Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri

Estimating forest biomass in a small-scale forest area is more accurate as it depends on actual field measurements. However, measuring the field to estimate forest biomass in a large region is not feasible because it is labour intensive, a lengthy process and expensive. Therefore, this study aimed (...

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Main Author:	Mohd Asri, Azinuddin
Format:	Thesis
Language:	English
Published:	2022
Subjects:	Aerial geography Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/60315/1/60315.pdf
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id	my-uitm-ir.60315
record_format	uketd_dc
spelling	my-uitm-ir.603152022-06-22T22:30:45Z Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri 2022-02 Mohd Asri, Azinuddin Aerial geography Neural networks (Computer science) Estimating forest biomass in a small-scale forest area is more accurate as it depends on actual field measurements. However, measuring the field to estimate forest biomass in a large region is not feasible because it is labour intensive, a lengthy process and expensive. Therefore, this study aimed (i) to classify the forest aboveground biomass by estimating crown projection area using object-based image analysis (OBIA) and (ii) to determine the accuracy assessment for estimating forest aboveground biomass using an artificial neural network (ANN) and Random Forest (RF). Object-based image analysis (OBIA) is one of the methods designed to classify the satellite image using multiresolution segmentation to obtain the value of the crown projection area (CPA). In contrast, machine learning is used to calculate the accuracy assessment of dependent between independent variables. A combination of approaches has been tested to estimate the forest's aboveground biomass and carbon stock for Selangor’s terrestrial ecosystem environment. The result shows the goodness of fit for the distance index (D) was 0.040. The total accuracy for 30 reference polygons was 96.6% and 96% for the total accuracy for distance index (D). The statistical values for min, max, mean, and standard deviation of carbon stock (kg/tree) were 4.891, 196.250, 101.142, and 46.340. The Random Forest algorithm was the best algorithm compared to the artificial neural network, which produced the highest R2 (0.998) and lowered RSME (55.067). The suitable independent variables (hL, DBH, and CPA) were vital to estimating the dependent variable (Sc) and producing a carbon stock map for the final result. The significant of this study is to prove that the application of object-based image analysis classification and machine learning algorithms for forest aboveground biomass and carbon stock estimation has excellent potential for the future management of forests to maintain their existence and growth. 2022-02 Thesis https://ir.uitm.edu.my/id/eprint/60315/ https://ir.uitm.edu.my/id/eprint/60315/1/60315.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 Neural networks (Computer science)
spellingShingle	Aerial geography Neural networks (Computer science) Mohd Asri, Azinuddin Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri
description	Estimating forest biomass in a small-scale forest area is more accurate as it depends on actual field measurements. However, measuring the field to estimate forest biomass in a large region is not feasible because it is labour intensive, a lengthy process and expensive. Therefore, this study aimed (i) to classify the forest aboveground biomass by estimating crown projection area using object-based image analysis (OBIA) and (ii) to determine the accuracy assessment for estimating forest aboveground biomass using an artificial neural network (ANN) and Random Forest (RF). Object-based image analysis (OBIA) is one of the methods designed to classify the satellite image using multiresolution segmentation to obtain the value of the crown projection area (CPA). In contrast, machine learning is used to calculate the accuracy assessment of dependent between independent variables. A combination of approaches has been tested to estimate the forest's aboveground biomass and carbon stock for Selangor’s terrestrial ecosystem environment. The result shows the goodness of fit for the distance index (D) was 0.040. The total accuracy for 30 reference polygons was 96.6% and 96% for the total accuracy for distance index (D). The statistical values for min, max, mean, and standard deviation of carbon stock (kg/tree) were 4.891, 196.250, 101.142, and 46.340. The Random Forest algorithm was the best algorithm compared to the artificial neural network, which produced the highest R2 (0.998) and lowered RSME (55.067). The suitable independent variables (hL, DBH, and CPA) were vital to estimating the dependent variable (Sc) and producing a carbon stock map for the final result. The significant of this study is to prove that the application of object-based image analysis classification and machine learning algorithms for forest aboveground biomass and carbon stock estimation has excellent potential for the future management of forests to maintain their existence and growth.
format	Thesis
qualification_level	Bachelor degree
author	Mohd Asri, Azinuddin
author_facet	Mohd Asri, Azinuddin
author_sort	Mohd Asri, Azinuddin
title	Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri
title_short	Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri
title_full	Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri
title_fullStr	Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri
title_full_unstemmed	Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri
title_sort	assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / azinuddin mohd asri
granting_institution	Universiti Teknologi Mara Perlis
granting_department	Faculty of Architecture, Planning and Surveying
publishDate	2022
url	https://ir.uitm.edu.my/id/eprint/60315/1/60315.pdf
_version_	1783735112556347392

Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri

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