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Article: Oakwood crown closure estimation by unmixing Landsat TM data
Title | Oakwood crown closure estimation by unmixing Landsat TM data |
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Authors | |
Issue Date | 2003 |
Citation | International Journal of Remote Sensing, 2003, v. 24, n. 22, p. 4422-4445 How to Cite? |
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. © 2003 Taylor & Francis Ltd. |
Persistent Identifier | http://hdl.handle.net/10722/296552 |
ISSN | 2023 Impact Factor: 3.0 2023 SCImago Journal Rankings: 0.776 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Pu, R. | - |
dc.contributor.author | Xu, B. | - |
dc.contributor.author | Gong, P. | - |
dc.date.accessioned | 2021-02-25T15:16:08Z | - |
dc.date.available | 2021-02-25T15:16:08Z | - |
dc.date.issued | 2003 | - |
dc.identifier.citation | International Journal of Remote Sensing, 2003, v. 24, n. 22, p. 4422-4445 | - |
dc.identifier.issn | 0143-1161 | - |
dc.identifier.uri | http://hdl.handle.net/10722/296552 | - |
dc.description.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. © 2003 Taylor & Francis Ltd. | - |
dc.language | eng | - |
dc.relation.ispartof | International Journal of Remote Sensing | - |
dc.title | Oakwood crown closure estimation by unmixing Landsat TM data | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/0143116031000095989 | - |
dc.identifier.scopus | eid_2-s2.0-0344688139 | - |
dc.identifier.volume | 24 | - |
dc.identifier.issue | 22 | - |
dc.identifier.spage | 4422 | - |
dc.identifier.epage | 4445 | - |
dc.identifier.eissn | 1366-5901 | - |
dc.identifier.isi | WOS:000186500100014 | - |
dc.identifier.issnl | 0143-1161 | - |