Originally developed to classify multispectral and hyperspectral images, spectral mapping methods were used to classify Light Detection and Ranging (LiDAR) data to estimate the vertical structure of vegetation for Fuel Type (FT) mapping. Three spectral mapping methods generated spatially comprehensive FT maps for Cabañeros National Park (Spain): (1) Spectral Mixture Analysis (SMA), (2) Spectral Angle Mapper (SAM), and (3) Multiple Endmember Spectral Mixture Analysis (MESMA). The Vegetation Vertical Profiles (VVPs) describe the vertical distribution of the vegetation and are used to define each FT endmember in a LiDAR signature library. Two different approaches were used to define the endmembers, one based on the field data collected in 1998 and 1999 (Approach 1) and the other on exploring spatial patterns of the singular FT discriminating factors (Approach 2). The overall accuracy is higher for Approach 2 and with best results when considering a five-FT model rather than a seven-FT model. The agreement with field data of 44% for MESMA and SMA and 40% for SAM is higher than the 38% of the official Cabañeros National Park FTs map. The principal spatial patterns for the different FTs were well captured, demonstrating the value of this novel approach using spectral mapping methods applied to LiDAR data. The error sources included the time gap between field data and LiDAR acquisition, the steep topography in parts of the study site, and the low LiDAR point density among others.

There is a well-established need within the remote sensing community for improved estimation and understanding of canopy structure and its influence on the retrieval of leaf biochemical properties. The main goal of this research was to assess the potential of optical spectral information from NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) to discriminate different canopy structural types. In the first phase, we assessed the relationships between optical metrics and canopy structural parameters obtained from LiDAR in terms of different canopy structural attributes (biomass (i.e., area under Vegetation Vertical Profile, VVPint), canopy height and vegetation complexity). Secondly, we identified and classified different “canopy structural types” by integrating several structural traits using Random Forests (RF). The study area is a heterogeneous forest in Sierra National Forest in California (USA). AVIRIS optical properties were analyzed by means of several sets of variables, including single narrow band reflectance and 1st derivative, sub-pixel cover fractions, narrow-band indices, spectral absorption features, optimized normalized difference indices and Principal Component Analysis (PCA) components. Our results demonstrate that optical data contain structural information that can be retrieved. The first principal component, used as a proxy for albedo, was the most strongly correlated optical metric with vegetation complexity, and it also correlated well with biomass (VVPint) and height. In conifer forests, the shade fraction was especially correlated to vegetation complexity, while water-sensitive optical metrics had high correlations with biomass (VVPint). Single spectral band analysis results showed that correlations differ in magnitude and in direction, across the spectrum and by vegetation type and structural variable. This research illustrates the potential of AVIRIS to analyze canopy structure and to distinguish several structural types in a heterogeneous forest. Furthermore, RF using optical metrics derived from AVIRIS proved to be a powerful technique to generate maps of structural attributes. The results emphasize the importance of using the whole optical spectrum, since all spectral regions contributed to canopy structure assessment.

From 2011 to 2016, California experienced a millennial-intensity drought, generating high levels of tree mortality. Remote sensing has been used to monitor the long-term impacts of drought; however, discriminating dead from live trees in arid and semiarid deciduous woodlands is challenging. The goals of this study were to assess and map the spatial patterns of drought-induced tree mortality in a blue oak (Quercus douglasii) woodland, a highly drought-tolerant species forming savannas along the lower foothills surrounding California's Central Valley. Airborne hyperspectral imagery was used to identify the most important wavelength regions predicting drought-induced blue oak mortality. The best metric to predict canopy stress was a normalized ratio using the spectral bands 937.53 and 1100.08 nm with a correlation with tree mortality of R2 = 0.83. The image prediction of mortality for nine field plots found that 16 of 98 trees died (17.9%) during the drought. We further evaluated tree mortality in 82 independent plots, and we found the greatest image predictive accuracy for tree mortality between 1% and 10%. When applied at the landscape level, the regression-based index found mortality ranged from 1% to more than 51% in oak stands with an average mortality of 10% over the entire study region. In addition, tree mortality at landscape level showed higher tree mortality in blue oaks on south-facing aspects probably because of higher insolation rates and in sites with low potential for accumulated drainage.

Our ability to measure and map plant function at multiple ecological scales is critical for understanding current and future changes in Earth's ecosystems and the global carbon budget. Conventional plant functional types (cPFTs) based on a few productivity-related traits have been previously used to simplify and represent major differences in global plant functions, but more recent research has directly focused on the use of functional trait information. Still, sampling limitations have constrained efforts to truly understand the variance and covariance of functional traits globally. Reflectance spectra offer a fast, repeatable, simultaneous measurement of a wide variety of leaf functional traits and could be used to optically define leaf functional types. To evaluate this concept, we measured leaf reflectance from a wide range of species in a diverse set of ecosystems across central and northern California, including observations from multiple individuals, sites, and seasons. Using principal components analysis, we analyzed spectral variation in relation to categorical attributes such as species and cPFTs, as well as to a set of functional trait metrics calculated from the spectra. We found the first three principal components (PCs) to be weakly related to categorical attributes and more strongly related to spectrally-derived functional metrics. Each PC was more strongly associated with different portions of the spectrum and contained different functional information. We applied a hybrid clustering algorithm to the PC coordinates of the observations to define potential optical leaf functional types. Twelve spectral clusters were identified, and these did not correspond directly to either single cPFTs or species. However, each cluster had a unique functional metric profile. Clusters represented both inter- and intra-species and cPFT functional differences driven by taxonomy, trait evolution and environmental responses, demonstrating their value as optical leaf functional types and the value of the clustering approach used here for defining optical types from leaf spectra. Our findings support the notion that cPFTs do not adequately capture differences in leaf function. They demonstrate that spectral measurements can be used to improve both the definition of PFTs as well as our knowledge regarding the covariance of functional traits within these classes.