干旱区生态环境知识资源中心(Arid): Automated mapping of Portulacaria afra canopies for restoration monitoring with convolutional neural networks and heterogeneous unmanned aerial vehicle imagery

Arid

DOI	10.7717/peerj.14219
	Automated mapping of Portulacaria afra canopies for restoration monitoring with convolutional neural networks and heterogeneous unmanned aerial vehicle imagery
	Galuszynski, Nicholas C.; Duker, Robbert; Potts, Alastair J.; Kattenborn, Teja
通讯作者	Potts, AJ
来源期刊	PEERJ
ISSN	2167-8359
出版年	2022
卷号	10
英文摘要	Ecosystem restoration and reforestation often operate at large scales, whereas monitoring practices are usually limited to spatially restricted field measurements that are (i) time- and labour-intensive, and (ii) unable to accurately quantify restoration success over hundreds to thousands of hectares. Recent advances in remote sensing technologies paired with deep learning algorithms provide an unprecedented opportunity for monitoring changes in vegetation cover at spatial and temporal scales. Such data can feed directly into adaptive management practices and provide insights into restoration and regeneration dynamics. Here, we demonstrate that convolutional neural network (CNN) segmentation algorithms can accurately classify the canopy cover of Portulacaria afra Jacq. in imagery acquired using different models of unoccupied aerial vehicles (UAVs) and under variable light intensities. Portulacaria afra is the target species for the restoration of Albany Subtropical Thicket vegetation, endemic to South Africa, where canopy cover is challenging to measure due to the dense, tangled structure of this vegetation. The automated classification strategy presented here is widely transferable to restoration monitoring as its application does not require any knowledge of the CNN model or specialist training, and can be applied to imagery generated by a range of UAV models. This will reduce the sampling effort required to track restoration trajectories in space and time, contributing to more effective management of restoration sites, and promoting collaboration between scientists, practitioners and landowners.
英文关键词	Machine learning Restoration ecology Ecosystem monitoring Spekboom Albany subtropical thicket Drone imagery Aerial imagery UAVs CNN Adaptive management
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000891524300003
WOS关键词	ALBANY SUBTROPICAL THICKET ; SUCCULENT THICKET ; EASTERN CAPE ; NAMA-KAROO ; DESERTIFICATION ; TRANSFORMATION ; BOUNDARY ; PATTERNS ; VALLEY ; CARBON
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393927
推荐引用方式 GB/T 7714	Galuszynski, Nicholas C.,Duker, Robbert,Potts, Alastair J.,et al. Automated mapping of Portulacaria afra canopies for restoration monitoring with convolutional neural networks and heterogeneous unmanned aerial vehicle imagery[J],2022,10.
APA	Galuszynski, Nicholas C.,Duker, Robbert,Potts, Alastair J.,&Kattenborn, Teja.(2022).Automated mapping of Portulacaria afra canopies for restoration monitoring with convolutional neural networks and heterogeneous unmanned aerial vehicle imagery.PEERJ,10.
MLA	Galuszynski, Nicholas C.,et al."Automated mapping of Portulacaria afra canopies for restoration monitoring with convolutional neural networks and heterogeneous unmanned aerial vehicle imagery".PEERJ 10(2022).