STTR Phase I: Registration of Below-Canopy, Above-Canopy, and Satellite Sensor Streams for Forest Inventories

这个小企业技术转让第一阶段项目的更广泛/商业影响是增加世界森林数据的数量并提高其准确性。目前，在收集森林数据时，调查人员必须在缓慢、费力的方法和快速但不准确的方法之间做出选择。该项目利用传感器和机器学习的最新进展，在不牺牲准确性的情况下大大提高数据收集速度。由此产生的丰富的数据集使森林的真正的“数字双胞胎”的建设，并打开了大门，为森林生长轨迹的高保真度建模。这一信息对于寻求最大限度地发挥其资产潜力的木材公司和预测当今变化如何影响森林作为长期碳汇的表现的环保团体都很有用。对美国公民的影响是广泛的。下面是两个例子：木材业效率的提高降低了成本，提高了原材料的质量，并将森林变成更密集的碳汇，有助于实现气候变化目标。这种广泛而深入的森林数据的可用性还可以推动对推动森林健康和生产力的更复杂和微妙关系的研究和理解的繁荣，围绕林业推出全新的子行业。STTR第一阶段项目探索的关键技术创新是构建最高保真的森林模型（数字孪生）通过结合不同的信息源，每一个都有自己的优点和缺点。背包上的光探测和测距（LiDAR）和摄像头传感器提供了高质量的库存指标，比手动测量快近1000倍，但仍然需要森林中的人佩戴背包。卫星图像几乎可以立即扩大到整个森林，也可以随着历史数据的时间推移扩大，但受到卫星自上而下的性质及其提供的分辨率的限制，特别是在考虑历史和免费数据来源时。基于无人机的图像介于两者之间，两者都有优点和缺点。在实践中，将以这种不同方式测量的信息源组合起来可能非常困难。在这个项目中，该团队探索如何表达基于LiDAR的指标，以最好地将它们与卫星和无人机的自上而下的图像联系起来。从这些关联中，人们可以构建强大的机器学习模型，并将其专门用于单个森林。这一能力使该公司能够为任何规模的木材公司提供森林资源清查和森林管理建议：仅使用卫星图像，或使用背包式激光雷达和卫星相结合，以获得整个森林的最高精度。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project is to increase the volume and improve the accuracy of data on the world’s forests. Presently, when collecting data on forests, surveyors must choose between slow, laborious methods, or quick but inaccurate ones. This project uses recent advances in sensors and machine learning to greatly improve data collection speed without sacrificing accuracy. The resulting rich datasets enable the construction of true “digital twins” of forests and open the door for higher fidelity modeling of forest growth trajectories. This information is useful both for timber firms seeking to maximize the potential of their assets and environmental groups projecting how changes today could impact a forest’s performance as a carbon-sink over the long term. The impacts on United States citizens are widespread. Here are two examples: improved efficiency in the timber industry brings down the cost and improves the quality of raw materials and turning forests into denser carbon sinks helps meet climate change goals. The availability of such broad and deep data on forests could also drive a boom in research and understanding about the more complex and nuanced relationships that drive forest health and productivity, launching entirely new sub-industries around forestry.The key technological innovations explored in this STTR Phase I project are in constructing the most high-fidelity forest model (digital twin) by combining disparate information sources, each with their own advantages and disadvantages. Light detecting and ranging (LiDAR) and camera sensors on backpacks provide high-quality inventory metrics nearly 1000 times faster than manual measurements, but still require someone in the forest to wear the backpack. Satellite imagery scales almost instantly to entire forests and also through time with historical data but is limited by the top-down nature of satellites and the resolution they offer, especially when historical and free data sources are considered. Drone-based imagery sits in-between, with advantages and disadvantages of both. In practice, combining information sources that measure in such different ways can be very difficult. In this project, the team explores how to express LiDAR-based metrics to best associate them with top-down imagery from satellites and drones. From these associations, one can then build powerful machine learning models and specialize them to individual forests. This ability may enable the company to provide forest inventories and forest management recommendations to timber companies at any scale: with satellite imagery only or with a combination of backpack-LiDAR and satellite for the highest accuracy over the entire forest.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.