Analisis Aboveground Carbon Stock Berbasis UAV dan Backpack LiDAR Point Clouds dengan Metode Pendekatan Karakter Fisiognomi Pohon

Prasetyo, Widodo Eko Praseyo (2025) Analisis Aboveground Carbon Stock Berbasis UAV dan Backpack LiDAR Point Clouds dengan Metode Pendekatan Karakter Fisiognomi Pohon. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pohon memiliki peran krusial dalam mitigasi perubahan iklim melalui kemampuan menyerap karbon atmosferik. Namun, estimasi biomassa dan cadangan karbon di atas permukaan tanah (Above Ground Biomass/AGB) masih banyak bergantung pada metode alometrik yang bersifat generalisasi, sehingga kurang merepresentasikan keragaman struktur morfologis pohon secara rinci. Penelitian ini bertujuan untuk mengestimasi AGB dan cadangan karbon individu pohon secara lebih akurat dengan memanfaatkan algoritma Adaptive Quantitative Structural Model (AdQSM) berbasis kombinasi data Backpack LiDAR dan UAV LiDAR. Data diperoleh dari tiga sistem LiDAR: (1) Low-cost Backpack LiDAR, (2) LiGrip H120, dan (3) UAV LiDAR L2 Sensor. Selanjutnya dilakukan segmentasi individu pohon secara manual, pemodelan struktur 3D batang dan cabang menggunakan AdQSM, serta perhitungan volume, AGB, dan cadangan karbon berdasarkan model geometrik. Hasil estimasi AdQSM dibandingkan dengan metode alometrik sebagai acuan evaluasi. Penelitian ini dilakukan pada 51 pohon di Kebun Bibit Wonorejo, Surabaya. Hasil menunjukkan bahwa estimasi AGB dan cadangan karbon menggunakan AdQSM secara konsisten lebih tinggi dibandingkan alometrik. Rata-rata AGB berdasarkan AdQSM mencapai 0,294 ton per pohon, meningkat 62,43% dari nilai alometrik sebesar 0,181 ton. Estimasi cadangan karbon juga meningkat sebesar 61,4%. Perbedaan ini mencerminkan keunggulan AdQSM dalam memodelkan geometri pohon secara lebih detail, terutama pada pohon dengan percabangan kompleks dan kanopi rapat (canopy openness <50%). Sebaliknya, pada pohon kecil, metode alometrik justru cenderung menghasilkan estimasi lebih tinggi, mengindikasikan potensi overestimasi akibat generalisasi. Temuan ini menegaskan pentingnya pendekatan struktural berbasis LiDAR dan AdQSM dalam mendukung estimasi karbon yang lebih akurat.
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Trees play a crucial role in mitigating climate change through their ability to absorb atmospheric carbon. However, the estimation of above-ground biomass (AGB) and carbon stock still heavily relies on allometric methods, which are generalized and less capable of accurately representing the morphological variability of tree structures. This study aims to estimate individual tree AGB and carbon stock more accurately by utilizing the Adaptive Quantitative Structural Model (AdQSM) algorithm based on combined data from Backpack LiDAR and UAV LiDAR systems. Data were acquired using three different LiDAR systems: (1) Low-cost Backpack LiDAR, (2) LiGrip H120, and (3) UAV LiDAR L2 Sensor. Individual trees were manually segmented, followed by 3D modeling of trunks and branches using AdQSM, and the calculation of volume, AGB, and carbon stock based on geometric models. The AdQSM estimates were then compared with allometric estimates as a reference for evaluation. The study was conducted on 51 trees at the Kebun Bibit Wonorejo urban forest in Surabaya. The results show that AGB and carbon stock estimated using AdQSM are consistently higher than those estimated using allometric methods. The average AGB based on AdQSM reached 0.294 tons per tree, an increase of 62.43% compared to the allometric value of 0.181 tons. The estimated carbon stock also increased by 61.4%. This difference highlights AdQSM's superiority in modeling tree geometry in greater detail, especially for trees with complex branching structures and dense canopies (canopy openness <50%). Conversely, in smaller trees, allometric methods tended to yield higher estimates, indicating potential overestimation due to model generalization. These findings underscore the importance of LiDAR- and AdQSM-based structural approaches in supporting more accurate carbon estimation.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	AdQSM, LiDAR, Above Ground Biomass, Carbon Stock, Alometrik, Pohon Individu, AdQSM, LiDAR, Above Ground Biomass, Carbon Stock, Allometric, Individual Tree
Subjects:	G Geography. Anthropology. Recreation > G Geography (General) > G70.217 Geospatial data G Geography. Anthropology. Recreation > GE Environmental Sciences > GE195.5 Green movement Q Science > QK Botany > QK710 Plant physiology T Technology > TD Environmental technology. Sanitary engineering > TD171.75 Climate change mitigation
Divisions:	Faculty of Civil Engineering and Planning > Geomatics Engineering > 29101-(S2) Master Thesis
Depositing User:	Widodo Eko Prasetyo
Date Deposited:	29 Jul 2025 01:23
Last Modified:	29 Jul 2025 01:23
URI:	http://repository.its.ac.id/id/eprint/122858

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