is above the AFA between June and September and the upper AFA boundary coincides well with the 5 °C isotherm of September.The PFA at the upper forest boundary extends up to the position of 5 °C isotherm in June, where the growing season obviously becomes very short at these high elevated places.As shown in Fig. 9 and 10 the PFA at the upper limit is overestimated and the upper AFA boundary generally has a natural limitation.

Discussion and conclusions
It was shown that the AFA and the forest lines coincide well with the local climate conditions.At the lower limit forests are restricted to a minimum annual precipitation of 250 mm.The upper forest line is combined to the 10 °C July-isotherm in most places and to the minimum monthly mean temperature of 5 °C for the period between June and September.In the more humid parts of the investigation area at the western and northern slopes of the NFR both forest lines have a steep gradient and the forest belt has its greatest vertical extension between 500 and 600 m and locally up to 900 m.This fits well to the findings of increasing vertical forest extension concurrent with increasing humidity and vice versa by Fickert (1998), Dai et al. (2013), and Klinge et al. (2003) in the surrounding regions.Besides temperature, rainfall influences the upper forest line because clouds reduce the air temperature by shadowing and reflection of solar insulation.This is explaining the steep gradient of the upper forest line at the windward side of the mountain ridges.
The comparison of the AFA with climate data reveals a strong relation between the distribution patterns at the upper boundary but divergences occur at the lower boundary.This indicates human impact on the forests at the mountain borders modifying the lower forest line, while the upper forest line represents the natural condition.Accordingly the PFA derived from relief parameters at lower elevations indicates additional area for more potential natural forest.The PFA at the upper boundary is overestimated by highest forest stands occurring at few climate favoured places, because we used the total vertical distance of forest distribution as a relief parameter instead of the standard variation presuming that extensive logging may also occur in the alpine meadow pastures.GIS-analysis combined with multispectral satellite images and DTM is well suited to determine forest lines and potential forest areas for semi-arid regions in a local to regional scale.For forest line delineation it is necessary to eliminate elevation values which are restricted by the relief conditions and do not represent climatic limitations.DTM-derived relief parameters slope aspect, gradient and solar radiation serve well as indicators for the climatic environment in the investigation area and help to transfer environmental settings to other places in the broader study area.Human impact is recognized by the evaluation of the parameter elevation.Therefore a forest line evaluation with respect to the general climatic conditions has to be performed before the parameter elevation is incorporated into the spatial delineation process of the PFA.In conclusion, the proposed workflow is a helpful method for the evaluation of the potential forest distribution and the delineation of human impact.It can be used to indicate local climate variability, for landscape analysis and for effective reforestation planning.
The mountain border is tectonically clearly accentuated against the alluvial fans and fanglomerats in approximately 1,500 m asl in the North and in 2,000 m asl in the southern intermountain basin following west to east trending fault lines.The mountains mainly consist of metamorphic and volcanic Carboniferous and Devonian rocks including several Palaeozoic granite bodies.Locally also Permian, Silurian and Jurassic rocks are distributed.

Figure 1 :Figure 3 :
Figure 1: Map overview showing the detailed investigation area (rectangle) in Central Asia

Figure 5 :
Figure 5: Frequency distribution of climate parameters for actual forest area (AFA, columns, left axes km 2 ) and the total study area (TSA, graph, right axes km 2 ).

Figure 6 :
Figure 6: The lower forest line and the catchment areas providing lower forest line values in the investigation area.

Figure 7 :Figure 8 :Figure 10 :
Figure 7: The upper forest line and the catchment areas providing upper forest line values in the investigation area.

Table 1 :
Confusion matrix showing the accuracy report of the supervised maximum likelihood

Table 3 :
Comparison between of the modelled area values of single relief parameter classes and of the combination of all four relief parameters.(AFA = Actual forest area, PFA = Potential forest area)