Modeling forest lines and forest distribution patterns with remote-sensing data in a mountainous region of semiarid central Asia

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Abstract

Satellite images and digital elevation models provide an excellent database to analyze forest distribution patterns and forest limits in the mountain regions of semiarid central Asia on the regional scale. For the investigation area in the northern Tien Shan, a strong relationship between forest distribution and climate conditions could be found. Additionally areas of potential human impact on forested areas are identified at lower elevations near the edge of the mountains based on an analysis of the differences in climatic preconditions and the present occurrence of forest stands. The distribution of spruce (Picea schrenkiana) forests is hydrologically limited by a minimum annual precipitation of 250 mm and thermally by a minimum monthly mean temperature of 5°C during the growing season. While the actual lower forest limit increases from 1600 m a.s.l. (above sea level) in the northwest to 2600 m a.s.l. in the southeast, the upper forest limit rises in the same direction from 1800 m a.s.l. to 2900 m a.s.l.. In accordance with the main wind directions, the steepest gradient of both forest lines and the greatest local vertical extent of the forest belt of 500 to 600 m to a maximum of 900 m occur at the northern and western mountain fronts. The forests in the investigation area are strongly restricted to north-facing slopes, which is a common feature in semi-arid central Asia. Based on the presumption that variations in local climate conditions are a function of topography, the potential forest extent was analyzed with regard to the parameters slope, aspect, solar radiation input and elevation. All four parameters showed a strong relationship to forest distribution, yielding a total potential forest area that is 3.5 times larger than the present forest remains of 502 km 2.

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Klinge, M., Böhner, J., & Erasmi, S. (2015). Modeling forest lines and forest distribution patterns with remote-sensing data in a mountainous region of semiarid central Asia. Biogeosciences, 12(10), 2893–2905. https://doi.org/10.5194/bg-12-2893-2015

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