Semi-automatic oil palm tree counting from pleiades satellite imagery and airborne LiDAR / Nurul Syafiqah Khalid

Oil palm is becoming an important source in its production of vegetable oil. Oil palm tree information is important for sustainability assessments and agriculture precision. Therefore, the oil palm tree counting technique is crucial to monitor the development of the oil palm plantations especially w...

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Main Author:	Khalid, Nurul Syafiqah
Format:	Thesis
Language:	English
Published:	2020
Subjects:	Aerial geography Remote Sensing Geographic information systems Geospatial data
Online Access:	https://ir.uitm.edu.my/id/eprint/33608/1/33608.pdf
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id	my-uitm-ir.33608
record_format	uketd_dc
spelling	my-uitm-ir.336082020-08-17T09:28:50Z Semi-automatic oil palm tree counting from pleiades satellite imagery and airborne LiDAR / Nurul Syafiqah Khalid 2020-08-17 Khalid, Nurul Syafiqah Aerial geography Remote Sensing Geographic information systems Geospatial data Oil palm is becoming an important source in its production of vegetable oil. Oil palm tree information is important for sustainability assessments and agriculture precision. Therefore, the oil palm tree counting technique is crucial to monitor the development of the oil palm plantations especially when it can to a large area. However, the most difficulties are to develop a method to detect, extract and count trees automatically from the image. This study aimed to develop the automatic oil palm tree counting using remote sensed data and two different algorithms at Felda Pasoh. There are three objectives, firstly, to produce tree height estimation by using the Canopy Height Model (CHM). Secondly, to develop the rule sets of watershed transformation segmentation and local maxima algorithm using Pleiades and LiDAR. Lastly, to compare the accuracy assessment of watershed transformation segmentation and local maxima algorithm. The data used is Pleiades high spatial resolution satellite imagery and LiDAR data. In this study, the software used for data processing and analysis includes eCognition, ERDAS, and ArcGIS. The study is to categorize and evaluates methods for automatic tree counting detection. For the methodology of this study, object-based image analysis (OBIA), watershed transformation segmentation and local maxima algorithm are applied. The CHM result shows the lowest height was determined at 0m and the highest was 9.376m. Therefore, the final output of tree crown shows the watershed transformation algorithm is the best method for use represented oil palm tree counting in the map which is the accuracy assessment is 38.9%. 2020-08 Thesis https://ir.uitm.edu.my/id/eprint/33608/ https://ir.uitm.edu.my/id/eprint/33608/1/33608.pdf text en public degree Universiti Teknologi Mara Perlis Faculty of Architecture, Planning and Surverying
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Aerial geography Remote Sensing Geographic information systems Geospatial data
spellingShingle	Aerial geography Remote Sensing Geographic information systems Geospatial data Khalid, Nurul Syafiqah Semi-automatic oil palm tree counting from pleiades satellite imagery and airborne LiDAR / Nurul Syafiqah Khalid
description	Oil palm is becoming an important source in its production of vegetable oil. Oil palm tree information is important for sustainability assessments and agriculture precision. Therefore, the oil palm tree counting technique is crucial to monitor the development of the oil palm plantations especially when it can to a large area. However, the most difficulties are to develop a method to detect, extract and count trees automatically from the image. This study aimed to develop the automatic oil palm tree counting using remote sensed data and two different algorithms at Felda Pasoh. There are three objectives, firstly, to produce tree height estimation by using the Canopy Height Model (CHM). Secondly, to develop the rule sets of watershed transformation segmentation and local maxima algorithm using Pleiades and LiDAR. Lastly, to compare the accuracy assessment of watershed transformation segmentation and local maxima algorithm. The data used is Pleiades high spatial resolution satellite imagery and LiDAR data. In this study, the software used for data processing and analysis includes eCognition, ERDAS, and ArcGIS. The study is to categorize and evaluates methods for automatic tree counting detection. For the methodology of this study, object-based image analysis (OBIA), watershed transformation segmentation and local maxima algorithm are applied. The CHM result shows the lowest height was determined at 0m and the highest was 9.376m. Therefore, the final output of tree crown shows the watershed transformation algorithm is the best method for use represented oil palm tree counting in the map which is the accuracy assessment is 38.9%.
format	Thesis
qualification_level	Bachelor degree
author	Khalid, Nurul Syafiqah
author_facet	Khalid, Nurul Syafiqah
author_sort	Khalid, Nurul Syafiqah
title	Semi-automatic oil palm tree counting from pleiades satellite imagery and airborne LiDAR / Nurul Syafiqah Khalid
title_short	Semi-automatic oil palm tree counting from pleiades satellite imagery and airborne LiDAR / Nurul Syafiqah Khalid
title_full	Semi-automatic oil palm tree counting from pleiades satellite imagery and airborne LiDAR / Nurul Syafiqah Khalid
title_fullStr	Semi-automatic oil palm tree counting from pleiades satellite imagery and airborne LiDAR / Nurul Syafiqah Khalid
title_full_unstemmed	Semi-automatic oil palm tree counting from pleiades satellite imagery and airborne LiDAR / Nurul Syafiqah Khalid
title_sort	semi-automatic oil palm tree counting from pleiades satellite imagery and airborne lidar / nurul syafiqah khalid
granting_institution	Universiti Teknologi Mara Perlis
granting_department	Faculty of Architecture, Planning and Surverying
publishDate	2020
url	https://ir.uitm.edu.my/id/eprint/33608/1/33608.pdf
_version_	1783734246385385472

Semi-automatic oil palm tree counting from pleiades satellite imagery and airborne LiDAR / Nurul Syafiqah Khalid

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