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

STTR 第一阶段：森林清查树冠下、树冠上和卫星传感器流的登记

• 批准号: 2234077
• 负责人: Peter McHale
• 金额: $ 27.5万
• 依托单位: GAIA AI, INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2234077&HistoricalAwards=false
• 关键词: STTR; Phase; Registration; Below; Canopy

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.

该项目的更广泛/委托是在收集有关森林的数据时，提高了TA的数量并提高TA的准确性在传感器和机器学习中，可以极大地提高数据收集速度，而没有产生的富数据集，可以使森林的“数字双胞胎”为森林增长轨迹的忠诚开放。 Environtal UPS预测今天的变化如何影响森林，这是两个例子：提高木材行业的效率带来了成本，并提高了原材料的质量和转向森林的质量气候变化目标。通过将不同的信息源结合在一起，最高级的模型（数字双胞胎），每个型号都具有自己的优势和缺点。测量值，但仍需要森林中的某人戴上图像几乎可以缩放到整个森林来源是在实践中的优点和缺点，以这种不同的方式来探讨了这两个项目的优势。他们与卫星和无人机的顶级图像相结合。被认为是值得对基金会的知识分子和更广泛的不足影响审查标准进行舒适评估的曲调的。