基于TM遥感影像的湿地松林生物量研究

Study on Biomass of Pinus elliottii Forest in Subtropical China Assisted with Remote Sensing

利用江西千烟洲地区2005年Landsat 5 TM遥感图像数据和同期野外调查获得的28个样方湿地松(Pinus elliottii)各器官生物量数据,分析了植被指数、影像变换(主成分分析,缨帽变换)结果与森林各器官生物量之间的相关关系,进而建立了光谱-植被指数与生物量多元回归模型。湿地松林各器官与遥感光谱、植被指数拟合相关性大小依次为:叶生物量>枝生物量>地上生物量>树干生物量。通过多元回归模型计算出湿地松林叶生物量平均为573 g.m-2,地上生物量平均为6 628 g.m-2,低于样地调查平均值。单一植被指数与生物量相关性较低,ND-VI并不适用于盖度较大的湿地松林;遥感影像经主成分分析后生物量光谱模型的相关系数略有提高,缨帽变换后反而使模型的相关系数降低。

Based on field survey data,TM imagery acquired in 2005 was applied for organ-specific biomass estimation of Pinus elliottii plantation as well as other forest types in Qianyanzhou of Jiangxi Province,China.A total of 28 plots was investigated and the relationship of biomass with vegetation indices was clarified using image analysis including PCA（Principal Component Analysis） and TASSEL（Tasseled cap transformation）.A series of regression models comparing biomass and spectra or vegetation index were established.The sequence of correlation coefficients from high to low was foliage biomass〉branch biomass〉above-ground biomass〉stem biomass.The average above-ground biomass of Pinus elliottii forest as estimated with multiple regression analysis was 6628 g·m^-2,with leaf biomass of 573 g·m^-2,less than the mean result by field survey.Correlation of biomass with a single vegetation index was quite low,indicating that NDVI alone is not sufficient for estimating biomass of densely closed forest.Results of regression analyses were slightly better using PCA and slightly worse with TASSEL.