The capability of the parameters derived from waveform data in discriminating objects is assessed and the effect of the relative calibration of full-waveform data in discriminating land-cover classes is evaluated. Firstly, a non-linear least-squares method with the Levenberg-Marquardt algorithm is used to fit the return waveforms by a Gaussian function. Gaussian amplitude, standard deviation, and energy are extracted. Secondly, a relative calibration method using the range between the sensor and the target based on a radar equation is applied to calibrate amplitude and energy. The change in transmit pulse energy is also considered in this process. A support vector machine classifier is used to distinguish the study area into non-vegetated area （including roads, buildings, and vacant lots）, grassland, needle-leaf forests, and broad- leaf forests. The overall classification accuracy ranges from 79.33% to 87.6%, with the combination of the two groups of the three studied parameters. Calibrated data classification accuracy is improved from 1.20% to 6.44%, thus resulting in better forest type discrimination. The result demonstrates that the parameters extracted from the waveforms can be applied effectively in identifying objects and that relative calibrated data can improve overall classification accuracy.
使用小兴安岭温带森林机载遥感-地面观测同步试验获取的机载激光雷达(light detection and ranging,Lidar)点云数据和地面实测样地数据,估测了典型森林类型的树叶、树枝、树干、地上、树根和总生物量等组分的生物量。从激光雷达数据中提取了两组变量(树冠高度变量组和植被密度变量组)作为自变量,并采用逐步回归方法进行自变量选择。结果表明:激光雷达数据得到的变量与森林各组分生物量有很强的相关性;对于针叶林、阔叶林和针阔叶混交林三种不同森林类型生物量的估测结果是:针叶林优于阔叶林,阔叶林优于针阔叶混交林;不区分森林类型的各组分生物量估测与地面实测值显著相关,模型决定系数在0.6以上;区分森林类型进行建模可以进一步提高生物量的估测精度。
森林对维护区域生态环境及全球碳平衡、缓解全球气候变化发挥着不可替代的作用,对森林地上生物量进行精确估测能够大大减小陆地生态系统碳储量的不确定性。本文结合机载激光雷达、星载激光雷达和成像光学遥感等数据进行大湄公河次区域的森林地上生物量估测,生成连续的森林地上生物量图。结果表明:①基于星机地协同观测数据可以有效地估测森林地上生物量,模型总体平均误差为34t/hm^2,相关系数为0.7;②估测结果与FAO FRA 2010报告以及其它报告公布的结果相比,一致性较好,平均差异为13.3%;③根据本文的遥感反演结果,大湄公河次区域森林生物量总量为62.72亿t,其中常绿阔叶林占71%,落叶阔叶林占10%,常绿针叶林占16%,混交林占3%;④从各国(地区)的生物量总量来看,缅甸森林地上生物量总量最大,占22%,其次是中国云南省、老挝、泰国、越南、中国广西壮族自治区和柬埔寨。
