As climate continues to warm, detailed geospatial spectral, structural, and thermal information, related to water, carbon, and energy cycling, are required for modeling the future state of the Arctic biome. To address these needs we have developed a cost-effective, multi-sensor UAS-based remote sensing platform for acquiring high resolution spectral, structural, and thermal measurements of Arctic vegetation. We successfully deployed this remote sensing platform in three sites along an elevation gradient near Nome, Alaska in summer 2017. Corresponding workflows have been further developed for assessing spectral, structural, and thermal patterns of arctic tundra biomes from the collected datasets. Our results demonstrate that the UAS platform can successfully (1) map heterogenous vegetation composition and (2) assess corresponding spectral, structural, and thermal patterns of Arctic tundra biomes. This study not only presents a novel and innovative approach for collecting high resolution spectral, structural and thermal information for characterizing Arctic tundra biome patterns, but also a basis for informing the modeling of climate feedbacks between the biosphere and the atmosphere in response to ongoing global change.

We presentPiCam(Pelican Imaging Camera-Array), an ultra-thin high performance monolithic camera array, that captures light fields and synthesizes high resolution images along with a range image (scene depth) through integrated parallax detection and superresolution. The camera is passive, supporting both stills and video, low light capable, and small enough to be included in the next generation of mobile devices including smartphones. Prior works [Rander et al. 1997; Yang et al. 2002; Zhang and Chen 2004; Tanida et al. 2001; Tanida et al. 2003; Duparré et al. 2004] in camera arrays have explored multiple facets of light field capture - from viewpoint synthesis, synthetic refocus, computing range images, high speed video, and micro-optical aspects of system miniaturization. However, none of these have addressed the modifications needed to achieve the strict form factor and image quality required to make array cameras practical for mobile devices. In our approach, we customize many aspects of the camera array including lenses, pixels, sensors, and software algorithms to achieve imaging performance and form factor comparable to existing mobile phone cameras. Our contributions to the post-processing of images from camera arrays include a cost function for parallax detection that integrates across multiple color channels, and a regularized image restoration (superresolution) process that takes into account all the system degradations and adapts to a range of practical imaging conditions. The registration uncertainty from the parallax detection process is integrated into a Maximum-a-Posteriori formulation that synthesizes an estimate of the high resolution image and scene depth. We conclude with some examples of our array capabilities such as postcapture (still) refocus, video refocus, view synthesis to demonstrate motion parallax, 3D range images, and briefly address future work.