Distribution Of Forest Biomass In The Southern Slope Of Qinling Mountains Based On Geostastistics

Since the20th century, global climate change has become an undisputed fact as theproduction and daily life of our human beings were impacted continually and seriously by theextreme weather conditions and natural disasters. The carbon storage in forest ecosystemaccounts for85-90%of terrestrial ecosystems, which plays an important role in responding toglobal climate change caused by CO2emission. Because of the reduction of forest vegetationtogether with the increase of CO2and other greenhouse gases, to estimate the forest biomassand carbon storage has increasingly become necessary. As one of the largest carbon storageforest areas in China, Qinling forest has a crucial influence on balancing the regional carboncontent. However, not enough research has been investigated in biomass in this region.In this study, data was collected in the permanent sample plots of Changjiaoba Town,Foping County, Shaanxi Province in2008. According to which, we calculated the biomass ofthe sample plots by using allometric growth models. On this basis, atethe spatial distributionof forest biomass by the Geostatistical techniques, also with the DEM date in that region, weanalyzed how do factors such as altitude, aspect and slope influence forest biomassdistributionOur objectivees are to provide a scientific basis for forest ecosystem restorationand reconstruction in the study area and help the forest managers withreasonablemanagement strategies. The main results of this research are summarized as follows:(1) The spatial heterogeneity of the biomass was analyzed by using the variogram ofstatistics, which leads to a better understanding in the spatial biomass distribution. Throughthe semivariogram fit with the sample plots data obtained in the study restrict, we found outthat the structural ratio of spherical model was0.971, the coefficient of determination was0.789and the residual was3760. It is shown that this model has the largest coefficient ofdetermination, the smallest residual, and a relatively high structural ratio. Thus, thespherical model was proved to be the optimal model for statistical interpolation aboutbiomass data in the study area.(2) It was a feasible way to obtain forest biomass in regional level by using the statistical interpolation method. With resolving the scale problem effectively, we eventually got the mapof spatial forest biomass distributionin the study restrict. By studying the pattern ofbiomassspatial distribution we concluded that the biomass in the northern, southeastern andsouthwestern regions is relatively higher than in the middle part of southern region. Thebiomass in this area ranged from36.78t/hm2to114.15t/hm2with an average biomass of75.15t/hm2and a total biomass of1.02Tg.(3) Different types and age groups of forests have different biomass levels. Coniferousforests in the near mature forest stage have a biomass of40.43t/hm2, who have the highestaverage biomass level in the study area. The biomass levels of broad-leaved forests increasedfrom young to mature forest. The investigated forest restricts were in the early successional?stage, most of the coniferous species were planted after large-scale logged, and it has notgrown to maturity. Therefore, the biomass of coniferous largely depends on the near matureforest. Pinus tabulaeformis and Quercus mixed forest have the highest biomass levelcompared with other forest types; Larix forest mixed with Quercus had a smaller average age,therefore hadthe lowest biomass level.(4) The biomass was significantly affected by the terrain factors. By analyzing thechanging rules of forest biomass along different altitudes, aspects and slopes, we concluded:forest biomass firstly increased and then decreased with the increasing of both elevation andslope; the accumulation of biomass in the shady slope was the highest, followed bysemi-shady and semi-sunny slope, and sunny slope was the smallest. In the slope aspect,biomass was mainly distributed in the steep slope from15-25°and the acute slope from26-35°. Both are higher than the plain areas and the slope of more than40°. With theincreasing of altitude, the biomass showed an increasing trend at the beginning and then adecreasing trend. The highest biomass level was found in the low altitude areas of1200-1600m.In this paper, statistical methods were applied to analyze the spatial distribution of forestbiomass based on the field survey data and terrain data. It is expected to provide scientificguidance for sustainable forest management as well as the restoration and reconstruction offorest ecosystem. More importantly, to afford scientific, rational and workable references formaking small-scale forest management plan forQingling mountains.