Continental and global mapping of forest biomass by fusion of InSAR, lidar and stereophotogrammetric satellite data

拟议的研究计划旨在开发新的三维遥感方法，用于在大陆和全球范围内绘制森林生物量以及高度和体积。这三个高度相关的结构参数的空间连续的估计将大大提高我们的碳循环的知识，同时为大规模的栖息地破碎化研究和全州不断增长的股票量的估计提供关键数据。 要对大陆或全球生物量总量作出无偏见的估计，就需要我们根据在非常广阔的地区所遇到的对比非常鲜明的森林和地形类型（即从封闭的阔叶林到开放的针叶林，从平坦到崎岖的地形等），在区域一级选择最适当的遥感工具，以及根据云量条件和植被物候，我们寻求最佳的融合方法来组合所选择的数据集。 我们的具体目标是：1）评估生成冠层高度模型的最佳策略（CHM = DSM-DTM）在大陆和全球尺度上，2）开发用于产生空间连续和无偏的森林结构属性估计的统计方法（生物量、高度和体积），以及3）用于通过偏振相干断层扫描（PCT）直接提取生物量估计值的详细方法，可选地由DSM和DTM限定。将测试下列数据源;立体数字空间模型：ASTER、SPOT等;对于干涉合成孔径雷达DSM：Tandem-X;对于DTM：机载激光雷达、滤波干涉合成孔径雷达DSM和基于PCT的DTM。将根据森林类型、地形、云量以及每一遥感数据集相对于图像获取时植被的物候状态的适当性进行区域划分。将采用各种统计技术从CHM或直接从断层图像预测生物量和体积。机载激光雷达数据在广泛的地区（几个1000平方公里），结合现场数据，将用于分析误差来源，并创建生物量的验证地图。

The proposed research program seeks to develop novel 3D remote sensing methods for mapping forest biomass, as well as height and volume, at continental and global scales. Spatially-continuous estimates of these three highly correlated structural parameters will greatly improve our knowledge of the carbon cycle while providing critical data for large scale habitat fragmentation studies and statewide estimates of growing stock volume. Producing continental or global unbiased estimates of total biomass will require that we choose the most appropriate remote sensing tools regionally as a function of the very contrasted forest and terrain types encountered over very wide areas (i.e. from closed broadleaf to open taiga forests, from flat to rugged topography, etc.), as well as according to cloudiness conditions and vegetation phenology, and that we seek the best fusion methods for combining the chosen datasets. Our specific objectives are to 1) evaluate the best strategies for generating canopy height models (CHM = DSM - DTM) at continental and global scales, 2) develop statistical methods for producing spatially-continuous and unbiased forest structural attribute estimates (biomass, height and volume) from the CHMs, and 3) elaborate methods for extracting biomass estimates directly by polarimetric coherence tomography (PCT), optionally bounded by DSMs and DTMs. The following data sources will be tested; for stereo DSMs: ASTER, SPOT, etc.; for InSAR DSMs: Tandem-X; for DTMs: airborne lidar, filtered InSAR DSMs, and PCT-based DTMs. Regionalization will be based on forest type, topography , cloudiness, and the adequacy of each remote sensing dataset relatively to the phenological state of the vegetation at the time of image acquisition. Various statistical techniques will be employed to predict biomass and volume from the CHMs, or directly from the tomograms. Airborne lidar data over extensive areas (several 1000 sq km), combined to field data, will be used to analyze the error sources and to create validation maps of biomass.