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All Journal Ecotrophic, Journal of Environmental Science Jurnal Sumberdaya Alam dan Lingkungan
Takahiro Osawa
Center for Research and Application of Satellite Remote Sensing (YUCARS) Yamaguchi University, Japan
Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Environmental Science

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CARBON STOCKS ESTIMATION ON URBAN VEGETATION USING UAV-SfM PHOTOGRAMMETRY METHOD Agus Sukma Yogiswara; Takahiro Osawa; I Wayan Nuarsa; Abd. Rahman As-syakur
ECOTROPHIC : Jurnal Ilmu Lingkungan (Journal of Environmental Science) Vol 17 No 1 (2023)
Publisher : Master Program of Environmental Science, Postgraduate Program of Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/EJES.2023.v17.i01.p04

Abstract

Global warming and biodiversity loss are critical issues, and forest retention and reforestation programs are crucial in mitigating climate change. However, discussions around these programs often exclude the role of urban vegetation in carbon sequestration. Preserving urban vegetation, such as trees, can also significantly reduce carbon emissions. Urban vegetation can be found in two main locations: Urban Green Open Spaces (UGS) and Road Landscapes (RL). In Denpasar Bali, Glodokan Tiang or Polyalthia longifolia trees are planted at those locations. Data management and carbon stock calculation mechanisms are required to demonstrate the contribution of urban vegetation in terms of carbon sequestration. The technology of Unmanned Aerial Vehicle (UAV) can be used as an alternative to efficiently calculate the estimated carbon stock. The calculation uses the Diameter Breast High (DBH) value approach using the canopy area and Canopy Height Model (CHM) obtained from UAV data processing using the Sfm method. UAV estimates show that the highest Above Ground Biomass (AGB) value at Bajra Sandhi Renon Field is 201.59 kg with a stored carbon content of 94.75 kg, while on I Gusti Ngurah Rai Bypass has the highest AGB value of 215.04 kg with a stored carbon content of 101.07 kg. These results have been validated by field observations, where the results of the regression analysis at the location of Bajra Sandhi Renon and I Gusti Ngurah Rai, show that between field observation data and estimation data with UAV there is no significant difference. While the results of the t-test: Paired Two Sample for Means at the Bajra Sandhi Renon Field and the Bypass I Gusti Ngurah Rai have a value above the significance level which proves that there is no significant difference between the carbon stock value from field observations and the carbon stock from the UAV approach. Keywords: Carbon Stock; Above Ground Biomass; Urban Vegetation; UAV-Sfm
Advancing Fauna Conservation through Machine Learning-Based Spectrogram Recognition: A Study on Object Detection using YOLOv5 Badrul Huda Husain; Takahiro Osawa
Jurnal Sumberdaya Alam dan Lingkungan Vol 10, No 2 (2023)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jsal.2023.010.02.2

Abstract

ABSTRACT The protection and monitoring of fauna species are essential for maintaining biodiversity and ensuring the sustainability of ecosystems. Traditional methods of fauna conservation and habitat monitoring rely heavily on manual observation and data collection, which can be time-consuming, and labor-intensive. In recent years, the application of machine learning techniques, such as object detection, has shown great potential in automating the identification of fauna species. In this study, we propose an approach to advancing fauna conservation through the utilization of machine learning-based spectrogram recognition. Specifically, we employ an object detection algorithm, YOLOv5, to detect and classify fauna species from spectrogram images obtained from acoustic recordings. The spectrograms provide a visual representation of audio signals, capturing distinct patterns and characteristics unique to different fauna species. Through extensive experimentation and evaluation, our approach achieved promising results, demonstrating a precision of 0.95, recall of 0.98, F1 score of 0.91, and mean Average Precision (mAP) of 0.934. These performance metrics indicate a high level of accuracy and reliability in fauna species detection. By automating the identification process, our approach provides a scalable solution for monitoring fauna populations over large geographical areas and enables the collection of comprehensive data, facilitating better decision-making and targeted conservation strategies. Keywords: acoustic recording, fauna conservation, machine learning, spectrogram, YOLOv5
Co-Authors Abd. Rahman As-syakur Agus Sukma Yogiswara Badrul Huda Husain I Wayan Nuarsa