Model for Estimating Above Ground Biomass of Reclamation Forest using Unmanned Aerial Vehicles

Among various stand parameters, the density of biomass volume is oftenly used as an indicator on evaluating the forest growth succes. The forest reclamation, which is intended to restore the land cover by revegetation process, the evaluation of biomass content has been a critical issue. Forest recla...

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Main Authors: Wahyuni, Sri (Author), Surati Jaya, I Nengah (Author), Puspaningsih, Nining (Author)
Format: EJournal Article
Published: Institute of Advanced Engineering and Science, 2016-12-01.
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Online Access:Get fulltext
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LEADER 02482 am a22003013u 4500
001 ijeecs6004_5379
042 |a dc 
100 1 0 |a Wahyuni, Sri  |e author 
700 1 0 |a Surati Jaya, I Nengah  |e author 
700 1 0 |a Puspaningsih, Nining  |e author 
245 0 0 |a Model for Estimating Above Ground Biomass of Reclamation Forest using Unmanned Aerial Vehicles 
260 |b Institute of Advanced Engineering and Science,   |c 2016-12-01. 
500 |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6004 
520 |a Among various stand parameters, the density of biomass volume is oftenly used as an indicator on evaluating the forest growth succes. The forest reclamation, which is intended to restore the land cover by revegetation process, the evaluation of biomass content has been a critical issue. Forest reclamation is expected to restore the land function to a proper state that might give better environment as well as productivity. In this study the authors develop a method for estimating above ground biomass (AGB), particularly in the ex open-pit coal mining area of PT. Bukit Asam Tbk using remotely-sended data taken from unmanned aerial vehicle (UAV) and developed using the least squares method. The main objective of this study is to develop a mathematical model of biomass estimation using UAV imagery having 10-cm spatial resolution. The study found that the best model of biomass estimation is: AGB(ton/ha)=0.2377Ci1.3688 with the correlation coefficient of 0.844, mean deviation of 2.29, aggregate deviation of -0.023, bias of 0.98, and Root Mean Square Error (RMSE) of 1.784 and mean deviation (MD) < 10% while Ci. This research concluded that UAV imagery could be used to estimate above ground biomass accurately. 
540 |a Copyright (c) 2016 Indonesian Journal of Electrical Engineering and Computer Science 
540 |a http://creativecommons.org/licenses/by-nc-nd/4.0 
546 |a eng 
690 |a Technology 
690 |a Electric Vehicle 
655 7 |a info:eu-repo/semantics/article  |2 local 
655 7 |a info:eu-repo/semantics/publishedVersion  |2 local 
655 7 |2 local 
786 0 |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 4, No 3: December 2016; 586-593 
786 0 |n 2502-4760 
786 0 |n 2502-4752 
786 0 |n 10.11591/ijeecs.v4.i3 
787 0 |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6004/5379 
856 4 1 |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6004/5379  |z Get fulltext