期刊
URBAN FORESTRY & URBAN GREENING
卷 58, 期 -, 页码 -出版社
ELSEVIER GMBH
DOI: 10.1016/j.ufug.2020.126958
关键词
Aerial photogrammetry; Multiple features; Random forest classification; Single tree segmentation; Tree height; UAV
资金
- Second Tibetan Plateau Scientific Expedition and Research Program [2019QZKK0405]
- National Key Research and Development Project of China [2018YFC0507300]
- Chinese Academy of Sciences [XDA2002040201]
Fine monitoring of tree species using UAV data and object-oriented classification at the single tree scale achieved high accuracy in detecting surface fragments, improving classification effects, and avoiding salt and pepper noise compared to traditional methods.
Fine monitoring of tree species is essential to supporting the urban forest management. Data acquired from unmanned aerial vehicles (UAVs) not only have very high spatiotemporal resolution, but also contain the vertical structure of trees which is important in the fine recognition of vegetation types. However, the research of combining multi-dimensional features in classification is still very limited. In our study, we extracted the spectral information, vegetation morphological parameters, texture information, and vegetation indexes based on UAV ultrahigh resolution images to build an object-oriented-based random forest (RF) classifier at the single tree scale. Establishing 6 classification scenarios that combines multiple data sources, multi-dimensional features, and multiple classification algorithms, our results show that: (1) UAV images can effectively detect surface fragments. The accuracy of RF classification based on UAV multiple features was high at 91.3 %, which was 20.5 % higher than the results by using high-resolution Baidu maps; (2) for mapping the tree species of urban forest, tree morphological characteristics, texture information, and vegetation indexes improved the classification accuracy by 2.9 %, 1.9 %, and 7.1 %, respectively, resulting in meaningful improvement of classification effects; and (3) the accuracy of RF classification based on UAV data was much higher than the maximum likelihood classification (MLC) results. Compared with the latter, the former can effectively avoid salt and pepper noise. The workflow of information extraction and urban forest classification based on UAV images in this paper yields high performance, which has important significance as a reference for future relevant research.
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