Remotely sensed data provide many opportunities for enhancing our understanding of the built and natural environment. Representations of the urban landscape from light detection and ranging (LiDAR) sensors and digital orthophotography from unmanned aerial systems (UAS) are quickly becoming essential for examining and maintaining infrastructure systems, estimating risk from extreme events, and improving urban sustainability. This includes community efforts toward energy resilience and the development of alternative energy systems, such as solar and wind. While LiDAR provides the means to model key characteristics of the urban landscape for solar energy planning, including slope, aspect and elevation, issues of spatial uncertainty and error persist in LiDAR data and have the potential to reduce the fidelity of solar energy assessments. In this paper, we use extremely high-resolution UAS data to improve solar energy audits and mitigate uncertainties associated with LiDAR data. The results suggest improvements in aggregate irradiation estimates by as much as 36 % when using digital orthophotos from a UAS when compared to LiDAR. This paper concludes with a detailed discussion of potential strategies for improving solar energy estimates for both researchers and practitioners.

遥感数据为增进我们对建筑和自然环境的理解提供了许多机会。光检测和测距（LiDAR）传感器以及无人航空系统（UAS）的数字正射影像对城市景观的表示，对于检查和维护基础设施系统，估计极端事件的风险以及改善城市的可持续性，正变得至关重要。这包括社区为提高能源弹性和开发替代能源系统（例如太阳能和风能）所做的努力。尽管LiDAR提供了对城市景观的关键特征进行建模（包括坡度，坡度和海拔）的方法，但LiDAR数据中仍然存在空间不确定性和误差问题，并有可能降低太阳能评估的准确性。在本文中，我们使用超高分辨率的UAS数据来改善太阳能审核并减轻与LiDAR数据相关的不确定性。结果表明，与LiDAR相比，使用来自UAS的数字正射影像时，总辐射估计值可提高多达36％。本文以对研究人员和从业人员改善太阳能估算的潜在策略进行了详细讨论。