Investigation related to multispectral imaging systems

A summary of technical progress made during a five year research program directed toward the development of operational information systems based on multispectral sensing and the use of these systems in earth-resource survey applications is presented. Efforts were undertaken during this program to: (1) improve the basic understanding of the many facets of multispectral remote sensing, (2) develop methods for improving the accuracy of information generated by remote sensing systems, (3) improve the efficiency of data processing and information extraction techniques to enhance the cost-effectiveness of remote sensing systems, (4) investigate additional problems having potential remote sensing solutions, and (5) apply the existing and developing technology for specific users and document and transfer that technology to the remote sensing community

Hyperspectral and Hypertemporal Longwave Infrared Data Characterization

The Army Research Lab conducted a persistent imaging experiment called the Spectral and Polarimetric Imagery Collection Experiment (SPICE) in 2012 and 2013 which focused on collecting and exploiting long wave infrared hyperspectral and polarimetric imagery. A part of this dataset was made for public release for research and development purposes. This thesis investigated the hyperspectral portion of this released dataset through data characterization and scene characterization of man-made and natural objects. First, the data were contrasted with MODerate resolution atmospheric TRANsmission (MODTRAN) results and found to be comparable. Instrument noise was characterized using an in-scene black panel, and was found to be comparable with the sensor manufacturer\u27s specication. The temporal and spatial variation of certain objects in the scene were characterized. Temporal target detection was conducted on man-made objects in the scene using three target detection algorithms: spectral angle mapper (SAM), spectral matched lter (SMF) and adaptive coherence/cosine estimator (ACE). SMF produced the best results for detecting the targets when the training and testing data originated from different time periods, with a time index percentage result of 52.9%. Unsupervised and supervised classication were conducted using spectral and temporal target signatures. Temporal target signatures produced better visual classication than spectral target signature for unsupervised classication. Supervised classication yielded better results using the spectral target signatures, with a highest weighted accuracy of 99% for 7-class reference image. Four emissivity retrieval algorithms were applied on this dataset. However, the retrieved emissivities from all four methods did not represent true material emissivity and could not be used for analysis. This spectrally and temporally rich dataset enabled to conduct analysis that was not possible with other data collections. Regarding future work, applying noise-reduction techniques before applying temperature-emissivity retrieval algorithms may produce more realistic emissivity values, which could be used for target detection and material identification

The design of a Space-borne multispectral canopy LiDAR to estimate global carbon stock and gross primary productivity

Understanding the dynamics of the global carbon cycle is one of the most challenging issues for the scientific community. The ability to measure the magnitude of terrestrial carbon sinks as well as monitoring the short and long term changes is vital for environmental decision making. Forests form a significant part of the terrestrial biosystem and understanding the global carbon cycle, Above Ground Biomass (AGB) and Gross Primary Productivity (GPP) are critical parameters. Current estimates of AGB and GPP are not adequate to support models of the global carbon cycle and more accurate estimates would improve predictions of the future and estimates of the likely behaviour of these sinks. Various vegetation indices have been proposed for the characterisation of forests including canopy height, canopy area, Normalised Difference Vegetation Index (NDVI) and Photochemical Reflectance Index (PRI). Both NDVI and PRI are obtained from a measure of reflectivity at specific wavelengths and have been estimated from passive measurements. The use of multi-spectral LiDAR to measure NDVI and PRI and their vertical distribution within the forest represents a significant improvement over current techniques. This paper describes an approach to the design of an advanced Multi-Spectral Canopy LiDAR, using four wavelengths for measuring the vertical profile of the canopy simultaneously. It is proposed that the instrument be placed on a satellite orbiting the Earth on a sun synchronous polar orbit to provide samples on a rectangular grid at an approximate separation of 1km with a suitable revisit frequency. The systems engineering concept design will be presented

Airborne thermography and ground geophysical investigation for detecting shallow ground disturbance under vegetation

This thesis discusses the potential of airborne thermal prospection for detecting shallow ground disturbance beneath vegetation based on images acquired by the NERC Airborne Thematic Mapper (ATM) at thermal infrared wavelengths. Shallow ground disturbance creates a differential heat flux due to a variation in the thermal properties between disturbed and undisturbed soils. When observed above a canopy, the effect of vegetation growth on the thermal regime of the underlying soils is poorly understood. The research extends current understanding by examining areas where ground disturbance is known to exist under variable vegetation cover at an archaeological site at Bosworth, Leicestershire and areas of abandoned mine activity on Baildon Moor, W. Yorkshire and in the N. Pennine Orefield, Weardale. The investigation focuses on qualitative image interpretation techniques, where anomalies on day and night thermal images are compared with those manifest on the multispectral images, and a more quantitative approach of Apparent Thermal Inertia (ATI) modelling. Physical thermal inertia is a parameter that is sensitive to volumetric variations in the soil, but cannot be measured directly using remote sensing techniques. However, an apparent thermal inertia is determined by examining the day and night temperature contrast of the surface, where spatial variations can signify potential features buried in the near-surface environment. Ground temperature profiling at the Bosworth site indicates that diurnal heat dissipates between 0.20-0.50m at an early stage in vegetation development with progressively lower diurnal amplitudes observed at 0.20m as the vegetation develops. Results also show that the time of diurnal maximum temperature occurs progressively later as vegetation develops, implying an importance for thermal image acquisition. The quantitative investigation concentrates on the Bosworth site where extensive ground geophysical prospection was performed and vertical soil samples extracted across features of variable multispectral, thermal and ATI response to enable comparison of the observed airborne thermal response with physical soil properties. Results suggest that there is a high correlation between ATI and soil moisture properties at 0.15-0.25m depth (R(^2)=0.99) at an early stage in cereal crop development but has a high correlation at a wider depth range (0.10-0.30m) at a later stage in development (R(^2)=0.98). The high correlation between physical ground disturbance and the thermal response is also corroborated qualitatively with the results of the resistivity surveys. The ATI modelling reveals similar features to those evident on day or night thermal images at an early stage in vegetation growth, suggesting that thermal imaging during the day at an early stage in vegetation growth may supply sufficient information on features buried in the near-surface environment. Airborne thermal imaging therefore provides a useful complementary prospection tool for archaeological and geological applications for surfaces covered by vegetation

Data Requirements for Oceanic Processes in the Open Ocean, Coastal Zone, and Cryosphere

The type of information system that is needed to meet the requirements of ocean, coastal, and polar region users was examined. The requisite qualities of the system are: (1) availability, (2) accessibility, (3) responsiveness, (4) utility, (5) continuity, and (6) NASA participation. The system would not displace existing capabilities, but would have to integrate and expand the capabilities of existing systems and resolve the deficiencies that currently exist in producer-to-user information delivery options

Technology Needs Assessment of an Atmospheric Observation System for Multidisciplinary Air Quality/Meteorology Missions, Part 2

The technology advancements that will be necessary to implement the atmospheric observation systems are considered. Upper and lower atmospheric air quality and meteorological parameters necessary to support the air quality investigations were included. The technology needs were found predominantly in areas related to sensors and measurements of air quality and meteorological measurements

Soil moisture and evapotranspiration predictions using Skylab data

The author has identified the following significant results. Multispectral reflectance and emittance data from the Skylab workshop were evaluated for prediction of evapotranspiration and soil moisture for an irrigated region of southern Texas. Wavelengths greater than 2.1 microns were required to spectrally distinguish between wet and dry fallow surfaces. Thermal data provided a better estimate of soil moisture than did data from the reflective bands. Thermal data were dependent on soil moisture but not on the type of agricultural land use. The emittance map, when used in conjunction with existing models, did provide an estimate of evapotranspiration rates. Surveys of areas of high soil moisture can be accomplished with space altitude thermal data. Thermal data will provide a reliable input into irrigation scheduling

Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data

Near-earth hyperspectral big data present both huge opportunities and challenges for spurring developments in agriculture and high-throughput plant phenotyping and breeding. In this article, we present data-driven approaches to address the calibration challenges for utilizing near-earth hyperspectral data for agriculture. A data-driven, fully automated calibration workflow that includes a suite of robust algorithms for radiometric calibration, bidirectional reflectance distribution function (BRDF) correction and reflectance normalization, soil and shadow masking, and image quality assessments was developed. An empirical method that utilizes predetermined models between camera photon counts (digital numbers) and downwelling irradiance measurements for each spectral band was established to perform radiometric calibration. A kernel-driven semiempirical BRDF correction method based on the Ross Thick-Li Sparse (RTLS) model was used to normalize the data for both changes in solar elevation and sensor view angle differences attributed to pixel location within the field of view. Following rigorous radiometric and BRDF corrections, novel rule-based methods were developed to conduct automatic soil removal; and a newly proposed approach was used for image quality assessment; additionally, shadow masking and plot-level feature extraction were carried out. Our results show that the automated calibration, processing, storage, and analysis pipeline developed in this work can effectively handle massive amounts of hyperspectral data and address the urgent challenges related to the production of sustainable bioenergy and food crops, targeting methods to accelerate plant breeding for improving yield and biomass traits

Earth resources applications of the Synchronous Earth Observatory Satellite (SEOS)

The results are presented of a four month study to define earth resource applications which are uniquely suited to data collection by a geosynchronous satellite. While such a satellite could also perform many of the functions of ERTS, or its low orbiting successors, those applications were considered in those situations where requirements for timely observation limit the capability of ERTS or EOS. Thus, the application presented could be used to justify a SEOS

PERSIANN-MSA: A precipitation estimation method from satellite-based multispectral analysis

Visible and infrared data obtained from instruments onboard geostationary satellites have been extensively used for monitoring clouds and their evolution. The Advanced Baseline Imager (ABI) that will be launched onboard the Geostationary Operational Environmental Satellite-R (GOES-R) series in the near future will offer a larger range of spectral bands; hence, it will provide observations of cloud and rain systems at even finer spatial, temporal, and spectral resolutions than are possible with the current GOES. In this paper, a new method called Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks-Multispectral Analysis (PERSIANN-MSA) is proposed to evaluate the effect of using multispectral imagery on precipitation estimation. The proposed approach uses a self-organizing feature map (SOFM) to classify multidimensional input information, extracted from each grid box and corresponding textural features of multispectral bands. In addition, principal component analysis (PCA) is used to reduce the dimensionality to a few independent input features while preserving most of the variations of all input information. The above method is applied to estimate rainfall using multiple channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite. In comparison to the use of a single thermal infrared channel, the analysis shows that using multispectral data has the potential to improve rain detection and estimation skills with an average of more than 50% gain in equitable threat score for rain/no-rain detection, and more than 20% gain in correlation coefficient associated with rain-rate estimation. © 2009 American Meteorological Society