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Canopy height model characteristics derived from airbone laser scanning and its effectiveness in discriminating various tropical moist forest types

Abstract : Mapping tropical forests to a sufficient level of spatial resolution and structural detail is a prerequisite for their rational management, which however remains a largely unmet challenge. We explore the degree to which a forest canopy height model CHM derived from airborne laser scanning ALS can discriminate between five forest types of similar height but varying structure or composition. We systematically compare various textural features Haralick, Fourier transform-based, and wavelet-based features and various classification procedures linear discriminant analysis LDA, random forestRF, and support vector machine SVM applied to two sizes of sampling units 64 m × 64 m and 32 m × 32 m. Simple height distribution statistics achieve at best 70% classification accuracy in our sample set comprising 120 sampling units of 64 m × 64 m. Using w avelet-based features, this accuracy increases to 79% but drops by 10% with smaller sampling units 32 m × 32 m. Classifier performance depends on the texture feature set used, but SVM and RF tend to perform better than LDA. High discrimination rates between forests types of similar height indicate that the ALS-derived CHM provides information suitable for mapping of tropical forest types. Wavelet-based texture features coupled with a SVM classifier was found to be the most promising combination of methods. Ancillary data derived from laser scans and notably topography could be used jointly for an improved segmentation scheme.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01713739
Contributor : Isabelle Gouat <>
Submitted on : Tuesday, February 20, 2018 - 7:57:49 PM
Last modification on : Thursday, July 2, 2020 - 1:58:02 PM

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Pol Kennel, Marie Tramon, Nicolas Barbier, Grégoire Vincent. Canopy height model characteristics derived from airbone laser scanning and its effectiveness in discriminating various tropical moist forest types. International Journal of Remote Sensing, Taylor & Francis, 2013, 34 (24), pp.8917-8935. ⟨10.1080/01431161.2013.858846⟩. ⟨lirmm-01713739⟩

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