Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensin

Multi-scalar remote sensing of the northern mixed prairie vegetation

Optimal scale of study and scaling are fundamental to ecological research, and have been made easier with remotely sensed (RS) data. With access to RS data at multiple scales, it is important to identify how they compare and how effectively information at a specific scale will potentially transfer between scales. Therefore, my research compared the spatial, spectral, and temporal aspects of scale of RS data to study biophysical properties and spatio-temporal dynamics of the northern mixed prairie vegetation. I collected ground cover, dominant species, aboveground biomass, and leaf area index (LAI) from 41 sites and along 3 transects in the West Block of Grasslands National Park of Canada (GNPC; +49°, -107°) between June-July of 2006 and 2007. Narrowband (VIn) and broadband vegetation indices (VIb) were derived from RS data at multiple scales acquired through field spectroradiometry (1 m) and satellite imagery (10, 20, 30 m). VIs were upscaled from their native scales to coarser scales for spatial comparison, and time-series imagery at ~5-year intervals was used for temporal comparison. Results showed VIn, VIb, and LAI captured the spatial variation of plant biophysical properties along topographical gradients and their spatial scales ranged from 35-200 m. Among the scales compared, RS data at finer scales showed stronger ability than coarser scales to estimate ground vegetation. VIn were found to be better predictors than VIb in estimating LAI. Upscaling at all spatial scales showed similar weakening trends for LAI prediction using VIb, however spatial regression methods were necessary to minimize spatial effects in the RS data sets and to improve the prediction results. Multiple endmember spectral mixture analysis (MESMA) successfully captured the spatial heterogeneity of vegetation and effective modeling of sub-pixel spectral variability to produce improved vegetation maps. However, the efficiency of spectral unmixing was found to be highly dependent on the identification of optimal type and number of region-specific endmembers, and comparison of spectral unmixing on imagery at different scales showed spectral resolution to be important over spatial resolution. With the development of a comprehensive endmember library, MESMA may be used as a standard tool for identifying spatio-temporal changes in time-series imagery. Climatic variables were found to affect the success of unmixing, with lower success for years of climatic extremes. Change-detection analysis showed the success of biodiversity conservation practices of GNPC since establishment of the park and suggests that its management strategies are effective in maintaining vegetation heterogeneity in the region. Overall, my research has advanced the understanding of RS of the northern mixed prairie vegetation, especially in the context of effects of scale and scaling. From an eco-management perspective, this research has provided cost- and time-effective methods for vegetation mapping and monitoring. Data and techniques tested in this study will be even more useful with hyperspectral imagery should they become available for the northern mixed prairie

Remote Sensing Methods and Applications for Detecting Change in Forest Ecosystems

Forest ecosystems are being altered by climate change, invasive species, and additional stressors. Our ability to detect these changes and quantify their impacts relies on detailed data across spatial and temporal scales. This dissertation expands the ecological utility of long-term satellite imagery by developing high quality forest mapping products and examining spatiotemporal changes in tree species abundance and phenology across the northeastern United States (US; the ‘Northeast’). Species/genus-level forest composition maps were developed by integrating field data and Landsat images to model abundance at a sub-pixel scale. These abundance maps were then used to 1) produce a more detailed, accurate forest classification compared to similar products and 2) construct a 30-year time-series of abundance for eight common species/genera. Analyzing the time-series data revealed significant abundance trends in notable species, including increases in American beech (Fagus grandifolia) at the expense of sugar maple (Acer saccharum). Climate was the dominant predictor of abundance trends, indicating climate change may be altering competitive relationships. Spatiotemporal trends in deciduous forest phenology – start and end of the growing season (SOS/EOS) – were examined based on MODIS imagery from 2001-2015. SOS exhibited a slight advancing trend across the Northeast, but with a distinct spatial pattern: eastern ecoregions showed advance and western ecoregions delay. EOS trended substantially later almost everywhere. SOS trends were linked to winter-spring temperature and precipitation trends; areas with higher elevation and fall precipitation anomalies had negative associations with EOS trends. Together, this work demonstrates the value of remote sensing in furthering our understanding of long-term forest responses to changing environmental conditions. By highlighting potential changes in forest composition and function, the research presented here can be used to develop forest conservation and management strategies in the Northeast