Oakwood Crown Closure Estimation by Unmixing of Landsat TM Data
Document Type
Article
Publication Date
11-2003
Digital Object Identifier (DOI)
https://doi.org/10.1080/0143116031000095989
Abstract
Using an unconstrained least squares solution (LSS) method and an artificial neural network (ANN) algorithm, we estimated oakwood crown closure from a Landsat Thematic Mapper (TM) image of Tulare County, California, USA. Fractions of endmembers (oak crown (f1), grass (f2) and soil (f3)) from mixed pixels were derived from aerial photographs (scale 1 : 40 000) scanned at 1 m ground resolution for training and testing the LSS and ANN algorithms. The aerial photographs were orthorectified using a digital photogrammetric software package with ground control points collected through a differential global positioning system (GPS). The TM image was georeferenced with respect to the corresponding orthorectified aerial photographs. The training and test samples were randomly selected from the TM image and their corresponding fractions of endmembers were derived from the orthophoto. A fourth endmember, shade (f4), was directly extracted from the TM image. Experimental results indicate that the ANN has performed better than the unconstrained LSS. To extract oakwood crown closure in mixed pixels, better results were obtained without using a shade endmember.
Was this content written or created while at USF?
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Citation / Publisher Attribution
International Journal of Remote Sensing, v. 24, issue 22, p. 4433-4445
Scholar Commons Citation
Pu, Ruiliang; Xu, B.; and Gong, P., "Oakwood Crown Closure Estimation by Unmixing of Landsat TM Data" (2003). School of Geosciences Faculty and Staff Publications. 393.
https://digitalcommons.usf.edu/geo_facpub/393
