Cover Image

Classification of tropical rainforest using different classification algorithm based on remote sensing imagery: A study of Gunung Basor

Remote sensing technologies are used globally to derive some of crucial spatial variable parameter such as vegetation cover. Three different classification algorithm, minimum distance classifier, Mahalanobis distance classifier and maximum likelihood algorithm was applied to classify the forest area...

Full description

Saved in:
Bibliographic Details
Main Author: Intan Noradybah Md Rodi
Format: Undergraduate Final Project Report
Language:English
Published: 2019
Online Access:http://discol.umk.edu.my/id/eprint/4513/1/Intan%20Noradybah%20Bt%20Md%20Rodi.pdf
http://discol.umk.edu.my/id/eprint/4513/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.umk.eprints.4513
record_format eprints
spelling my.umk.eprints.45132022-05-23T21:52:36Z http://discol.umk.edu.my/id/eprint/4513/ Classification of tropical rainforest using different classification algorithm based on remote sensing imagery: A study of Gunung Basor Intan Noradybah Md Rodi Remote sensing technologies are used globally to derive some of crucial spatial variable parameter such as vegetation cover. Three different classification algorithm, minimum distance classifier, Mahalanobis distance classifier and maximum likelihood algorithm was applied to classify the forest area in Gunung Basor. The study area is located in Gunung Basor, Jeli. The area is a high potential growing region for different tree species. The main objectives is to develop a forest tree recognition techniques and build a classification strategy for forest tree area segmentation. By producing classification map, accuracy for the classification can be determined. Thehighest accuracy for classification map of Gunung Basor is by using maximum likelihood algorithm with an accuracy of 82.90%. Thus, this project is importantto increase theaccuracy offorest classification by usingminimumdistance classifier, Mahalanobis distance classifier and maximum likelihood algorithm to develop a techniques for forest tree recognition based on remote sensing imagery. Hence, the result from this study represent the synergistic use of high resolution opticalimagerycanbeefficienttoimprovethecharacterizationoftropicalrainforest. 2019 Undergraduate Final Project Report NonPeerReviewed text en http://discol.umk.edu.my/id/eprint/4513/1/Intan%20Noradybah%20Bt%20Md%20Rodi.pdf Intan Noradybah Md Rodi (2019) Classification of tropical rainforest using different classification algorithm based on remote sensing imagery: A study of Gunung Basor. Final Year Project thesis, Universiti Malaysia Kelantan. (Submitted)
institution Universiti Malaysia Kelantan
building Perpustakaan Universiti Malaysia Kelantan
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Kelantan
content_source UMK Institutional Repository
url_provider http://umkeprints.umk.edu.my/
language English
description Remote sensing technologies are used globally to derive some of crucial spatial variable parameter such as vegetation cover. Three different classification algorithm, minimum distance classifier, Mahalanobis distance classifier and maximum likelihood algorithm was applied to classify the forest area in Gunung Basor. The study area is located in Gunung Basor, Jeli. The area is a high potential growing region for different tree species. The main objectives is to develop a forest tree recognition techniques and build a classification strategy for forest tree area segmentation. By producing classification map, accuracy for the classification can be determined. Thehighest accuracy for classification map of Gunung Basor is by using maximum likelihood algorithm with an accuracy of 82.90%. Thus, this project is importantto increase theaccuracy offorest classification by usingminimumdistance classifier, Mahalanobis distance classifier and maximum likelihood algorithm to develop a techniques for forest tree recognition based on remote sensing imagery. Hence, the result from this study represent the synergistic use of high resolution opticalimagerycanbeefficienttoimprovethecharacterizationoftropicalrainforest.
format Undergraduate Final Project Report
author Intan Noradybah Md Rodi
spellingShingle Intan Noradybah Md Rodi
Classification of tropical rainforest using different classification algorithm based on remote sensing imagery: A study of Gunung Basor
author_facet Intan Noradybah Md Rodi
author_sort Intan Noradybah Md Rodi
title Classification of tropical rainforest using different classification algorithm based on remote sensing imagery: A study of Gunung Basor
title_short Classification of tropical rainforest using different classification algorithm based on remote sensing imagery: A study of Gunung Basor
title_full Classification of tropical rainforest using different classification algorithm based on remote sensing imagery: A study of Gunung Basor
title_fullStr Classification of tropical rainforest using different classification algorithm based on remote sensing imagery: A study of Gunung Basor
title_full_unstemmed Classification of tropical rainforest using different classification algorithm based on remote sensing imagery: A study of Gunung Basor
title_sort classification of tropical rainforest using different classification algorithm based on remote sensing imagery: a study of gunung basor
publishDate 2019
url http://discol.umk.edu.my/id/eprint/4513/1/Intan%20Noradybah%20Bt%20Md%20Rodi.pdf
http://discol.umk.edu.my/id/eprint/4513/
_version_ 1763303424629145600
score 13.211869

Similar Items