DOI: 10.1016/j.jag.2017.09.016
Scopus记录号: 2-s2.0-85036561343
论文题名: Ensemble classification of individual Pinus crowns from multispectral satellite imagery and airborne LiDAR
作者: Kukunda C ; B ; , Duque-Lazo J ; , González-Ferreiro E ; , Thaden H ; , Kleinn C
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2018
卷: 65 起始页码: 12
结束页码: 23
语种: 英语
英文关键词: Data integration
; Ensemble regression and classification
; Individual tree crown segmentation
; Machine learning
; Spectrally and structurally similar tree species
Scopus关键词: airborne sensing
; classification
; data processing
; ensemble forecasting
; lidar
; machine learning
; regression analysis
; satellite imagery
; tree
; vegetation index
; Alpes de Haute Provence
; Barcelonnette Basin
; France
; Provence-Alpes-Cote d'Azur
; Pinus sylvestris
; Pinus uncinata
英文摘要: Distinguishing tree species is relevant in many contexts of remote sensing assisted forest inventory. Accurate tree species maps support management and conservation planning, pest and disease control and biomass estimation. This study evaluated the performance of applying ensemble techniques with the goal of automatically distinguishing Pinus sylvestris L. and Pinus uncinata Mill. Ex Mirb within a 1.3 km2 mountainous area in Barcelonnette (France). Three modelling schemes were examined, based on: (1) high-density LiDAR data (160 returns m−2), (2) Worldview-2 multispectral imagery, and (3) Worldview-2 and LiDAR in combination. Variables related to the crown structure and height of individual trees were extracted from the normalized LiDAR point cloud at individual-tree level, after performing individual tree crown (ITC) delineation. Vegetation indices and the Haralick texture indices were derived from Worldview-2 images and served as independent spectral variables. Selection of the best predictor subset was done after a comparison of three variable selection procedures: (1) Random Forests with cross validation (AUCRFcv), (2) Akaike Information Criterion (AIC) and (3) Bayesian Information Criterion (BIC). To classify the species, 9 regression techniques were combined using ensemble models. Predictions were evaluated using cross validation and an independent dataset. Integration of datasets and models improved individual tree species classification (True Skills Statistic, TSS; from 0.67 to 0.81) over individual techniques and maintained strong predictive power (Relative Operating Characteristic, ROC = 0.91). Assemblage of regression models and integration of the datasets provided more reliable species distribution maps and associated tree-scale mapping uncertainties. Our study highlights the potential of model and data assemblage at improving species classifications needed in present-day forest planning and management. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79908
Appears in Collections: 气候变化事实与影响
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作者单位: University of Goettingen, Forest Inventory and Remote Sensing, Faculty of Forest Sciences, Büsgenweg 5, Göttingen, Germany; Department of Forestry, School of Agriculture and Forestry, University of Córdoba, Laboratory of Dendrochronology, DendrodatLab-ERSAF Edf Leonardo da Vinci, Campus de Rabanales s/n, Córdoba, Spain; Unidade de Xestión Forestal Sostible (UXFS) – Departamento de Enxeñería Agroforestal, Escola Politécnica Superior, R/Benigno Ledo, Campus Terra, Lugo, Spain; University of Goettingen, Chair for Statistics, Humboldallee 3, Göttingen, Germany
Recommended Citation:
Kukunda C,B,, Duque-Lazo J,et al. Ensemble classification of individual Pinus crowns from multispectral satellite imagery and airborne LiDAR[J]. International Journal of Applied Earth Observation and Geoinformation,2018-01-01,65