Richard Fernandes

Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products: proposition of the CEOS-BELMANIP

This study investigates the representativeness of land cover and leaf area index (LAI) sampled by a global network of sites to be used for the evaluation of land biophysical products, such as LAI or fAPAR, derived from current satellite systems. The networks of sites considered include 100 sites where ground measurements of LAI or fAPAR have been performed for the validation of medium resolution satellite land biophysical products, 188 FLUXNET sites and 52 AERONET sites. All the sites retained had less than 25% of water bodies within a 8times8 km 2 window, and were separated by more than 20 km. The ECOCLIMAP global classification was used to quantify the representativeness of the networks. It allowed describing the Earths surface with seven main types and proposed a climatology for monthly LAI values at a spatial resolution around 1 km. The site distribution indicates a large over representation of the northern midlatitudes relative to other regions, and an under-representation of bare surfaces, grass, and evergreen broadleaf forests. These three networks represent all together 295 sites after elimination of sites that were too close. They were thus completed by 76 additional sites to improve the representativeness in latitude, longitude, and surface type. This constitutes the BELMANIP network proposed as a benchmark for intercomparison of land biophysical products. Suitable approaches to conducting intercomparison at the sites are recommended

Evaluation of national and global LAI products derived from optical remote sensing instruments over Canada

Leaf area index (LAI) is an important surface variable for monitoring the status of vegetation and as input in a number of ecosystem process models. There are currently several coarse-resolution LAI maps over Canada, including a Canada Centre for Remote Sensing ten-day, 1-km resolution, Canada-wide product based on SPOT-4 VEGETATION (VGT), a MODIS eight-day, 1-km resolution, global product and a monthly, 7-km resolution, global map produced using POLDER-1. These products are difficult to validate because of their large spatial extent and coarse resolution. In this study we use in situ LAI measurements collected over a wide range of forest types and ecological zones in Canada to derive 30-m resolution reference LAI maps based on robust error-in-measurement regressions to Landsat Enhanced Thematic Mapper Plus vegetation indices. The reference maps and LAI products were aggregated to a coarser resolution (3 km for MODIS and VGT and 7 km for POLDER) before comparison to account for registration errors, and variability in sensor projected point spread functions. Spatially corresponding aggregated pixels with both high-quality reference and coarse scale LAI retrievals were compared. The comparison shows reasonable agreement (biases less than 25% or one LAI) between the VGT and reference LAI. The MODIS LAI product showed weak correlations (R2<0.25) over all sites at the scale of comparison and typically overestimated reference LAI in mixed forests by approximately 200%. The POLDER LAI product, only available in June 1997, showed almost no correlation to the summer reference LAI datasets. It underestimated reference LAI for an early growing season with an extent, in some cases, greater than the seasonal differences in LAI. This independent validation of three large area LAI products suggests that there may be substantial biases due to the lack of regional tuning of retrieval algorithms. These biases are far larger than the uncertainties in the reference-based LAI scenes in the case of the MODIS product. This suggests that reliable LAI maps may require regional calibration to meet the Global Terrestrial Observing System mapping requirements of plusmn15% uncertainties

Generating historical AVHRR 1 km baseline satellite data records over Canada suitable for climate change studies

Generating historical AVHRR 1 km baseline satellite data records over Canada suitable for climate change studies Rasim Latifovic, Alexander P. Trishchenko, Ji Chen, William B. Park, Konstantin V. Khlopenkov, Richard Fernandes, Darren Pouliot, Calin Ungureanu, Yi Luo, Shusen Wang, Andrew Davidson, and Josef Cihlar Pages 324-346 Abstract. Satellite data are an important component of the global climate observing system (GCOS). To serve the purpose of climate change monitoring, these data should satisfy certain criteria in terms of the length of observations and the continuity and consistency between different missions and instruments. Despite the great potential and obvious advantages of satellite observations, such as frequent repeat cycles and global coverage, their use in climate studies is hindered by substantial difficulties arising from large data volumes, complicated processing, and significant computer resources required for archiving and analysis. Successful examples of satellite earth observation (EO) data in climate studies include, among others, analyses of the earths radiation budget (Earth Radiation Budget Experiment (ERBE), Scanner for Radiation Budget (ScaRaB), and Cloud and the Earths Radiant Energy System (CERES)), cloudiness (International Satellite Cloud Climatology Project (ISCCP)), vegetation research (Global Inventory Modeling and Mapping Studies (GIMMS)), and the National Oceanic and Atmospheric Administration – National Aeronautics and Space Administration (NOAA–NASA) Pathfinder Program. Despite several attempts, the great potential of the advanced very high resolution radiometer (AVHRR) 1 km satellite data for climate research remains substantially underutilized. To address this issue, the generation of a comprehensive satellite data archive of AVHRR data and products at 1 km spatial resolution over Canada for 1981–2004 (24 years) has been initiated, and a new system for processing at level 1B has been developed. This processing system was employed to generate baseline 1 day and 10 day year-round clear-sky composites for a 5700 km × 4800 km area of North America. This region is centred over Canada but also includes the northern United States, Alaska, Greenland, and surrounding ocean regions. The baseline products include top-of-atmosphere (TOA) visible and near-infrared reflectance, TOA band 4 and band 5 brightness temperature, a cloud – clear – shadow – snow and ice mask, and viewing geometry. Details of the data processing system are presented in the paper. An evaluation of the system characteristics and comparison with previous results demonstrate important improvements in the quality and efficiency of the data processing. The system can process data in a highly automated manner, both for snow-covered and snow-free scenes, and for daytime and nighttime orbits, with high georeferencing accuracy and good radiometric consistency for all sensors from AVHRR NOAA-6 to AVHRR NOAA-17. Other processing improvements include the implementation of advanced algorithms for clear sky – cloud – shadow – snow and ice scene identification, as well as atmospheric correction and compositing. At the time of writing, the assembled dataset is the most comprehensive AVHRR archive at 1 km spatial resolution over Canada that includes all available observations from AVHRR between 1981 and 2004. The archive and the processing system are valuable assets for studying different aspects of land, oceans, and atmosphere related to climate variability and climate change.

Modelling Watersheds as Spatial Object Hierarchies: Structure and Dynamics

The generation, transport and fate of non-point source pollutants in surface water systems is recognized as a major threat to water supplies, aquatic and coastal ecosystems. The transformation and movement of water, carbon and nutrients through watersheds integrates a set of ecosystem processes along hydrologic flowpaths. Human individual and institutional interactions with these processes involve direct addition or abstraction of these substances, or the alteration of land cover and drainage systems. In natural and developed catchments, these processes often vary at granularities ranging from below the level of a hillslope, up through regional watersheds. This suggests the need for the development of hierarchical analysis tools that can address the integration of a set of biophysical, biogeochemical and socioeconomic processes over a spectrum of scales. We describe and illustrate the use of a watershed model implemented as a spatial object hierarchy, representing successively contained landform classes associated with class specific processes as member functions. The model has been linked in a range of looser and tighter couplings with GRASS and ArcView, supplemented by specific terrain analytical functions. We illustrate the data and model system for an instrumented catchment monitored as part of the Baltimore Ecosystem Study (BES), a Long Term Ecological Research (LTER) site centering on integrated carbon, water and nutrient cycling.

From need to product: a methodology for completing a land cover map of Canada with Landsat data

Despite its very large territory and the best Landsat archive in the world, Canada has made very limited use of Landsat data for land cover mapping. The primary difficulty has been the prohibitive cost of information extraction and the earlier (and now overcome for Landsat-7 enhanced thematic mapper plus data) high cost of data purchase. The solution to this remaining obstacle lies in decreasing the cost of Landsat data processing and analysis while ensuring the high quality of the extracted information. In this paper, we present an efficient and effective approach to mapping land cover in Canada from Landsat thematic mapper data (single or multiple satellites). The key features of this approach are an increase in the ratio of computer to human analysis and automation for high data volume or large area processing. However, it is essential that the final product quality not suffer because of the greater reliance on computer processing, thus the algorithm performance becomes critical. We describe the overall approach, discuss key challenges, explain the principles behind key algorithms developed to respond to the challenges, present evidence demonstrating the effectiveness of these algorithms in a boreal landscape setting, and consider implementation issues. With a processing system developed to handle large numbers (tens to hundreds) of Landsat scenes, which incorporates most of the algorithms discussed here, the stage is nearly set for large-scale processing leading to a Landsat-based land cover classification product(s) for Canada.

Multi‐temporal Mapping of Burned Forest over Canada Using Satellite‐based Change Metrics

Abstract A procedure for continental‐scale mapping of burned boreal forest at 10‐day intervals was developed for application to coarse resolution satellite imagery. The basis of the technique is a multiple logistic regression model parameterized using 1998 SPOT‐4 VEGETATION clear‐sky composites and training sites selected across Canada. Predictor features consisted of multi‐temporal change metrics based on reflectance and two vegetation indices, which were normalized to the trajectory of background vegetation to account for phenological variation. Spatial‐contextual tests applied to the logistic model output were developed to remove noise and increase the sensitivity of detection. The procedure was applied over Canada for the 1998‐2000 fire seasons and validated using fire surveys and burned area statistics from forest fire management agencies. The area of falsely mapped burns was found to be small (3.5% commission error over Canada), and most burns larger than 10 km2 were accurately detected and mapped (R2 = 0.90, P<0.005, n = 91 for burns in two provinces). Canada‐wide satellite burned area was similar, but consistently smaller by comparison to statistics compiled by the Canadian Interagency Forest Fire Centre (by 17% in 1998, 16% in 1999, and 3% in 2000).

On Line Validation Exercise (OLIVE): A Web Based Service for the Validation of Medium Resolution Land Products. Application to FAPAR Products

The OLIVE (On Line Interactive Validation Exercise) platform is dedicated to the validation of global biophysical products such as LAI (Leaf Area Index) and FAPAR (Fraction of Absorbed Photosynthetically Active Radiation). It was developed under the framework of the CEOS (Committee on Earth Observation Satellites) Land Product Validation (LPV) sub-group. OLIVE has three main objectives: (i) to provide a consistent and centralized information on the definition of the biophysical variables, as well as a description of the main available products and their performances (ii) to provide transparency and traceability by an online validation procedure compliant with the CEOS LPV and QA4EO (Quality Assurance for Earth Observation) recommendations (iii) and finally, to provide a tool to benchmark new products, update product validation results and host new ground measurement sites for accuracy assessment. The functionalities and algorithms of OLIVE are described to provide full transparency of its procedures to the community. The validation process and typical results are illustrated for three FAPAR products: GEOV1 (VEGETATION sensor), MGVIo (MERIS sensor) and MODIS collection 5 FPAR. OLIVE is available on the European Space Agency CAL/VAL portal), including full documentation, validation exercise results, and product extracts.

Recent advancements in optical field leaf area index, foliage heterogeneity, and foliage angular distribution measurements

In-situ estimations of leaf area index (LAI), leaf clumping, and leaf angular distribution are often performed from canopy gap fraction measurements with optical sensors. Two new procedures are used in this study to improve the estimation of gap fraction from digital camera photographs,: 1) the digital number (DN) of mixed sky-canopy pixels is used to estimate the within pixel gap fraction instead of the usual threshold used to separate a pixel in gap or a foliage pixel, and 2), the within pixel gap fraction is calculated at different view zenith and azimuth angles to take into account multiple scattering effects. To estimate foliage clumping, a gap size distribution is calculated from a narrow view zenith angle range (less than 1/spl deg/). The clumping index is then extracted using 3 methods: 1) a refined gap size distribution theory developed for the TRAC instruments; 2) the Lang and Xiang (1986) logarithm gap fraction averaging and 3) a combination of 1) and 2). Clumping index variations with view zenith angle in the range from 15/spl deg/ to 70/spl deg/ are derived using the individual and combined methods. Analysis of the digital hemispherical photographs shows that 1) the three methods give different clumping estimates, but the angular variation patterns are similar, and 2) canopies with significant angular variation in clumping can induce large errors in the inverted leaf angle distribution when the clumping angular variation is not included in the retrieval. The practical implication of these findings is that LAI, clumping index, and foliage orientation can all be reliably retrieved using digital hemispherical photographs, considerably reducing the number and cost of instruments needed in fieldwork.

ALOS PALSAR L-band polarimetric SAR data and in situ measurements for leaf area index assessment

Leaf area index (LAI), a key parameter controlling crop growth and yield models, has been widely estimated using optical satellite measurements. The estimation of LAI from high-resolution optical satellite data is limited by cloudy conditions and this may be a problem when systematic monitoring during the growing season is required. Synthetic Aperture Radar data are less susceptible to atmospheric effects than optical data and have been related to standing biomass over a number of landscapes. Here we quantify the relationship between LAI and both Advanced Land Observing Satellite (ALOS) Phased Array Synthetic Aperture Radar (PALSAR) L-band data and ENVISAT Advanced Synthetic Aperture Radar C-band data under relatively uniform soil moisture conditions. Digital hemispherical photographs were taken from large corn, soybean and pasture fields and forest plots on 4–5 July 2006 and processed using the CANEYE software to estimate in situ LAI. Estimates derived from PALSAR L-band polarimetric radar backscatter of crop (corn and soybean) fields and forest plots were in good agreement with measured LAI values, but the C-band Advanced Synthetic Aperture Radar imagery showed weak relationships. The study shows that PALSAR L-band polarimetric data have the potential to provide useful estimates of LAI, providing a possible alternative when optical data are limited by cloud cover. However, additional work is required to characterize the temporal variability of the relationship between PALSAR backscatter and LAI over varying soil moisture and soil surface conditions.

Landsat ETM+ mosaic of northern Canada

Mapping northern Canada with medium spatial resolution (30 m) Landsat data is important to complement national multiagency activities in forested and agricultural regions, and thus to achieve full Canadian coverage. Northern mapping presents unique challenges due to limited availability of field data for calibration or class labeling. Additional problems are caused by variability between individual Landsat scenes acquired under different atmospheric conditions and at different times. Therefore, the generation of radiometrically consistent coverage is highly desirable to reduce the amount of reference data required for land cover mapping and to increase mapping efficiency and consistency by stabilizing spectra of land cover classes among hundreds of Landsat scenes. The production chain and dataset of a normalized, 90 m resolution Landsat enhanced thematic mapper plus (ETM+) mosaic of northern Canada is presented in this research note. A robust regression technique called Thiel–Sen (TS) is used to normalize Landsat scenes to consistent coarse-resolution VEGETATION (VGT) imagery. The derived dataset is available for any interested user and can be employed in applications aimed at studying processes in the Canadian Arctic regions above the tree line.