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

这项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，测量员必须在缓慢而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了数据收集速度。由此产生的丰富数据集能够构建真正的森林“数字双胞胎”，并为更高保真度的森林生长轨迹建模打开了大门。这些信息对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碳汇的长期表现的环保组织都很有用。对美国公民的影响是广泛的。这里有两个例子：木材工业效率的提高降低了成本，提高了原材料的质量；将森林变成密度更大的碳汇有助于实现气候变化目标。获得如此广泛和深入的森林数据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，从而围绕林业推出全新的子产业。STTR第一期项目探索的关键技术创新是通过结合不同的信息源构建最高保真的森林模型（数字孪生），每个信息源都有自己的优缺点。背包上的光探测和测距（激光雷达）和相机传感器提供高质量的库存指标，比人工测量快近1000倍，但仍然需要有人在森林里穿着背包。卫星图像几乎可以立即缩放到整个森林，也可以通过历史数据进行时间缩放，但由于卫星自上而下的性质及其提供的分辨率，特别是在考虑到历史和免费数据源时，卫星图像受到限制。基于无人机的图像介于两者之间，具有两者的优点和缺点。在实践中，结合以不同方式度量的信息源可能非常困难。在这个项目中，团队探索了如何表达基于激光雷达的指标，以最好地将它们与来自卫星和无人机的自上而下的图像相关联。从这些关联中，人们可以建立强大的机器学习模型，并将其专门用于单个森林。这种能力使该公司能够向木材公司提供任何规模的森林清单和森林管理建议：仅使用卫星图像或将背包激光雷达和卫星相结合，以获得整个森林的最高精度。该奖项反映了美国国家科学基金会的法定使命，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.