HYPERSPECTRAL MAPPING OF RE-VEGETATED AREAS AFFECTED BY SMELTING. OPERATIONS IN SUDBURY, CANADA. H. Peter White and Abdelgadir ...
MONITORING ENVIRONMENTAL REMEDIATION: HYPERSPECTRAL MAPPING OF RE-VEGETATED AREAS AFFECTED BY SMELTING OPERATIONS IN SUDBURY, CANADA H. Peter White and Abdelgadir Abuelgasim Canada Centre for Remote Sensing, Natural Resources Canada, Ottawa, Ontario, Canada ABSTRACT Space borne imaging spectrometry provides spectral information which can be used to extract parameters of interest in monitoring regional environmental remediation initiatives. The environment surrounding the City of Greater Sudbury in Ontario Canada has been affected by deposition of sulphur-dioxide and metals from a century of smelting operations. Impacts have been noted up to 100 km downwind of the smelter sites, with areas of barren rock replacing natural forested cover. Regional remediation measures have been undertaken in the area since the late1970s, involving a reduction of smelter emissions and a revegetation initiative. Space borne hyperspectral acquisitions from EO-1 Hyperion are evaluated for providing regional information on land cover change and relative vegetation health in support of remediation monitoring measures. Index Terms— remote sensing, hyperspectral, mine-site remediation
vegetation,
1. INTRODUCTION Awareness of the environmental and human health effects of mining and smelting has led to increased public interest in reducing these impacts. Technological advances which support long term environmental performance are being pursued to meet both public expectations and regulatory scrutiny. Hyperspectral remote sensing has the potential to expand an understanding of these environmental impacts and to help develop appropriate strategies to monitor the long term success of remediation efforts. This paper evaluates the potential application of hyperspectral remote sensing as a tool to monitor the effectiveness of regional re-vegetation efforts in areas affected by mineral smelting. The targeted approach is to first assess the imaging spectrometry data and correct for sensor system artefacts, and then to examine the surrounding vegetation state. This is done through deriving canopy water content (or equivalent water thickness, EWT) [1] and leaf areas index (LAI) through inversion of a radiative transfer model. EWT is used to constrain the maximum reflectance of the vegetation using a leaf level
radiative transfer model, and the remotely observed spectral bidirectional reflectance factors (BRF) are then used in the inverse FLAIR Model (Four-scaLe Model for AnIsotropic Reflectance) [2][3] to derive LAI. This demonstration study is done within the context of the Sudbury Regional Land Reclamation Program, using data collected as part of a 2003 field campaign [4]. 2. REGIONAL MINING IMPACTS AT SUDBURY The Sudbury region lies on the southern border of the Canadian Shield in a boreal forest zone. Known for rich mineral deposits, this region has supported the local mining industry for over 100 years. This is due to a Precambrian bedrock rich in iron-sulphides containing one of the largest known deposits of nickel-copper-platinum group elements. Mining activities began in the 1880’s with the use of open-bed roast yards, producing significant sulphur-dioxide emissions that left localized areas barren. By the 1930’s, metal smelting became dominant and emissions, now distributed through smokestacks, contained both sulphur dioxide copper and nickel particles. This long-term contamination has impacted over 5500 km2 [5]. By 1970 the surroundings, originally characterized by stands of pine mixed with tolerant hardwood species, had notably diminished, and in some areas become completely barren. Emissions reductions with improved ore processing technologies introduced in the 1970’s created the potential to restore these areas [6]. In a remediation initiative, barren regions were limed to reduce soil acidity and immobilize metal contaminants. This was followed by fertilizer applications, planting of grasses, and a final planting of trees and shrubs, primarily species of pine (Pinus resinosa, P. strobus, P. banksinana). Natural re-colonization has also occurred as conditions improved, primarily white birch (Betula papyrifera) and trembling aspen (Populus tremuloides). The remediation program continues, however the long-term, dynamic nature of the re-vegetation process makes the availability of cost-effective and rigorous regional monitoring an essential element of evaluating its progress. Hyperspectral remote sensing provides a potential solution to the issue of monitoring.
Her Majesty the Queen in right of Canada 2010 Canada Centre for Remote Sensing, Earth Science Sector, Contribution 20100011
3. DATA ACQUISITIONS As part of this preliminary study, a cloud-free EO-1 Hyperion image was acquired on August 13, 2003. The acquisition footprint follows a north-south transect approximately centered over the Copper Cliff tailings and smelter site (see Figure 1). Hyperion provides 242 contiguous spectral bands ranging from 400 nm to 2500 nm, with bandwidths ranging from 8 nm to 14 nm. The spatial resolution is 30 m by 30 m with a swath width of 7.7 km. Field measurements made in August were used to be concurrent to the Hyperion acquisition. Twelve 10 by 10 m plots were randomly selected within the sensor footprint. In each plot, the number and species of trees were recorded, and LAI derived. Additional data from the City of Greater Sudbury’s restoration planning database included areas within the scene which have been remediated since 1979. For a regional evaluation, information was extracted from plots representing impact zones around the smelter site, at distances of 3 km, 8 km, 30 km and 60 km. The area within 3 km of the emissions sites contain areas where no forest was present in the 1970’s. Within 8 km of the smelters, forest cover was limited to valleys. Within 30 km, the forest cover was below regional norms [7]. A zone containing the 30 km to 60 km region from the smelter was selected to compare affected zones to areas of lesser impact. 4. PRE-PROCESSING AND ANALYSIS Before analysis, a data quality check was performed. As a technology demonstrator, Hyperion imagery is impacted by signal-to-noise, vertical striping, spectral curvature, detector misalignment, and gain and offset issues [8][9]. Preprocessing and information extraction was performed using a suite of modules developed at the Canada Centre for Remote Sensing (CCRS) and implemented in the Imaging Spectrometer Data Analysis System (ISDAS) [10]. 4.1. Noise Reduction This module evaluates for random noise that originates in the detection process and sensor electronics [8]. The method first identifies spectra of similar shape and magnitude (within a well defined noise boundary of the sensor, and within a defined spatial relationship). An average spectrum is then produced to replace the original spectrum. In this process each pixel is examined individually. 4.2. Vertical De-Striping Striping artefacts in various bands within an image cannot only interfere with a visual interpretation, but can severely affect subsequent analyses. The Spectral Moment Matching
(SMM) method, as developed at CCRS, evaluates for the existence of stripes, and then removes them [11]. This method averages measured means and standard deviations of corresponding along-track columns in highly correlated bands. This information is then used to derive adjustments to the gain and offset of the striped column, and these values are then applied to replace the striped column. 4.3. Spectral Curvature Spectral curvature, or spectral smile, is inherent with most hyperspectral sensors. It is a variation of the band centre wavelength as a function of across track pixel. A method has been developed to determine the spectral band centre and bandwidth of every element of the two dimensional array, using both the location and shape of atmospheric spectral absorption features (CO2, O2, H2O, CH4) [12]. This spectral calibration is essential to derive high quality surface reflectance data. As atmospheric absorption features that contribute to the measured at-sensor radiance are spectrally very narrow, spectral characterisation of the sensor is required to be very accurate (
