Forest Biomass Estimation And Driving Analysis Of Its Changes In Zhangguangcai And Wanda Mountain

Forest, the important resource on surface, is the natural resource on which human survival depends. It is not only an ecosystem with the biggest area, the most widely distribution, complex composition and abundant material resources, but also a main carbon sink of the terrestrial ecosystem. However, people had cut large numbers of woods during 70s and 80s that led to heavy destruction of the ecosystem. During the period of natural forest protection, forest biomass and carbon storage have undergone great changes that have a positive effect on forest ecosystems. Therefore, it is significant to study changes of forest biomass, especially in natural forest protection time.Northeast forest in China, one of the large temperate forests in the world occupied over a third of national forest area and volume, plays an important role in global carbon cycle and entironment constructions. However, researches on forest carbon cycling in Northeast are not comprehensive. Carbon cycle estimating, modeling and forecasting in the region are also needed in Chinese and the global research. This paper analyzes and discusses some estimating methods of forest biomass and its rules in temporal and spatial changes, combining with RS and GIS technology.This paper develops biomass estimation models of Changbai mountain using many relevant factors, such as remote sensing factors, GIS factors, meteorological factors and economic factors, then analyses information of temporal and spatial changes. It also focuses on natural driving factors, factitious driving factors and economic driving factors, and analyses each factors for biomass changes quantitatively. Comparing with formerly qualitative and linear quantitative analysis, this paper finds the most proper method to simulate the driving mechanism and makes nonlinear analysis of biomass changes and driving factors, which provides references for investigating forest productions, carbon budgets and carbon cycling models.Some conclusions were made according to above problems:1. Due to atmospheric conditions, lighting conditions, surface fluctuations and other factors of remote sensing image in large area and large time scales, images in the same region have large radiative differences. These differences have important quantitative effects on extracting information. Only when geometric correction, radiometric correction, atmospheric correction, sun elevation angle calibration, radiation normalization of multi-temporal remote sensing images are made before forest biomass estimation and tempory-spatial analysis can precision have good results.2. Models for estimating forest biomass quantitatively were discussed and established by using multivariate stepwise regression, linear and nonlinear partial least-squares (PLS) method, back-propagation (BP) neural network model and back-propagation neural network based on gaussian error function (Erf-BP) model. The best predicted model among them was Erf-BP neural network with the precision of 84.91%. The second one was nonlinear PLS model with the fitted and predicted precision of 85.8% and 83.08% respectively. The third was linear PLS model with the fitted and predicted precision of 83.08% and 83.09%. The fourth was BP neural network model with the precision of 80.97%. The last one was multivariate stepwise regression with the fitted and predicted precision of 76% and 75%. Because of the limitations of calculating time, PLS and neural network models were hard to popularize and apply.3. The impacts of some factors on biomass variations, such as natural factors and man-made factors were analysed quantitatively in this study. Linear and nonlinear PLS models were used to calculate variables'VIP values of atmosphere, management and economy. These VIP values were useful for analyzing driving mechanism of biomass changes. The results indicated that the main driving factors of biomass changes between 70s and 80s,80s and 90s,90s and 2000s were management factors, all of the three factors, natural and social factors respectively. This result showed the effects of Natural Forest Protection Project.In this paper, not only correct methods were used for predicting biomass of the study region, but also biomass changes, changes mechanism and driving factors were identified and analyzed.