Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada

Jones, Trevor; Coops, Nicholas C.; Sharma, Tara

doi:10.1016/j.rse.2010.07.002

Cited by 196 publications

(140 citation statements)

References 60 publications

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“…McNemar's test has been frequently used for testing the statistical significance of two classifiers [4,20,21,50]. Foody [51] suggests the use of McNemar's test instead of a z-test in cases where the same test samples are used for each accuracy assessment, resulting in the assumption of independence of Figure 4.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers

et al. 2016

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Abstract:The identification of tree species can provide a useful and efficient tool for forest managers for planning and monitoring purposes. Hyperspectral data provide sufficient spectral information to classify individual tree species. Two non-parametric classifiers, support vector machines (SVM) and random forest (RF), have resulted in high accuracies in previous classification studies. This research takes a comparative classification approach to examine the SVM and RF classifiers in the complex and heterogeneous forests of Muir Woods National Monument and Kent Creek Canyon in Marin County, California. The influence of object-or pixel-based training samples and segmentation size on the object-oriented classification is also explored. To reduce the data dimensionality, a minimum noise fraction transform was applied to the mosaicked hyperspectral image, resulting in the selection of 27 bands for the final classification. Each classifier was also assessed individually to identify any advantage related to an increase in training sample size or an increase in object segmentation size. All classifications resulted in overall accuracies above 90%. No difference was found between classifiers when using object-based training samples. SVM outperformed RF when additional training samples were used. An increase in training samples was also found to improve the individual performance of the SVM classifier.

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Section: Accuracy Assessmentmentioning

confidence: 99%

Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers

et al. 2016

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“…SVM classifier produced more reasonable classification map although it did not obtain the highest classification accuracy in most of the classification runs. Previous researches also suggested that SVM classifier outperformed conventional approaches to classify high-dimensional and multi-source data in complex environments (Jones, Coops, & Sharma, 2010). Due to non-linear and adaptive fitting capacities of RBF kernel, SVM was powerful to cope with high dimension data (Verrelst et al, 2015).…”

Section: Classifiermentioning

confidence: 99%

“…SVM separates the classes with a decision surface that maximizes the margin between the classes. SVM was reported to outperform when cope with high dimension, limited samples and multi-source data representing complex environments (Jones, Coops, & Sharma, 2010).…”

Section: Introductionmentioning

confidence: 99%

Assessing the Utility of Uav-Borne Hyperspectral Image and Photogrammetry Derived 3d Data for Wetland Species Distribution Quick Mapping

Wong

Fung

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Lightweight unmanned aerial vehicle (UAV) loaded with novel sensors offers a low cost and minimum risk solution for data acquisition in complex environment. This study assessed the performance of UAV-based hyperspectral image and digital surface model (DSM) derived from photogrammetric point clouds for 13 species classification in wetland area of Hong Kong. Multiple feature reduction methods and different classifiers were compared. The best result was obtained when transformed components from minimum noise fraction (MNF) and DSM were combined in support vector machine (SVM) classifier. Wavelength regions at chlorophyll absorption green peak, red, red edge and Oxygen absorption at near infrared were identified for better species discrimination. In addition, input of DSM data reduces overestimation of low plant species and misclassification due to the shadow effect and inter-species morphological variation. This study establishes a framework for quick survey and update on wetland environment using UAV system. The findings indicate that the utility of UAV-borne hyperspectral and derived tree height information provides a solid foundation for further researches such as biological invasion monitoring and bio-parameters modelling in wetland.

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“…Buckley et al (2013) combined hyperspectral and LiDAR data for geological out crop analysis, floodplain classification (Verrelst et al, 2009) and civil engineering structural monitoring (Brook et al, 2010). Most of the other work on LiDAR and imaging spectroscopy data fusion (Colgan et al, 2012;Dalponte et al, 2012;Ghosh et al, 2013;Jones et al, 2010); focus on vegetation applications. The growing interest in urban precise engineering mapping, deformation monitoring and the critical time factor involved in selecting training sites for supervised classification, especially for large areas or during emergency situations, motivates this study.…”

Section: Ajasmentioning

confidence: 99%

Imaging Spectroscopy and Light Detection and Ranging Data Fusion for Urban Features Extraction

Idrees

Shafri

Saeidi

2013

American Journal of Applied Sciences

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This study presents our findings on the fusion of Imaging Spectroscopy (IS) and LiDAR data for urban feature extraction. We carried out necessary preprocessing of the hyperspectral image. Minimum Noise Fraction (MNF) transforms was used for ordering hyperspectral bands according to their noise. Thereafter, we employed Optimum Index Factor (OIF) to statistically select the three most appropriate bands combination from MNF result. The composite image was classified using unsupervised classification (k-mean algorithm) and the accuracy of the classification assessed. Digital Surface Model (DSM) and LiDAR intensity were generated from the LiDAR point cloud. The LiDAR intensity was filtered to remove the noise. Hue Saturation Intensity (HSI) fusion algorithm was used to fuse the imaging spectroscopy and DSM as well as imaging spectroscopy and filtered intensity. The fusion of imaging spectroscopy and DSM was found to be better than that of imaging spectroscopy and LiDAR intensity quantitatively. The three datasets (imaging spectrocopy, DSM and Lidar intensity fused data) were classified into four classes: building, pavement, trees and grass using unsupervised classification and the accuracy of the classification assessed. The result of the study shows that fusion of imaging spectroscopy and LiDAR data improved the visual identification of surface features. Also, the classification accuracy improved from an overall accuracy of 84.6% for the imaging spectroscopy data to 90.2% for the DSM fused data. Similarly, the Kappa Coefficient increased from 0.71 to 0.82. on the other hand, classification of the fused LiDAR intensity and imaging spectroscopy data perform poorly quantitatively with overall accuracy of 27.8% and kappa coefficient of 0.0988.

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Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada

Cited by 196 publications

References 60 publications

Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers

Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers

Assessing the Utility of Uav-Borne Hyperspectral Image and Photogrammetry Derived 3d Data for Wetland Species Distribution Quick Mapping

Imaging Spectroscopy and Light Detection and Ranging Data Fusion for Urban Features Extraction

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