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Spatial pattern of forest biomass and its influencing factors in the Great Xing’an Mountains, Heilongjiang Province, China.

WANG Xiao-li1,2, CHANG Yu1, CHEN Hong-wei1, HU Yuan-man1, JIAO Lin-lin1,2, FENG Yu-ting3, WU Wen1,2, WU Hai-feng1,2   

  1. (1State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China; 2University of Chinese Academy of Sciences, Beijing 100049, China; 3Liaoning Academy of Environmental Sciences, Shenyang 110031, China)
  • Online:2014-04-18 Published:2014-04-18

Abstract: Based on field inventory data and vegetation index EVI (enhanced vegetation index), the spatial pattern of the forest biomass in the Great Xing’an Mountains, Heilongjiang Province was quantitatively analyzed. Using the spatial analysis and statistics tools in ArcGIS software, the impacts of climatic zone, elevation, slope, aspect and vegetation type on the spatial pattern of forest biomass were explored. The results showed that the forest biomass in the Great Xing’an Mountains was 350 Tg and spatially aggregated with great increasing potentials. Forest biomass density in the cold temperate humid zone (64.02 t·hm-2) was higher than that in the temperate humid zone (60.26 t·hm-2). The biomass density of each vegetation type was in the order of mixed coniferous forest (65.13 t·hm-2) > sprucefir forest (63.92 t·hm-2) > Pinus pumila-Larix gmelinii forest (63.79 t·hm-2) > Pinus sylvestris var. mongolica forest (61.97 t·hm-2) > Larix gmelinii forest (61.40 t·hm-2) > deciduous broadleaf forest (58.96 t·hm-2). With the increasing elevation and slope, the forest biomass density first decreased and then increased. The forest biomass density in the shady slopes was greater than that in the sunny slopes. The spatial pattern of forest biomass in the Great Xing’an Mountains exhibited a heterogeneous pattern due to the variation of climatic zone, vegetation type and topographical factor. This spatial heterogeneity needs to be accounted when evaluating forest biomass at regional scales.