Remote Sensing Projects in the Santa Monica Mountains, California Remote sensing of soil properties in the Santa Monica Mountains observed with AVIRIS A significant problem for soil analysis is the presence of vegetation in most pixels. Because the signatures of soils and vegetation are so different, the lesser variability contained within the soil component is not significant enough for soil discrimination when vegetation is also present in the pixel. For this model FBA was modified to project the spectra into a property-specific axis of continuous variation. To examine the application of this method to extract and improve detection of soil properties using an imaging spectrometer, we applied it to map the spatial distribution of organic matter content from samples in two watersheds in the Santa Monica Mountains Recreation Area. The purpose of this work was to test the performance of HFBA (Hierarchical Foreground and Background Analysis) applied to AVIRIS data for the discrimination of these soils and soil properties.(PDF File) Palacios-Orueta, A., J. E. Pinzon, D. A. Roberts, and S. L. Ustin. (1998). ìRemote sensing of soil properties in the Santa Monica Mountains: Hierarchical Foreground and Background Analysis.î Seventh Annual JPL Airborne Earth Science Workshop, Pasadena, CA. January 12-16, 1998   Mapping the Distribution of Wildfire Fuels Using AVIRIS in the Santa Monica Mountains  Catastrophic wildfires, such as the 1990 Painted Cave Fire in Santa Barbara or Oakland fire of 1991, attest to the destructive potential of fire in the wildland/urban interface. For example, during the Painted Cave Fire, 673 structures were consumed over a period of only six hours at an estimated cost of 250 million dollars (Gomes et al., 1993). One of the primary sources of fuels is chaparral, which consists of plant species that are adapted to frequent fires and may actually promote its ignition and spread of through volatile organic compounds in foliage (Philpot, 1977). As one of the most widely distributed plant communities in Southern California (Weislander and Gleason, 1954), and one of the most common vegetation types along the wildland urban interface, chaparral represents one of the greatest sources of wildfire hazard in the region.  Roberts, D.A., M. Gardner, J. Regelbrugge, D. Pedreros and S.L. Ustin (1998) Mapping the Distribution of Wildfire Fuels Using AVIRIS in the Santa Monica Mountains.     Remote Sensing of Soil Properties in the Santa Monica Mountains. I. Spectral Analysis  AVIRIS (Advanced Visible/Infrared Imaging Spectrometer) bands were simulated from laboratory spectra to test their performance in analyzing soil properties from a semiarid region. Multivariate analysis, specifically Principal Component Analysis and Canonical Discriminant Analysis, as well as band depth analysis were applied to study the effect of organic matter, iron content, and texture in a set of samples from two valleys in the Santa Monica Mountains Recreation Area, California. Results showed that total iron and organic matter content were main factors affecting spectral shape, although sand content significantly affected the spectral contrast of the absorption features. It was shown as well that the elimination of the atmospheric water bands from the analysis did not strongly affect the retrieval of spectral information related to these properties.  Palacios-Orueta A., S.L. Ustin. (1998), Remote Sensing of Soil Properties in the Santa Monica Mountains. I. Spectral Analysis. Remote Sensing of Environment 65(2):170-183.    Remote Sensing of Soil Properties in the Santa Monica Mountains. II.  Hierarchical Foreground and Background Analysis  Hierarchical foreground and background analysis (HFBA) was used to discriminate soils and soil properties from two valleys in the Santa Monica Mountains Recreation Area, California. The analysis is organized in two levels. In the first level, spectral data from laboratory measured soil samples were used to train the vector used to classify the soils in AVIRIS images between valleys. The prediction of organic matter and iron contents is performed at a second level of resolution. Results showed that in the laboratory soils could be classified at a high level of accuracy. When applied to the image, HFBA did not separate the spatial patterns of the two valleys, but the spatial predictions of organic matter and iron content were in accordance with the first level of classification. The presence of vegetation and the high variability of soils in each valley affect adversely the ability to resolve these soil properties. A further analysis of the terrain characteristics could explain these results better.   Palacios-Orueta A., J.E. Pinzon, S.L. Ustin, D.A. Roberts. (1998), Remote Sensing of Soils in the Santa Monica Mountains:  II.  Hierarchical Foreground and Background Analysis. Remote Sensing of Environment (in press). Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models  California chaparral is one of the most important natural vegetation communities in Southern California, representing a significant source of species diversity and, through a high susceptibility to fire, playing a major role in ecosystem dynamics. The high human cost of fire arid intimate mixing along the urban interface combine to modify the natural fire regime as well as provide additional impetus for a better understanding of how to predict fire and its management. A study was initiated in the Santa Monica Mountains to investigate the use of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for providing improved maps of chaparral coupled with direct estimates of canopy attributes (e.g. biomass, leaf area, fuel load).- The Santa Monica Mountains are an east-west trending range located approximately 75 kilometers north of Los Angeles extending westward into Ventura County. Within the Santa Monica Mountains a diverse number of ecosystems are located, including four distinct types of chaparral, wetlands, riparian habitats, woodlands, and coastal sage scrub. In this study we focus on mapping three types of chaparral, oak woodlands and grasslands. Chaparral mapped included coastal sage scrub, chemise chaparral and mixed chaparral that consisted predominantly of two species of Ceanothus.  Roberts, D.A., M. Gardner, R. Church, S.L. Ustin, G.J. Scheer, and R.O. Green. (1997) Mapping chaparral in the Santa Monica Mountains using multiple endmember  spectral mixture models. Remote Sensing of Environment 65:267-279.  Optimum Strategies for Mapping Vegetation using Multiple Endmember Spectral Mixture Models  Improved vegetation maps are required for fire management and biodiversity assessment, form critical inputs for hydrological and biogeochemical models and represent a means for scaling-up point measurements. At scales greater than 10 meters, vegetation communities are typically mixed consisting of leaves, branches, exposed soil and shadows. To map mixed vegetation, many researchers employ spectral mixture analysis (SMA). In most SMA applications, a single set of spectra consisting of green vegetation, soil, non-photosynthetic vegetation and shade are used to "unmix" images. However, because most scenes contain more than four components, this simple approach leads to fraction errors and may fail to differentiate many vegetation types. In this work, we apply a new approach called multiple endmember spectral mixture analysis (MESMA), in which the number and types of endmembers vary per-pixel. Using this approach, hundreds of unique models are generated that account for community specific differences in plant chemistry, physical attributes and phenology. Additionally, we describe a new strategy for developing and organizing regionally specific spectral libraries. We present results from a study in the Santa Monica Mountains using AVIRIS data, in which we map grassland and chaparral communities, mapping species dominance in some cases to a high degree of accuracy.  Roberts, D.A., M. Gardner, R. Church, S.L. Ustin, and R.O. Green, (1997) Optimum strategies for mapping vegetation using multiple endmember spectral mixture models, SPIE Conf. Vol 3118, Imaging Spectrometry III, 12 p. Presented at the 42nd Annual SPIE meeting, July 27 to Aug. 1, 1997, San Diego, CA. Vol. 3118, pp. 108-119.   Estimating Canopy Water Content of Chaparral Shrubs Using Optical Methods  Predicting fire hazard in fire-prone ecosystems in urbanized landscapes, such as the chaparral systems of California, is critical to risk assessment and mitigation. Understanding the dynamics of fire spread, topography and vegetation condition are necessary to increase the accuracy of fire risk assessments. One vital input to fire models is spatial and temporal estimates of canopy water content. However, timely estimates of such a dynamic ecosystem property cannot be provided for more than periodic point samples using ground based methods. This study examined the potential of three quazi-physical methods for estimating water content from remotely sensed Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data from chaparral systems in the Santa Monica Mountains, California. We examined estimates of water content at the leaf, canopy and at the image level and compared them to each other and to ground based estimates of plant water content. These methods predicted water content (with R2 between 0.5-0.95) but differ in their ease of use and the need for ancillary data inputs. The prospect for developing regional estimates for canopy water content at high spatial resolution (20 m) from high resolution optical sensors appears promising.   Ustin, S.L., G.J. Scheer, C. Castaneda, S. Jacquemoud, J.E. Pinzon, A. Palacios, D.A. Roberts, J.A. Gamon and R.O. Green. (1996), Estimating Canopy Water Content of Chaparral Shrubs Using Optical Methods.Remote Sens. Environ. Special Issue on Image Spectrometry, May 1 1996.  in 6th Airborne AVIRIS Workshop, R.O. Green (ed)., Jet Propulsion Laboratory, Pasadena, CA, March, 1996 JPL 96-4 Vol. 96-4, 235-238.     A Ground Truthing Method for AVIRIS Overflights Using Canopy Absorption Spectra  Remote sensing for ecological field studies requires ground truthing for accurate interpretation of remote imagery. However, traditional vegetation sampling methods are time consuming and hard to relate to the scale of an AVIRIS scene. The large errors associated with manual field sampling, the contrasting formats of remote and ground data, and problems with coregistration of field sites with AVIRIS pixels can lead to difficulties in interpreting AVIRIS data. As part of a larger study of fire risk in the Santa Monica Mountains of southern California (see Roberts et al. and Ustin et al., this volume), we explored ground-based optical method of sampling vegetation using spectrometers mounted both above and below vegetation canopies. The goal was to use optical methods to provide a rapid, consistent, and objective means of "ground truthing" that could be related both to AVIRIS imagery and to conventional ground sampling (e.g., plot harvests and pigment assays).  Gamon, J.A., L. Serrano, D.A. Roberts, and S.L. Ustin. (1996) A Ground Truthing Method for AVIRIS Overflights Using Canopy Absorption Spectra.  In 6th Airborne AVIRIS Workshop, R.O. Green (ed)., Jet Propulsion Laboratory, Pasadena, CA, March, 1996 JPL 96-4 Vol. 1, 93-96.      Canopy Reflectance Measurements in the Santa Monica Mountains  This document is a report describing the events that took place during June 8 through June 12, 1995 for the Santa Monica Mountains experiment.  The experiment was to obtain field canopy reflectance measurements and fresh weight canopy measurements to provide enough information to calibrate AVIRIS images to surface reflectance. Three sites, Zuma Ridge, Castro Crest and Encino Reservoir, were chosen as representative of the vegetation under study and presenting vegetation in different stages of growth.     Using Endmembers In AVIRIS Images To Estimate Changes In Vegetative Biomass  Field techniques for estimating vegetative biomass are labor intensive, and rarely are used to monitor changes in biomass over time. Remote-sensing offers an attractive alternative to field measurements; however, because there is no simple correspondence between encoded radiance in multispectral images and biomass, it is not possible to measure vegetative biomass directly from AVIRIS images. We are investigating ways to estimate vegetative biomass by identifying community types and then applying biomass scalars derived from field measurements.  Our objective is to develop an improved method based on spectral mixture analysis to characterize and identify vegetative communities, that can be applied to multi-temporal AVIRIS and other types of images.  Smith, M. O., J. B. Adams, S. L. Ustin, and D. A. Roberts. (1992). Using Endmembers in AVIRIS Images to Estimate Changes in Vegetative Biomass. Summaries of the Third Annual JPL Airborne Geoscience Workshop, Pasadena, CA, 92-14, 69-71.