Field Reflectance Measurements Research Articles

Field Reflectance Measurements at Night of Beach and Desert Sands within a Particulate BRDF Model

The Bidirectional Reflectance Distribution Function (BRDF) of soils is frequently assumed to be Lambertian in remote sensing applications, even though many soils are observed to have a distinct backscatter. This is largely due to the difficulty of obtaining accurate in-situ BRDF of these materials over the entire hemisphere. The objective of the work presented here is to describe a new technique that combines the measurement of field BRDF data on desert and beach sands measured at night with a model that reproduces the backscatter except in a small region around zero phase angle. It is found that this combination produces parameterized BRDF of sufficient accuracy to allow its inclusion in a remote sensing materials database. The measurement technique was developed in the laboratory and transitioned to perform in-plane BRDF field measurements of sands at night. Field measurements were obtained using a portable light source and field spectrometer over the range of 0.4–2.4 micrometers. The spectrometer sensor optics and light source were mounted on a portable, manually operated goniometer allowing in-plane measurements to within 3 degrees of backscatter. Unlike daytime measurements, corrections for background were unnecessary, and BRDF was obtained by referencing to a Spectralon calibration standard. Analysis of the spectrometer field data used a variant of the Shadow Hiding Opposition Effect (SHOE) wherein a series expansion of Legendre Polynomials was substituted for the angularly dependent backscatter function. It is shown that when the out-of-plane scattering and the backscatter peak are small, this model provides an adequate description of the BRDF. The combination of the nocturnal in-situ measurements with an analysis using a model that can describe the backscatter produces parameterized BRDF for these sands of sufficient accuracy to allow inclusion in the NEFDS materials database. Measurement and analysis techniques are assessed, sample results for both the laboratory and field data are shown, and the extension to future soils measurements are discussed.

Laboratory, field, mast-borne and airborne spectral reflectance measurements of boreal landscape during spring

Abstract. We publish and describe a surface spectral reflectance data record of seasonal snow (dry, wet, shadowed), forest ground (lichen, moss) and forest canopy (spruce and pine, branches) constituting the main elements of the boreal landscape. The reflectances are measured with spectro(radio)meters covering the wavelengths from visible (VIS) to short-wave infrared (SWIR) (350 to 2500 nm). In this paper, we describe the instruments used and how the spectral observations at different scales along with the concurrent in situ reference data have been collected, processed and archived. Information on the quality of the data and factors causing uncertainty are discussed. The main experimental site is located in the Sodankylä Arctic Space Centre in northern Finland (67.37∘ N, 26.63∘ E; 179 m a.s.l) and the surrounding region. The collection includes highly controlled snow and conifer branch laboratory spectral measurements, portable field spectroradiometer observations of snow and snow-free ground at different locations, and continuous mast-borne reflectance time series data of a pine forest and forest opening. In addition to the surface level spectral reflectance, data from airborne imaging spectrometer campaigns over the Sodankylä boreal forest and Saariselkä fell region at selected spectral bands are included in the collection. All measurements of the data record correspond to a typical polar-orbiting satellite observation event in the high-latitude spring season regarding their Sun or illumination source (calibrated lamp) zenith angle and close-to-nadir instrument viewing angle. For all measurement geometries, observations are given in surface reflectance quantity corresponding to the typical representation of a satellite observation quantity to facilitate their comparison with other data sources. The openly accessible spectral reflectance data at multiple scales are suitable to climate and hydrological research and remote sensing model validation and development. To facilitate easy access to the data record the four datasets described here are deposited in a permanent data repository (http://www.zenodo.org/communities/boreal_reflectances/) (Hannula et al., 2019). Each dataset of a distinct scale has its own unique DOI – laboratory: https://doi.org/10.5281/zenodo.3580078 (Hannula and Heinilä, 2018a); field: https://doi.org/10.5281/zenodo.3580825 (Heinilä et al., 2019a); mast-borne: https://doi.org/10.5281/zenodo.3580096 (Hannula and Heinilä, 2018b); and airborne: https://doi.org/10.5281/zenodo.3580451 (Heinilä, 2019a) and https://doi.org/10.5281/zenodo.3580419 (Heinilä, 2019b).

Hyperspectral analysis of objects under shadow conditions based on field reflectance measurements.

Shaded objects in remote sensing data are usually neglected because of their low reflectance. The common treatment for detecting shadows consists of simple identification and straightforward removal; however, this approach is often criticized owing to the complexity and difficulty of its application and the incompleteness of the treated data. Recent efforts with hyperspectral field reflectance measurements have provided alternatives to scrutinize the issues of shadows. To demonstrate our proposed framework, we first gathered basic hyperspectral data with a spectrometer at Peking University, Beijing, China. Subsequently, we classified the spectral reflectances into three types: sunlight reflectance, shadow reflectance, and simulated reflectance. The spectral characteristics of shadows can provide pertinent classification information despite their low reflectance. Under full shadow conditions, the simulated reflectance was close to the sunlight reflectance because the same reduction ratio was used for the spectralon panel during the measurement process. In addition, the ratio of the shadow reflectance to the sunlight reflectance seemed to be related to the characteristics of the obstruction. The findings in this paper can contribute to a comprehensive understanding of the influence of shadows in remote sensing research, demonstrating the potential of fully utilizing typically neglected shadow information.

Assessing leaf area index of wheat (Triticum astvium L.) crop with hyperspectral field reflectance measurements

Assessing leaf area index of wheat (Triticum astvium L.) crop with hyperspectral field reflectance measurements

On characterization and measurement of average solar field mirror reflectance in utility-scale concentrating solar power plants

Due to the emerging need for the development of acceptance test codes for commercial concentrating solar power (CSP) plants, an effort is made here to develop a mirror reflectance model suitable for CSP applications as well as a general procedure to measure the average mirror reflectance of a solar field. Typically, a utility-scale solar field includes hundreds of thousands of mirror panels (if not more), and their reflectance is subject to many factors, such as weather and planned washing schedule. The newly developed mirror reflectance model can be used to characterize different types of mirror materials and can be directly used to perform optical performance evaluation of solar collectors. The newly proposed procedure for average solar field reflectance measurements includes a baseline comprehensive measurement and an individual factor measurement: the former allows a comprehensive survey of mirror reflectance across the whole solar field, and the latter can provide correcting factors for selected individual factors to further improve the accuracy of the baseline measurements. A detailed test case implementing the general procedure is applied to a state-of-the-art commercial parabolic trough plant and validates the proposed mirror reflectance model and average reflectance measurement procedure. In the test case, the plant-wide reflectance measurements at a commercial utility-scale solar plant were conducted and can shed light on relevant analysis of CSP applications. The work can also be naturally applied to other types of solar plants, such as power towers and linear Fresnel plants.

Absolute vicarious calibration of OCM2 and AWiFS sensors using a reflectance-based method over land sites in the Rann of Kutch, Gujarat

A study was carried out to estimate vicarious calibration coefficients for the OCM2 (Ocean Color Monitor) sensor onboard Oceansat-2 and also the AWiFS (Advanced Wide Field Sensor) sensor onboard Resourcesat-1 using reflectance measurements over three land sites – Dhrangadhra, Desalpar, and Bhachau – in the Rann of Kutch, Gujarat, India, on four dates (17 October 2010, 25 and 29 April 2011, and 1 May 2011). Hyperspectral field reflectance measurements of the study sites (of extent ˜2 km × 2 km) in the wavelength range 325–2500 nm, along with measurements of atmospheric parameters (aerosol optical depth (AOD), water vapour, ozone) and sensor spectral response functions, were input to the 6S atmosphere correction code to compute top-of-atmosphere (TOA) at-satellite radiance in the eight visible and near infrared (NIR) bands of OCM2 and the four visible, NIR, and shortwave infrared (SWIR) bands of the AWiFS sensor. The uncertainty in vicarious calibration coefficients due to measured spatial variability of field reflectance, aerosol optical thickness (AOT), water vapour, and ozone, was also computed for the OCM2 sensor for three dates (25 and 29 April 2011, 1 May 2011). The effect of surface anisotropy on TOA radiance was studied using a 15 day Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) product covering the study sites. The results show that there is an indication of change in calibration coefficients in OCM2, for band 2 (25 April Desalpar data), bands 2 and 5 (29 April Desalpar data), and bands 2–5 and 7 in Bhachau (1 May data), all at the 1σ level. For these bands, in the inverse mode, the 6S corrected surface reflectance was closer to field surface reflectance when estimated at-sensor radiances were used as input to the code. For AWiFS, there was no evidence of change in calibration coefficients of all four bands at the 1σ level. It was found that site spatial variability was a critical factor in estimating change in sensor calibration coefficients and influencing uncertainty in TOA radiance for all three sites.

Assessment of Radiometric Correction Methods for ADS40 Imagery

This article presents the results of an assessment of radiometric correction methods of images taken by the large-format aerial, photogrammetric, multispectral pushbroom camera Leica Geosystems ADS40. The investigation was carried out in the context of the multi-site EuroSDR project “Radiometric aspects of digital photogrammetric images”. Images were collected at the forestry research test site Hyytiala, Finland in August, 2008. Two processing workflows were evaluated: one based on the photogrammetric software Leica XPro, which in radiometric processes relies on physical modeling and information collected from the imagery only, and one based on ATCOR-4, which is software dedicated to physical atmospheric correction of airborne multi-, hyperspectral and thermal scanner data, and can be operated either with or without in-situ reflectance and atmospheric observations. Outputs of these processes are reflectance images. Three participants processed the data with several processing options which resulted in a total of 12 different radiometrically corrected reflectance images. The data analysis was based on field and laboratory reflectance measurements of reference reflectance targets and field measurements of permanent targets (asphalt, grass, gravel). Leica XPro provided up to 5 % reflectance accuracy without any ground reference and ATCOR-4 provided reflectance accuracy better than 5 % with vicarious inflight radiometric calibration of the sensor. The results show that the radiometric correction of multispectral aerial images is possible in an efficient way in the photogrammetric production environment.

Existence of characteristic spectral signatures for agricultural crops – potential for automated crop mapping by hyperspectral imaging

Image classification for material mapping using independent training data is emerging as an automated method for hyperspectral image analysis. Possibility of using independent training data for image classification depends upon material type and its spectral behaviour. Identification and spectral discrimination of materials which exhibit characteristic spectral behaviour are critical for developing hyperspectral material detection and mapping methods. We identify and evaluate characteristic reflectance signature of winter rape relative to its co-occurring crops from a hyperspectral image classification perspective. Spectrallibraries developed using field reflectance measurements of agricultural crops: alfalfa, winter barley, winter rape, winter rye and winter wheat collected during four growing seasons are searched through for the classification of a HyMap image acquired for a separate site by spectral angle mapper and spectralfeature fitting methods. Results indicate the existence of a characteristic spectral signature for winter rape and meaningful matching between image and field spectra, which can be used for automatic mapping of winter rape by hyperspectral imaging.

Transferring spectral libraries of canopy reflectance for crop classification using hyperspectral remote sensing data

Spectral library search is emerging as an automated method for exploiting finer spectral details available in hyperspectral remote sensing data. We report on the potential of transferring independent crop spectral libraries for classifying various agricultural crops using airborne hyperspectral image. Spectral libraries constructed from multi-season field reflectance measurements for five agricultural crops (alfalfa, winter barley, winter rape, winter rye, and winter wheat) are used for the per-pixel and per-field classification of HyMAP airborne hyperspectral image by the spectral library search method. Results obtained from this method are compared with the results obtained from the per-field object-oriented, and per-pixel support vector machines (SVM) supervised image classification using image-based training data. Results from the spectral library search approach (best overall accuracy: 82%) exhibit strong correlation with the results obtained from both the object-oriented and SVM-supervised classification approach. However, per-field object-oriented classification shows moderate increase in the classification performance. In spite of significant reduction in the overall accuracy, the resultant overall accuracy of about 82% obtained from the spectral library search is fairly high, given the large spatial and temporal differences maintained between the image data and the field reflectance measurements. Results indicate the existence of a meaningful spectral matching between image and reflectance library spectra for some of the crops considered, showing their potential for transferring reflectance spectral libraries for image classification. Incomplete library coverage and phenological variations are found to be the key issues that influence the prospect of transferring spectral libraries for image classification.

Identification of robust hyperspectral indices on forest leaf water content using PROSPECT simulated dataset and field reflectance measurements

AbstractIn this study, we aim at finding efficient and robust hyperspectral indices for estimating forest leaf water content parameters (equivalent water thickness, EWT and fuel moisture content, FMC), which are useful for the understanding of terrestrial ecosystem functioning and evaluating fire risk. The most efficient hyperspectral indices have been identified (both on the context of index types and wavelength domains) using both a simulated dataset generated from the calibrated leaf reflectance model, PROSPECT, and datasets of field measured reflectance. Results indicate that high precision can be obtained using the type of vegetation index of two wavelength bands based on reflectance derivatives to estimate both parameters, with overall R2 and RMSE of 0·60 ∼ 0·75 and 0·0009 ∼ 0·0012 g cm−2 for EWT, 0·63 ∼ 0·87 and 0·12 ∼ 0·20 g g−1 for FMC, respectively. The best indices identified in this study for vegetation water status in temperate deciduous forests were dSR (1510, 1560) for EWT and dSR (2110, 2260) for FMC, with widths of wavebands (Δλ) be variable up to 50 nm for both dSR indices. Despite the obvious discrepancies found in fit when applying the identified indices to different datasets, the indices identified in this study are applicable to various species (Dataset III), various phenological stages, different sites (Dataset I) and various leaf anatomies (Dataset II), and therefore suitable for an all inclusive wide range of application especially in temperate deciduous forests. Copyright © 2011 John Wiley & Sons, Ltd.

Use of field reflectance data for crop mapping using airborne hyperspectral image

Recent developments in hyperspectral remote sensing technologies enable acquisition of image with high spectral resolution, which is typical to the laboratory or in situ reflectance measurements. There has been an increasing interest in the utilization of in situ reference reflectance spectra for rapid and repeated mapping of various surface features. Here we examined the prospect of classifying airborne hyperspectral image using field reflectance spectra as the training data for crop mapping. Canopy level field reflectance measurements of some important agricultural crops, i.e. alfalfa, winter barley, winter rape, winter rye, and winter wheat collected during four consecutive growing seasons are used for the classification of a HyMAP image acquired for a separate location by (1) mixture tuned matched filtering (MTMF), (2) spectral feature fitting (SFF), and (3) spectral angle mapper (SAM) methods. In order to answer a general research question “what is the prospect of using independent reference reflectance spectra for image classification”, while focussing on the crop classification, the results indicate distinct aspects. On the one hand, field reflectance spectra of winter rape and alfalfa demonstrate excellent crop discrimination and spectral matching with the image across the growing seasons. On the other hand, significant spectral confusion detected among the winter barley, winter rye, and winter wheat rule out the possibility of existence of a meaningful spectral matching between field reflectance spectra and image. While supporting the current notion of “non-existence of characteristic reflectance spectral signatures for vegetation”, results indicate that there exist some crops whose spectral signatures are similar to characteristic spectral signatures with possibility of using them in image classification.

Passive field reflectance measurements

The results of reflectance measurements performed with a three-band passive radiometer with independent channels for solar irradiance reference are presented. Comparative operation between the traditional method that uses downward-looking field and reference white panel measurements and the new approach involving duplicated downward- and upward-looking spectral channels (each latter one with its own diffuser) is analyzed. The results indicate that the latter method performs in very good agreement with the standard method and is more suitable for passive sensors under rapidly changing atmospheric conditions (such as clouds, dust, mist, smog and other scatterers), since a more reliable synchronous recording of reference and incident light is achieved. Besides, having separate channels for the reference and the signal allows a better balancing of gains in the amplifiers for each spectral channel. We show the results obtained in the determination of the normalized difference vegetation index (NDVI) corresponding to the period 2004–2007 field experiments concerning weed detection in soybean stubbles and fertilizer level assessment in wheat. The method may be used to refine sensor-based nitrogen fertilizer rate recommendations and to determine suitable zones for herbicide applications.

Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements

This paper examines how reflectance spectrometry used in the laboratory to estimate clay and calcium carbonate (CaCO 3) soil contents can be applied to field and airborne measurements for soil property mapping. A continuum removal (CR) technique quantifying specific absorption features of clay (2206 nm) and CaCO 3 (2341 nm) was applied to laboratory, field and airborne HYMAP reflectance measurements collected in 2003 (33 sites) and 2005 (19 sites) over bare soil sites of a few meters within the La Peyne Valley area, southern France. Nine intermediate stages from the laboratory up to HYMAP sensor measurements were considered for separately evaluating the possible degradation of estimation performances when going across scales and sensors, e.g. radiometric calibration, spectral resolution, spatial variability, illumination conditions, and surface status including roughness, soil moisture and presence and nature of pebbles. Significant relationships were observed between clay and CaCO 3 contents and CR values computed respectively at 2206 nm and 2341 nm from reflectance measurements at the laboratory level with an ASD spectrophotometer up to the HYMAP spectro-imaging sensor. Performances of clay and CaCO 3 estimations decreased from the laboratory to airborne scales. The main factors inducing uncertainties in the estimates were radiometric and wavelength calibration uncertainties of the HYMAP sensor as well as possible residual atmospheric effects.

Using Remote Sensing to Assess Biological Control of Saltcedar

Research was conducted to evaluate the potential of using remote sensing technology to assess feeding damage on the invasive woody plant species, saltcedar, Tamarix ramosissima Ledeb., by a leaf beetle, Diorhabda elongata (Brulle). Field reflectance measurements showed that saltcedar plants exhibiting feeding damage had lower visible green reflectance than healthy plants, and could be distinguished from healthy plants on conventional color aerial photographs. Accuracy assessments performed on supervised classification maps of aerial photographs from two years showed that damaged and healthy saltcedar plants had producer and user accuracies ranging from 88% to 100%.

Using spatial information technologies for detecting and mapping Eurasian watermilfoil

This paper describes the spectral light reflectance characteristics of Eurasian watermilfoil (Myriophyllum spicatum L.) and the application of aerial colour-infrared (CIR) photography and videography for distinguishing infestations of this invasive, exotic, submersed aquatic weed in Texas waterways. Airborne videography was integrated with global positioning system (GPS) and geographic information system (GIS) technologies for mapping the distribution of Eurasian watermilfoil. Field reflectance measurements showed that Eurasian watermilfoil could be spectrally distinguished from other associated plant species in either the visible green, visible red or near-infrared regions of the electromagnetic spectrum. Eurasian watermilfoil submerged at depths greater than 5 cm below the water surface had similar visible reflectance to water. Surfaced Eurasian watermilfoil could be distinguished on CIR aerial photography and videography where it had a greyish-pink or faint pink image response. Integration of the GPS with the video imagery permitted latitude-longitude coordinates of Eurasian watermilfoil infestations to be recorded on each image. A stretch of the Rio Grande River in southwest Texas was flown with the photographic and video systems to detect Eurasian watermilfoil infestations. The GPS coordinates on the CIR video scenes depicting Eurasian watermilfoil infestations were entered into a GIS to map the distribution of this invasive weed in the Rio Grande.

Hyperspectral field reflectance measurements to estimate wheat grain yield and plant height

Hyperspectral crop reflectance data are useful for several remote sensing applications in agriculture, but there is still a need for studies to define optimal wavebands to estimate crop biophysical parameters. The objective of this work is to analyze the use of narrow and broad band vegetation indices (VI) derived from hyperspectral field reflectance measurements to estimate wheat (Triticum aestivum L.) grain yield and plant height. A field study was conducted during the winter growing season of 2003 in Campinas, São Paulo State, Brazil. Field canopy reflectance measurements were acquired at six wheat growth stages over 80 plots with four wheat cultivars (IAC-362, IAC-364, IAC-370, and IAC-373), five levels of nitrogen fertilizer (0, 30, 60, 90, and 120 kg of N ha-1) and four replicates. The following VI were analyzed: a) hyperspectral or narrow-band VI (1. optimum multiple narrow-band reflectance, OMNBR; 2. narrow-band normalized difference vegetation index, NB_NDVI; 3. first- and second-order derivative of reflectance; and 4. four derivative green vegetation index); and b) broad band VI (simple ratio, SR; normalized difference vegetation index, NDVI; and soil-adjusted vegetation index, SAVI). Hyperspectral indices provided an overall better estimate of biophysical variables when compared to broad band VI. The OMNBR with four bands presented the highest R² values to estimate both grain yield (R² = 0.74; Booting and Heading stages) and plant height (R² = 0.68; Heading stage). Best results to estimate biophysical variables were observed for spectral measurements acquired between Tillering II and Heading stages.

Wheat yield estimation using remote sensing and the STICS model in the semiarid Yaqui valley, Mexico

During the 1999/2000 agricultural seasons, an experiment was carried out on winter wheat fields in the semiarid Yaqui Valley (Northwest Mexico). This data set was used to calibrate the evolution of the leaf area index (LAI) simulated by STICS, which was found to be in excellent agreement with estimates obtained from field reflectance measurements. After calibration, STICS was able to simulate satisfactorily the seasonal levels and trends observed in net radiation, soil moisture and evapotranspiration, but the crop temperature was overestimated by about 2.5 °C. On a larger scale, STICS was run on 16 fields with contrasting management practices. The simulations indicate that yield predictability is significantly lower for later sowing dates, consistent with observations. The seasonal variations of field and satellite data (Landsat-ETM+, Terra-MODIS and VEGETATION) NDVI were very close. However, some difficulties were noted: saturation of NDVI at high LAI values and smoothed variability on a 1-km spatial scale, as well as the need for a sound methodology for processing satellite data.

CANOPY DAMAGE AND RECOVERY AFTER SELECTIVE LOGGING IN AMAZONIA: FIELD AND SATELLITE STUDIES

We combined a detailed field study of canopy gap fraction with spectral mixture analyses of Landsat 7 ETM+ satellite imagery to assess landscape and regional dynamics of canopy damage following selective logging in an eastern Amazon forest. Our field studies encompassed measurements of ground damage and canopy gap fractions along multitemporal sequences of post‐harvest regrowth of 0.5–3.5 yr. Areas used to stage harvested logs prior to transport, called log decks, had the largest forest gap fractions, but their contribution to the landscape‐level gap dynamics was minor. Tree falls were spatially the most extensive form of canopy damage following selective logging, but the canopy gap fractions resulting from them were small. Reduced‐impact logging resulted in consistently less damage to the forest canopy than did conventional logging practices. This was true at the level of individual landscape strata such as roads, skids, and tree falls as well as at the area‐integrated scale.A spectral mixture model was employed that utilizes bundles of field and image spectral reflectance measurements with Monte Carlo analysis to estimate high spatial resolution (subpixel) cover of forest canopies, exposed nonphotosynthetic vegetation, and soils in the Landsat imagery. The method proved highly useful for quantifying forest canopy cover fraction in log decks, roads, skids, tree fall, and intact forest areas, and it tracked canopy damage up to 3.5 yr post‐harvest. Forest canopy cover fractions derived from the satellite observations were highly and inversely correlated with field‐based canopy gap fraction. Subsequent regional‐scale estimates of forest gap fraction were derived from the combination of field‐ and satellite‐based measurements. A 450‐km2 study of gap fraction showed that approximately one‐half of the canopy opening caused by logging is closed within one year of regrowth following timber harvests. This is the first regional‐scale study utilizing field measurements, satellite observations, and models to quantify forest canopy damage and recovery following selective logging in the Amazon.

Remote Sensing of Macartney Rose in the Texas Coastal Prairie

Macartney rose (Rosa bracteata) is an evergreen shrub that often creates a range management problem in southeastern Texas. A study was conducted to determine the potential of using remote sensing technology to distinguishMacartney rose on the Texas Coastal Prairie. Field reflectance measurements showed that Macartney rose had higher near-infrared reflectance than associated plant species and mixtures of species in February and March. Macartney rose had a bright red image tonal response on color-infrared aerial photographs obtained in late winter. Computer analysis of a color-infrared photographic transparency showed that Macartney rose populations could be quantified. An accuracy assessment performed on the classified image showed that both the user's accuracy and producer's accuracy for Macartney rose were 100%.

Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data

Knowledge of the distribution of vegetation on the landscape can be used to investigate ecosystem functioning. The sizes and movements of animal populations can be linked to resources provided by different plant species. This paper demonstrates the application of imaging spectroscopy to the study of vegetation in Yellowstone National Park (Yellowstone) using spectral feature analysis of data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). AVIRIS data, acquired on August 7, 1996, were calibrated to surface reflectance using a radiative transfer model and field reflectance measurements of a ground calibration site. A spectral library of canopy reflectance signatures was created by averaging pixels of the calibrated AVIRIS data over areas of known forest and nonforest vegetation cover types in Yellowstone. Using continuum removal and least squares fitting algorithms in the US Geological Survey's Tetracorder expert system, the distributions of these vegetation types were determined by comparing the absorption features of vegetation in the spectral library with the spectra from the AVIRIS data. The 0.68 μm chlorophyll absorption feature and leaf water absorption features, centered near 0.98 and 1.20 μm, were analyzed. Nonforest cover types of sagebrush, grasslands, willows, sedges, and other wetland vegetation were mapped in the Lamar Valley of Yellowstone. Conifer cover types of lodgepole pine, whitebark pine, Douglas fir, and mixed Engelmann spruce/subalpine fir forests were spectrally discriminated and their distributions mapped in the AVIRIS images. In the Mount Washburn area of Yellowstone, a comparison of the AVIRIS map of forest cover types to a map derived from air photos resulted in an overall agreement of 74.1% (kappa statistic=0.62).