【作者】 孙丽；

【导师】 陈蜀江；

【作者基本信息】 新疆师范大学 ， 地图学与地理信息系统， 2011， 硕士

【摘要】 生长量可以作为评定立地条件好坏及经营措施效果的指标,正确地分析研究并掌握林木的生长规律,采用相应的经营管理措施,可以改善树木的生长状况,提高生长量。以特克斯县为研究对象,利用样地实测数据结合GIS手段提取相关因子,并进行因子的数量化,分析了各遥感因子与连年生长量之间的相关关系,运用统计软件进行回归分析,建立了相关性显著的回归模型。并将分析结果经过检验,再将拟合较好的模型进行比较,最终选定最优模型,并对特克斯县的云杉生长量进行了遥感反演。通过以上研究得到主要结论如下:(1)根据云杉分布状况,选择云杉长势较好的特克斯县,并采用1996年、2001年和2006年的51个样地中1365株样木的胸径数据。遥感影像选择Landsat-7-ETM+数据以及SRTM-DEM数据。(2)利用云杉单株胸径数据,经过统计计算,推算出特克斯县的一元材积表及材积经验式为V = 0. 00010447D2.54202946,由此计算各年份的云杉材积,并计算出定期生长量与连年生长量,可得51个样地的样木在1996-2006年定期生长量为3.64463m~3,1996-2006年连年生长量为0.364463m~3。(3)由于生长量与海拔梯度增加呈现单峰型变化格局,故需对海拔的实际值进行数量化,即海拔依据生长量分级,根据海拔数据结合大量文献资料,可将海拔分为5级。坡向数据也需进行数量化处理,坡向可以分为8个方向,分级方法是将360°根据坡向进行划分,北坡为等级8,南坡为等级1。(4)根据生长量数据对影响因子进行相关性分析,分析的因子有红光、绿光、蓝光、近红外、短波红外、NDVI及立地因子的海拔、坡度、坡向,共九个因子。通过主成分分析和相关分析筛选因子。最后留下4个影响因子,分别为绿光、NDVI、高程、坡向。(5)通过对4个因子的多元线性计算和对其进行回归方程显著性F检验,并对回归系数进行T检验,结果表明:绿光波段不能通过回归系数检验,故选择3和2因子模型进行计算,对所得4个方程进行对比比较,并进行误差检验,得到最优预测模型为3因子NDVI、高程、坡向的多元回归方程: Y=-0.003716+0.018369*N+0.001966*E+0.000666*A。(6)利用预测模型对特克斯县的云杉连年生长量进行遥感反演,可得云杉连年生长量为1.332684×10~4m~3,云杉的面积为9.523737×10~4hm~2。与高程、坡向叠加分析,可得生长量的变化趋势表现为中海拔处最大,即在1900～2599m的云杉生长量为1.126898×10~4m~3,占84.56%。生长量增加最多的是阴坡,即北坡,生长量为4.65614×10~3 m~3,占34.94%。更多还原

【Abstract】 Growth can serve as the site conditions quality testing and evaluation index of management measure effect, correctly analysis and master of forest tree growth rule, using relevant management measures; it can change the growth situation, and improve the growth of the trees. As the research object in Tekes County to combine the measured data with GIS means extract relevant factors, and factors related to the discrimination, analyzed the relationship between each RS factors and successive growth ,and used statistical software for regression analysis, established significant relationship to regression model . After that analyze the results after inspection and then compared the model better fit, and ultimately select the best model, And Picea schrenkiana growth by remote sensing inversion in Tekes County. Through the above research get the main conclusion as follows:(1)According to the distribution of Picea schrenkiana, choose Picea schrenkiana grew better in Tekes County, and adopted the 51 sample plots of 1365 sample wood DBH data in 1996、2001 and 2006 . RS image Select Landsat-7-ETM + data and SRTM-DEM data.(2)Use Diameter at breast height of per plant by Picea schrenkiana data, through statistical calculation, calculate a Picea schrenkiana volume table and volume of experience formulation for V = 0. 00010447D2.54202946 in Tekes County, which calculated the year of the Picea schrenkiana volume and the volume and periodic annual increment growth, the 51 sample plots of the periodic growth for 3.64463m~3 and the annual increment growth for 0.364463m~3 in 1996-2006.(3)Due to the increased growth with the elevation gradient showed a single peak pattern of change, it takes the actual value of the elevation of discrimination that is classification of elevation based on the increased growth, elevation data based on combining a large number of documents, it can be divided into 5 levels. Aspect of discrete data also in need of treatment, and aspect can be divided into eight directions, according to aspect that classification method to divide the 360°, the north aspect for grade 8, and south aspect for grade 1.(4)According to the data on the impact of growth factors correlation analysis, analysis of a total of nine factors are red, green, blue, near infrared, shortwave infrared, NDVI and site factors elevation, slope, aspect. By principal components’analysis and correlation analysis to select factor. Finally four factors are green, NDVI, elevation, aspect. (5)Through four factors of the multivariate linear to compute and conducts the regression equation significant F for inspection and regression coefficients for T inspection, the result shows: Green have not through regression coefficients, so choose three and two factors of the multivariate linear to compute,and Compared to the four equations and the error test. The optimal prediction model for NDVI, elevation, aspect of multiple regression equation: Y=-0.003716+0.018369*N+0.001966*E+0.000666*A.(6)Using the prediction model to Picea schrenkiana growth by remote sensing inversion in Tekes County, it showed successive growth that is 1.332684×10~4m~3, Picea schrenkiana covers an area of 9.523737×10~4hm~2. And elevation, slope to stack analysis, the trend growth performance of the largest in the elevations, Picea schrenkiana growth is 1.126898×10~4m~3in 1900～2599m, accounting for 84.56%.The largest increase on North Slope, growth is 4.65614×10~3m~3, accounting for 34.94%.更多还原