Dominant tree species classification using remote sensing data and object-based image analysis [OBIA] / Juhaida Jamal

Rainforest was a type of forest that is rich in flora and fauna species. Over the last few decades, forests have been the victims of over logging and deforestation due to forest transformation activities and high demand for a piece of wood. Uncontrolled of this activity gave an impact to the tree sp...

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Main Author:	Jamal, Juhaida
Format:	Thesis
Language:	English
Published:	2021
Subjects:	Aerial geography Remote Sensing SD Forestry
Online Access:	https://ir.uitm.edu.my/id/eprint/43233/1/43233.pdf
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id	my-uitm-ir.43233
record_format	uketd_dc
spelling	my-uitm-ir.432332021-03-19T08:07:34Z Dominant tree species classification using remote sensing data and object-based image analysis [OBIA] / Juhaida Jamal 2021-03-09 Jamal, Juhaida Aerial geography Remote Sensing SD Forestry Rainforest was a type of forest that is rich in flora and fauna species. Over the last few decades, forests have been the victims of over logging and deforestation due to forest transformation activities and high demand for a piece of wood. Uncontrolled of this activity gave an impact to the tree species to be endangered and some of the species experienced in the extinction phase. A detailed inventory of tree species is needed to manage and plan the forest on a sustainable basis. Many techniques had been done to identify the tree species, but in the recent three decades, remote sensing technique was widely used to study the distribution of tree species for the large and inaccessible area. In this study, an object-based image analysis (OBIA) with a combination of high-resolution multispectral satellite imagery (WV-2) and airborne laser scanning (LiDAR) data was tested for classification of individual tree crowns of tropical tree species at Forest Research Institute Malaysia (FRIM) forest, Selangor. LiDAR data was taken using fixed-wing aircraft with Gemini Airborne Laser Terrain Mapper (ALTM) laser with 0.15m and 0.25 resolution for horizontal and vertical. WV-2 was captured with a 0.5m spatial resolution. In this study, hyperspectral data captured using bayspec sensor mount at UAV with height 220m from the ground and have 0.3 resolution was used to extract the spectral reflectance of tree species. Study area with total area 3.5 hectare were grid into 35 rectangular subplots with each subplot have 500m2 area. Segmentation of the image was performed using multi-resolution segmentation in eCognition software. Accuracy assessment for segmentation was done by measure the ‘goodness fit’ (D value) between training object and output segmentation. The overall accuracy of the segmentation was 86%. For species classification, the accuracy assessment was performed using the error matrix confusion technique to 7 classes of tree species. The result had shown the overall accuracy classification was 64%. 2021-03 Thesis https://ir.uitm.edu.my/id/eprint/43233/ https://ir.uitm.edu.my/id/eprint/43233/1/43233.pdf text en public degree Universiti Teknologi MARA Perlis Faculty of Architecture, Planning and Surveying
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Aerial geography Remote Sensing SD Forestry
spellingShingle	Aerial geography Remote Sensing SD Forestry Jamal, Juhaida Dominant tree species classification using remote sensing data and object-based image analysis [OBIA] / Juhaida Jamal
description	Rainforest was a type of forest that is rich in flora and fauna species. Over the last few decades, forests have been the victims of over logging and deforestation due to forest transformation activities and high demand for a piece of wood. Uncontrolled of this activity gave an impact to the tree species to be endangered and some of the species experienced in the extinction phase. A detailed inventory of tree species is needed to manage and plan the forest on a sustainable basis. Many techniques had been done to identify the tree species, but in the recent three decades, remote sensing technique was widely used to study the distribution of tree species for the large and inaccessible area. In this study, an object-based image analysis (OBIA) with a combination of high-resolution multispectral satellite imagery (WV-2) and airborne laser scanning (LiDAR) data was tested for classification of individual tree crowns of tropical tree species at Forest Research Institute Malaysia (FRIM) forest, Selangor. LiDAR data was taken using fixed-wing aircraft with Gemini Airborne Laser Terrain Mapper (ALTM) laser with 0.15m and 0.25 resolution for horizontal and vertical. WV-2 was captured with a 0.5m spatial resolution. In this study, hyperspectral data captured using bayspec sensor mount at UAV with height 220m from the ground and have 0.3 resolution was used to extract the spectral reflectance of tree species. Study area with total area 3.5 hectare were grid into 35 rectangular subplots with each subplot have 500m2 area. Segmentation of the image was performed using multi-resolution segmentation in eCognition software. Accuracy assessment for segmentation was done by measure the ‘goodness fit’ (D value) between training object and output segmentation. The overall accuracy of the segmentation was 86%. For species classification, the accuracy assessment was performed using the error matrix confusion technique to 7 classes of tree species. The result had shown the overall accuracy classification was 64%.
format	Thesis
qualification_level	Bachelor degree
author	Jamal, Juhaida
author_facet	Jamal, Juhaida
author_sort	Jamal, Juhaida
title	Dominant tree species classification using remote sensing data and object-based image analysis [OBIA] / Juhaida Jamal
title_short	Dominant tree species classification using remote sensing data and object-based image analysis [OBIA] / Juhaida Jamal
title_full	Dominant tree species classification using remote sensing data and object-based image analysis [OBIA] / Juhaida Jamal
title_fullStr	Dominant tree species classification using remote sensing data and object-based image analysis [OBIA] / Juhaida Jamal
title_full_unstemmed	Dominant tree species classification using remote sensing data and object-based image analysis [OBIA] / Juhaida Jamal
title_sort	dominant tree species classification using remote sensing data and object-based image analysis [obia] / juhaida jamal
granting_institution	Universiti Teknologi MARA Perlis
granting_department	Faculty of Architecture, Planning and Surveying
publishDate	2021
url	https://ir.uitm.edu.my/id/eprint/43233/1/43233.pdf
_version_	1783734684503506944

Dominant tree species classification using remote sensing data and object-based image analysis [OBIA] / Juhaida Jamal

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