Biometric Properties Estimated from High Resolution Imagery in the Amazon and the Cerrado Regions
Michael
Palace, Complex System Research Center, UNH, Morse Hall, 8 College Road, Durham, NH 03824, michael.palace@unh.edu
(Presenting)
Stephen
Hagen, Applied GeoSolutions, LLC, 87 Packers Falls Road, Durham, NH 03824, steve.hagen@agsemail.com
Bobby
Braswell, Atmospheric and Environmental Research, Inc., 131 Hartwell Avenue, Lexington, MA 02421-3136, bbraswel@aer.com
Mercedes
Bustamante, UnB - Universidade de Brasília,, Departamento de Ecologia ICC-Campus Universitario Asa Norte, Brasilia DF 70910-970, mercedes@unb.br
Laerte
Ferreira, UFG - LAPIG, UFG - LAPIG Universidade Federal de Goias - UFG Instituto de Estudos, Socio-Ambientais - IESA Campus II - Caixa Postal 131, Goiania, GO 74001-970, Brazil, laerte@iesa.ufg.br
The Amazon and Cerrado regions are unique ecotypes with complex and varied forest and vegetation structure. Because these two tropical regions have and are undergoing rapid change due to human encroachment, understanding the forests structure in these ecotypes aids in efforts to quantify carbon dynamics on both regional and global scales. We estimated forest structure by applying a suite of image and statistical analysis tools to 11,014 image tiles or sections (1 km2 each) extracted from 300 IKONOS images. Our preprocessing of this data included: (1) calculation of top of atmosphere reflectance based on observation conditions; (2) archiving of 1000x1000 m tiles; (3) calculation of summary statistics including mean NDVI for each tile; (4) a 5-category land cover characterization based on discriminant analysis with 100+ manually selected training points. The summary statistics and landuse classification was used to determine which areas within an IKONOS tile would be analyzed using textural methods. Our textural methods include estimates of lacunarity, semivariance, power spectrum, entropy, and a crown characterization algorithm. We found significant differences of textural measurements between some vegetation classes indicating that vegetation structure is able to be discerned using textural methods and that this structure is able to used to differentiate vegetation types. Finally, we associated high resolution forest structure estimates with coarser scale MODIS pixel values in an effort to scale across the Amazon and Cerrado regions. Our analytical methods for such scaling used both multivariate linear methods and Bayesian nonlinear regressions to match derived canopy characteristics from high resolution images (one set of variables for each tile) with spectral and angular moderate resolution reflectance data.
Presentation Type: Poster
Poster Session: Carbon Cycle Science
NASA TE Funded Awards Represented:
Keller, Michael
A Historical Reconstruction of Vegetation Change and a Carbon Budget for the Brazilian Cerrado Using Multiple Satellite Sensors and Historical Aerial Photography
Palace, Michael
Scaling Forest Biometric Properties Derived from High Resolution Imagery to the Amazon Basin using Moderate Resolution Spectral Reflectance Data