Ensemble Classification of Individual Pinus Crowns from Multispectral Satellite Imagery and Airborne LiDAR

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dc.contributor.authorKukunda, Collins B.
dc.contributor.authorDuque-Lazob, Joaquín
dc.contributor.authorGonzález-Ferreiro, Eduardo
dc.contributor.authorThaden, Hauke
dc.contributor.authorKleinna, Christoph
dc.date.accessioned2022-06-09T18:37:46Z
dc.date.available2022-06-09T18:37:46Z
dc.date.issued2018
dc.description.abstractDistinguishing 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 1.3km2 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.en_US
dc.identifier.citationKukunda, C. B., Duque-Lazo, J., González-Ferreiro, E., Thaden, H., & Kleinn, C. (2018). Ensemble classification of individual Pinus crowns from multispectral satellite imagery and airborne LiDAR. International journal of applied earth observation and geoinformation, 65, 12-23.https://doi.org/10.1016/j.jag.2017.09.016en_US
dc.identifier.issn1569-8432
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/3863
dc.language.isoenen_US
dc.publisherInternational journal of applied earth observation and geoinformationen_US
dc.subjectIndividual tree crown segmentation; Ensemble regression and classification; Machine learning; Data integration; Spectrally and structurally similar tree speciesen_US
dc.titleEnsemble Classification of Individual Pinus Crowns from Multispectral Satellite Imagery and Airborne LiDARen_US
dc.typeArticleen_US
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