Mapping Northern Exposure With POLDER: Application for Circumpolar Methane Exchange

Three images types from POLDER airborne imagery, nadir looking multi-spectral bands, multiple view-angle images of the red band, and combined datasets, were used to classify land cover types at the NASA southern BOREAS study site in Saskatchewan, Canada. We found that combining multiple view-angle imagery with nadir viewing multi-spectral bands provided the greatest accuracy and discrimination of the largest number of inundated and non-inundated land cover classes. While the nadir looking multi-spectral band data correctly separated open water from other land cover classes it did not separate the inundated vegetated regions that define boreal wetlands. Multiple view-angle data could separate two upland vegetation types and open water but only one inundated vegetation type

AVIRIS spectra of California wetlands

Spectral data gathered by the AVIRIS from wetlands in the Suisun Bay area of California on 13 October 1987 were analyzed. Spectra representing stands of numerous vegetation types (including Sesuvium verrucosum, Scirpus acutus and Scirpus californicus, Xanthium strumarium, Cynadon dactylon, and Distichlis spicata) and soil were isolated. Despite some defects in the data, it was possible to detect vegetation features such as differences in the location of the chlorophyll red absorption maximum. Also, differences in cover type spectra were evident in other spectral regions. It was not possible to determine if the observed features represent noise, variability in canopy architecture, or chemical constituents of leaves

Measuring dry plant residues in grasslands: A case study using AVIRIS

Grasslands, savannah, and hardwood rangelands are critical ecosystems and sensitive to disturbance. Approximately 20 percent of the Earth's surface are grasslands and represent 3 million ha. in California alone. Developing a methodology for estimating disturbance and the effects of cumulative impacts on grasslands and rangelands is needed to effectively monitor these ecosystems. Estimating the dry biomass residue remaining on rangelands at the end of the growing season provides a basis for evaluating the effectiveness of land management practices. The residual biomass is indicative of the grazing pressure and provides a measure of the system capacity for nutrient cycling since it represents the maximum organic matter available for decomposition, and finally, provides a measure of the erosion potential for the ecosystem. Remote sensing presents a possible method for measuring dry residue. However, current satellites have had limited application due to the coarse spatial scales (relative to the patch dynamics) and insensitivity of the spectral coverage to resolve dry plant material. Several hypotheses for measuring the biochemical constituents of dry plant material, particularly cellulose and lignin, using high spectral resolution sensors were proposed. The use of Airborne Visible/Infrared Imaging Spectrometers (AVIRIS) to measure dry plant residues over an oak savannah on the eastern slopes of the Coast Range in central California was investigated and it was asked what spatial and spectral resolutions are needed to quantitatively measure dry plant biomass in this ecosystem

Imaging Spectroscopic Analysis of Biochemical Traits for Shrub Species in Great Basin, USA

The biochemical traits of plant canopies are important predictors of photosynthetic capacity and nutrient cycling. However, remote sensing of biochemical traits in shrub species in dryland ecosystems has been limited mainly due to the sparse vegetation cover, manifold shrub structures, and complex light interaction between the land surface and canopy. In order to examine the performance of airborne imaging spectroscopy for retrieving biochemical traits in shrub species, we collected Airborne Visible Infrared Imaging Spectrometer—Next Generation (AVIRIS-NG) images and surveyed four foliar biochemical traits (leaf mass per area, water content, nitrogen content and carbon) of sagebrush (Artemesia tridentata) and bitterbrush (Purshia tridentata) in the Great Basin semi-desert ecoregion, USA, in October 2014 and May 2015. We examined the correlations between biochemical traits and developed partial least square regression (PLSR) models to compare spectral correlations with biochemical traits at canopy and plot levels. PLSR models for sagebrush showed comparable performance between calibration (R2: LMA = 0.66, water = 0.7, nitrogen = 0.42, carbon = 0.6) and validation (R2: LMA = 0.52, water = 0.41, nitrogen = 0.23, carbon = 0.57), while prediction for bitterbrush remained a challenge. Our results demonstrate the potential for airborne imaging spectroscopy to measure shrub biochemical traits over large shrubland regions. We also highlight challenges when estimating biochemical traits with airborne imaging spectroscopy data

Limited Wildlife Diversity at Highway Right-of-Way Crossings

This project included estimation of animal occurrence near and using structures (e.g., culverts, street, and RR crossing structures) to cross the Interstate-80 right-of-way in the Sierra Nevada, California. A combination of track plates near over-crossings and remote cameras at under-crossings was used to index wildlife occurrence and crossings. Diversity was relatively low in the highway right-of-way and at highway under-crossings. Across six highway under-crossings, only eight of 38 possible species were observed moving through these crossings from one side of the highway right-of-way to the other. Alpha diversity at highway crossings ranged widely for wildlife near street under and over-crossings, but was not related to nearby land development. Wildlife use of existing under-crossing structures was inversely proportional to the presence of humans and frequency of human use of the same structures. This has important implications for effectiveness of existing structures and purpose-built “wildlife crossings” to provide for wildlife movement

Estimating Surface Soil Moisture in Simulated AVIRIS Spectra

Soil albedo is influenced by many physical and chemical constituents, with moisture being the most influential on the spectra general shape and albedo (Stoner and Baumgardner, 1981). Without moisture, the intrinsic or matrix reflectance of dissimilar soils varies widely due to differences in surface roughness, particle and aggregate sizes, mineral types, including salts, and organic matter contents. The influence of moisture on soil reflectance can be isolated by comparing similar soils in a study of the effects that small differences in moisture content have on reflectance. However, without prior knowledge of the soil physical and chemical constituents within every pixel, it is nearly impossible to accurately attribute the reflectance variability in an image to moisture or to differences in the physical and chemical constituents in the soil. The effect of moisture on the spectra must be eliminated to use hyperspectral imagery for determining minerals and organic matter abundances of bare agricultural soils. Accurate soil mineral and organic matter abundance maps from air- and space-borne imagery can improve GIS models for precision farming prescription, and managing irrigation and salinity. Better models of soil moisture and reflectance will also improve the selection of soil endmembers for spectral mixture analysis