Andrew Dyk

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+.

Pixel-Based Image Compositing for Large-Area Dense Time Series Applications and Science

Abstract Free and open access to the more than 40 years of data captured in the Landsat archive, combined with improvements in standardized image products and increasing computer processing and storage capabilities, have enabled the production of large-area, cloud-free, surface reflectance pixel-based image composites. Best-available-pixel (BAP) composites represent a new paradigm in remote sensing that is no longer reliant on scene-based analysis. A time series of these BAP image composites affords novel opportunities to generate information products characterizing land cover, land cover change, and forest structural attributes in a manner that is dynamic, transparent, systematic, repeatable, and spatially exhaustive. Herein, we articulate the information needs associated with forest ecosystem science and monitoring in a Canadian context, and indicate how these new image compositing approaches and subsequent derived products can enable us to address these needs. We highlight some of the issues and opportunities associated with an image compositing approach and demonstrate annual composite products at a national-scale for a single year, with more detailed analyses for two prototype areas using 15 years of Landsat data. Recommendations concerning how to best link compositing decisions to the desired use of the composite (and the information need) are presented, along with future research directions. Résumé L’accès libre et gratuit à plus de 40 ans de données dans l’archive Landsat combiné à l’amélioration des produits d’imagerie standardisés et l’augmentation des capacités de traitement et de stockage informatiques ont permis la production d’images composites basées sur les pixels de réflectance de surface de grande superficie sans nuages. Les composites du « meilleur pixel disponible » (best-available-pixel; BAP) représentent un nouveau paradigme en matière de télédétection qui ne dépend plus de l’analyse par scène. Une série chronologique de ces images composites BAP offre de nouvelles occasions de générer des produits d’information qui caractérisent la couverture terrestre, le changement de la couverture terrestre et les attributs structurels de la forêt d’une manière dynamique, transparente, systématique, répétable et spatialement exhaustive. Ici, nous articulons les besoins d’information liés à la science et à la surveillance des écosystèmes forestiers dans un contexte canadien, et nous indiquons comment ces nouvelles approches de composition d’image et les produits qui en découlent peuvent nous permettre de répondre à ces besoins. Nous soulignons quelques-uns des problèmes et des possibilités associés à une approche de composition d’image et nous démontrons des produits composites annuels à l’échelle nationale pour une année, avec des analyses plus détaillées pour deux zones prototypes utilisant 15 ans de données Landsat. Des recommandations concernant la meilleure façon de lier des décisions de composition d’images à l’utilisation souhaitée du composite (et le besoin d’information) ainsi que les orientations futures de la recherche sont présentées.

Comparison of methods for estimation of Kyoto Protocol products of forests from multitemporal Landsat

The Kyoto Protocol requires nations to report on their reforestation, afforestation, and deforestation (RAD). Using 1990 as a baseline, nations are also required to monitor changes in carbon stocks leading up to the reporting period 2008 to 2012. A study was conducted using three dates of boreal summer Landsat 5 imagery to estimate above-ground carbon for a forested test site near Hinton, Alberta. The carbon estimates were compared with those derived from Canadas national forest inventory. The remote sensing estimates for areas that had not changed were consistent year to year within 3%. The experiment was repeated with the addition of leaf-on and leaf-off image pairs and Landsat-7 imagery. A comparison was made of the classification accuracies achieved for forest classes with single date and paired leaf-on and leaf-off image sets. Spatial properties were incorporated into the image analysis by first creating a multitemporal segmentation. This paper reports on the classification methods used, compares the classification accuracies achieved, and gives recommendations for the creation of Kyoto Protocol products for temperate forests derived from remotely sensed imagery.

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.

Comparison of Aviris and AISA Airborne Hyperspectral Sensing for Above-Ground Forest Carbon Mapping

Monitoring of the 418 million ha of forests in Canada is needed to ensure the sustainable development of these forests. Hyperspectral sensing can provide mapping of forest species, forest health, and above-ground biomass. Airborne two-meter AISA hyperspectral and LIDAR data were acquired by the University of Victoria (UVic) over the Greater Victoria Watershed District (GVWD) test site and compared to NASAs AVIRIS data that had been acquired in the summer of 2002 at 4 m spatial resolution. Tree heights derived from LIDAR data, and allometric equations were used to provide independent ground estimates of biomass. Between-sensor calibration calibrated the AISA data to the same basis as the AVIRIS data. The calibrated reflectance data were used to generate forest species classifications, and biomass estimates for the test site. Average classification accuracies exceeded 89% in mapping major forest species. These products were used to create a map of above-ground carbon for the forested portion of the GVWD test site.

Forest information from hyperspectral sensing

Remote sensing of forests is a major application for Canadas new hyperspectral satellite (HERO). Hyperspectral remote sensing can provide forest information products for applications in forest inventory, forest chemistry, and for some Kyoto Protocol information products. Through several projects, we have demonstrated that airborne and satellite hyperspectral sensing can provide accurate maps of west coast forest species. High correlations have been demonstrated between ground measurements of foliar nitrogen and estimates derived from hyperspectral sensing. Data for these experiments have included airborne AVIRIS and CASI, and satellite Hyperion data. To achieve operational accuracies of 80% or better for forest species classification, it is essential that the hyperspectral data are well calibrated. Also, sensor artifacts, such as smile, keystone, and striping, must be corrected. Methods have also been developed for mapping forest health status bioindicators: leaf chlorophyll, nitrogen, and water content. Methods have also been developed for determining canopy fractions to levels as low as 20%. If HERO generates a 1% improvement in forest product sales, this would amount to a benefit

Monitoring forests with Hyperion and ALI

700 million annually in Canada alone. This paper will report on the current state of hyperspectral sensing of forests, and present the hyperspectral sensor specifications needed for forest applications.

Combining Hyperspectral Remote Sensing and Physical Modeling for Applications in Land Ecosystems

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.

EVEOSD forest information products from AVIRIS and Hyperion

Land ecosystems, in particular forest ecosystems, are under increasing pressure from environmental changes such as population growth, global warming, wildfires, forest insects, and diseases. Data from hyperspectral sensors can be used to map forest species and determine biophysical and biochemical properties. Modeling plays an important role in accurate determination of ecosystem properties. Radiative transfer models are used to understand how radiation interacts with the atmosphere and the Earths terrestrial surface and to correct observed radiances to surface reflectance. Canopy models are used to infer through inversion quantitative information from hyperspectral data on canopy structure and foliage biochemistry. This article presents an overview on combining hyperspectral sensing with canopy radiative transfer models to derive ecosystem information products.

Geometric correction and validation of Hyperion and ALI data for EVEOSD

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.