J.J. van der Sanden

Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements

Leaf area index (LAI) is one of the surface parameters that has importance in climate, weather, and ecological studies, and has been routinely estimated from remote sensing measurements. Canada-wide LAI maps are now being produced using cloud-free Advanced Very High-Resolution Radiometer (AVHRR) imagery every 10 days at 1-km resolution. The archive of these products began in 1993. LAI maps at the same resolution are also being produced with images from the SPOT VEGETATION sensor. To improve the LAI algorithms and validate these products, a group of Canadian scientists acquired LAI measurements during the summer of 1998 in deciduous, conifer, and mixed forests, and in cropland. Common measurement standards using the commercial Tracing Radiation and Architecture of Canopies (TRAC) and LAI-2000 instruments were followed. Eight Landsat Thematic Mapper (TM) scenes at 30-m resolution were used to locate ground sites and to facilitate spatial scaling to 1-km pixels. In this paper, examples of Canada-wide LAI maps are presented after an assessment of their accuracy using ground measurements and the eight Landsat scenes. Methodologies for scaling from high- to coarse-resolution images that consider surface heterogeneity in terms of mixed cover types are evaluated and discussed. Using Landsat LAI images as the standard, it is shown that the accuracy of LAI values of individual AVHRR and VEGETATION pixels was in the range of 50–75%. Random and bias errors were both considerable. Bias was mostly caused by uncertainties in atmospheric correction of the Landsat images, but surface heterogeneity in terms of mixed cover types were also found to cause bias in AVHRR and SPOT VEGETATION LAI calculations. Random errors come from many sources, but pixels with mixed cover types are the main cause of random errors. As radiative signals from different vegetation types were quite different at the same LAI, accurate information about subpixel mixture of the various cover types is identified as the key to improving the accuracy of LAI estimates. D 2002 Elsevier Science Inc. All rights reserved.

Water resource applications with RADARSAT-2 – a preview

Abstract Fresh water is arguably the most vital resource for many aspects of a healthy and stable environment. Monitoring the extent of surface water enables resource managers to detect perturbations and long term trends in water availability, and set consumption guidelines accordingly. Potential end-users of water-related observations are numerous and reflect society as a whole. They encompass scientists and managers at all levels of government, aboriginal groups, water/power utility managers, farmers, planners, engineers, hydrologists, medical researchers, climate scientists, recreation enthusiasts, public school to post-graduate students, many special interest groups and the general public. Water data and analyses generate information products that benefit water resources planning and management, engineering design, plant operations, navigation activities, health research, water quality assessments and ecosystem management. As well, they serve as inputs for flood and drought warnings and weather and climate prediction models. Radar data in general, and RADARSAT in particular, are very good for detecting open surface water and have been used operationally for flood monitoring in many countries. Significant radar data archives now exist to analyse seasonal, annual and decadal trends, in order to attain a better understanding of the freshwater cycle. Radar data are also useful for wetland classification and soil moisture estimation. With the increasing pressure on water resources, both from a quality as well as a quantity perspective, the need will continue to increase for reliable information. RADARSAT-2 has several innovations that will enhance the ability to provide useful information about water resources. This paper provides an overview of the use of radar in general, and RADARSAT-2 in particular, for the generation of information products useful to water resource managers.

Anticipated applications potential of RADARSAT-2 data

In this paper we assess how RADARSAT-2s technical enhancements in terms of polarization, spatial resolution, look direction, and orbit control will impact the potential utility of its data products for 32 applications in the fields of agriculture, cartography, disaster management, forestry, geology, hydrology, oceans, and sea and land ice. Our assessment relies on bibliographic sources and, in particular, case studies drawn from ongoing applications development work at the Canada Centre for Remote Sensing and the Canadian Ice Service. The applications potential of RADARSAT-2 data compared with that of RADARSAT-1 data is anticipated to improve in a major, moderate, and minor fashion for 3, 18, and 10 of the identified applications, respectively. For one of the applications considered, the increase in potential of RADARSAT-2 vis-à-vis RADARSAT-1 cannot be assessed because this application relies completely on RADARSAT-2s new full polarimetric capability.

A semi-automated tool for surface water mapping with RADARSAT-1

Fresh water is arguably the most vital resource for all aspects of a healthy and stable environment and society. Monitoring the extent of surface water enables resource managers to detect perturbations and long-term trends in water availability and set consumption guidelines accordingly. Radar in general, and RADARSAT-1 in particular, is very good at detecting open surface water and has been used operationally for flood monitoring in many countries. Significant radar data archives now exist to analyse seasonal, annual, and decadal trends in surface water availability. A software tool (Forest non-Forest Class Extraction or FnFCE), based on software initially designed for deforestation classification with RADARSAT-1 data and later modified to include a flood mapping capability, has been adapted for this surface water application. Three test sites have been selected to demonstrate this surface water mapping tool, including Fort Mackay, Alberta, a tar sands area with water used for industrial purposes; Cache Lake in Tuktut Nogait National Park, Northwest Territories, where the many park lakes are currently poorly monitored and understood; and Old Crow Flats, Yukon Territory, a large area of ponds and lakes thought to be at risk from permafrost degradation and important to caribou migration and oil and gas development. This paper describes the software tool and demonstrates preliminary results of monitoring seasonal and annual changes in surface water distribution using RADARSAT-1 imagery for the three test sites. An overview of the successes and limitations of the approach is given, as well as a summary of the current state of evolution of the method and the envisioned software development plan.

Potential of airborne radar to support the assessment of land cover in a tropical rain forest environment

The potential of airborne radar systems as tools for collecting information in support of the assessment of tropical primary forests and derived cover types was examined. SAR systems operating with high spatial resolutions and different wavelengths (i.e., X-, C-, L- and P-band) acquired data in Guyana and Colombia. Three fundamentally different information sources from the radar return signal were considered in the study: its strength or backscatter, polarization and phase, and spatial variability or texture. Radiometric, polarimetric, and textural attributes were computed from predefined image regions selected to represent five types of primary forest, selectively logged forest, secondary forest, and a mixture of nonforest cover types. Texture was found to be the most important source of information in high resolution X- and C-band images. Textural attributes computed per region made modest to good bases for automated classifications of the land cover types studied. Primary forests and logged-over forests were found to display particularly distinctive textural patterns. Backscatter values computed per region from L- and P-band radar images also made modest to good classification bases. Backscatter measurements in either a single L- or P-band channel enabled accurate classification of nonforest cover types. Reliable identification of secondary forests and logged-over forests generally asked for measurements in a minimum of two C-, L-, and/or P-band radar channels. Similarly, reliable assessment of primary forest types required observations in a minimum of three C-, L-, and/or P-band radar channels.

Review of relationships between grey-tone co-occurrence, semivariance, and autocorrelation based image texture analysis approaches

In the present paper we review relationships between commonly used statistical approaches to analysis of image texture. The approaches considered characterize image texture by means of the statistics of grey-tone co-occurrence contrast, grey-tone co-occurrence correlation, semivariance, and autocorrelation. In the literature, the relationships between these textural measures are rarely discussed and sometimes overlooked. Grey-tone co-occurrence contrast and semivariance are shown to represent identical statistics. The expectations for the statistic of grey-tone co-occurrence correlation and the coefficient of autocorrelation are demonstrated to agree. Grey-tone variance is shown to link the statistics of grey-tone co-occurrence contrast and grey-tone co-occurrence correlation.

Identification of polarimetric and nonpolarimetric C-band SAR parameters for application in the monitoring of lake ice freeze-up

Polarimetric and nonpolarimetric C-band SAR parameters were assessed for their potential to discriminate open water versus lake ice. Analysis was done for incidence angles between 18° and 50°. Open water was sampled and modelled at wind speeds of 2–24 m/s, from various directions. Lake ice samples were for new ice, less than two months old. Low-quality data, caused by low-wind slicks and ice slicks, were identified using polarimetric data. Nonparametric testing was used to initially identify parameters with discrimination potential. These parameters were then evaluated throughout the incidence angle range; bounds and thresholds were statistically estimated to provide robust discrimination potential. Classified images were both qualitatively and quantitatively assessed. For single-polarized data, the VV polarization is recommended over HH. Cross-polarized data were limited by sensor noise floors, and are not recommended. Dual co-polarized data, using the co-polarized ratio, were useful discriminators at incidence angles > 31°. The co-polarized ratio was insensitive to wind speed, and it was also the best parameter for polarimetric data. Anisotropy was a robust parameter at incidence angles <28°. Accuracies ranged from 76% to 99%, if wind speed thresholds were observed. The identification of robust discriminators enhances monitoring of lake ice freeze-up, an operational concern for departments within the Government of Canada.

Using MERIS to Assess Insect Defoliation in Canadian Aspen Forests

This paper describes the prelim inary results of a study into the potential of images from the Medium Resolution Imaging Spectrometer (MERlS) for the mapping and monitoring of aspen defoliation in Canada. Information relating to aspen defoliation supports environmental and sustainable development initiatives. More specifically, the information needs relate to the location, extent and severity of defoliation events. Our results indicate that with the help of MERIS images, the assessment of location and extent of defoliation is straightforward but the assessment of defoliation severity is challenging. MERIS derived defoliation information products offer advantages over conventional defoliation maps that are produced on the basis of aerial surveys.

RADARSAT-2; are its technical capabilities expected to provide potential for remote sensing applications ?

In this paper, we preview and demonstrate how the technical improvements included in RADARSAT-2 will impact the systems potential utility for 32 applications in the fields of agriculture, cartography, disaster management, forestry, geology, hydrology, oceans, and sea and land ice.

The AIRSAR-93 and SAREX-92 campaigns in Guyana and Colombia

At tropical rain forest study sites in Guyana (Mabura Hill) and Colombia (Araracuara and San Jose del Guaviare) a systematic research program is carried out using satellite remote sensing data and radar data acquired during airborne campaigns. In April 1992 the SAREX-92 airborne radar remote sensing campaign of ESA and in May/June 1993 the AIRSAR South American Deployment of NASA were successfully carried out. Results of SAREX-92 and some first results of the AIRSAR-93 campaigns are discussed.<<ETX>>