A.S. Bhogal

Calibration of forest chemistry for hyperspectral analysis

A primary advantage of hyperspectral sensors is the ability to provide measurements of canopy chemistry. Canopy chemistry can be used to estimate new and old foliage, detect damage, identify trees under stress, and map chemical distributions in the forests. We have begun a new EO-1 project, Evaluation and Validation of EO-1 for Sustainable Development of forests (EVEOSD). NASAs EO-1 satellite was successfully launched on November 21, 2000. In preparation for airborne and spaceborne data collection and calibration, we collected in September 2000 foliar canopy and ground cover chemistry samples from 54 plots distributed across the Greater Victoria Watershed (GVWD) test site. Treetop samples were collected from helicopters. Differential GPS was used to provide sample positioning to within 1 m. The foliar samples were divided into new and old foliage. Organic and inorganic chemistry analyses were done. Spectral calibration samples were collected over ground targets, over stacks of foliar samples, and over ground vegetation. Landsat-7 and Radarsat data were collected at the same time. The chemistry samples were placed into a database and integrated with GIS files of topography and forest cover. We obtained 1 m aerial orthophotography that allowed us to investigate the spectral components making up the Landsat-7 and EO-1 pixels.

Monitoring forests with Hyperion and ALI

Hyperion, a hyperspectral sensor, and the Advanced Land Imager (ALI) are carried on NASAs 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 US. Extensive fieldwork has been conducted at four of these sites. A comparison is made of forest classification results from Hyperion, ALI, and the ETM+ of Landsat-7 for the Greater Victoria Watershed. The data have been radiometrically corrected and ortho-rectified. Feature selection and statistical transforms are used to reduce the Hyperion feature space from 220 channels to 12 features. Classes chosen for discrimination included Douglas Fir, Hemlock, Western Red Cedar, Lodgepole Pine and Red Alder. Overall classification accuracies obtained for each sensor were: Hyperion 92.9%, 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.

Geometric correction and validation of Hyperion and ALI data for EVEOSD

Precise geometric correction of EO-1s Hyperion data is essential to link ground spectral data and satellite hyperspectral data. Two scenes have been selected from sites of the EVEOSD (Evaluation and Validation of EO-1 for Sustainable Development of Forests) project. One site is the Greater Victoria Watershed District (GVWD) located on south Vancouver Island, BC and the other is Hoquiam located in southwestern Washington State. Ground Control Point (GCP) collection has been performed using a feature fitting method in which high accuracy, orthorectified photo-derived polygons of features are used for tie-down. For example lakes are adjusted to match the same feature obvious in the hyperspectral imagery. This technique allows for easier estimation of a GCPs precise fit to the imagery. A third (11) of the GCPs were identified as check points to validate the geometric models. GCPs were collected independently from both the VNIR and SWIR arrays of the Hyperion sensor to determine the adjustment factor required to remove the displacement and skew between these arrays. The adjustment can then be applied to GCPs collected from one array to make a compatible geometric correction model for both arrays. The polynomial and rational function correction methods have been applied to both scenes with various orders applied to each function. The effect of terrain distortion removal is evaluated in using the rational function method. Hyperion data can be geocorrected with surprising accuracy. For example, we obtained 10 m RMS on check points with the rational function. With a second order polynomial we achieved 13 m RMS without terrain correction. The accuracy of this latter result is due to the small swath width of the sensor. Applying terrain correction does improve the accuracy of geometric correction in areas with high relief. A similar procedure was applied to EO-1s ALI sensor and this paper compares the results for Hyperion and ALI geometric fidelity.

Determination of above ground carbon in Canada's forests-a multi-source approach

Canada is a signatory to the Kyoto Protocol and must report on reforestation, afforestation and deforestation activities since 1990. Reporting commitments also include a baseline estimate of forest carbon stocks in 1990 and the monitoring of changes in carbon stocks leading up to the reporting period 2008 to 2012. Canada has 10% of the worlds forests (418 million hectares), which account for a significant amount of stored carbon. The determination of above-ground carbon stocks in the forest can be based on several sources: remote sensing, models of vegetation growth, book-keeping carbon models, and traditional forest inventories. Estimating above-ground carbon with remote sensing requires the fusion and integration of remote sensing data with topographic, forest cover and other geospatial information. Multi-temporal LANDSAT TM imagery was used in conjunction with GIS data to compute above-ground biomass from which the carbon content is determined. In addition to biomass, other key factors, which play a role in the determination of carbon stocks, include species and age distribution, forest structure, and climate variables. The paper reports on remote sensing experiments to determine the above-ground carbon stocks for a test site near Hinton, Alberta, Canada. It is expected that this approach will be useful in supporting Canadas reporting commitments on the sustainability its forest resources.

Case-based reasoning and software agents for intelligent forest information management

To perform forest information management, SEIDAM integrates forest cover descriptions, topographic maps and remote sensing imagery. SEIDAM relies on an online robotic data storage device, image and GIS metadata databases, software agents and a case-based reasoning system to deliver information to decision makers in a timely fashion. The image and GIS metadata databases contain information about the sources of data, where the data are stored, where they have been delivered and the processing they have undergone. The software agents perform the actual processing by running image analysis, GIS, database and other software to accomplish specific tasks. The case-based reasoning system relies on the software agents, past experience from domain experts and information from the metadata databases to determine what processing is required to deliver products satisfying user goals. This paper describes the intelligent inventory update function in SEIDAM and its AI methodology.

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.

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.

Planning and Learning in a Natural Resource Information System

The paper presents PALERMO — a planner used to answer queries in the SEIDAM information system for forestry. The information system is characterized by the large complexity of software and data sets involved. PALERMO uses previously answered queries and several planning techniques to put together plans that, when executed, produce products by calling the appropriate systems (GIS, image analysis, database, models) and ensures the proper flow on information between them. Experimental investigation of several planning techniques indicates that analogical planning cuts down the search involved in planning without experiencing the utility problem.

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.

Automated methods for atmospheric correction and fusion of multispectral satellite data for national monitoring

The Earth Observation for Sustainable Development of Canadas forests (EOSD) project monitors Canadas forests from space. Canada contains ten-percent of the worlds forests. Initial EOSD products are land cover, forest change, forest biomass, and automated methods. There are more than 500 LANDSAT TM or ETM+ scenes required for a single coverage of Canadas forests. Multi-temporal analysis using satellite data requires automation for conversion of these data to common units of exoatmospheric radiance or ground reflectance. During the next ten years the EOSD project will use a variety of Landsat optical and Radarsat sensors. A diverse set of ancillary and satellite data formats exist which require the development of adaptable data ingest and processing streams. Legacy LANDSAT TM and ETM+ data are available in a number of different formats from several national and US suppliers. In this paper, we present an automated system for managing processing streams for calibration and atmospheric correction of LANDSAT TM and ETM+ data to create data sets ready to analyze for EOSD products. Using known forest attributes from GIS data and field measurements, we validated our results of studies undertaken to assess spectral signal variability using both at-sensor radiance and ground reflectance for LANDSAT TM and ETM+ for a test site on Vancouver Island, BC. We present a strategy for correcting and fusing multi-source and multitemporal satellite data for meeting EOSD requirements.