N: 71.2719 S: -2.86 E: -54.96 W: -156.613
Description
The BigFoot project gathered 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. These surfaces were produced from Landsat ETM+ imagery to explicitly characterize the land cover at the BigFoot Sites to provide validation of the MODIS land cover product. The land cover scheme is consistent with the categories defined by the MOD12 IGBP (http://geography.bu.edu/landcover/userguidelc/index.html) strategy. Each BigFoot land cover product covers approximately a 7 x 7 km extent and consists of the land cover surface image 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. For an in depth discussion of methods used to produce these surfaces, please see references.Additional information on land cover 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.
Product Summary
Citation
Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.
Copy Citation
Documents
USER'S GUIDE
GENERAL DOCUMENTATION
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 | |
| WarmingInduced Earlier Greenup Leads to Reduced Stream Discharge in a Temperate Mixed Forest Catchment | Kim, Ji Hyun, Hwang, Taehee, Yang, Yun, Schaaf, Crystal L., Boose, Emery, Munger, J. William | Land Use/Land Cover Classification | |
| Increased water yield due to the hemlock woolly adelgid infestation in New England | Kim, Jihyun, Hwang, Taehee, Schaaf, Crystal L., Orwig, David A., Boose, Emery, Munger, J. William | Land Use/Land Cover Classification | |
| 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 | |
| Impacts of including forest understory brightness and foliage clumping information from multiangular measurements on leaf area index mapping over North America | Pisek, Jan, Chen, Jing M., Alikas, Krista, Deng, Feng | Land Use/Land Cover Classification | |
| 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 | |
| Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America | Pisek, Jan, Chen, Jing M. | Land Use/Land Cover Classification | |
| Validation of the MODIS Bidirectional Reflectance Distribution Function and Albedo Retrievals Using Combined Observations From the Aqua and Terra Platforms | Salomon, J.G., Schaaf, C.B., Strahler, A.H., Feng Gao, Yufang Jin | Land Use/Land Cover Classification |