Minitaurized VNIR-SWIR Hyperspectral Sensor Technology Platform for Unmanned Aerial Systems

Period of Performance: 03/02/2016 - 09/21/2016


Phase 1 SBIR

Recipient Firm

Headwall Photonics Inc.
601 River Street Array
Fitchburg, MA 01420
Firm POC
Principal Investigator


Earth System Modeling (ESM) of terrestrial areas is important for understanding the carbon cycle. To properly model the carbon cycle, several inputs are required. One important input to the carbon model is the type and quantity of vegetation that is affecting carbon uptake and release. However, current models of vegetation, through Plant Functional Types (PFTs) are limited and offer a very coarse understanding of the true (meter-by-meter) global variation in vegetation. One method to improve and more distinctly define PFTs is higher resolution remote sensing capabilities. The ability of deploying Unmanned Aerial Systems (UAS) allows for rapid deployment of sensors, such as Hyperspectral Instruments, to help determine the vegetation types on a fine-scale by using spectral characteristics of the vegetation sampled. The largest problem with this concept is that Hyperspectral Sensor Technology (HST) Systems have not scaled in size with the UAS and thus it has been difficult to attach HST Systems to UAS with restricted Size, Weight, and Power (SWaP) payload capacities. The following Phase I Objective is to develop a full-spectrum (350nm to 2500nm) HST System that will be miniaturized while maintaining the expected spatial and spectral resolution of legacy and well-known remote-sensing hyperspectral systems. Phase I research will focus on a new full spectrum HST System that will be fabricated, assembled, and radiometrically characterized prior to being integrated and flown on a 5-10 lb. payload capacity UAS. Flight tests will be undertaken over dense and diversified vegetation plots in Western Massachusetts. From the flight tests chlorophyll maps and PFT maps will be developed along with benchmarking the performance of the HST System. Carbon cycle modeling based on plants is limited now by current measurements, which are not diverse and specific. To improve modeling, more precision based modeling is required using unmanned aerial systems and light weight (