| Recently, the research on forest carbon storage attracted more and more attention against the global warming. As the basis of analyzing terrestrial carbon cycle and storage and its dynamic change, forest biomass estimation has become one of the important contents of ecology and global change study. Shangri-La county, located in the alpine and gorge region in the northwest of Yunnan province, is laid at the hinterland of the largest world natural heritage site "Three Parallel Rivers". The forest biomass and carbon storage research at this region plays a significant role for developing plateau terrestrial carbon cycle study, evaluating the ecological services of natural heritage and its role in global climate change scientifically and improving the whole society awareness of the ecological value of protected areas. As the development of GIS, RS and GPS techniques, a rapid and effective research can be provided for studying forest biomass and carbon storage in large scale.In this study, Shangri-La was chosen as study area. The research on remote sensing estimation of forest biomass and carbon storage was carried out, which was supported by National Natural Science Foundation project (No:40861009). The Landsat data TM of 2008 and 2009, the forest management inventory of 2008 and 186 supplemental field samples were integrated. The study area is located in the high-mountain gorge area, the complex terrain (elevation from 1503m to 5545m) resulted in remote sensing data pre-processing and standardizing very difficult. For this case, the RS data terrain shadow elimination, forest biomass RS feature extraction and modeling means was focused on in this study. A forest biomass RS estimation model was built. And the forest biomass and carbon storage estimation for major forest ecosystems was completed in Shangri-La. The main conclusions as below:(1) The RS data, located in the high-mountain gorge area was influenced seriously by the terrain. The correlation between RS data and forest volume was improved in this study by employing the terrain radiation correction.The study result indicated that the correlation between each band and forest volume was very low by using Radiometric Calibration only. The six bands of TM data and forest volume were failed to significance test. After atmospheric correction, the correlation was improved in some degree. But there were also only the TM4 correlation was significant (p=0.05). By using terrain correction, the correlation between the bands and sample biomass was improved obviously based on atmosphere correction. There were three bands correlation had passed the significance test. What is more, the correlation coefficient of TM4 passed the significant test (p=0.01).(2) Compared with numerous traditional RS features, the shadow percent of forest obtained by pixel unmixing had represented much better correlation with forest biomass than the most other ones among the RS features in the study.The study result indicated that the correlation between shadow richness image and forest biomass after pixel unmixing was better in some degree than the correlation of single band. All correlation had passed the significance test (p=0.01) except Spruce-fir. It showed that the shadow feature obtained by pixel unmixing could be a significant factor in the forest biomass estimation based on RS.(3) The result of three model means showed that the accuracy of the linear regression model was the worst, the neural network was followed, and the support vector machine regression model was the highest. With the same sample data set, biomass estimation model was performed by using multiple linear regression, neural networks and support vector machine approach for different forest types, respectively. The results showed that multiple linear regressions leaded to estimates of the model less effective. It was because that the data distribution was required to meet some hypothesis (e.g. linearity, normality, equal variance, independence, etc.), which caused only a few variables (2-3) left in the model equation, and then it resulted in the insufficient use of remote sensing information.The neural network can obtain a smaller training error, for the new and untrained data, however, its generalization ability was poor and there were some learning problems. Consequently, its model estimation accuracy was not high.Unlike traditional means such as neural networks to minimize the training error, the Support Vector Machine made the training error as an optimization constraint, and minimized the value of the confidence range as its optimization objective. The generalization ability of SVM was superior to the traditional study means. Moreover, the SVM solving was transform into quadratic programming solving. Therefore, the SVM solution was unique and global optimal one. In this study results, the correctness and effectiveness of SVM theory was fully demonstrated by that Support Vector Machine model had the highest accuracy.(4) Dominant forest ecosystems carbon storage of the study areaWith the help of Support Vector Machine Model, carbon storage of Shangri-La can be estimated, the total carbon storage of Shangri-La is 302.984 TgC. The carbon storage of arbor Layer, Shrub Layer, Herb Layer, Litter Layer and Soil Layer is 60.196 TgC,5.433 TgC,1.080 TgC,3.582 TgC and 232.692 TgC respectively, and it accounts for 19.87%, 1.79%,0.36%,1.18% and 76.80% of total carbon storage respectively. According to the amount of their carbon storage, the sequence was Soil Layer>Tree Layer>Shrub Layer> Litter Layer>Herb Layer.(5) Dominant forest ecosystem carbon density of the study areaThe average carbon density of Shangri-La dominant forest ecosystem is 403.480 t/hm2. Spruce-fir has the greatest carbon density (576.889 t/hm2). The follow are Oak with 326.947 t/hm2. Alpine larch with 279.993 t/hm2 and Yunnan Pine 255.792 t/hm2 respectively. The average carbon density of Shangri-La was higher than that of its neighboring Sichuan area, which is estimated to be 232.81 t/hm2. It is demonstrated that Shangri-La, locating in the greatest world natural heritage site, was not only significant in its nature, humanities and biological value, but also a vital carbon pool, which plays an important role in maintaining stability and balance for the whole ecosystem of Shangri-La county.
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