Spectral Analysis of Absorption Features for Mapping Vegetation Cover and Microbial Communities in Yellowstone National Park Using AVIRIS Data

This report summarizes the application of imaging spectroscopy to the study of biotic components of Yellowstone National Park ecosystems. Maps of vegetation cover and hot-spring microorganisms were generated using spectral-feature analysis of data from the airborne visible and infrared imaging spectrometer (AVIRIS). AVIRIS data were calibrated to surface reflectance using a radiative-transfer model and a ground-calibration target. A spectral library of canopy-reflectance signatures was created by averaging pixels of reflectance data over known occurrences of 27 vegetation cover types in Yellowstone. Distributions of these vegetation types were determined by comparing absorption features of the vegetation in the spectral library with every pixel of the AVIRIS data using continuum removal and spectral analysis in the U.S. Geological Survey’s Tetracorder expert system. Analysis of the chlorophyll- and leaf-water-absorption features (centered near 0.68, 0.98, and 1.20 μm, respectively) allowed accurate identification of vegetation cover types. Conifer cover types of lodgepole pine, whitebark pine, Douglas fir, and a mixed Engelmann spruce/subalpine fir class were spectrally identified and their distributions were mapped in AVIRIS images. Field-reflectance measurements revealed a distinct spectral signature of hot-spring microorganisms. These field measurements were added to the vegetation spectral library, and maps showing the distributions of microbial mats in the geyser basins of Yellowstone were produced

Plant phenolics and absorption features in vegetation reflectance spectra near 1.66μm

AbstractPast laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66μm is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic CH bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf (r2=0.95, RMSE=1.0%, n=56), fresh leaf (r2=0.79, RMSE=2.1%, n=56), and canopy (r2=0.78, RMSE=1.0%, n=13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63μm, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the spectral influences of plant phenolics and terpenes relative to dominant leaf biochemistry (water, chlorophyll, protein/nitrogen, cellulose, and lignin)

Comparison of Methods for Modeling Fractional Cover Using Simulated Satellite Hyperspectral Imager Spectra

Remotely sensed data can be used to model the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil in natural and agricultural ecosystems. NPV and soil cover are difficult to estimate accurately since absorption by lignin, cellulose, and other organic molecules cannot be resolved by broadband multispectral data. A new generation of satellite hyperspectral imagers will provide contiguous narrowband coverage, enabling new, more accurate, and potentially global fractional cover products. We used six field spectroscopy datasets collected in prior experiments from sites with partial crop, grass, shrub, and low-stature resprouting tree cover to simulate satellite hyperspectral data, including sensor noise and atmospheric correction artifacts. The combined dataset was used to compare hyperspectral index-based and spectroscopic methods for estimating GV, NPV, and soil fractional cover. GV fractional cover was estimated most accurately. NPV and soil fractions were more difficult to estimate, with spectroscopic methods like partial least squares (PLS) regression, spectral feature analysis (SFA), and multiple endmember spectral mixture analysis (MESMA) typically outperforming hyperspectral indices. Using an independent validation dataset, the lowest root mean squared error (RMSE) values were 0.115 for GV using either normalized difference vegetation index (NDVI) or SFA, 0.164 for NPV using PLS, and 0.126 for soil using PLS. PLS also had the lowest RMSE averaged across all three cover types. This work highlights the need for more extensive and diverse fine spatial scale measurements of fractional cover, to improve methodologies for estimating cover in preparation for future hyperspectral global monitoring missions

Mapping changing distributions of dominant species in oil-contaminated salt marshes of Louisiana using imaging spectroscopy

The April 2010 Deepwater Horizon (DWH) oil spill was the largest coastal spill in U.S. history. Monitoring subsequent change in marsh plant community distributions is critical to assess ecosystem impacts and to establish future coastal management priorities. Strategically deployed airborne imaging spectrometers, like the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), offer the spectral and spatial resolution needed to differentiate plant species. However, obtaining satisfactory and consistent classification accuracies over time is a major challenge, particularly in dynamic intertidal landscapes.Here, we develop and evaluate an image classification system for a time series of AVIRIS data for mapping dominant species in a heavily oiled salt marsh ecosystem. Using field-referenced image endmembers and canonical discriminant analysis (CDA), we classified 21 AVIRIS images acquired during the fall of 2010, 2011 and 2012. Classification results were evaluated using ground surveys that were conducted contemporaneously to AVIRIS collection dates. We analyzed changes in dominant species cover from 2010 to 2012 for oiled and non-oiled shorelines.CDA discriminated dominant species with a high level of accuracy (overall accuracy=82%, kappa=0.78) and consistency over three imaging dates (overall2010=82%, overall2011=82%, overall2012=88%). Marshes dominated by Spartina alterniflora were the most spatially abundant in shoreline zones (â¤28m from shore) for all three dates (2010=79%, 2011=61%, 2012=63%), followed by Juncus roemerianus (2010=11%, 2011=19%, 2012=17%) and Distichlis spicata (2010=4%, 2011=10%, 2012=7%).Marshes that were heavily contaminated with oil exhibited variable responses from 2010 to 2012. Marsh vegetation classes converted to a subtidal, open water class along oiled and non-oiled shorelines that were similarly situated in the landscape. However, marsh loss along oil-contaminated shorelines doubled that of non-oiled shorelines. Only S. alterniflora dominated marshes were extensively degraded, losing 15% (354,604m2) cover in oiled shoreline zones, suggesting that S. alterniflora marshes may be more vulnerable to shoreline erosion following hydrocarbon stress, due to their landscape position

Comparison of Laboratory and Field Remote Sensing Methods to Measure Forage Quality

Recent research in range ecology has emphasized the importance of forage quality as a key indicator of rangeland condition. However, we lack tools to evaluate forage quality at scales appropriate for management. Using canopy reflectance data to measure forage quality has been conducted at both laboratory and field levels separately, but little work has been conducted to evaluate these methods simultaneously. The objective of this study is to find a reliable way of assessing grassland quality through measuring forage chemistry with reflectance. We studied a mixed grass ecosystem in Grasslands National Park of Canada and surrounding pastures, located in southern Saskatchewan. Spectral reflectance was collected at both in-situ field level and in the laboratory. Vegetation samples were collected at each site, sorted into the green grass portion, and then sent to a chemical company for measuring forage quality variables, including protein, lignin, ash, moisture at 135 °C, Neutral Detergent Fiber (NDF), Acid Detergent Fiber (ADF), Total Digestible, Digestible Energy, Net Energy for Lactation, Net Energy for Maintenance, and Net Energy for Gain. Reflectance data were processed with the first derivative transformation and continuum removal method. Correlation analysis was conducted on spectral and forage quality variables. A regression model was further built to investigate the possibility of using canopy spectral measurements to predict the grassland quality. Results indicated that field level prediction of protein of mixed grass species was possible (r2 = 0.63). However, the relationship between canopy reflectance and the other forage quality variables was not strong

Iron oxide minerals in dust-source sediments from the Bodélé Depression, Chad: Implications for radiative properties and Fe bioavailability of dust plumes from the Sahara

Atmospheric mineral dust can influence climate and biogeochemical cycles. An important component of mineral dust is ferric oxide minerals (hematite and goethite) which have been shown to influence strongly the optical properties of dust plumes and thus affect the radiative forcing of global dust. Here we report on the iron mineralogy of dust-source samples from the Bodélé Depression (Chad, north-central Africa), which is estimated to be Earth’s most prolific dust producer and may be a key contributor to the global radiative budget of the atmosphere as well as to long-range nutrient transport to the Amazon Basin. By using a combination of magnetic property measurements, Mössbauer spectroscopy, reflectance spectroscopy, chemical analysis, and scanning electron microscopy, we document the abundance and relative amounts of goethite, hematite, and magnetite in dust-source samples from the Bodélé Depression. The partition between hematite and goethite is important to know to improve models for the radiative effects of ferric oxide minerals in mineral dust aerosols. The combination of methods shows (1) the dominance of goethite over hematite in the source sediments, (2) the abundance and occurrences of their nanosize components, and (3) the ubiquity of magnetite, albeit in small amounts. Dominant goethite and subordinate hematite together compose about 2% of yellow-reddish dust-source sediments from the Bodélé Depression and contribute strongly to diminution of reflectance in bulk samples. These observations imply that dust plumes from the Bodélé Depression that are derived from goethite-dominated sediments strongly absorb solar radiation. The presence of ubiquitous magnetite (0.002-0.57 wt. %) is also noteworthy for its potentially higher solubility relative to ferric oxide and for its small sizes, including PM<0.1m. For all examined samples, the average iron apportionment is estimated at about 33% in ferric oxide minerals, 1.4 % in magnetite, and 65% in ferric silicates. Structural iron in clay minerals may account for much of the iron in the ferric silicates. We estimate that the mean ferric oxides flux exported from the Bodélé Depression is 0.9 Tg/yr with greater than 50% exported as ferric oxide nanoparticles (<0.1m). The high surface-to-volume ratios of ferric oxide nanoparticles once entrained into dust plumes may facilitate increased atmospheric chemical and physical processing and affect iron solubility and bioavailability to marine and terrestrial ecosystems

Titan's cold case files - Outstanding questions after Cassini-Huygens

Abstract The entry of the Cassini-Huygens spacecraft into orbit around Saturn in July 2004 marked the start of a golden era in the exploration of Titan, Saturn's giant moon. During the Prime Mission (2004–2008), ground-breaking discoveries were made by the Cassini orbiter including the equatorial dune fields (flyby T3, 2005), northern lakes and seas (T16, 2006), and the large positive and negative ions (T16 & T18, 2006), to name a few. In 2005 the Huygens probe descended through Titan's atmosphere, taking the first close-up pictures of the surface, including large networks of dendritic channels leading to a dried-up seabed, and also obtaining detailed profiles of temperature and gas composition during the atmospheric descent. The discoveries continued through the Equinox Mission (2008–2010) and Solstice Mission (2010–2017) totaling 127 targeted flybys of Titan in all. Now at the end of the mission, we are able to look back on the high-level scientific questions from the start of the mission, and assess the progress that has been made towards answering these. At the same time, new scientific questions regarding Titan have emerged from the discoveries that have been made. In this paper we review a cross-section of important scientific questions that remain partially or completely unanswered, ranging from Titan's deep interior to the exosphere. Our intention is to help formulate the science goals for the next generation of planetary missions to Titan, and to stimulate new experimental, observational and theoretical investigations in the interim