| Global climate change is an indisputable fact, extreme weather and frequent natural disasters have seriously affected human production and life. Though it does not reach a binding agreement on emission issues in Copenhagen conference, the development of low-carbon economy and the promotion of energy conservation are becoming a basic consensus of all countries. Meanwhile, it shows that climate change is an extremely complex issue, far beyond the traditional scope of the environment. It involves political, economic, international law and many other complex issues. Therefore, global carbon cycle becomes a widespread concern research topic in scientific community. About 77% of the vegetation carbon stores in forest biomass in terrestrial ecosystems. So forest biomass is the most important parameter in terrestrial ecosystem carbon cycle, which directly reacts to forest carbon stocks. Northeast forest of China, one of the world's three large temperate forests which occupied above 1/3 of the country's total forest area and volume, plays an important role in China and global carbon cycle, forestry and ecological environment construction. However, forest carbon cycling in the northeast forest is not yet comprehensive, carbon cycle assessment, modeling and forecasting of our country and global are still needed research results in this region. This study is based on two scientific northeast forest-related issues, one is how to accurately obtain the large-scale forest biomass, and the other is analysis of spatial and temporal changes in forest biomass and quantitative analysis of driving. Remote sensing and geographic information systems are key technologies to solve the problem. The researches and corresponding conclusions according to the above issues are as followed:1,Remote sensing data processing, including geometric correction, radiometric correction and radiation normalized at different phases of large-scale remote sensing data is the right base work for using remote sensing information.2,The northeastern forest was divided into Daxing'an Mountain, Xiaoxing'an Mountain and Changbai Mountain by the geographical distribution of vegetation. Changbai Mountain is divided into Jilin and Heilongjiang Changbai Mountains combined with the administrative divisions. Forest biomass of four regions was estimated separately.3,The establishment of ground biomass model is the basis of estimating forest biomass. Two methods were used to establish ground biomass model in this study, one is conventional statistical model used in Daxing'an Mountain and Changbai Mountain, including 7 main tree species of Daxing'an Mountain and 18 main tree species of Changbai Mountain, the other is uniform biomass model. "Chebyshev orthogonal polynomial with partial least square to establish ground biomass unified model" was proposed in this study. It takes the biomass model as an element in continuous function space. A group bases in this space were found to express as a linear combination for tree biomass unified model. Rigorous mathematical derivation was in model proposed process, and parameters were calculated using partial least squares method. Model results were compared with existing biomass model results and ground-measurement data. The whole modeling process is scientific and rigor. The model is a general type which is a new approach for above-ground biomass modeling. The model was applied to forest of Xiaoxing'an Mountain with the establishment of unified biomass model of same independent variable DBH for 16 major tree species. The average accuracy of unified biomass model is 5% higher than conventional statistical models.4,Forest biomass in inventory plot was calculated according to the tree, shrub and grass biomass models of different regions, as the basis for the establishment of remote sensing data model.5,Forest biomass estimation models in various regions were established by stepwise regression, BP neutral network and Erf-Bp neural network methods. The results show that the stepwise regression model was less precise, about 75%, which was difficult to achieve accuracy; Erf-BP neural network has high precision, about 80%, but is difficult to promote in large region for its own algorithm characteristics.6,Partial least squares model is a new method to estimate forest biomass. Compared with the regression model, the accuracy of this method to estimate forest biomass is higher, above 80%. In particular, nonlinear partial least squares model is better. However, the algorithm is complex and costs long running time. Because of the uncertainty of nonlinear model form, it is a problem to estimate by remote sensing in large area, so the method only stops at a small area experiment.7,Joint equations and measurement error model to estimate the forest biomass is a new statistical method to extract spatial parameters in remote sense. The results of the researches show that this method is more than reasonable than conventional statistical methods. Joint equations and measurement error model has good application in forest stand growth model, but have not yet applied to remote sensing information extraction project. In this study, joint equations and measurement error model is introduced into the remote sensing estimation of forest biomass, on one hand to explore a new remote sensing estimation model, on the other hand to provide a new method to estimate information of joint multi-sensor. Multi-angle remote sense was used in the joint equations of biomass and leaf area index. And the biomass estimation was combined with physical model and statistical model, with the average test accuracy of 83.3%, RMSR of 17.72 in needle model, and the average test accuracy of 83.0%, RMSE of 20.28 in broad-leaves. Laser radar was used in the joint equations of biomass and tree heights, with the average test accuracy of 81.0%, RMSR of 15.19. Microwave remote sense was used in the joint equations of biomass and backscattering coefficients. The joint method of active and passive remote sense for forest biomass estimation was tried, with the average test accuracy of 83.9%, RMSR of 20.36. To some extent, these methods all improved the estimation accuracy of forest biomass. Finally, considered the implementability of biomass estimation, the joint equations of biomass and crown density were chose in this study, with the average test accuracy of 83.1%, RMSR of 20.01.8,Analysis of spatial and temporal changes in forest biomass. Under the foundation of the GIS spatial analysis and geo-statistical analysis, a computer program for analyzing change-driven factors of regional forest biomass was developed for calculating importance value of each factor to biomass change quantitatively, using canonical correlation analysis, principal component analysis and partial least squares algorithm based on extensive collections of regional meteorological data, business activity data and socio-economic data for several years.According to the conclusions of the study, it is found that the stepwise regression model can not meet the required precision, the neural network model and partial least squares algorithm with high precision are complex and only used in small experimental area, the joint equations and the measurement error model is the best with high precision, simple algorithm and suitability for remote sensing estimation in large area. On the analysis of spatial and temporal changes in forest biomass, it showed that management measures are the main driving factors of forest biomass changes in the 70-80 years; three categories of factors all have played important roles in the 80-90 years from the view of importance values of various factors affecting biomass change; from the lat 90s to the present, the significance of management measures reduced, but natural factors and socio-economic factors increased, which indicated the initial success of natural forest protection project.
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