Keely L. Roth

Mapping Plant Functional Types at Multiple Spatial Resolutions Using Imaging Spectrometer Data

Imaging spectrometer data have been used to map plant functional types (PFTs—plant species grouped by similarities in their resource use, ecosystem function, and responses to environmental conditions) at spatial resolutions of 30 m and finer, but not at coarser spatial resolutions that may be necessary for global PFT mapping. This study uses spatially resampled Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) data acquired over the Wasatch Mountains of northern Utah, USA to examine changes in PFT classification accuracy as spatial resolution is degraded from 20 to 60 m. Accuracy was dependent on the spatial resolution of the classified data and the spatial resolution of endmembers used in the multiple endmember spectral mixture analysis classifier.

Identifying Santa Barbara's urban tree species from AVIRIS imagery using canonical discriminant analysis

In this research, we classify 15 common urban trees in downtown Santa Barbara, California, using crown-level canonical discriminant analysis (CDA) on airborne visible/infrared imaging spectrometer (AVIRIS) imagery. We compare the CDA classification accuracy against results obtained from stepwise discriminant analysis. We also examine the impact of various crown-level aggregation techniques and training sample size on classification results. An overall classification accuracy of 86% was achieved using CDA. Species-specific results were highest for dense crowns with high normalized difference vegetation index values. Bands chosen using forward feature selection spanned AVIRIS full spectral range illustrating a need for retaining a full complement of spectral information. Nevertheless, there is some indication that bands along the green edge, green peak and yellow edge are particularly valuable for discriminating structurally similar urban trees.

Monitoring the Impacts of Severe Drought on Southern California Chaparral Species using Hyperspectral and Thermal Infrared Imagery

Airborne hyperspectral and thermal infrared imagery acquired in 2013 and 2014, the second and third years of a severe drought in California, were used to assess drought impacts on dominant plant species. A relative green vegetation fraction (RGVF) calculated from 2013–2014 Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data using linear spectral unmixing revealed seasonal and multi-year changes relative to a pre-drought 2011 reference AVIRIS image. Deeply rooted tree species and tree species found in mesic areas showed the least change in RGVF. Coastal sage scrub species demonstrated the highest seasonal variability, as well as a longer-term decline in RGVF. Ceanothus species were apparently least well-adapted to long-term drought among chaparral species, showing persistent declines in RGVF over 2013 and 2014. Declining RGVF was associated with higher land surface temperature retrieved from MODIS-ASTER Airborne Simulator (MASTER) data. Combined collection of hyperspectral and thermal infrared imagery may offer new opportunities for mapping and monitoring drought impacts on ecosystems.

Linking Physical Geography Education and Research through the Development of an Environmental Sensing Network and Project-Based Learning.

Geographic education is more effective when students actively participate by developing hypotheses, designing experiments, collecting and analyzing data, and discussing results. We describe an innovative pedagogical approach, in which students learn physical geography concepts by analyzing environmental data collected in contrasting environments in Santa Barbara County, CA. The major components of this approach include a local network of micrometeorology stations (the Innovative Datasets for Environmental Analysis by Students (IDEAS network)), student field trips, a web portal (www.geog.ucsb.edu/ideas) and analysis tools which support student education and research. Examples of student work, graded rubrics, course evaluation scores, and instructor observations demonstrate the effectiveness of this approach. The most serious limitation is the high cost of equipment given the low number of students initially involved, a weakness that can be addressed through expanded use of this facility by other physical geography classes and institutions, facilitated by the IDEAS website, and increased enrollment in existing classes.

Spectral sensitivity of radiative transfer inversion for seasonal canopy pigments estimation from aviris data in a woodland savanna ecosystem

Leaf pigments are an important contributor to a plants physiological and ecological functioning. Their measurement on a seasonal basis is important for understanding plant response and adaptation to stress, such as the extended California drought. In this preliminary study, chlorophylls a+b and carotenoids content were estimated at canopy level using a coupled leaf-canopy radiative transfer model (DART and PROSPECT) and a look-up table inversion approach, from AVIRIS imagery acquired in spring, summer and fall 2013. The study area is a woodland savanna. Results showed high sensitivity to soil background, low canopy cover, and LAI values, resulting in a low spectral contribution of the pigments to the remote sensing spectra. Selected spectral intervals proved more robust for estimating pigments than vegetation indices such as TCARI and OSAVI for chlorophyll, and a carotenoid band ratio.

Relationships between species composition, fractional cover and land surface temperature in a mediterranean ecosystem

HyspIRI is a proposed satellite mission combining two 60 m spatial resolution sensors: a Visible-Shortwave Infrared (VSWIR) imaging spectrometer and a multispectral thermal infrared (TIR) scanner. HyspIRI offers the potential to combine the ability of a VSWIR sensor to discriminate plant species and estimate accurate surface fractions of biotic and abiotic materials using multispectral emissivity with improved Land Surface Temperature (LST) retrieval from the TIR sensor. We evaluate potential synergies between AVIRIS maps of species/cover and fractions and MASTER LST utilizing multiple flight lines acquired in July 2011 in the Santa Barbara area. Cover was mapped using Multiple Endmember Spectral Mixture Analysis using a spectral library derived from the 7.5 m imagery and endmembers selected using Iterative Endmember Selection. Temperature Emissivity Separation (TES) was accomplished using the MASTER TES algorithm. Pixel-based accuracy was 55% for 21 classes, but 84.7% based on pixel majority in reference polygons. An inverse relationship was observed between green vegetation (GV) fractions and LST, but varied by plant species, generating unique LST-GV clusters.