K.O. Niemann

Processing Hyperion and ALI for forest classification

Hyperion (a hyperspectral sensor) and the Advanced Land Imager (ALI) (a multispectral sensor) are carried on the National Aeronautics and Space Administrations Earth Observing 1 (EO-1) satellite. The Evaluation and Validation of EO-1 for Sustainable Development (EVEOSD) is our project supporting the EO-1 mission. With 10% of the worlds forests and the second largest country by area in the world, Canada has a natural requirement for effective monitoring of its forests. Eight test sites have been selected for EVEOSD, with seven in Canada and one in the United States. Extensive fieldwork has been conducted at four of these sites. A comparison is made of forest classification results from Hyperion, ALI, and the Enhanced Thematic Mapper Plus (ETM+) of Landsat-7 for the Greater Victoria Watershed. The data have been radiometrically corrected and orthorectified. Feature selection and statistical transforms are used to reduce the Hyperion feature space from 198 channels to 11 features. Classes chosen for discrimination included Douglas-fir, hemlock, western redcedar, lodgepole pine, and red alder. Overall classification accuracies obtained for each sensor were Hyperion 90.0%, ALI 84.8%, and ETM+ 75.0%. Hyperspectral remote sensing provides significant advantages and greater accuracies over ETM+ for forest discrimination. The EO-1 sensors, Hyperion and ALI, provide data with excellent discrimination for Pacific Northwest forests in comparison to Landsat-7 ETM+.

EVEOSD forest information products from AVIRIS and Hyperion

Hyperspectral remote sensing can provide forest information products for applications in forest inventory, forest chemistry, and the Kyoto Protocol. One of the forest information products is the high accuracy forest species map produced by the classification of hyperspectral data. As part of the Evaluation and Validation of EO-1 for Sustainable Development (EVEOSD) Project, Hyperion data were acquired in 2001 and 2002. Corresponding AVIRIS data were also acquired. All hyperspectral data acquired were calibrated to reflectance and orthorectified. Experiments were conducted to compare the accuracies of the data sets for mapping forest species. Operational accuracies for forest species recognition were achieved with both AVIRIS and Hyperion. Bioindicators were also developed for mapping chlorophyll, nitrogen, and moisture content. These bioindicators were stratified by forest type. In the Greater Victoria Watershed District and the Hoquiam, Washington State test sites, foliar samples were collected, analyzed, and databases were built, which include foliar chemistry and plot parameters. These sites were used to develop the indicators and to validate their success. The forest information products produced under the EVEOSD project demonstrate some of the benefits to be achieved from an operational hyperspectral satellite.

A practical alternative for fusion of hyperspectral data with high resolution imagery

Data fusion techniques are currently used to merge low resolution multispectral with high resolution panchromatic data. Various authors have expended efforts on attempting to achieve a fusion where the spectral characteristics of one image are combined with the spatial attributes of the second producing a new, fused image. This philosophy has a number of inherent problems. The first is that of retaining the spectral fidelity of the original hyperspectral data in the fused data set. Techniques currently used to achieve the fusion usually do not retain the spectral characteristics accurately. Associated with this is the assumption that the intensity of distributions associated with the high spatial resolution (often panchromatic) image, usually collected in the visible portion of the spectrum, are consistent throughout the entire spectral range in question. Another problem is that of image size. To fuse a 20 meter MSS with a 1 meter panchromatic image represents an associated 400 fold increase in data base size. The approach in this extracts the spatial attributes of the high resolution data set (in our case the individual tree crowns). Once extracted, the spatial properties of the extracted attributes are assessed and translated into a spatial coverage consistent with the resolution of the spectral data for further analyses. The advantages of this approach are firstly that the data are used at the resolutions that they were originally collected. Secondly, it avoids the creation of large, unmanageable images.

Shoreline feature extraction from remotely-sensed imagery

Different methods of delineation were used to extract shoreline features from images with different spatial and spectral resolutions acquired by airborne and spaceborne sensors. The exact location of the shoreline is difficult to obtain from the images and therefore the definition of shoreline was based upon the geomorphological and oceanographic characteristics of the area of study. Extracted shoreline vectors were then compared to existing official shoreline vectors to assess their accuracy and software efficiency. It is expected that the generation of shoreline vectors with a high accuracy will greatly improve the time of work and number of specialised personnel, and allow for the integration of the resulting shoreline vectors into cartographic databases.

Remote sensing of relative moisture status in old growth Douglas fir

One of the limiting factors affecting the growth of trees is the presence or absence of sufficient moisture. In locations where seasonal moisture deficits are frequent this can lead to substantial variations in the magnitude of tree growth. In these situations the detection of variations of canopy moisture through remote sensing techniques can improve forest mapping and management. This Letter reports on a study examining the potential of utilizing optical remotely sensed data to detect variations in canopy reflectance at a number of growth-limited sites located on southern Vancouver Island, Canada. Topographic variations coupled with rapidly drained soils and precipitation induced moisture deficits promote spatial variations in growth rates of the dominant tree species, coastal Douglas fir. Optical remotely sensed data were collected using the AVIRIS sensor and comparison of annual growth rates with reflectance data made.

Hyperspectral remote sensing of conifer chemistry and moisture

The chemical and moisture composition of conifer foliage in the Greater Victoria Watershed District (GVWD), Vancouver Island, Canada, was explored using hyperspectral remote sensing data. Imagery acquired from the airborne sensor Advanced Visible/Infrared Imaging Spectrometer (AVIRIS) were evaluated along with sampled foliar chemical and moisture measurements to provide insight into ecological processes occurring within the watershed. Concentrations of nitrogen, total chlorophyll and moisture were used to provide an analysis of the forest canopy, comprised of Coastal Douglas-fir and Western Redcedar. The AVIRIS data were processed to correct atmospheric and geometric distortion. The AVIRIS data were used to investigate the relationship between the hyperspectral imagery and the sampled chemical data. A total of 45 plots in the GVWD were samples from a helicopter. These samples provided both organic and inorganic analysis of the forest canopy. A Partial Least Squares regression was used to analyze the relationship between the data sets in order to extract chemical constituents in the forest canopy. Results indicate that the regression equation explains 81%, 79% and 70% of the variation in nitrogen, total chlorophyll and moisture, respectively. An analysis of the chemical characteristics of the canopy can provide insight into factors controlling growth such as nutrient levels and water deficiencies at the foliar level.

Pixel unmixing for hyperspectral measurement of foliar chemistry in Pacific Northwest coastal forests

Studies for the detection and mapping of variation in foliar chemistry have concentrated on the correlation of wavelength specific reflection and concentrations of foliar pigments and nutrients either through ground-based radiometric measurements or airborne data. The advantage of the former is that the scene components can be effectively controlled so that a relatively simple reflectance model can be constructed and end members extracted. In the case of using airborne data, however, the influence of scene components that mask, or subdue, the reflectance-chemical signal, may dominate. This has led to the development of methodologies for which the various scene components can readily be isolated and accounted. Pixel unmixing to isolate canopy reflectance from other scene components has long been used in the assessment of foliar characteristics and processes. Unfortunately the traditional methods of unmixing rely on distinct spectral signatures from the various scene components. This paper details a method developed to isolate the scene components when they are not spectrally dissimilar.

Multi-temporal evaluation with CHRIS of coastal forests

— In 2001, ESA launched the Project for On-Board Autonomy (PROBA) small satellite as a technology demonstrator. One of the instruments on board was the Compact High Resolution Imaging Spectrometer (CHRIS), which acquires imagery from 415 nm to 1050 nm. In Mode 4, CHRIS acquires 18 bands at the full spatial resolution of 18 m. CHRIS acquires 5 hyperspectral images over an angular range of 55 degrees along track. In 2004, five clear CHRIS image sets were acquired throughout the summer over the Greater Victoria Watershed District (GVWD). One image set was acquired within one day of a Hyperion acquisition. These CHRIS acquisitions were undertaken for our Evaluation and Validation of CHRIS for National Forests Project. The many new challenges of working with multi-angle and multi-temporal imagery such as orthorectification and atmospheric correction related to producing forest products are discussed in this paper. This paper reports on the analysis of the CHRIS data for creating forest data products for species recognition for sustainable forest management. Earlier experiments with airborne, 1m multi-angle data (FIFEDOM sensor from MDA) over the GVWD demonstrated that the following forest information could be obtained: individual tree location, stand density, tree height, crown size, crown shape, and gap probability.

AVIRIS imagery for forest attribute information: anisotropic effects and limitations in multitemporal data

Hyperspectral data can provide valuable forest information, such as forest species, stand density, biochemistry, and forest structure. It is also well known that optical radiometric properties of forest objects vary with the angles of illumination and view angle. The anisotropy of the forest canopy can restrict the determination of the forest parameters of interest. In high relief areas such as Vancouver Island, Canada the impact of illumination effects presents numerous additional complexities. The authors present the results of a study undertaken to assess forest attribute determination from AVIRIS data acquired over the Greater Victoria Watershed District Test Site (GVWD) on Vancouver Island B.C., Canada on two dates. A comparison of data from a number of test plots is carried out using AVIRIS imagery acquired in 1993 and 1994. Inventory information (such as stem density, species distribution, biomass, etc.) for these plots is known as a result of field sampling and data fusion of the AVIRIS Hyperspectral data with high spatial resolution (1 m) MEIS data and AirSAR data For GVWD, the dominant forest species is Douglas fir. Similarly aged stands on different slopes and at various aspects provide a sampling of view angles. Acquisitions at different times of the day sample the variation in illumination angles. AVIRIS reflectances from 1993 and 1994 are used to determine the limitations imposed by a range of off-nadir angles and BRDF effects.

Development of hyperspectral biochemistry through the use of statistical modeling and spectral unmixing

Prior attempts at mapping the biochemical characteristics of the forest canopy have met with mixed results. The use of simple regression or stepwise multiple regression has resulted in ambiguous or inconsistent correlations. The current project attempted to integrate two promising techniques: partial least squares (PLS) regression and spectral mixture analysis (SMA). The analysis demonstrate results that are consistent with other published results using the PLS approach. An incremental increase in the explanatory power of the model (to a maximum r/sup 2/ of 0.877 for foliar nitrogen) was observed with the inclusion of the SMA results.