Version 1
: Received: 6 August 2024 / Approved: 7 August 2024 / Online: 8 August 2024 (12:57:18 CEST)
How to cite:
Conti, L. A.; Barcellos, R. L.; Oliveira, P.; Nascimento-Neto, F. C.; Cunha-Lignon., M. S. B. Geographic Object-Oriented Analysis of UAV Multispectral Images for Tree Distribution Mapping in Mangroves. Preprints2024, 2024080539. https://doi.org/10.20944/preprints202408.0539.v1
Conti, L. A.; Barcellos, R. L.; Oliveira, P.; Nascimento-Neto, F. C.; Cunha-Lignon., M. S. B. Geographic Object-Oriented Analysis of UAV Multispectral Images for Tree Distribution Mapping in Mangroves. Preprints 2024, 2024080539. https://doi.org/10.20944/preprints202408.0539.v1
Conti, L. A.; Barcellos, R. L.; Oliveira, P.; Nascimento-Neto, F. C.; Cunha-Lignon., M. S. B. Geographic Object-Oriented Analysis of UAV Multispectral Images for Tree Distribution Mapping in Mangroves. Preprints2024, 2024080539. https://doi.org/10.20944/preprints202408.0539.v1
APA Style
Conti, L. A., Barcellos, R. L., Oliveira, P., Nascimento-Neto, F. C., & Cunha-Lignon., M. S. B. (2024). Geographic Object-Oriented Analysis of UAV Multispectral Images for Tree Distribution Mapping in Mangroves. Preprints. https://doi.org/10.20944/preprints202408.0539.v1
Chicago/Turabian Style
Conti, L. A., Francisco Cordeiro Nascimento-Neto and Marilia Stela Bispo Cunha-Lignon.. 2024 "Geographic Object-Oriented Analysis of UAV Multispectral Images for Tree Distribution Mapping in Mangroves" Preprints. https://doi.org/10.20944/preprints202408.0539.v1
Abstract
Mangroves are critical ecosystems providing essential environmental services, making their conservation crucial. High-resolution remote sensing, particularly through unmanned aerial vehicles (UAVs), offers unprecedented resolution in ecosystem mapping. This study utilizes a multispectral sensor mounted on a UAV to map mangroves at Cardoso Island on Brazil’s southeastern coast and Suape on the northeastern coast. Using Geographic Object-Based Image Analysis (GEOBIA) for segmentation and classification, we employed machine learning algorithms to classify the primary mangrove species: Laguncularia racemosa, Rhizophora mangle, and Avicennia schaueriana. Two flight altitudes were used: a higher altitude for broad-scale mapping and a lower altitude for classification training, supplemented by field measurements for calibration. This method enabled the precise delineation of tree crowns and provided insights into species distribution and zonation, influenced by the distinct estuarine conditions of tides and sediment input. The high-resolution mapping achieved in this study enhances detailed analyses of mangroves, improving ecological models and biophysical estimations such as biomass and blue carbon stocks. This work highlights the potential of UAV-based multispectral imaging in advancing mangrove research and conservation efforts.
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
