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利用多光谱无人机数据建立红树林物种分布图的方案

Protocol for establishing a map of mangrove species distribution using multispectral unmanned aerial vehicle data.

作者信息

Ngo Dung Trung, Nguyen Hieu Huu Viet, Nguyen Binh Thi Thanh, Dang Ngoc Thi

机构信息

Institute of Tropical Ecology, Joint Vietnam-Russia Tropical Science and Technology Research Center, No. 63, Nguyen Van Huyen Str., Cau Giay District, Hanoi, Vietnam.

Forest Inventory and Planning Institute (FIPI), Ngoc Hoi Str., Vinh Quynh Commune, Thanh Tri District, Hanoi, Vietnam.

出版信息

STAR Protoc. 2024 Dec 20;5(4):103425. doi: 10.1016/j.xpro.2024.103425. Epub 2024 Nov 1.

DOI:10.1016/j.xpro.2024.103425
PMID:39488838
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11567038/
Abstract

Unmanned aerial vehicle (UAV) or drone image data are useful tools for creating machine learning-based mangrove categorization maps. Here, we present a protocol for creating a taxonomy map of mangrove species using machine learning and multispectral UAV images. We describe steps for gathering and analyzing UAV images and field data and categorizing mangroves. We then detail procedures for building a library of spectral reflectance and normalized difference vegetation index (NDVI) values and a tree classification map. For complete details on the use and execution of this protocol, please refer to Dung..

摘要

无人机（UAV）或无人驾驶飞机图像数据是创建基于机器学习的红树林分类地图的有用工具。在这里，我们展示了一种使用机器学习和多光谱无人机图像创建红树林物种分类地图的方案。我们描述了收集和分析无人机图像及实地数据以及对红树林进行分类的步骤。然后，我们详细介绍了构建光谱反射率和归一化植被指数（NDVI）值库以及树木分类地图的程序。有关此方案的使用和执行的完整详细信息，请参考邓……

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96bb/11567038/6393126caa4b/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96bb/11567038/75892ee5b628/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96bb/11567038/c80d96e8e07a/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96bb/11567038/7da3ac91b182/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96bb/11567038/f5dcd0af0231/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96bb/11567038/c9fb34c39465/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96bb/11567038/d0ec273a3dc7/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96bb/11567038/6393126caa4b/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96bb/11567038/75892ee5b628/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96bb/11567038/c80d96e8e07a/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96bb/11567038/7da3ac91b182/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96bb/11567038/f5dcd0af0231/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96bb/11567038/c9fb34c39465/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96bb/11567038/d0ec273a3dc7/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96bb/11567038/6393126caa4b/gr6.jpg

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本文引用的文献

1
Mapping tree species of wetlands using multispectral images of UAVs and machine learning: A case study of the Dong Rui Commune.利用无人机多光谱图像和机器学习绘制湿地树种图：以董瑞公社为例
Heliyon. 2024 Jul 25;10(15):e35159. doi: 10.1016/j.heliyon.2024.e35159. eCollection 2024 Aug 15.
2
Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation.利用全自动地块分割的摄影测量法验证基于无人机的紫花苜蓿生物量预测的准确性。
Sci Rep. 2021 Feb 8;11(1):3336. doi: 10.1038/s41598-021-82797-x.