基于森林群落和光谱曲线特征分异的福建省森林生物量遥感反演  

Forest biomass based on the forest communities and image spectral curve characteristics: a remote sensing estimation in Fujian Province

作　　者：苏华[1] 李静[1,2] 陈修治[2] 廖吉善[2] 温达志[2] 

机构地区：[1]福州大学地理空间信息技术国家地方联合工程研究中心空间数据挖掘与信息共享教育部重点实验室,福州350002 [2]中国科学院华南植物园,广州510650

出　　处：《生态学报》2017年第17期5742-5755,共14页Acta Ecologica Sinica

基　　金：国家自然科学基金(31500357;41401055;41430529;41601444);广东省自然科学基金(2014A030310233;2015A030313809;2015A030313811);广州市科技计划(201510010240;2016J2200001);广东省水利厅科技创新项目(2016-16);海西政务大数据应用协同创新中心资助;中国科学院战略性先导科技专项(XDA05050200);福建省自然科学基金(2017J01657);中国博士后科学基金面上资助(2016M600495)

摘　　要：基于福建省Landsat8 OLI影像,利用混合像元分解模型筛选出"纯净"的植被像元,提取296个调查样地对应植被像元的红光和近红外波段的中心波长(分别CWR和CWNIR)及其对应的反射率(分别R和NIR),构建以(NIR-R)/(CWNIR-CWR)为特征指数的叶生物量回归模型。然后根据针叶林、阔叶林及针阔混交林叶生物量与干、枝、叶所组成的地上生物量的关系方程,结合福建省植被覆盖分类数据,估测了整个福建省针叶林、阔叶林、混交林的地上生物量,并绘制了福建省地上生物量分布图。结果表明:红光和近红外两个波段反射率和其中心波长所组成的斜率与叶生物量相关性显著,与针叶林、阔叶林、混交林叶生物量的精度分别达到70.55%、68.89%、51.75%,采用这种方法对福建省叶生物量和地上总生物量进行估算,并进行精度验证,其中,针叶林、阔叶林、混交林叶物量的模型误差(RMSE)分别达到29.2467 t/hm^2(R^2=66.64%)、14.0258 t/hm^2(R^2=61.13%)、10.1788 t/hm^2(R^2=55.43%),地上总生物量的模型精度分别达到49.8315 t/hm^2(R^2=54.65%)、45.1820 t/hm^2(R^2=49.01%)、41.5131 t/hm^2(R^2=38.79%),这说明,采用红光波段和近红外波段与其中心波长所组成的斜率估测森林叶生物量,进而估算其地上总生物量的方法是可行的。Forest is one of the vital renewable resources for sustainable development of renewable resources; it plays an important role in global climate change, water and soil conservation, and carbon cycle in terrestrial ecosystem. Forest biomass, therefore, is now attracting attention worldwide from both scholars and policy makers. Using Landsat8 OLI images and 296 survey samples in Fujian Province, we found that the leaf biomass is negatively correlated with the reflectance of near infrared wave band and the slope of near infrared and red band. Therefore, the slope of the near infrared and red band reflectance can be used as an effective indicator for describing the differences in leaf biomass of forest. Currently, empirical models are mainly used to estimate forest biomass such as the vegetation-index models based on multiple spectra remote sensing imageries and backscattering-coefficient inversion models based on active microwave remote sensing imageries. However, most of these empirical models lack physical mechanism. Here, we established a spectral-slope based model building on the spectral characteristic of multiple spectra remote sensing of forest. We firstly used the pixel unmixing model to select the pure vegetation pixels from the images and calculated the slope （Slope（red, infrared）） of the reflectivity of red band and near infrared band from image spectral curve characteristics based on the pure vegetation pixels of forest communities. We then set up the leaf biomass （LB） reversion models based on the relationship between the spectral slope and the leaf biomass by using the linear regression method to estimate the leaf biomass of coniferous forest, broad-leaved forest, and mixed forest in Fujian Province. We finally verified the results by using the in situ biomass data. The spectral-slope-based estimation algorithms for retrieving the leaf biomass of coniferous forest, broad-leaved forest, and mixed forest are LBconifer=59.358-38.948×Slope（red, infrared） （R2=70.55%）, LBbroad=28.622-12.5

关 键 词：地上生物量 叶生物量 光谱斜率 Landsat8 OLI 遥感反演模型 

分 类 号：S718.556[农业科学—林学]