气候变化领域集成服务门户(CCPortal): Individual tree detection and species classification of Amazonian palms using UAV images and deep learning

CCPortal

DOI	10.1016/j.foreco.2020.118397
	Individual tree detection and species classification of Amazonian palms using UAV images and deep learning
	Ferreira M.P.; Almeida D.R.A.D.; Papa D.D.A.; Minervino J.B.S.; Veras H.F.P.; Formighieri A.; Santos C.A.N.; Ferreira M.A.D.; Figueiredo E.O.; Ferreira E.J.L.
发表日期	2020
ISSN	0378-1127
卷号	475
英文摘要	Information regarding the spatial distribution of palm trees in tropical forests is crucial for commercial exploitation and management. However, spatially continuous knowledge of palms occurrence is scarce and difficult to obtain with conventional approaches such as field inventories. Here, we developed a new method to map Amazonian palm species at the individual tree crown (ITC) level using RGB images acquired by a low-cost unmanned aerial vehicle (UAV). Our approach is based on morphological operations performed in the score maps of palm species derived from a fully convolutional neural network model. We first constructed a labeled dataset by dividing the study area (135 ha within an old-growth Amazon forest) into 28 plots of 250 m × 150 m. Then, we manually outlined all palm trees seen in RGB images with 4 cm pixels. We identified three palm species: Attalea butyracea, Euterpe precatoria and Iriartea deltoidea. We randomly selected 22 plots (80%) for training and six plots (20%) for testing. We changed the plots for training and testing to evaluate the variability in the classification accuracy and assess model generalization. Our method outperformed the average producer's accuracy of conventional patch-wise semantic segmentation (CSS) in 4.7%. Moreover, our method correctly identified, on average, 34.7 percentage points more ITCs than CSS, which tended to merge trees that are close to each other. The producer's accuracy of A. butyracea, E. precatoria and I. deltoidea was 78.6 ± 5.5%, 98.6 ± 1.4% and 96.6 ± 3.4%, respectively. Fortunately, one of the most exploited and commercialized palm species in the Amazon (E. precatoria, a.k.a, Açaí) was mapped with the highest classification accuracy. Maps of E. precatoria derived from low-cost UAV systems can support management projects and community-based forest monitoring programs in the Amazon. © 2020 Elsevier B.V.
英文关键词	Amazon forest; Açaí; DeepLabv3+; Drone; Euterpe precatoria
语种	英语
scopus关键词	Aircraft detection; Antennas; Convolutional neural networks; Costs; Forestry; Image classification; Mathematical morphology; Semantics; Unmanned aerial vehicles (UAV); Classification accuracy; Commercial exploitation; Conventional approach; Individual tree crown; Individual tree detections; Morphological operations; Semantic segmentation; Species classification; Deep learning; algorithm; artificial neural network; detection method; forest management; pixel; segmentation; tree; tropical forest; unmanned vehicle; Accuracy; Classification; Costs; Forestry; Images; Maps; Testing; Trees; Amazonia; Attalea butyracea; Euterpe precatoria; Iriartea deltoidea
来源期刊	Forest Ecology and Management
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/155026
作者单位	Cartographic Engineering Section, Military Institute of Engineering (IME), Praça Gen. Tibúrcio 80, Rio de Janeiro, RJ 22290-270, Brazil; Forest Sciences Department, University of São Paulo (USP), Av. Pádua Dias 11, Piracicaba, SP, Brazil; Embrapa Acre, Rodovia BR-364, km 14, Rio Branco, AC 69900-056, Brazil; Federal University of Acre, Rodovia BR 364, Km 04 - Distrito Industrial, Rio Branco, AC 69920-900, Brazil; Departament of Forestry, Federal University of Paraná (UFPR), Pref. Lothário Meissner Avenue 900, Curitiba, PR 80210-170, Brazil; Technology Foundation of Acre State, Distrito Industrial, Rio Branco - State of Acre, Rio Branco, AC 69920-202, Brazil; National Institute of Amazonian Research (INPA), Estrada Dias Martins, 3868, Bairro Chácara Ipê, Rio Branco, AC 69917-560, Brazil
推荐引用方式 GB/T 7714	Ferreira M.P.,Almeida D.R.A.D.,Papa D.D.A.,et al. Individual tree detection and species classification of Amazonian palms using UAV images and deep learning[J],2020,475.
APA	Ferreira M.P..,Almeida D.R.A.D..,Papa D.D.A..,Minervino J.B.S..,Veras H.F.P..,...&Ferreira E.J.L..(2020).Individual tree detection and species classification of Amazonian palms using UAV images and deep learning.Forest Ecology and Management,475.
MLA	Ferreira M.P.,et al."Individual tree detection and species classification of Amazonian palms using UAV images and deep learning".Forest Ecology and Management 475(2020).