Michael A. Spanner

Remote sensing of temperate coniferous forest leaf area index. The influence of canopy closure, understory vegetation and background reflectance.

Abstract The relationship between the leaf area index (LAI) of temperate coniferous forests in the western United States and Thematic Mapper (TM) data corrected for atmospheric effects and Sun-surface-sensor geometry was influenced by canopy closure, understory vegetation and background reflectance. Strong inverse curvilinear relationships were observed between coniferous forest LAI and both TM bands 3 (0-63-0-69μm) and 5 (1-55-1-75μm). The inverse relationships are explained by increased reflectance of understory vegetation and background in open stands of lower LAI and decreased reflectance of the overstory in closed canopy stands with higher LAI. A strong positive relationship was observed between LAI and TM band 4 (0-76-0-90μm) radiance in stands with greater than 89 per cent canopy closure. Open stands with low overstory LAI had elevated band 4 radiances caused by understory vegetation and/or a highly reflective granite background. Old growth stands with incomplete overstories had low band 4 radiance...

Relationship of Thematic Mapper Simulator Data to Leaf Area Index of Temperate Coniferous Forests

Abstract Regional relationships between remote sensing data and the leaf area index (LAI) of coniferous forests were analyzed using data acquired by an Airborne Thematic Mapper (ATM). Eighteen coniferous forest stands with a range of projected leaf area index of 0.6–16.1 were sampled from an environmental gradient in moisture and temperature across west-central Oregon. Spectral radiance measurements to account for atmospheric effects were acquired above the canopies from a radiometer mounted on a helicopter. A strong positive relationship was observed between the LAI of closed canopy forest stands and the ratio of near-infrared (0.76–0.90 μm) and red (0.63–0.69 μm) spectral bands. A linear regression based on LAI explained 83% of the variation in the ratio of the atmospherically corrected bands. A log-linear equation fit the asymptotic characteristic of the relationship better, explaining 91% of the variance. The positive relationship is explained by a strong asymptotic inverse relationship between LAI and red radiation and a relatively flat response between LAI and near infrared radiation.

The seasonality of AVHRR data of temperate coniferous forests: relationship with leaf area index.

Abstract Advanced Very High Resolution Radiometer (AVHRR) data from the National Oceanic and Atmospheric Administration (NOAA)-9 satellite were acquired of the western United States from March 1986 to November 1987. Monthly maximum value composites of AVHRR normalized difference vegetation index (NDVI) [(near infrared — visible)/(near infrared + visible)] were calculated for 19 coniferous forest stands in Oregon, Washington, Montana, and California. The leaf area index (LAI) of the conifer forests explained 70% and 79% of the variation of the summer maximum AVHRR NDVI in July 1986 and July 1987, respectively. The seasonal variation of NDVI was related to phenological changes in LAI, as well as the proportion of surface cover types contributing to the overall reflectance. The varying solar zenith angles in the summer and winter months complicated analyses of the seasonal differences in LAI of the forest stands by reducing NDVI values in the winter months. It is concluded that AVHRR NDVI data from July were related to the seasonal maximum leaf area index of coniferous forests of the western United States, and that seasonal differences in the AVHRR NDVI were related to: a) phenological changes in LAI caused by climate, b) the proportions of surface cover types contributing to the overall reflectance, and c) large variations in the solar zenith angle.

Remote sensing of forest canopy and leaf biochemical contents

Abstract Remote detection and measurement of the nitrogen and lignin contents of forest canopies could allow predictions of biogeochemical processes such as productivity, decomposition, and nutrient turnover rates. Spectral absorption features characteristic of proteins (containing nitrogen), lignin and other leaf constituents occur throughout the shortwave infrared region (1200–2400 nm). The lignin and nitrogen concentration of dried and ground deciduous leaves have been predicted from reflectance spectra obtained in the laboratory. The optimum wavelengths for prediction were selected using stepwise multiple linear regression. The prediction errors were comparable to chemical techniques. Analysis of the reflectance spectra of fresh, whole leaves has been limited thus far to conifer leaves but indicate spectral features predictive of nitrogen and lignin also found in airborne spectra. Airborne Imaging Spectrometer (AIS) were evaluated for whole forest canopy physical and chemical properties. Variations in spectral brightness were associated with variations in total water content of the foliar biomass. Comparison of forest spectra with spectrally flat targets revealed absorption features common to the canopy spectra between 1500 and 1700 nm which were tentatively attributed to absorption by lignin and starch. The AIS and laboratory data indicate strongly that absorption in the infrared region is influenced by biochemical characteristics.

Remote Sensing of Coniferous Forest Leaf Area

Many important ecological questions concern eco? system processes occurring over large areas. However, our understanding of ecosystem functions is derived primarily from research executed on small, intensively studied sites, and extrapolation to large areas is diffi? cult. For example, it is not known definitively whether the land biota act as a source or a sink in the global carbon cycle, or whether increases in carbon dioxide concentrations and the subsequent predicted global warming would stimulate or suppress land vegetation (Bolin 1977, Woodwell et al. 1983). Much of this indecision results from our inability to directly measure important vegetation properties on large spatial scales. Estimates of the global carbon content of terrestrial plant biomass range from 450 x 1015 to 1000 x 1015 g. These estimates are derived by extrapolation of data from sites of intensive study to the areal coverage of vegetation assumed to be equivalent; hence no direct means of verification exist. As a consequence, no defensible estimate of energy and mass exchange rates is possible for large areas of terrestrial vegetation. Four recent workshops have evaluated the potential of advanced satellite technology for direct measure? ment of critical vegetation characteristics over large areas (Botkin 1982, 1985,Goody 1982, Wittwer 1983). Each workshop ultimately identified leaf area index (LAI, the area of leaf over a given area of ground) as the single variable both amenable to measurement by satellite and of greatest importance for quantifying en? ergy and mass exchange by plant canopies over landscapes. Characterizing vegetation in terms of LAI, rath? er than species composition, is a critical simplification for regional and global comparison of different terres? trial ecosystems. Previous research in crops and grass? lands has shown leaf area and biomass to be correlated with reflectance values measured by satellite-based sensors (Wiegand et al. 1979). We report here the first attempt to measure the LAI of coniferous forests using optical scanners of satellite resolution.

Forest canopy closure from classification and spectral unmixing of scene components-multisensor evaluation of an open canopy

Three types of remote sensing data, color infrared aerial photography (CIR), compact airborne spectrographic imager (CASI) imagery, and airborne visible/infrared imaging spectrometer (AVIRIS) imagery, have been used to estimate forest canopy closure for an open-canopy forest environment. The high-spatial-resolution CIR and CASI images were classified to generate forest canopy closure estimates. These estimates were used to validate the forest canopy closure estimation accuracy obtained using the AVIRIS image. Reflectance spectra extracted from the spectral-mode CASI image were used to normalize the raw AVIRIS image to a reflectance image. Classification and spectral unmixing methods have been applied to the AVIRIS image. Results indicate that both the classification and the spectral unmixing methods can produce reasonably accurate estimates of forest canopy closure (within 3 percent agreement) when related statistics are extracted from the AVIRIS image and relatively pure reflectance spectra are extracted from the CASI image. However, it is more challenging to use the spectral unmixing technique to derive subpixel-scale components whose reflectance spectra cannot be directly extracted from the AVIRIS image. >

Analysis of Forest Structure Using Thematic Mapper Simulator Data

Remotely sensed data from forested landscapes contain information on both cover type and structure. Structural properties include crown closure, basal area, leaf area index, and tree size. Cover type and structure together are useful variables for designing forest volume inventories. The potential of Thematic Mapper Simulator (TMS) data for sensing forest structure has been explored by principal components and feature selection techniques. Improved discrimination over multispectral scanner (MSS) data proved possible in a mixed conifer forest in Idaho for estimating crown closure and tree size (saplings/seedlings, pole, sawtimber). Classification accuracy increased monotonically with the addition of new channels up to seven; the four optimum channels were 4, 7, 5, and 3. The analysis of TMS data for 123 field sites in Sequoia National Park indicated that canopy closure could be well estimated by a variety of bands or band ratios (r = 0.62-0.69) without reference to forest type. Estimation of basal area was less successful ( r = 0.51 or less) on average, but improved for certain forest types when data were stratified by floristic composition. To achieve such a stratification, sites were ordinated by a detrended correspondence analysis (DECORANA) based on the canopy of dominant species. Within forest types, canopy closure continued to be the best predictor of spectral variation. Total basal area could be predicted in certain forest types with improved or moderate reliability using various linear ratios of TMS bands (e. g., red fir, 5/4, r = 0.76; lodgepole pine, 4/3, r = 0.82).

Unmixing AVHRR imagery to assess clearcuts and forest regrowth in Oregon

Advanced Very High Resolution Radiometer imagery provides frequent and low-cost coverage of the Earth, but its coarse spatial resolution (1.1 km by 1.1 km) does not lend itself to standard techniques of automated categorization of land cover classes because the pixels are generally mixed; that is, the extent of the pixel includes several land use/cover classes. Unmixing procedures were developed to extract land use/cover class signatures from mixed pixels, using Landsat Thematic Mapper data as a source for the training set, and to estimate fractions of class coverage within pixels. Application of these unmixing procedures to mapping forest clearcuts and regrowth in Oregon indicated that unmixing is a promising approach for mapping major trends in land cover with AVHRR bands 1 and 2. Including thermal bands by unmixing AVHRR bands 1-4 did not lead to significant improvements in accuracy, but experiments with unmixing these four bands did indicate that use of weighted least squares techniques might lead to improvements in other applications of unmixing. >

Feature selection and the information content of Thematic Mapper Simulator data for forest structural assessment

The information content of Thematic Mapper Simulator (TMS) data was investigated for a forested region in northern Idaho to determine the sensitivity of TMS data to forest structural characteristics (crown closure and site class). Feature selection performed via principal components analysis and a Monte Carlo simulation indicated that TMS channels 4 (0.77–0.90 μm), 7 (10.32–12.33 μm), 5 (1.53–1.73 μm), and 3 (8.63–0.69 μm) were the four optimal channels for forest structural analysis. These four channels utilized the full spectral capability of the Thematic Mapper, representing wavelengths from the visible, the near-infrared (IR), the mid-IR, and the thermal portions of the electromagnetic spectrum. As the number of channels supplied to the Monte Carlo feature selection routine increased, classification accuracy increased. The information content of the TMS data was analyzed by performing supervised maximum likelihood classifications on three data sets: 1) 7-channel 30-m 8-bit data, 2) the 4-optimal-channel 30-m 8-bit data, and 3) TMS data degraded to Landsat multispectral scanner (MSS)specifications, 3-channel 60-m 6-bit data. The greatest sensitivity to forest structural parameters, which included crown closure, site class, and succession within clearcuts, was obtained from the 7-channel TMS data, the 4-optimal-channel TMS data, and the simulated MSS data, respectively. The increased number of spectral hands was largely responsible for the increased accuracy of the TMS data over the simulated MSS data. The improved spatial resolution of the TMS data did not improve classification performance. Variance within the TMS scene was largely due to the structural characteristics of the forest canopy.

Analysis Of Forest Disturbance Using TM and AVHRR Data

A methodology that will be used to determine the proportions of undisturbed, successional vegetation and recently disturbed land cover within coniferous forests using remotely sensed data from the advanced very high resolution radiometer (AVHRR) is presented. The method uses thematic mapper (TM) data to determine the proportions of the three stages of forest disturbance and regrowth for each AVHRR pixel in the sample areas, and is then applied to interpret all AVHRR imagery. Preliminary results indicate that there are predictable relationships between TM spectral response and the disturbance classes. Analysis of ellipse plots from a TM classification of the disturbed forested landscape indicates that the forest classes are separable in the red (0.63-0.69 micron) and near-infrared (0.76-0.90 micron) bands, providing evidence that the proportion of disturbance classes may be determined from AVHRR data.