Grouping Land Cover Using Orthophoto in Small Islands: An Application of Low-Cost UAV on Mansinam Island
DOI:
https://doi.org/10.23917/forgeo.v38i2.4870Keywords:
UAV, Land cover, Mansinam, Forest, Small islandAbstract
Land cover is crucial for island management, but the lack of accessible and high-resolution remote sensing data has reduced investigations on small islands, including land cover identification. Therefore, this study aimed to investigate land cover using Unmanned Aerial Vehicle (UAV) technology, providing very high-resolution images. Classification and delineation were conducted using automatic segmentation followed by manual reinterpretation and visual verification. The results showed 14 cover classes, consisting of 8 vegetated and six non-vegetated categories. Forest cover on Mansinam island accounted for 75.5% or 302.4 ha, which was evenly distributed. Furthermore, primary forest covered 31.91% or 127.74 ha, and secondary covered 43.63% or 174.68 ha. The classification achieved an overall accuracy of 96% and a kappa coefficient of 0.94. Low-cost UAVs effectively produced high-resolution aerial images of small islands for land cover identification. Therefore, future studies were recommended to consider whether segmentation can reliably distinguish between primary and secondary forests, as well as assess the impact of flight altitude on segmentation accuracy using ground control points. The results were also expected to support spatial planning or sustainable forest and environment management on Mansinam Island.
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Akca, S., & Polat, N. (2022). Semantic segmentation and quantification of trees in an orchard using UAV orthophoto. Earth Science Informatics, 15(4), 2265–2274. doi: 10.1007/s12145-022-00871-y [Google Scholar] [CrossRef]
Akturk, E., & Altunel, A. O. (2019). Accuracy assesment of a low-cost UAV derived digital elevation model (DEM) in a highly broken and vegetated terrain. Measurement: Journal of the International Measurement Confederation, 136, 382–386. doi: 10.1016/j.measurement.2018.12.101 [Google Scholar] [CrossRef]
Aldyan, F., Hernawati, R., Minarno, P., Afif, H., & Solikhin, A. (2018). Kajian Tutupan Lahan Berbasis Obyek Menggunakan Data Uav Trimble Ux5 (Wilayah Studi: Desa Pagak, Kab. Purworejo Jawa Tengah). Seminar Nasional Geomatika, 2, 517. doi: 10.24895/sng.2017.2-0.449 [Google Scholar] [CrossRef]
Arfan, A., Nyompa, S., Maru, R., Nurdin, S., & Juanda, M. F. (2021). Mapping Analysis of Mangrove Areas using Unmanned Aerial Vehicle (UAV) Method in Maros District South Sulawesi. Journal of Physics: Conference Series, 2123(1). doi: 10.1088/1742-6596/2123/1/012010 [Google Scholar] [CrossRef]
Arham, I., Sjaf, S., & Darusman, D. (2019). Strategi Pembangunan Pertanian Berkelanjutan di Pedesaan Berbasis Citra Drone (Studi Kasus Desa Sukadamai Kabupaten Bogor). Jurnal Ilmu Lingkungan, 17(2), 245. doi: 10.14710/jil.17.2.245-255 [Google Scholar] [CrossRef]
Banu, T. P., Borlea, G. F., & Banu, C. (2016). The Use of Drones in Forestry. Journal of Environmental Science and Engineering B, 5(11), 557–562. doi: 10.17265/2162-5263/2016.11.007 [Google Scholar] [CrossRef]
Bispo dos Santos, G. A., & Conti, L. A. (2022). Coastal land cover mapping using UAV imaging on the southeast coast of Brazil. Journal of Coastal Conservation, 26(5). https://doi.org/10.1007/s11852-022-00886-z[Google Scholar] [CrossRef]
BKP. (2020). Indeks Ketahanan Pangan 2020. In Badan Ketahanan Pangan (Vol. 0). Retrieved from http://bkp.pertanian.go.id/storage/app/media/2021/ikp-2020-20210120fix.pdf
BPS. (2023). Papua Barat Province In Figures 2023. Manokwari: Statistics Agency of Papua Barat Province.
Cámara-Leret, R., Frodin, D. G., Adema, F., Anderson, C., Appelhans, M. S., Argent, G., … van Welzen, P. C. (2020). New Guinea has the world’s richest island flora. Nature, 584(7822), 579–583. doi: 10.1038/s41586-020-2549-5 [Google Scholar] [CrossRef]
Cámara–Leret, R., & Dennehy, Z. (2019). Indigenous Knowledge of New Guinea’s Useful Plants: A Review. Economic Botany, 73(3), 405–415. doi: 10.1007/s12231-019-09464-1 [Google Scholar] [CrossRef]
Chen, Y., Chen, Q., & Jing, C. (2021). Multi-resolution segmentation parameters optimization and evaluation for VHR remote sensing image based on meanNSQI and discrepancy measure. Journal of Spatial Science, 66(2), 253–278. doi: 10.1080/14498596.2019.1615011 [Google Scholar] [CrossRef]
Congalton, R., & Green, K. (1957). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. In The Photogrammetric Record, 25(130), 204-205. doi: 10.1111/j.1477-9730.2010.00574_2.x [Google Scholar] [CrossRef]
Corlett, R. T. (2018). Tropical Rainforests and Climate Change. In Encyclopedia of the Anthropocene (1st ed.). Elsevier Inc, 2, 25-29. doi: 10.1016/B978-0-12-809665-9.09501-X [Google Scholar] [CrossRef]
Cover, T. M., & Hart, P. E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27. doi: 10.1109/TIT.1967.1053964 [Google Scholar] [CrossRef]
Deliry, S. I., & Avdan, U. (2021). Accuracy of Unmanned Aerial Systems Photogrammetry and Structure from Motion in Surveying and Mapping: A Review. Journal of the Indian Society of Remote Sensing, 49(8), 1997–2017. doi: 10.1007/s12524-021-01366-x [Google Scholar] [CrossRef]
Diack, I., Diene, S. M., Louise, L., Aziz, D. A., Benjamin, H., Olivier, R., … Moussa, D. (2024). Combining UAV and Sentinel-2 Imagery for Estimating Millet FCover in a Heterogeneous Agricultural Landscape of Senegal. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 7305–7322. doi: 10.1109/JSTARS.2024.3373508 [Google Scholar] [CrossRef]
DJI. (2016). Phantom 3 Profesional : User Manual. 3–6. Retrieved From https://dl.djicdn.com/downloads/phantom_3/User Manual/Phantom_3_Professional_User_Manual_v1.8_en.pdf
DJI. (2024). Mavic Pro Specs. Retrieved May 29, 2024, Retrived From https://www.dji.com/id/mavic/info
El-naggar, A. M. (2018). Determination of optimum segmentation parameter values for extracting building from remote sensing images. Alexandria Engineering Journal, 57(4), 3089–3097. doi: 10.1016/j.aej.2018.10.001 [Google Scholar] [CrossRef]
Elkhrachy, I. (2021). Accuracy Assessment of Low-Cost Unmanned Aerial Vehicle (UAV) Photogrammetry. Alexandria Engineering Journal, 60(6), 5579–5590. doi: 10.1016/j.aej.2021.04.011 [Google Scholar] [CrossRef]
Ghosh, S., Kumar, D., & Kumari, R. (2022). Cloud-based large-scale data retrieval, mapping, and analysis for land monitoring applications with Google Earth Engine (GEE). Environmental Challenges, 9, 100605. doi: 10.1016/j.envc.2022.100605 [Google Scholar] [CrossRef]
Harto, A. B., Prastiwi, P. A. D., Ariadji, F. N., Suwardhi, D., Dwivany, F. M., Nuarsa, I. W., & Wikantika, K. (2019). Identification of banana plants from unmanned aerial vehicles (UAV) photos using object based image analysis (OBIA) method (a case study in Sayang Village, Jatinangor District, West Java). HAYATI Journal of Biosciences, 26(1), 7–14. doi: 10.4308/hjb.26.1.7 [Google Scholar] [CrossRef]
Hematang, F. (2021). Pendugaan dimensi tegakan hutan di pulau mansinam kabupaten manokwari : pendekatan aplikasi uav (unmanned aerial vehicle) (Unpublished thesis). Papua University.
Hematang, F., Murdjoko, A., & Hendri. (2021). Model Pendugaan Diameter Pohon Berbasis Citra Unmanned Aerial Vehicle (Uav) Pada Hutan Hujan Tropis Papua: Studi Di Pulau Mansinam Papua Barat. Jurnal Penelitian Kehutanan Faloak, 5, 16–30. [Google Scholar]
Hematang, F., Murdjoko, A., Hendri, H., & Tokede, M. (2022). Application of Unmanned Aerial Vehicle ( UAV ) For Estimation of Tree Height in Heterogeneous Forest. Biosaintifika: Journal of Biology & Biology Education, 14(2), 168–179. [Google Scholar]
Horning, N., Fleishman, E., Ersts, P. J., Fogarty, F. A., & Wohlfeil, M. (2020). Mapping of land cover with open-source software and ultra-high-resolution imagery acquired with unmanned aerial vehicles. Remote Sensing in Ecology and Conservation, 6(4), 487–497. doi: 10.1002/rse2.144 [Google Scholar] [CrossRef]
Hyeok, K. G., & Wan, C. J. (2017). Land cover classifcation with high spatial resolution using orthoimage and DSM based on fixed-wing UAV. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 35(1), 1–10. doi: 10.7848/ksgpc.2017.35.1.1 [Google Scholar] [CrossRef]
Jodhani, K. H., Patel, D., Madhavan, N., & Singh, S. K. (2023). Soil Erosion Assessment by RUSLE, Google Earth Engine, and Geospatial Techniques over Rel River Watershed, Gujarat, India. Water Conservation Science and Engineering, 8(1). doi: 10.1007/s41101-023-00223-x [Google Scholar] [CrossRef]
Jumaat, N. F. H., Ahmad, B., & Dutsenwai, H. S. (2018). Land cover change mapping using high resolution satellites and unmanned aerial vehicle. IOP Conference Series: Earth and Environmental Science, 169(1). doi: 10.1088/1755-1315/169/1/012076 [Google Scholar] [CrossRef]
KLHK. (2021). Statistik Bidang Planalogi Kehutanan dan Tata Lingkungan Tahun 2020. Kementerian Lingkungan Hidup Dan Kehutanan, 2013–2015.
KLHK. (2022). Sistem Informasi Geospasial KLHK. Retrieved May 27, 2024, Retrived From https://geoportal.menlhk.go.id/Interaktif2/
Koman, W. A. F., Shofiyal Izza, N., & Candraningtyas, D. (2022). Perbandingan parameter segmentasi OBIA dalam klasifikasi tutupan lahan. Jurnal Paradigma: Jurnal Multidisipliner Mahasiswa Pascasarjana Indonesia, 3(2), 104–113. [Google Scholar]
Kushardono, D. (2017). Klasifikasi Digital Pada Penginderaan Jauh. In IPB Press (Vol. 1).
Li, Z., Zan, Q., Yang, Q., Zhu, D., Chen, Y., & Yu, S. (2019). Remote estimation of mangrove aboveground carbon stock at the species level using a low-cost unmanned aerial vehicle system. Remote Sensing, 11(9). doi: 10.3390/rs11091018 [Google Scholar] [CrossRef]
Liu, D., & Xia, F. (2010). Assessing object-based classification: Advantages and limitations. Remote Sensing Letters, 1(4), 187–194. doi: 10.1080/01431161003743173 [Google Scholar] [CrossRef]
Lubis, K. A., Rusdi, M., & Sugianto, S. (2021). Proses Segmentasi Citra Satelit Untuk Pemetaan Tutupan Lahan. Jurnal Ilmiah Mahasiswa Pertanian, 6(4), 691–698. doi: 10.17969/jimfp.v6i4.18414 [Google Scholar] [CrossRef]
Marwa, J., Sineri, A. S., & Hematang, F. (2020). Biecological Carrying Capacity of Forest and Land in Manokwari Regency, West Papua Province. Jurnal Sylva Lestari, 8(2), 197. doi: 10.23960/jsl28197-206 [Google Scholar] [CrossRef]
Miraki, M., Sohrabi, H., & Immitzer, M. (2023). Tree Species Mapping in Mangrove Ecosystems Using UAV-RGB Imagery and Object-Based Image Classification. Journal of the Indian Society of Remote Sensing, 51(10), 2095–2103. doi: 10.1007/s12524-023-01752-7 [Google Scholar] [CrossRef]
Moon, H.-G., Lee, S.-M., & Cha, J.-G. (2017). Land cover classification using UAV imagery and object-based image analysis-focusing on the Maseo-Myeon, Seocheon-Gun, Chungcheongnam-Do. Journal of the Korean Association of Geographic Information Studies, 20(1), 1–14. [Google Scholar]
Murdjoko, A., Brearley, F. Q., Ungirwalu, A., Djitmau, D. A., & Benu, N. M. H. (2022). Secondary Succession after Slash-and-Burn Cultivation in Papuan Lowland Forest, Indonesia. Forests, 13(3), 1–14. [Google Scholar]
Murdjoko, A., Ungirwalu, A., Mardiyadi, Z., Tokede, M. J., Djitmau, D. A., & Benu, N. M. H. (2021). Floristic Composition of Buah Hitam Habitats in Lowland Tropical Mixed Forest of West Papua, Indonesia. Floresta e Ambiente, 28(3). doi: 10.1590/2179-8087-FLORAM-2021-0042 [Google Scholar] [CrossRef]
Nagendran, S. K., Tung, W. Y., & Mohamad Ismail, M. A. (2018). Accuracy assessment on low altitude UAV-borne photogrammetry outputs influenced by ground control point at different altitude. IOP Conference Series: Earth and Environmental Science, 169(1). doi: 10.1088/1755-1315/169/1/012031 [Google Scholar] [CrossRef]
Navarro, A., Young, M., Allan, B., Carnell, P., Macreadie, P., & Ierodiaconou, D. (2020). The application of Unmanned Aerial Vehicles (UAVs) to estimate above-ground biomass of mangrove ecosystems. Remote Sensing of Environment, 242, 111747. doi: 10.1016/j.rse.2020.111747 [Google Scholar] [CrossRef]
Němec, P. (2015). Comparison of modern forest inventory method with the common method for management of tropical rainforest in the Peruvian Amazon. Journal of Tropical Forest Science, 27(1), 80–91. [Google Scholar]
Otero, V., Van De Kerchove, R., Satyanarayana, B., Martínez-Espinosa, C., Fisol, M. A. Bin, Ibrahim, M. R. Bin, … Dahdouh-Guebas, F. (2018). Managing mangrove forests from the sky: Forest inventory using field data and Unmanned Aerial Vehicle (UAV) imagery in the Matang Mangrove Forest Reserve, peninsular Malaysia. Forest Ecology and Management, 411, 35–45. doi: 10.1016/j.foreco.2017.12.049 [Google Scholar] [CrossRef]
Park, G., Park, K., Song, B., & Lee, H. (2022). Analyzing Impact of Types of UAV-Derived Images on the Object-Based Classification of Land Cover in an Urban Area. Drones, 6(3). doi: 10.3390/drones6030071 [Google Scholar] [CrossRef]
Pasaribu, R. A., Aditama, F. A., & Setyabudi, P. (2021). Object-based image analysis (OBIA) for mapping mangrove using Unmanned Aerial Vehicle (UAV) on Tidung Kecil Island, Kepulauan Seribu, DKI Jakarta Province. IOP Conference Series: Earth and Environmental Science, 944(1). doi: 10.1088/1755-1315/944/1/012037 [Google Scholar] [CrossRef]
Polat, N., & Kaya, Y. (2021). Investigation of the Performance of Different Pixel-Based Classification Methods in Land Use/Land Cover (LULC) Determination. Türkiye İnsansız Hava Araçları Dergisi, 3(1), 1–6. doi: 10.51534/tiha.829656 [Google Scholar] [CrossRef]
Purba, H. A., & Perwira. (2021). Penggunaan Teknologi Uav Pada Pemetaan Pantai Dengan Pendekatan Berbasis Objek Geografis. 2(2), 6. [Google Scholar]
Rahardjo, N., Aunurrahim, A., Hayun, G., & Asri, M. (2021). Unmanned Aerial Vehicle (UAV) Data as a Land Cover Data Renewal in Pandanrejo Village, Kaligesing Sub-District, Purworejo. International Journal of Sciences: Basic and Applied Research (IJSBAR) International Journal of Sciences: Basic and Applied Research, 55(1), 247–261. Retrieved from http://gssrr.org/index.php?journal=JournalOfBasicAndApplied[Google Scholar]
Rahul, T. S., Brema, J., & Wessley, G. J. J. (2023). Evaluation of surface water quality of Ukkadam lake in Coimbatore using UAV and Sentinel-2 multispectral data. International Journal of Environmental Science and Technology, 20(3), 3205–3220. doi: 10.1007/s13762-022-04029-7 [Google Scholar] [CrossRef]
Ramadhani, Y. H., K, A. P., & Susanti, R. (2015a). Pemetaan Pulau Kecil dengan Pendekatan Berbasis Objek Menggunakan Data Unmanned Aerial Vehicle (UAV) Studi Kasus di Pulau Pramuka , Kepulauan Seribu. Majalah Ilmiah Globe, 17(2), 125–134. [Google Scholar]
Ramadhani, Y. H., K, A. P., & Susanti, R. (2015b). Pemetaan Pulau Kecil dengan Pendekatan Berbasis Objek Menggunakan Data Unmanned Aerial Vehicle (UAV) Studi Kasus di Pulau Pramuka , Kepulauan Seribu. Majalah Ilmiah Globe, 17(2), 125–134. [Google Scholar]
Raweyai, J., Sadsoeitoeboen, B., Tokede, M., Kesaulija, F., Mentansan, G., & Mardiyadi, Z. (2023). Rekognisi MBKM bentuk free form dalam riset tematik pemanfaatan lahan oleh masyarakat di Pulau Mansinam. IGKOJEI: Jurnal Pengabdian Masyarakat, 4(1), 50–60. doi: 10.46549/igkojei.v4i1.358 [Google Scholar] [CrossRef]
Rohman, A., & Prasetya, D. B. (2019). Rapid Mapping for Simple Flood Mitigation Using Commercial Drone at Way Galih Village, Lampung, Indonesia. Forum Geografi, 33(1), 101–113. doi: 10.23917/forgeo.v33i1.8421 [Google Scholar] [CrossRef]
Sari, N. M., & Kushardono, D. (2014). Klasifikasi Penutup Lahan Berbasis Obyek Pada Data Foto Uav Untuk Mendukung Penyediaan Informasi Penginderaan Jauh Skala Rinci ( Object Based Classification Of Land Cover On Uav Photo Data To Support The Provision Of Detailed-Scale. Remote Sensing Informat, 11(2), 114–127. [Google Scholar]
Shashkov, M., Ivanova, N., Shanin, V., & Grabarnik, P. (2019). Ground Surveys Versus UAV Photography: The Comparison of Two Tree Crown Mapping Techniques. In Springer Proceedings in Earth and Environmental Science. Springer International Publishing, 2, 48–56. doi: 10.1007/978-3-030-11720-7_8 [Google Scholar] [CrossRef]
Sitompul, J. R., Ruswanti, C. D., Sukandar, H., & Ganesa, A. S. (2019). Klasifikasi Vegetasi dan Tutupan Lahan Pada Citra UAV Menggunakan Metode Object-Based Image Analysis di Segara Anakan , Kabupaten Cilacap Classification of Vegetation and Land Cover in UAV Images Using the Object-Based Image Analysis Method in Segara Anak. Researcgate, 2, 504–511. [Google Scholar]
Sohl, M. A., Mahmood, S. A., & Rasheed, M. U. (2024). Comparative performance of four machine learning models for land cover classification in a low-cost UAV ultra-high-resolution RGB-only orthomosaic. Earth Science Informatics, (0123456789). doi: 10.1007/s12145-024-01318-2 [Google Scholar] [CrossRef]
Sonbait, L. Y., Manik, H., Warmetan, H., Lina, Y., Wambrauw, D., Sagrim, M., … Murdjoko, A. (2021). The natural resource management to support tourism : A traditional knowledge approach in Pegunungan Arfak Nature Reserve, West Papua, Indonesia. Biodiversitas, 22(10), 4466–4474. doi: 10.13057/biodiv/d221040 [Google Scholar] [CrossRef]
Sorondanya, N., Peday, H., & Runtuboi, Y. (2021). Type and Distribution of Forest Ecosystems in Mansinam Island, Manokwari. Jurnal manajemen Hutan Tropica, 7(1), 99–120. [Google Scholar]
Sothe, C., Dalponte, M., de Almeida, C. M., Schimalski, M. B., Lima, C. L., Liesenberg, V., … Tommaselli, A. M. G. (2019). Tree species classification in a highly diverse subtropical forest integrating UAV-based photogrammetric point cloud and hyperspectral data. Remote Sensing, 11(11). doi: 10.3390/rs11111338 [Google Scholar] [CrossRef]
Tawer, P., Maturbongs, R., Murdjoko, A., Jitmau, M., Djitmau, D., Siburian, R., … Tambing, J. (2021). Vegetation dynamic post-disturbance in tropical rain forest of bird’s head peninsula of west papua, indonesia. Annals of Silvicultural Research, 46(1), 48–58. doi: 10.12899/ASR-2145 [Google Scholar] [CrossRef]
Torresan, C., Berton, A., Carotenuto, F., Di Gennaro, S. F., Gioli, B., Matese, A., … Wallace, L. (2017). Forestry applications of UAVs in Europe: a review. International Journal of Remote Sensing, 38(8–10), 2427–2447. doi: 10.1080/01431161.2016.1252477 [Google Scholar] [CrossRef]
Uddin, K., & Matin, M. A. (2021). Potential flood hazard zonation and flood shelter suitability mapping for disaster risk mitigation in Bangladesh using geospatial technology. Progress in Disaster Science, 11(3), 100185. doi: 10.1016/j.pdisas.2021.100185 [Google Scholar] [CrossRef]
Umarhadi, D. A., Danoedoro, P., Wicaksono, P., Widayani, P., Nurbandi, W., & Juniansah, A. (2018). The Comparison of Canopy Density Measurement Using UAV and Hemispherical Photography for Remote Sensing Based Mapping. Proceedings - 2018 4th International Conference on Science and Technology, ICST 2018, 2–6. doi: 10.1109/ICSTC.2018.8528670 [Google Scholar] [CrossRef]
Vaggela, A., Sanapala, H., & Mokka, J. R. (2022). Monitoring Land Use and Land Cover Changes Prospects Using Remote Sensing and GIS for Mahanadi River Delta, Orissa, India. Geoplanning, 9(1), 47–60. doi: 10.14710/geoplanning.9.1.47-60 [Google Scholar] [CrossRef]
Waromi, J. (2021). Dinamika Perubahan Tutupan Hutan Dan Pemanfaatan Lahan Pulau Mansinam. Universitas Papua.
Wasehun, E. T., Hashemi Beni, L., & Di Vittorio, C. A. (2024). UAV and satellite remote sensing for inland water quality assessments: a literature review. Environmental Monitoring and Assessment, 196(3). doi: 10.1007/s10661-024-12342-6 [Google Scholar] [CrossRef]
Weih, R. C., & Riggan, N. D. (2010). Object-based classification vs. pixel-based classification: Comparitive importance of multi-resolution imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII, 1–6.
Wijaya, M. S., Ramadhani, Y. H., Rudiastuti, A. W., Nurteisa, Y. T., Rahadian, A., Pujawati, I., & Hartini, S. (2019). Kajian Wahana Udara Nir-Awak Untuk Akuisisi Data Survei Parameter Biofisik Vegetasi Mangrove. Seminar Nasional Geomatika 2018 :Penggunaan Dan Pengembangan Produk Informasi Geospasial Mendukung Daya Saing Nasional, 3, 947. doi: 10.24895/sng.2018.3-0.1061 [Google Scholar] [CrossRef]
Wulan, R. T., Ambarwulan, W., Putra, A. S., Maulana, E., Maulia, N., Putra, M. D., … Raharjo, T. (2016). Uji Akurasi Data Uav (Unmanned Aerial Vehicle) di KAwasan Pantai Pelangi, Parangtritis, Kretek, Kabupaten Bantul. Prosiding Seminar Nasional Kelautan, (September), 232–240. doi: 10.13140/RG.2.2.23481.57448 [Google Scholar] [CrossRef]
Yilmaz, V., Levent, T., Cigdem, Y., & Oguz, G. (2017). Determination of Tree Crown Diameters with Segmentation of a UAS-Based Canopy Height Determination of Tree Crown Diameters with Segmentation of a UAS-Based Canopy Height Model. IPSI BgD Transactions on Internet Research, 13(7), 63–67. [Google Scholar]
Zhang, Y., Wu, H., & Yang, W. (2019). Forests Growth Monitoring Based on Tree Canopy 3D Reconstruction Using UAV Aerial Photogrammetry. Forests, 10(12), 1–16. doi: 10.3390/f10121052 [Google Scholar] [CrossRef]
Zhao, Y., Zhou, L., Chen, C., Li, X., Du, H., Yu, J., … Song, M. (2023). Urban Forest Above-Ground Biomass Estimation Based on UAV 3D Real Scene. Drones, 7(7). doi: 10.3390/drones7070455 [Google Scholar] [CrossRef]
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