气候变化领域集成服务门户(CCPortal): Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks

CCPortal

DOI	10.1016/j.rse.2021.112322
	Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks
	Mäyrä J.; Keski-Saari S.; Kivinen S.; Tanhuanpää T.; Hurskainen P.; Kullberg P.; Poikolainen L.; Viinikka A.; Tuominen S.; Kumpula T.; Vihervaara P.
发表日期	2021
ISSN	00344257
卷号	256
英文摘要	During the last two decades, forest monitoring and inventory systems have moved from field surveys to remote sensing-based methods. These methods tend to focus on economically significant components of forests, thus leaving out many factors vital for forest biodiversity, such as the occurrence of species with low economical but high ecological values. Airborne hyperspectral imagery has shown significant potential for tree species classification, but the most common analysis methods, such as random forest and support vector machines, require manual feature engineering in order to utilize both spatial and spectral features, whereas deep learning methods are able to extract these features from the raw data. Our research focused on the classification of the major tree species Scots pine, Norway spruce and birch, together with an ecologically valuable keystone species, European aspen, which has a sparse and scattered occurrence in boreal forests. We compared the performance of three-dimensional convolutional neural networks (3D-CNNs) with the support vector machine, random forest, gradient boosting machine and artificial neural network in individual tree species classification from hyperspectral data with high spatial and spectral resolution. We collected hyperspectral and LiDAR data along with extensive ground reference data measurements of tree species from the 83 km2 study area located in the southern boreal zone in Finland. A LiDAR-derived canopy height model was used to match ground reference data to aerial imagery. The best performing 3D-CNN, utilizing 4 m image patches, was able to achieve an F1-score of 0.91 for aspen, an overall F1-score of 0.86 and an overall accuracy of 87%, while the lowest performing 3D-CNN utilizing 10 m image patches achieved an F1-score of 0.83 and an accuracy of 85%. In comparison, the support-vector machine achieved an F1-score of 0.82 and an accuracy of 82.4% and the artificial neural network achieved an F1-score of 0.82 and an accuracy of 81.7%. Compared to the reference models, 3D-CNNs were more efficient in distinguishing coniferous species from each other, with a concurrent high accuracy for aspen classification. Deep neural networks, being black box models, hide the information about how they reach their decision. We used both occlusion and saliency maps to interpret our models. Finally, we used the best performing 3D-CNN to produce a wall-to-wall tree species map for the full study area that can later be used as a reference prediction in, for instance, tree species mapping from multispectral satellite images. The improved tree species classification demonstrated by our study can benefit both sustainable forestry and biodiversity conservation. © 2021 The Author(s)
英文关键词	Convolutional neural network; Deep learning; Hyperspectral imaging; Tree species classification
语种	英语
scopus关键词	Aerial photography; Antennas; Biodiversity; Conservation; Convolution; Convolutional neural networks; Decision trees; Deep learning; Deep neural networks; Forestry; Learning systems; Optical radar; Random forests; Remote sensing; Spectroscopy; Support vector machines; Biodiversity conservation; Canopy Height Models; Feature engineerings; Forest biodiversity; Hyper-spectral imageries; Individual tree species classifications; Multispectral satellite image; Sustainable forestry; Classification (of information); artificial neural network; biodiversity; forest inventory; image analysis; lidar; pine; prediction; satellite data; sustainability; three-dimensional modeling; Finland; Picea abies; Pinus sylvestris; Populus tremula
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178928
作者单位	Finnish Environment Institute (SYKE), Biodiversity Centre, Latokartanonkaari 11, Helsinki, FI-00790, Finland; Department of Environmental and Biological Sciences, University of Eastern Finland, P.O. Box 111, Joensuu, FI-80101, Finland; Department of Geographical and Historical Studies, University of Eastern Finland, Yliopistonkatu 7, Joensuu, FI-80101, Finland; Department of Forest Sciences, University of Helsinki, Helsinki, FI-00014, Finland; Earth Change Observation Laboratory, Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, Helsinki, 00014, Finland; Finnish Environment Institute (SYKE), Environmental Policy Centre, Latokartanonkaari 11, Helsinki, FI-00790, Finland; Natural Resources Institute Finland (Luke), Latokartanonkaari 9, Helsinki, FI-00790, Finland
推荐引用方式 GB/T 7714	Mäyrä J.,Keski-Saari S.,Kivinen S.,et al. Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks[J],2021,256.
APA	Mäyrä J..,Keski-Saari S..,Kivinen S..,Tanhuanpää T..,Hurskainen P..,...&Vihervaara P..(2021).Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks.Remote Sensing of Environment,256.
MLA	Mäyrä J.,et al."Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks".Remote Sensing of Environment 256(2021).