Data Acquisition Tree Height and Areal Parameters of Mangrove Vegetation Using UAV

Open Access

Issue		BIO Web Conf. Volume 89, 2024 The 4^th Sustainability and Resilience of Coastal Management (SRCM 2023)


Article Number		07002
Number of page(s)		10
Section		Geo-Marine and Mapping and Application for Coastal Area
DOI		https://doi.org/10.1051/bioconf/20248907002
Published online		23 January 2024

Cai, W. & Wei, Z. Remote Sensing Image Classification Based on a Cross-Attention Mechanism and Graph Convolution. IEEE Geoscience and Remote Sensing Letters 19, (2022). [Google Scholar]
Moeinizade, S., Pham, H., Han, Y., Dobbels, A. & Hu, G. An applied deep learning approach for estimating soybean relative maturity from UAV imagery to aid plant breeding decisions. Machine Learning with Applications 7, 100233 (2022). [CrossRef] [Google Scholar]
Chen, xiaogang et al. Journal of Geophysical Research: Oceans. J Geophys Res Oceans 123, 6962–6979 (2018). [CrossRef] [Google Scholar]
Alongi, D. M. Global Significance of Mangrove Blue Carbon in Climate Change Mitigation (Version 1). Sci 2, 57 (2020). [CrossRef] [Google Scholar]
Taillardat, P., Friess, D. A. & Lupascu, M. Mangrove blue carbon strategies for climate change mitigation are most effective at the national scale. Biol Lett 14, (2018). [Google Scholar]
Carugati, L. et al. Impact of mangrove forests degradation on biodiversity and ecosystem functioning. Sci Rep 8, (2018). [CrossRef] [PubMed] [Google Scholar]
Prabu, E. & Gokul, S. MANGROVES: AN INCREDIBLE ECOSYSTEM FOR SUSTAINABLE FISHERIES. aqua tropis 32, 397–411 (2017). [Google Scholar]
Del Valle, A., Eriksson, M., Ishizawa, O. A. & Miranda, J. J. Mangroves protect coastal economic activity from hurricanes. PNAS 117, 265–270 (2020). [CrossRef] [PubMed] [Google Scholar]
Temmerman, S. et al. Marshes and Mangroves as Nature-Based Coastal Storm Buffers. annual review of marine 15, 95–118 (2022). [Google Scholar]
PUTRI, A. E., KHADIJAH, U. L. S. & NOVIANTI, E. Community empowerment in the development of mangrove tourism in batu karas of pangandaran, West Java. Geojournal of Tourism and Geosites 31, 972–978 (2020). [CrossRef] [Google Scholar]
Al-Khayat, J. A., Abdulla, M. A. & Alatalo, J. M. Diversity of benthic macrofauna and physical parameters of sediments in natural mangroves and in afforested mangroves three decades after compensatory planting. Aquat Sci 81, (2018). [Google Scholar]
Estoque, R. C. et al. Assessing environmental impacts and change in Myanmar’s mangrove ecosystem service value due to deforestation (2000–2014). Glob Chang Biol 24, 5391–5410 (2018). [CrossRef] [PubMed] [Google Scholar]
Hamilton, S. E. & Casey, D. Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21). Global Ecology and Biogeography 25, 729–738 (2016). [CrossRef] [Google Scholar]
Wang, D. et al. Mapping height and aboveground biomass of mangrove forests on Hainan Island using UAV-LiDAR sampling. Remote Sens (Basel) 11, (2019). [Google Scholar]
Bunting, P. et al. Global Mangrove Watch: Monthly Alerts of Mangrove Loss for Africa. Remote Sens (Basel) 15, (2023). [PubMed] [Google Scholar]
Tian, Y. et al. Mangrove Biodiversity Assessment Using UAV Lidar and Hyperspectral Data in China’s Pinglu Canal Estuary. Remote Sens (Basel) 15, (2023). [PubMed] [Google Scholar]
Kuenzer, C., Bluemel, A., Gebhardt, S., Quoc, T. V. & Dech, S. Remote sensing of mangrove ecosystems: A review. Remote Sensing vol. 3 878–928 Preprint at https://doi.org/10.3390/rs3050878 (2011). [CrossRef] [Google Scholar]
Matese, A. & Di Gennaro, S. F. Practical applications of a multisensor UAV platform based on multispectral, thermal and RGB high resolution images in precision viticulture. Agriculture (Switzerland) 8, (2018). [Google Scholar]
Dash, J. P., Watt, M. S., Paul, T. S. H., Morgenroth, J. & Hartley, R. Taking a closer look at invasive alien plant research: A review of the current state, opportunities, and future directions for UAVs. Methods in Ecology and Evolution vol. 10 2020–2033 Preprint at https://doi.org/10.1111/2041-210X.13296 (2019). [CrossRef] [Google Scholar]
Li, Y. et al. Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images. Remote Sens (Basel) 14, (2022). [Google Scholar]
Yang, B., Hawthorne, T. L., Torres, H. & Feinman, M. Using object-oriented classification for coastal management in the east central coast of Florida: A quantitative comparison between UAV, satellite, and aerial data. Drones 3, 1–15 (2019). [Google Scholar]
You, H., Liu, Y., Lei, P., Qin, Z. & You, Q. Segmentation of individual mangrove trees using UAV-based LiDAR data. Ecol Inform 77, (2023). [Google Scholar]
Saliu, I. S. et al. An accuracy analysis of mangrove tree height mensuration using forestry techniques, hypsometers and UAVs. Estuar Coast Shelf Sci 248, (2021). [Google Scholar]
Yin, D. & Wang, L. Individual mangrove tree measurement using UAV-based LiDAR data: Possibilities and challenges. Remote Sens Environ 223, 34–49 (2019). [CrossRef] [Google Scholar]
Durgun, H. et al. GAZİ JOURNAL OF ENGINEERING SCIENCES "Evaluation of Tree Diameter and Height Measurements in UAV Data through the Integration of Remote Sensing and Machine Learning Methods Evaluation of Tree Diameter and Height Measurements in UAV Data through the Integration of Remote Sensing and Machine Learning Methods. Gazi Journal of Engineering Sciences 9, 113–125 (2023). [Google Scholar]
Moity, N., Delgado, B. & Salinas-de-Leon, P. Mangroves in the Galapagos islands: Distribution and dynamics. PLoS One 14, (2019). [Google Scholar]
Qiu, P. et al. Finer resolution estimation and mapping of mangrove biomass using UAV LiDAR and worldview-2 data. Forests 10, (2019). [Google Scholar]
Flores-de-Santiago, F., Valderrama-Landeros, L., Rodríguez-Sobreyra, R. & Flores-Verdugo, F. Assessing the effect of flight altitude and overlap on orthoimage generation for UAV estimates of coastal wetlands. J Coast Conserv 24, (2020). [CrossRef] [Google Scholar]
Flores-de-Santiago, F., Valderrama-Landeros, L., Rodríguez-Sobreyra, R. & Flores-Verdugo, F. Assessing the effect of flight altitude and overlap on orthoimage generation for UAV estimates of coastal wetlands. J Coast Conserv 24, (2020). [CrossRef] [Google Scholar]
Pachehkenari, M. S. & Fadaei, H. Tree height estimation, Unmanned aerial systems (UASs), Ground control points (GCPs), Normalized digital surface model (nDSM). American Journal of Geographic Information System 2020, 55–63 (2020). [Google Scholar]
Chapman V.J. Mangrove Vegetation. (1976). [Google Scholar]
Krause, S., Sanders, T. G. M., Mund, J. P. & Greve, K. UAV-based photogrammetric tree height measurement for intensive forest monitoring. Remote Sens (Basel) 11, (2019). [Google Scholar]
Zarco-Tejada, P. J., Diaz-Varela, R., Angileri, V. & Loudjani, P. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. European Journal of Agronomy 55, 89–99 (2014). [CrossRef] [Google Scholar]
Panagiotidis, D., Abdollahnejad, A., Surový, P. & Chiteculo, V. Determining tree height and crown diameter from high-resolution UAV imagery. Int J Remote Sens 38, 2392–2410 (2017). [CrossRef] [Google Scholar]
Castellanos-Galindo, G. A., Casella, E., Tavera, H., Zapata Padilla, L. A. & Simard, M. Structural Characteristics of the Tallest Mangrove Forests of the American Continent: A Comparison of Ground-Based, Drone and Radar Measurements. Frontiers in Forests and Global Change 4, (2021). [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.