Intuitive Image Analyzing on Plant Data High-Throughput Plant Analysis with LemnaTec Image Processing
By: Stefan Paulus, T. Dornbusch, Marcus Jansen
Abstract
For digital plant phenotyping huge amounts of 2D images are acquired. This is known as one part of the phenotyping bottleneck. This bottleneck can be addressed by well-educated plant analysts, huge experience and an adapted analysis software.
Lasers Without Lost Cities: Using Drone Lidar to Capture Architectural Complexity at Kuelap, Amazonas, Peru
By: Parker VanValkenburgh, K. C. Cushman, Luis Jaime Castillo Butters, Carol Rojas Vega, Carson B. Roberts, Charles Kepler, James Kellner
Abstract
We report the results of drone lidar survey at a high-elevation archaeological site in the Chachapoyas region of Peruvian Amazonia. Unlike traditional airborne remote sensing, drone lidar produces very high-density measurements at a wide range of scan angles by operating at low altitudes and slow flight speeds. These measurements can resolve near vertical surfaces and novel dimensions of variability in architectural datasets.
Machine learning-driven hyperspectral imaging for non-destructive origin verification of green coffee beans across continents, countries, and regions
By: Joy Sim, Yash Dixit, Cushla Mcgoverin, Indrawati Oey, Russell Frew, Marlon M. Reis, Biniam Kebede
Abstract
Coffee is a target for geographical origin fraud. More rapid, cost-effective, and sustainable traceability solutions are needed. The potential of hyperspectral imaging-near-infrared (HSI-NIR) and advanced machine learning models for rapid and non-destructive origin classification of coffee was explored for the first time (i) to understand the sensitivity of HSI-NIR for classification across various origin scales (continental, country, regional), and (ii) to identify discriminant wavelength regions. HSI-NIR analysis was conducted on green coffee beans from three continents, eight countries, and 22 regions.
Mapping Barrier Island Soil Moisture using a Radiative Transfer Model of Hyperspectral Imagery from an Unmanned Aerial System
By: Rehman S. Eon, Charles M. Bachmann
Abstract
The advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system.
MethaneAIR: A High-Resolution Infrared Imaging Spectrometer for Airborne Measurements of CH4 and CO2
By: Jenna Samra, Jonathan E. Franklin, Bruce Daube, Peter Cheimets, Scott Milligan, Martin H. Ettenberg, Josh Benmergui, Kelly Chance, Apisada Chulakadabba, Eamon Conway, Xiong Liu, Christopher Miller, Amir H. Souri, Kang Sun, Steven Wofsy
Abstract
Measures total-column dry air mole fraction of CH4 and CO2 over a 23.7° swath at high spatial resolution. Precursor to MethaneSAT, which will revolutionize measurements and modeling of CH4 emissions across the globe. Observations facilitate advances in spectroscopy and retrievals needed for precise emissions measurements from MethaneSAT. By itself represents a major advance in the state of the art of airborne remote sensing of CH4 and CO2
Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion
By: Danilo Roberti Alves de Almeida, Eben North Broadbent, Matheus Pinheiro Ferreira, Paula Meli, Angelica Maria Almeyda Zambrano, Eric Bastos Gorgens, Angelica Faria Resende, Catherine Torres de Almeida, Cibele Hummel do Amaral, Ana Paula Dalla Corte, Carlos
Abstract
Remote sensors, onboard orbital platforms, aircraft, or unmanned aerial vehicles (UAVs) have emerged as a promising technology to enhance our understanding of changes in ecosystem composition, structure, and function of forests, offering multi-scale monitoring of forest restoration. UAV systems can generate high-resolution images that provide accurate information on forest ecosystems to aid decision-making in restoration projects. However, UAV technological advances have outpaced practical application; thus, we explored combining UAV-borne lidar and hyperspectral data to evaluate the diversity and structure of restoration plantings.
More than Skin Deep: Photonics Protects Our Cultural Heritage, Freebody
By: Marie Freebody
Abstract
For hundreds of years, analysis trumped preservation when it came to irreplaceable cultural heritage objects such as paintings, icons, and written works. Today, conservators avoid taking even tiny samples from works of priceless art, making photonics technology an invaluable addition to cultural heritage research.
Movements of moisture and acid in gastric milk clots during gastric digestion: Spatiotemporal mapping using hyperspectral imaging
By: Siqi Li, Yash Dixit, Marlon M. Reis, Harjinder Singh, Aiqian Ye
Abstract
Ruminant milk is known to coagulate into structured clots during gastric digestion. Hyperspectral imaging was used to investigate the movements of moisture and acid in skim milk clots formed during dynamic gastric digestion and the effects of milk type (regular or calcium-rich) and the presence/absence of pepsin.
NASA Goddard’s LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager
By: Bruce D. Cook, Lawrence A. Corp, Ross F. Nelson, Elizabeth M. Middleton, Douglas C. Morton, Joel T. McCorkel, Jeffrey G. Masek, Kenneth J. Ranson, Vuong Ly, Paul M. Montesano
Abstract
The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a compact, lightweight and portable system.
NASA Operation IceBridge 2018
By: Nathan Kurtz
Abstract
Ice surface elevation data over ice sheets, glaciers, and sea ice to bridge the gap between ICESat and ICESat-2 missions • New measurements critical to ice sheet models: bed topography, grounding line position, ice and snow thickness
