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Examines the link between leaf reflectance and transmittance and canopy hyperspectral data for chlorophyll content estimation in remote sensing images of the Algoma Region. Estimates of leaf pigment by hyperspectral remote sensing of closed forest canopies; Pigment estimation by model inversion using red-edge indices; Data on reflectance spectra.

Objective measures of forest ecosystem condition are needed to gauge the effects of management activities and natural phenomena on sustainability. The Bioindicators of Forest Condition Project seeks to develop a Forest Condition Rating (FCR) system using a physiological, remote sensing approach. In particular, the goal of the project is to test whether hyperspectral remote sensing may be used to infer stand-level information about pigment concentration, chlorophyll fluorescence, and other physiological features of condition. The project spans a four-year period of intensive sampling in tolerant hardwood forests in Ontario using the Compact Airborne Spectrographic Imager (CASI). For each airborne campaign, concurrent ground-based sampling for leaf physiological features was performed. Controlled laboratory and greenhouse studies were also conducted to derive relationships between leaf-based spectral measurements and physiology in the presence of environmental stresses. The project has identified several promising bioindicators of strain that are discernible from hyperspectral images and related to ground-based physiology. The most promising remote indicator for senti-operational testing is estimation of chlorophyll content, which can be used to classify maple stands on a five-stage scale of health. Chlorophyll fluorescence has also been discerned from spectral signatures, but our studies indicate it may be confounded by chlorophyll content. The intent here is to update the forestry community on progress made, insights gained, and the practical implications of the research. [ABSTRACT FROM AUTHOR]

Abstract: Automated tree species classification using high density airborne light detection and ranging (LiDAR) data will support more precise forest inventory but further research is required to improve the associated methods. Most existing methods rely on geometric and vertical distribution features, which often do not accurately represent the internal foliage and branch patterns of an individual tree. Our study objective was to develop novel algorithms to characterize internal structures of an individual tree crown and to test their effectiveness for use in classifying tree species. We derived several LiDAR features to describe the three-dimensional texture, foliage clustering degree relative to tree envelop, foliage clustering scale, and gap distribution of an individual tree in both horizontal and vertical directions. Features were selected using a genetic algorithm and then tree species were classified using linear discriminant analysis based on the selected features. The four species, sugar maple (Acer saccharum Marsh.), trembling aspen (Populus tremuloides Michx.), jack pine (Pinus banksiana Lamb.) and eastern white pine (Pinus strobus L.), were classified with an overall accuracy of 77.5% and a Kappa coefficient of 0.7. The results demonstrate the significance of the derived structural features as aids to classify tree species. Our investigation also showed a positive linear correlation (R 2 =0.88) between LiDAR point density and species classification accuracy. [Copyright &y& Elsevier]

Abstract: Hyperspectral remote sensing has great potential for accurate retrieval of forest biochemical parameters. In this paper, a hyperspectral remote sensing algorithm is developed to retrieve total leaf chlorophyll content for both open spruce and closed forests, and tested for open forest canopies. Ten black spruce (Picea mariana (Mill.)) stands near Sudbury, Ontario, Canada, were selected as study sites, where extensive field and laboratory measurements were carried out to collect forest structural parameters, needle and forest background optical properties, and needle biophysical parameters and biochemical contents chlorophyll a and b. Airborne hyperspectral remote sensing imagery was acquired, within one week of ground measurements, by the Compact Airborne Spectrographic Imager (CASI) in a hyperspectral mode, with 72 bands and half bandwidth 4.25–4.36 nm in the visible and near-infrared region and a 2 m spatial resolution. The geometrical–optical model 4-Scale and the modified leaf optical model PROSPECT were combined to estimate leaf chlorophyll content from the CASI imagery. Forest canopy reflectance was first estimated with the measured leaf reflectance and transmittance spectra, forest background reflectance, CASI acquisition parameters, and a set of stand parameters as inputs to 4-Scale. The estimated canopy reflectance agrees well with the CASI measured reflectance in the chlorophyll absorption sensitive regions, with discrepancies of 0.06%–1.07% and 0.36%–1.63%, respectively, in the average reflectances of the red and red-edge region. A look-up-table approach was developed to provide the probabilities of viewing the sunlit foliage and background, and to determine a spectral multiple scattering factor as functions of leaf area index, view zenith angle, and solar zenith angle. With the look-up tables, the 4-Scale model was inverted to estimate leaf reflectance spectra from hyperspectral remote sensing imagery. Good agreements were obtained between the inverted and measured leaf reflectance spectra across the visible and near-infrared region, with R 2 =0.89 to R 2 =0.97 and discrepancies of 0.02%–3.63% and 0.24%–7.88% in the average red and red-edge reflectances, respectively. Leaf chlorophyll content was estimated from the retrieved leaf reflectance spectra using the modified PROSPECT inversion model, with R 2 =0.47, RMSE=4.34 μg/cm2, and jackknifed RMSE of 5.69 μg/cm2 for needle chlorophyll content ranging from 24.9 μg/cm2 to 37.6 μg/cm2. The estimates were also assessed at leaf and canopy scales using chlorophyll spectral indices TCARI/OSAVI and MTCI. An empirical relationship of simple ratio derived from the CASI imagery to the ground-measured leaf area index was developed (R 2 =0.88) to map leaf area index. Canopy chlorophyll content per unit ground surface area was then estimated, based on the spatial distributions of leaf chlorophyll content per unit leaf area and the leaf area index. [Copyright &y& Elsevier]