The demand for precise mapping and monitoring of forest resources, such as above ground biomass (AGB), has increased rapidly. National accounting and monitoring of AGB requires regularly updated information based on consistent methods. While remote sensing technologies such as airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) have been shown to deliver the necessary 3D spatial data for AGB mapping, the capacity of repeat acquisition, remotely sensed, vegetation structure data for AGB monitoring has received less attention. Here, we use vegetation height models (VHMs) derived from repeat acquisition DAP data (with ALS terrain correction) to map and monitor woody AGB dynamics across Switzerland over 35 years (1983-2017 inclusive), using a linear least-squares regression approach. We demonstrate a consistent relationship between canopy height derived from DAP and field-based NFI measures of woody AGB across four inventory periods. Over the environmentally heterogeneous area of Switzerland, our models have a comparable predictive performance (R² = 0.54) to previous work predicting AGB based on ALS metrics. Pearson correlation coefficients between measured and predicted changes in woody AGB over time increased with shorter time gaps (< 2 years) between image capture and field-based measurements, ranging between 0.76 and 0.34. A close temporal match between field surveys and remote sensing data acquisition is thus key to reliable mapping and monitoring of AGB dynamics, especially in areas where forest management and natural disturbances trigger relatively fast canopy dynamics. We show that VHMs derived from repeat DAP capture constitute a cost effective and reliable approach to map and monitor changes in woody AGB at a national extent and can provide an important information source for national carbon accounting and monitoring of ecosystem service provisioning.

对诸如地上生物量（AGB）等森林资源进行精确制图和监测的需求迅速增长。AGB的国家会计和监控要求根据一致的方法定期更新信息。虽然已显示出机载激光扫描（ALS）和数字航空摄影测量（DAP）等遥感技术可提供用于AGB映射的必要3D空间数据，但已经获得了用于AGB监测的重复采集，遥感植被结构数据的能力少注意。在这里，我们使用线性最小二乘回归方法，从重复采集的DAP数据（具有ALS地形校正）得出的植被高度模型（VHM），来绘制和监视瑞士35年间（包括1983-2017年）的木质AGB动态。我们证明了在四个清查期中，从DAP得出的树冠高度与木质AGB的基于实地的NFI度量之间的一致关系。在瑞士的环境异质地区，我们的模型具有可比的预测性能（R²  = 0.54）到以前基于ALS指标预测AGB的工作。木质AGB随时间变化的实测和预测变化之间的Pearson相关系数随着图像捕获和基于实地的测量之间的较短时间间隔（<2年）而增加，介于0.76和0.34之间。因此，实地调查和遥感数据采集之间的紧密时间匹配是可靠地绘制和监测AGB动态的关键，尤其是在森林管理和自然干扰触发相对快速的冠层动态的地区。我们表明，重复DAP捕获产生的VHM构成了一种经济有效且可靠的方法，可以在全国范围内绘制和监测木质AGB的变化，并且可以为国家碳核算和生态系统服务供应的监测提供重要的信息来源。