Logistic regression modelling of thematic mapper data for rubber (Hevea Brasiliensis) area mapping / Mohd Nazip Suratman ... [et al.]

Logistic regression modelling of thematic mapper data for rubber (Hevea Brasiliensis) area mapping / Mohd Nazip Suratman ... [et al.]

Logistic regression modelling of Landsat Thematic Mapper (TM) was applied for mapping the area of rubber plantations in the study area ofSelangor, Malaysia. TM bands 2-5 and 7 were included in the final logistic regression model, and all were highly significant at the 0.0001 level. The tf value (23...

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Main Authors: Suratman, Mohd Nazip, LeMay, Valerie M., Gary, Q. Bull, Donald, G. Leckie, Walsworth, Nick, Peter, L. Marshall
Format: Article
Language:English
Published: Faculty of Applied Sciences 2005
Subjects:
Mathematical statistics. Probabilities
Malaysia
Online Access:http://ir.uitm.edu.my/id/eprint/11797/
http://ir.uitm.edu.my/id/eprint/11797/1/AJ_MOHD%20NAZIP%20SURATMAN%20SL%2005.pdf
id uitm-11797
recordtype eprints
spelling uitm-117972016-09-07T02:03:47Z http://ir.uitm.edu.my/id/eprint/11797/ Logistic regression modelling of thematic mapper data for rubber (Hevea Brasiliensis) area mapping / Mohd Nazip Suratman ... [et al.] Suratman, Mohd Nazip LeMay, Valerie M. Gary, Q. Bull Donald, G. Leckie Walsworth, Nick Peter, L. Marshall Mathematical statistics. Probabilities Malaysia Logistic regression modelling of Landsat Thematic Mapper (TM) was applied for mapping the area of rubber plantations in the study area ofSelangor, Malaysia. TM bands 2-5 and 7 were included in the final logistic regression model, and all were highly significant at the 0.0001 level. The tf value (23247.9) for the model was highly statistically significant (P<0.0001), which implies the estimated model fitted the model building data. TM bands 4 and 5 were the two most influential variables affecting the odds of rubber area occurrence on the imagery. Using probabilities of > 0.5, the model correctly classified 94.5% of the observations in both the training and validation data sets. This high accuracy suggests that the model is appropriate for predicting the presence of rubber trees in the pixels based on selected spectral bands measured by Landsat TM. Faculty of Applied Sciences 2005 Article PeerReviewed text en http://ir.uitm.edu.my/id/eprint/11797/1/AJ_MOHD%20NAZIP%20SURATMAN%20SL%2005.pdf Suratman, Mohd Nazip and LeMay, Valerie M. and Gary, Q. Bull and Donald, G. Leckie and Walsworth, Nick and Peter, L. Marshall (2005) Logistic regression modelling of thematic mapper data for rubber (Hevea Brasiliensis) area mapping / Mohd Nazip Suratman ... [et al.]. Science Letters, 2 (1). pp. 79-85. ISSN 1675-7785
repository_type Digital Repository
institution_category Local University
institution Universiti Teknologi MARA
building UiTM Institutional Repository
collection Online Access
language English
topic Mathematical statistics. Probabilities
Malaysia
spellingShingle Mathematical statistics. Probabilities
Malaysia
Suratman, Mohd Nazip
LeMay, Valerie M.
Gary, Q. Bull
Donald, G. Leckie
Walsworth, Nick
Peter, L. Marshall
Logistic regression modelling of thematic mapper data for rubber (Hevea Brasiliensis) area mapping / Mohd Nazip Suratman ... [et al.]
description Logistic regression modelling of Landsat Thematic Mapper (TM) was applied for mapping the area of rubber plantations in the study area ofSelangor, Malaysia. TM bands 2-5 and 7 were included in the final logistic regression model, and all were highly significant at the 0.0001 level. The tf value (23247.9) for the model was highly statistically significant (P<0.0001), which implies the estimated model fitted the model building data. TM bands 4 and 5 were the two most influential variables affecting the odds of rubber area occurrence on the imagery. Using probabilities of > 0.5, the model correctly classified 94.5% of the observations in both the training and validation data sets. This high accuracy suggests that the model is appropriate for predicting the presence of rubber trees in the pixels based on selected spectral bands measured by Landsat TM.
format Article
author Suratman, Mohd Nazip
LeMay, Valerie M.
Gary, Q. Bull
Donald, G. Leckie
Walsworth, Nick
Peter, L. Marshall
author_facet Suratman, Mohd Nazip
LeMay, Valerie M.
Gary, Q. Bull
Donald, G. Leckie
Walsworth, Nick
Peter, L. Marshall
author_sort Suratman, Mohd Nazip
title Logistic regression modelling of thematic mapper data for rubber (Hevea Brasiliensis) area mapping / Mohd Nazip Suratman ... [et al.]
title_short Logistic regression modelling of thematic mapper data for rubber (Hevea Brasiliensis) area mapping / Mohd Nazip Suratman ... [et al.]
title_full Logistic regression modelling of thematic mapper data for rubber (Hevea Brasiliensis) area mapping / Mohd Nazip Suratman ... [et al.]
title_fullStr Logistic regression modelling of thematic mapper data for rubber (Hevea Brasiliensis) area mapping / Mohd Nazip Suratman ... [et al.]
title_full_unstemmed Logistic regression modelling of thematic mapper data for rubber (Hevea Brasiliensis) area mapping / Mohd Nazip Suratman ... [et al.]
title_sort logistic regression modelling of thematic mapper data for rubber (hevea brasiliensis) area mapping / mohd nazip suratman ... [et al.]
publisher Faculty of Applied Sciences
publishDate 2005
url http://ir.uitm.edu.my/id/eprint/11797/
http://ir.uitm.edu.my/id/eprint/11797/1/AJ_MOHD%20NAZIP%20SURATMAN%20SL%2005.pdf
first_indexed 2023-09-18T22:48:33Z
last_indexed 2023-09-18T22:48:33Z
_version_ 1777417389006127104

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