2025
|
Hendriatna, Adis; Prasetyo, Lilik B; Kusrini, Mirza Dikari; Setiawan, Yudi Multi-sensor data utilization of unmanned aerial vehicle for wildlife monitoring in Komodo National Park Journal Article In: Ecological Engineering & Environmental Technology, vol. 26, iss. 3, pp. 315-329, 2025. @article{nokey,
title = {Multi-sensor data utilization of unmanned aerial vehicle for wildlife monitoring in Komodo National Park},
author = {Adis Hendriatna and Lilik B Prasetyo and Mirza Dikari Kusrini and Yudi Setiawan},
doi = {https://doi.org/10.12912/27197050/200185},
year = {2025},
date = {2025-02-01},
journal = {Ecological Engineering & Environmental Technology},
volume = {26},
issue = {3},
pages = {315-329},
abstract = {The use of unmanned aerial vehicles (UAVs) equipped with multispectral and thermal sensors provides a promising approach to wildlife monitoring, especially in the dynamic environment of Komodo National Park. This study explores the effectiveness of UAVs in tracking Komodo dragons and other wildlife using thermal imaging, which distinguishes animals based on body temperature contrasts with the surrounding environment. Thermal sensors detect wildlife more effectively in the afternoon, as animals like the Komodo dragon exhibit higher body temperatures compared to the cooler surroundings. Challenges, however, arise in the morning when animals body temperatures are closer to the environment, making them harder to detect. Factors such as fog, animal movement, and sensor limitations also impact detection accuracy. The study highlights the advantages of combining UAV thermal imaging with multispectral data to enhance monitoring accuracy. Despite the challenges, this method proves to be an efficient tool for wildlife management and conservation in remote, vast areas like Komodo National Park.},
keywords = {komodo, UAV},
pubstate = {published},
tppubtype = {article}
}
The use of unmanned aerial vehicles (UAVs) equipped with multispectral and thermal sensors provides a promising approach to wildlife monitoring, especially in the dynamic environment of Komodo National Park. This study explores the effectiveness of UAVs in tracking Komodo dragons and other wildlife using thermal imaging, which distinguishes animals based on body temperature contrasts with the surrounding environment. Thermal sensors detect wildlife more effectively in the afternoon, as animals like the Komodo dragon exhibit higher body temperatures compared to the cooler surroundings. Challenges, however, arise in the morning when animals body temperatures are closer to the environment, making them harder to detect. Factors such as fog, animal movement, and sensor limitations also impact detection accuracy. The study highlights the advantages of combining UAV thermal imaging with multispectral data to enhance monitoring accuracy. Despite the challenges, this method proves to be an efficient tool for wildlife management and conservation in remote, vast areas like Komodo National Park. |
2019
|
Sujaswara, Azwar A; Setiawan, Yudi; Prasetyo, Lilik B; Hudjimartsu, Sahid A; Wijayanto, Arif K Utilization of UAV technology for vegetation cover mapping using object based image analysis in restoration area of Gunung Halimun Salak National Park, Indonesia Proceedings Article In: Sixth International Symposium on LAPAN-IPB Satellite, pp. 1137221, International Society for Optics and Photonics 2019. @inproceedings{sujaswara2019utilization,
title = {Utilization of UAV technology for vegetation cover mapping using object based image analysis in restoration area of Gunung Halimun Salak National Park, Indonesia},
author = {Azwar A Sujaswara and Yudi Setiawan and Lilik B Prasetyo and Sahid A Hudjimartsu and Arif K Wijayanto},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11372/1137221/Utilization-of-UAV-technology-for-vegetation-cover-mapping-using-object/10.1117/12.2540566.short},
doi = {10.1117/12.2540566},
year = {2019},
date = {2019-01-01},
booktitle = {Sixth International Symposium on LAPAN-IPB Satellite},
volume = {11372},
pages = {1137221},
organization = {International Society for Optics and Photonics},
abstract = {Halimun Salak Corridor (HSC) is an important area that connects the Mount Halimun and Mount Salak, and has important role of animals movements. As the corridor have become degraded over the last ten years, ecosystem restoration action is required. In order to monitor that restoration program, then, it is necessary to mapping the vegetation cover in the corridor. Unmanned Aerial Vehicle (UAV) technology is an alternative technology that can be used to provide a detail vegetation cover map based on a high resolution image. This research aim to mapping vegetation cover based on a combination of structural characteristics of height and vegetation indices by using Object Based Image Analysis (OBIA) method. Structural characteristics was defined from the canopy height model (CHM) using the Structure from Motion (SfM) method, meanwhile, several spectral indices (NDVI, NDWI, and SAVI) were produced from multispectral images. We applied Object Based Image Analysis (OBIA) to classify vegetation cover based on their structure and spectral characteristics. The results shown that the most dominant vegetation cover is the tree class, which is 70.74 ha (77.31 % of the 91.5 ha mapped area) and accuracy test revealed 73.11% of overall accuracy.},
keywords = {UAV},
pubstate = {published},
tppubtype = {inproceedings}
}
Halimun Salak Corridor (HSC) is an important area that connects the Mount Halimun and Mount Salak, and has important role of animals movements. As the corridor have become degraded over the last ten years, ecosystem restoration action is required. In order to monitor that restoration program, then, it is necessary to mapping the vegetation cover in the corridor. Unmanned Aerial Vehicle (UAV) technology is an alternative technology that can be used to provide a detail vegetation cover map based on a high resolution image. This research aim to mapping vegetation cover based on a combination of structural characteristics of height and vegetation indices by using Object Based Image Analysis (OBIA) method. Structural characteristics was defined from the canopy height model (CHM) using the Structure from Motion (SfM) method, meanwhile, several spectral indices (NDVI, NDWI, and SAVI) were produced from multispectral images. We applied Object Based Image Analysis (OBIA) to classify vegetation cover based on their structure and spectral characteristics. The results shown that the most dominant vegetation cover is the tree class, which is 70.74 ha (77.31 % of the 91.5 ha mapped area) and accuracy test revealed 73.11% of overall accuracy. |
