N: 71.2719 S: -2.86 E: -54.96 W: -156.613
Description
The BigFoot project gathered leaf area index (LAI) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2003. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. LAI was measured at plots within each site for at least two years using standard direct and optical methods at each site. Direct measurement approaches included periodic area harvest for non-forest sites and application of allometric equations to tree diameter data for forest sites. LAI was also estimated indirectly using the Li-Cor LAI-2000 Plant Canopy Analyzers (Gower et al. 1999). LAI was measured three times each year at the forest sites and four to six times at other sites depending upon the phenology of LAI development for a given ecosystem. To develop LAI surfaces at any given site, the Landsat ETM+ image closest in date to maximum LAI was chosen as a reference and images from other dates radiometrically normalized to it. Each LAI surface has a grain of 25 meters and covers a 7 x 7 km extent. The data set consists of the LAI surface images in standard geotiff format, an accompanying text file which provides metadata specific to the image (such as projection, data type, class names, etc), and associated auxiliary and world files. Additional information on LAI measurements and surface development can be found on the BigFoot website at http://www.fsl.orst.edu/larse/bigfoot/ovr_mthd.html. BigFoot Project Background: Reflectance data from MODIS, the Moderate Resolution Imaging Spectrometer onboard NASA's Earth Observing System (EOS) satellite Terra (http://landval.gsfc.nasa.gov/MODIS/index.php), is used to produce several science products including land cover, leaf area index (LAI) and net primary production (NPP). The overall goal of the BigFoot Project was to provide validation of these products. To do this, BigFoot combined ground measurements, additional high resolution remote sensing data, and ecosystem process models at nine flux tower sites representing different biomes to evaluate the effects of the spatial and temporal patterns of ecosystem characteristics on MODIS products. BigFoot characterized up to a 7 x 7 km area (49 MODIS pixels) surrounding the CO2 flux towers located at each of the nine sites. We collected multi-year, in situ measurements of ecosystem structure and functional characteristics related to the terrestrial carbon cycle. Our sampling design allowed us to examine scales and spatial pattern of these properties, the inter-annual variability and validity of MODIS products, and provided for a field-based ecological characterization of the flux tower footprint. BigFoot was funded by NASA's Terrestrial Ecology Program.
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Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Reprocessed MODIS Version 6.1 Leaf Area Index Dataset and Its Evaluation for Land Surface and Climate Modeling | Lin, Wanyi, Yuan, Hua, Dong, Wenzong, Zhang, Shupeng, Liu, Shaofeng, Wei, Nan, Lu, Xingjie, Wei, Zhongwang, Hu, Ying, Dai, Yongjiu | Land Use/Land Cover Classification, Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Canopy Characteristics | |
| Global Gap-Free MERIS LAI Time Series (2002-2012) | Tum, Markus, Gunther, Kurt, Bottcher, Martin, Baret, Frederic, Bittner, Michael, Brockmann, Carsten, Weiss, Marie | Canopy Characteristics | |
| Bayesian maximum entropy data fusion of field-observed leaf area index (LAI) and Landsat Enhanced Thematic Mapper Plus-derived LAI | Li, Aihua, Bo, Yanchen, Chen, Ling | Canopy Characteristics | |
| Estimating time-series leaf area index based on recurrent nonlinear autoregressive neural networks with exogenous inputs | Chai, Linna, Qu, Yonghua, Zhang, Lixin, Liang, Shunlin, Wang, Jindi | Canopy Characteristics, Land Use/Land Cover Classification | |
| Hyperspectral remote sensing of foliar nitrogen content | Knyazikhin, Yuri, Schull, Mitchell A., Stenberg, Pauline, Mottus, Matti, Rautiainen, Miina, Yang, Yan, Marshak, Alexander, Latorre Carmona, Pedro, Kaufmann, Robert K., Lewis, Philip, Disney, Mathias I., Vanderbilt, Vern, Davis, Anthony B., Baret, Frederic, Jacquemoud, Stephane, Lyapustin, Alexei, Myneni, Ranga B. | Canopy Characteristics, Land Use/Land Cover Classification | |
| Bayesian maximum entropy data fusion of field observed LAI and Landsat ETM+ derived LAI | Li, Aihua, Bo, Yanchen, Chen, Ling | Canopy Characteristics | |
| Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling | Yuan, Hua, Dai, Yongjiu, Xiao, Zhiqiang, Ji, Duoying, Shangguan, Wei | Canopy Characteristics | |
| A Bayesian network algorithm for retrieving the characterization of land surface vegetation | Qu, Yonghua, Wang, Jindi, Wan, Huawei, Li, Xiaowen, Zhou, Guoqing | Canopy Characteristics, Land Use/Land Cover Classification | |
| LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products | Weiss, Marie, Baret, Frederic, Garrigues, Sebastien, Lacaze, Roselyne | Canopy Characteristics |