This data set provides local LAI maps for the Larose (Ontario) site in Canada. These derived maps may also be useful for validating other LAI maps over this same site given that the area is protected from disturbance. The maps should be used for the given period of validity. The LAI data are suitable for use in modeling carbon, water, energy, and trace gas exchange between the land surface and the atmosphere at regional scales. The dataset may also be useful for monitoring changes in the land surface. A complete description of the methods used to produce the maps for the Larose site and the ground measurement campaign is provided in the companion document Larose2003FTReport.pdf.
The Leaf Area Index (LAI) maps are at 30-m resolution for the 3x3-km Larose site. LAI is defined here as half the total (all-sided) live foliage area per unit horizontal projected ground surface area. Overstory LAI corresponds to all tree foliage except for treeless areas where it corresponds to total foliage. The algorithms were developed from ground measurements and Landsat TM and ETM+ images (Fernandes et. al., 2003). A mask was developed using the Landsat ETM+/TM5 image and available land cover maps to identify only those areas with land cover belonging to the sample land cover classes and with Landsat ETM+/TM5 spectral reflectance values that fell within the convex hull of the spectral reflectance values over the plots. LAI was mapped within the masked region using the Landsat ETM+/TM5 image and the developed transfer function. The final LAI map was scaled by a factor of 20 (offset 0). The LAI maps are in Tagged Image File Format (TIFF).
The VALERI project (Validation of Land European Remote sensing Instruments) is dedicated to the validation of products derived from medium resolution satellite sensors ( http://www.avignon.inra.fr/valeri/ ). The objectives of the VALERI project are: (1) to evaluate the absolute accuracy of the biophysical products (LAI, fAPAR, fCover (the percentage of soil covered by vegetation)) derived from large swath sensors (e.g., AVHRR, POLDER, VEGETATION, SEAWIFS, MSG, MERIS, AATSR, MODIS, MISR, GLI) using a range of possible algorithms; and (2) to inter-compare the products derived from different sensors and algorithms. For this purpose, the VALERI project has developed a network of sites distributed over the Earth's surface and a methodology designed to directly measure the biophysical variables of interest at proper spatial and temporal scales.
Cite this data set as follows:
Fernandes, R. A., A. Abduelgasim, L. Sylvain, S. K. Khurshid, and C. Butson. 2006. Leaf Area Index Maps at 30-m Resolution, VALERI Site, Larose, Canada. Data set. Available on-line [http://daac.ornl.gov/] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. doi:10.3334/ORNLDAAC/829.
Project: VALERI (Validation of Land European Remote Sensing Instruments)
Derivation of LAI maps at 30-m resolution by the Natural Resources Canada (NRCAN), Earth Sciences Sector (ESS), Canada Centre of Remote Sensing (CCRS), Groundwater Program.
The lead investigator was R.A. Fernandes. You may contact him at Richard.Fernandes@ccrs.nrcan.gc.ca.
Additional Contact:
Frédéric Baret, VALERI Research Director
Institut National de la Recherche Agronomique
Avignon, France
Email: baret@avignon.inra.fr
VALERI Web Site:
Site-Specific Map Product Overview:
Leaf Area Index (LAI) map of Larose (Ottawa), Canada Validation of European Remote Sensing Instruments (VALERI) site for August, 2003 at 30m resolution. The map was produced by scaling up a set of in situ optically based LAI estimates over 33 plots of approximately 1 ha each within the mapped area. Plots were located using a random sampling with stratification by land cover. In situ LAI estimates were derived using multiple (>10) digital hemispherical camera photographic measurements per plot for both the understory and overstory and processed using the CAN-EYE (Version 4.1)software provided by Institute National Agronomie de France. A regression-based transfer function was applied to paired in-situ LAI and Landsat TM5 at surface reflectance values over each plot. A mask was developed using the Landsat TM5 image and available land cover map to identify only those areas with land cover belonging to the sample land cover classes and with Landsat 5 TM spectral reflectance values that fell within the convex hull of the spectral reflectance values over the plots. LAI was mapped within the masked region using the Landsat TM 5 image and the developed transfer function. The final LAI map was scaled by a factor of 20 (offset 0).
Validation of Land European Remote Sensing Instruments Overview:
The VALERI project (Validation of Land European Remote sensing Instruments) is dedicated to the validation of products derived from medium resolution satellite sensors ( http://w3.avignon.inra.fr/valeri/ ). The objectives of the VALERI project are: (1) to evaluate the absolute accuracy of the biophysical products (LAI, fAPAR, fCover) derived from large swath sensors (e.g., AVHRR, POLDER, VEGETATION, SEAWIFS, MSG, MERIS, AATSR, MODIS, MISR, GLI) using a range of possible algorithms; and (2) to inter-compare the products derived from different sensors and algorithms. For this purpose, the VALERI project has developed a network of sites distributed over the Earth's surface and a methodology designed to directly measure the biophysical variables of interest at proper spatial and temporal scales.
VALERI sites were selected to represent, with the other validation projects, the large variation of biomes and conditions observed over the Earth's surface. Each site is about 3.3 km in size and is relatively flat and homogeneous at the medium resolution scale. For each site, the methodology used to generate the high spatial resolution biophysical variable maps is mainly based on concurrent use of local ground measurements and a high spatial resolution satellite image, generally SPOT-HRV. Local ground measurements are representative of elementary sampling units (ESUs) that have approximately the same order of size as that of SPOT-HRV. The ground measurements mainly consist of gap fraction measurements achieved with LAI2000 or hemispherical photographs. The ESUs are selected over the whole 3.3 km² site in order to sample the vegetation types observed and to allow the derivation of variogrammes. A transfer function is then established over the ESUs to relate the ground measurements of the biophysical variables to the high spatial resolution satellite image. Finally, co-kriging is applied to generate the high spatial resolution map of the biophysical variables over the 3.3 km².
The VALERI database is organized by site and field campaign date, and provides ground measurements, satellite images, and high spatial resolution maps of biophysical variables, where available [ http://www.avignon.inra.fr/valeri/fic_htm/database/main.php ]. Ground measurements include LAI2000 data (.xls) and GPS data (.xls) for most sites, and hemispherical photos (.dat), land use map (.zip), sunphotometer data (.dat), atmospheric data (.dat), and/or global and diffuse radiation measurements (.dat) for some sites. Satellite data include a Readme file (.php), Quick Look images (.htm), and Zip SPOT images 8x11 TOA [a zipped generic binary array file, in band interleaved by line (BIL) format]. Also available for many of the VALERI sites are MODIS LAI/FPAR, 8-day, 1km (MOD15A2), MODIS Surf. Ref., 8-day, 500m (MOD09A1), MODIS Surf. Ref., 8-day, 250m (MOD09Q1), and Landsat 7 ETM+ data provided by the Committee for Earth Observation Satellites, Land Product Validation (CEOS-LPV) subgroup. A report on each measurement campaign accompanies the data set.
The main output of the VALERI validation activity is a series of high spatial resolution maps of biophysical variables derived from ground measurements and high resolution satellite images. High resolution LAI maps, retrieved using multiple regression between high resolution reflectance images and LAI measurements, are available for several VALERI sites; other maps are in preparation. [ http://www.avignon.inra.fr/valeri/fic_htm/database/main.php ].
Study Area: (All latitude and longitude given in degrees and fractions)
| Site (Region) | Westernmost Longitude | Easternmost Longitude | Northernmost Latitude | Southernmost Latitude | Geodetic Datum |
|---|---|---|---|---|---|
| Larose, Ottawa, Canada | -75.236353 | -75.197931 | 45.394033 | 45.367100 | WGS84 |
Center latitude and longitude: 45.380567 -75.217136
A complete description of the methods used to produce the maps for the Larose site and the ground measurement campaign is provide in the companion document Larose2003FTReport.pdf.
Time period:
The ground measurements were carried out from 05/08/2003 to 08/08/2003, while the high spatial resolution imagery (HRVIR1 on SPOT4) was acquired on 08/19/2003.
Image data:
Landsat Thematic Mapper (TM5) and Enhanced Thematic Mapper Plus (ETM+) images covering the study site were collected and processed.
Projection and Datum:
The LAI map and the Mask map are in UTM Zone 18N projection and WGS-84 datum.
Spatial Resolution:
The Landsat TM5 and ETM+ images have a pixel resolution of 30 x 30 m.
Ground Measurements:
In-situ optically based LAI estimates were measured using optical instruments [Digital Hemispherical Camera Photographs (DHPs)] over plots of approximately 1 hectare within the mapped area.
Parameters or Variables:
LAI is defined as half the all sided area of all foliage per horizontal projected ground surface area ((UN FAO, 2002, Chen and Black, 1992).
Mask indicator designates area where LAI was mapped.
Data File Information:
These map files are provided for each site. The site name is prepended to each file to ensure unique names.
File Name Description
larose_2003_lai30m.tif LAI map in Tagged Image File Format (TIFF)
larose_2003_mask30m.tif Mask map in TIFF format (Value mark with 1 are the mapped area)
Application of data:
This data set provides local LAI maps for the Larose site in Canada. These derived maps may also be useful for validating other LAI maps over these same sites given that the areas are protected from disturbance. The maps should be used for the given period of validity. The LAI data are suitable for use in modeling the carbon, water, energy, energy and trace gas exchange between the land surface and the atmosphere at regional scales. The data set may also be useful for monitoring changes in the land surface.
Larose (Ontario) 2003 LAI map: This data set is intended as a local LAI map for the Larose forest region circa August 2003. The data set may also be useful for validating other LAI maps over this region from the 2000 - 2005 period given that the area is protected from disturbance. The map should not be used prior to 2000 due to an ice storm event.
Theory of Measurements:
Landsat-5 thematic mapper (TM) and Landsat-7 enhanced thematic mapper plus (ETM+) images across Canada were used to produce fine-resolution (30 m) LAI estimates using a uniform methodology traceable to in situ measurements (Fernandes et al. 2003). Residuals between coarse-scale LAI estimates based on data from the Satellite pour l'observation de la terre (SPOT) VEGETATION (VGT) sensor, and Landsat LAI estimates were quantified. Furthermore, a number of scaling treatments were performed on the Landsat scenes to isolate the relative contributions of land cover and reflectance scaling versus atmospheric correction and bidirectional distribution function (BRDF) normalization on coarse-scale LAI errors. Uncertainties because of atmospheric correction and acquisition geometry normalization were identified as the largest source of scene-wide bias errors.
Derivation Techniques and Algorithms:
A complete description of this technique for producing 30-m resolution maps is provided in Fernandes et al. 2003. Software provided by Sylvain Leblanc, Canada Centre of Remote Sensing (CCRS) was used for processing data from LAI-2000 and TRAC instruments. CAN-EYE software (Jonckheere et al. 2004, Weiss et al. 2004) and provided by Institute National Agronomie de France was used for processing digital hemispherical photographs [ http://w3.avignon.inra.fr/valeri/Meeting_Reports/VALERImeetingMarch2005/Baret_Can-Eye.ppt ].
Accuracy Report:
The accuracy is given by the confidence errors of prediction of the regression transfer function assuming the sample plots are representative of the region mapped and no-spatial correlations in residuals. A summary table is given below:
Site Month_Year #_of_Samples MAE LAI-min LAI-median LAI-max P-min P-median P-max Larose August 2003 33 0.55 0.81 4.05 6.45 0.5 0.75 1.25
Note: MAE is the Median Absolute Error; P is the Prediction 1 sigma confidence interval at minimum, median and maximum estimated LAI.
Limitations of the Data:
The data set may be useful for validating other LAI maps over these sites given that the area is protected from disturbance. The map should be used for the given period of validity as noted in Section 3.
For the Larose Site, accuracy is reported relative to in-situ LAI estimates. The accuracy is given by the confidence errors of prediction of the regression transfer function assuming the sample plots are representative of the region mapped and there are no-spatial correlations in residuals. In-situ LAI estimates were cross-checked for 5 plots using two different approaches and three different operators for deriving LAI from the same digital cameras. Effective LAI varied by less than 5% of total effective LAI between approaches or operators. LAI (corrected for clumping) could vary by up to 20% based on the approach taken. However, the difference between these methods is generally proportional to effective LAI and hence represents an arbitrary bias factor.
Remote Sensing data: Landsat TM5 and ETM+ images were acquired with corresponding 30-m resolution (1:50,000 scale) land cover maps for the Larose site (Fernandes et al. 2003).
In-situ data: A standard methodology was used to collect and process the optical in situ data. The optical instruments used for measurements are summarized below:
Site Instrument
Larose Digital Hemispherical Camera
Processing Steps:
1. Acquire 30-m resolution (1:50,000 scale) land cover map of region.
2. Acquire 1:50,000 scale road vector map of region (<10m positional accuracy 2 sigma).
3. Georeference land cover map using road vectors manually.
4. Sample 50 points in the land cover map ensuring 3 points per land cover class and balance of points placed according to area of each class. All points are separated by at least 200m.
5. Perform field survey using standard VALERI sampling scheme for both up and down looking digital hemispherical photographs (box with cross pattern + 3 random samples in box; cross extends past box for forest stands). (Larose2003FTReport.pdf (link))
6. Process images for each plot (37 in total) using CAN-EYE software to get LAI. Only retain plots where effective LAI from all zenith algorithm algorithm is within 10% of effective LAI from 57 degree angle estimate (33 in total).
7. Correct LAI estimates using in-situ samples of needle-to-shoot area ratios per species (3 in total for site).
8. Georeference con-current (<1 month delay) 30-m resolution Landsat satellite image to road vectors.
9. Calibrate Landsat ETM+/TM5 satellite image and correct to surface reflectance as in Butson et al. 2003.
10. Identify Landsat ETM+/TM5 pixel values co-incident with surface plots (usually 3x3-km window average).
11. Generate various vegetation indices based on pixel values and perform Thiel-Sen regression (Fernandes and Leblanc, 2005) to generate transfer function. Select transfer function with best prediction confidence intervals using lowest dimensionality data set. In this case the transfer function used the Infrared Simple Ratio (Fernandes et al. 2003). Lowest dimensionality was used to maximize mappable area.
12. Determine mappable area as those pixels within the convex hull of the spectral index values of the surface plots and matching the land cover sampled.
13. Apply the transfer function to the Landsat image to map LAI over the mappable area.
14. Scale the LAI by a factor of 20 (offset=0) for coding purposes and save as an 8 bit raster.
15. Save the mappable area as a value of 1 in another 8 bit raster with same extents as LAI map.
This data is available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC) or the EOS Data Gateway.
E-mail: uso@daac.ornl.gov
Telephone: +1 (865) 241-3952
FAX: +1 (865) 574-4665
Requested data can be provided electronically on the ORNL DAAC's anonymous HTTP site or on various media by request.
Butson, C., and Fernandes, R.A. (2003). A consistency analysis of surface reflectance and leaf area index retrieval from overlapping clear-sky Landsat ETM+ imagery. Remote Sensing of Environment, 89, 369-380.
Chen, J. M. and Black,T.A, (1992). Defining leaf area index for non-flat leaves. Plant, Cell and Environment, 15, 421-429.
Chen, J.M., Rich, P.M., Gower, S.T., Norman, J.M., and Plummer S. (1997). Leaf area index of boreal forests: Theory, techniques, and measurements. Journal of Geophysical Research, 102(D24),pp.29429-29442.
Fernandes, R. A., and Leblanc, S.G. (in press.2005). Parametric and non-parametric (Thiel-Sen) linear regressions for predicting biophysical parameters in the presence of measurement errors.
Fernandes, R., Butson, C., Leblanc, S. G., & Latifovic, R. (2003). Landsat-5 TM and Landsat-7 ETM+ based accuracy assessment of leaf area index products for Canada derived from SPOT-4 VEGETATION data . Canadian Journal of Remote Sensing, 29(2), 241-258.
Kaufman, Y.J., A.E. Wald, L.A. Remer, B. Gao, R. Li, and L. Flynn (1997).The MODIS 2.1 um Channel Correlation with Visible Reflectance for Use in Remote Sensing of Aerosol, IEEE Trans. Geoscience and. Remote Sensing, vol. 35, pp. 1286-1297.
Kaufman, Y. J., and Tanre, D. (1998). Algorithm for remote sensing of tropospheric aerosol from MODIS. ATDB reference number ATBD-MOD-02. Washington-DC, USA: Goddard Space Flight Centre-NASA (Online distribution: http://ftp.www.gsfc.nasa.gov/MODIS-Atmosphere/ _docs/atbd_mod02.pdf).
Liang, S., Fang, H., and Chen, M. (2001). Atmospheric correction of Landsat ETM+ Land Surface Imagery: Part 1. Methods. IEEE Transactions on Geoscience and Remote Sensing, 39, 2490-2498.
Nemani, R., L. Pierce, S. Running and L. Band (1993). Forest ecosystem processes at the watershed scale: sensitivity to remotely-sensed leaf area index. Int. J. Rem. Sens. 14:2519-2534.
Vermote, E.F., D. Tanre, J.L Deuz, M. Herman, and J. Morcrette (1997). Second simulation of the satellite signal in the solar spectrum, 6S: An overview. IEEE Trans. Geosc. Remote Sens.,vol. 35, no. 3, pp. 675-686.
UN FAO, (2002). Global terrestrial observing system at http://www.fao.org/gtos/ [accessed 16 August 2002].
Jonckheere, Inge, Stefan Fleck, Kris Nackaerts, Bart Muysa,Pol Coppin, Marie Weiss, Frédéric Baret. 2004. Review of methods for in situ leaf area index determination. Part I. Theories, sensors and hemispherical photography. Agricultural and Forest Meteorology 121: 1935.
Weiss, M., F. Baret, G.J. Smith, I. Jonckheere, P. Coppin. 2004. Review of methods for in situ leaf area index determination. Part II. Estimation of LAI, errors and sampling. Agricultural and Forest Meteorology 121: 37.53