Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu–Natal, South Africa

Peerbhay, Kabir; Mutanga, Onisimo; Ismail, Riyad

doi:10.1016/j.isprsjprs.2013.01.013

Cited by 120 publications

(72 citation statements)

References 52 publications

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“…Our results are consistent with those of earlier studies of other types of vegetation that also show that the exclusion of wavebands that provide little information related to the response variable improves the PLS-DA based classification of plant communities [47,81]. For example, using airborne AISA Eagle hyperspectral imagery, Peerbhay et al [47] showed that a PLS-DA model based on the 78 wavebands that were most relevant for the classification of forest species gave 8.17% higher overall classification accuracy than a model utilising all 230 AISA Eagle wavebands.…”

Section: Pls-da Based Classification Of Grassland Age-classessupporting

confidence: 92%

“…For example, using airborne AISA Eagle hyperspectral imagery, Peerbhay et al [47] showed that a PLS-DA model based on the 78 wavebands that were most relevant for the classification of forest species gave 8.17% higher overall classification accuracy than a model utilising all 230 AISA Eagle wavebands. Our results are also consistent with the results of Peerbhay et al [47] in that they show that a high number of hyperspectral wavebands can be compressed into a few latent variables with the help of PLS, reducing the risk of model overfitting and, at the same time providing a successful classification of different vegetation types.…”

Section: Pls-da Based Classification Of Grassland Age-classesmentioning

confidence: 99%

“…Spectral measurements near the green reflectance peak at 550 nm have previously been shown to be important for the classification of vegetation communities (e.g., [47]). …”

Section: Spectral Dissimilarities Between Grasslands Belonging To Difmentioning

confidence: 99%

“…Partial least squares (PLS) [43] regression is commonly used in hyperspectral remote sensing and has shown to be a powerful technique for studying grassland vegetation [44][45][46]. In addition, partial least squares discriminant analysis (PLS-DA), using pre-selected wavebands, is increasingly used in remote sensing-based classification of plant communities (e.g., [47]). …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery

et al. 2014

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Plant communities differ in their species composition, and, thus, also in their functional trait composition, at different stages in the succession from arable fields to grazed grassland. We examine whether aerial hyperspectral (414-2501 nm) remote sensing can be used to discriminate between grazed vegetation belonging to different grassland successional stages. Vascular plant species were recorded in 104.1 m 2 plots on the island of Öland (Sweden) and the functional properties of the plant species recorded in the plots were characterized in terms of the ground-cover of grasses, specific leaf area and Ellenberg indicator values. Plots were assigned to three different grassland age-classes, representing 5-15, 16-50 and >50 years of grazing management. Partial least squares discriminant analysis models were used to compare classifications based on aerial hyperspectral data with the age-class classification. The remote sensing data successfully classified the plots into age-classes: the overall classification accuracy was higher for OPEN ACCESSRemote Sens. 2014, 6 7733 a model based on a pre-selected set of wavebands (85%, Kappa statistic value = 0.77) than one using the full set of wavebands (77%, Kappa statistic value = 0.65). Our results show that nutrient availability and grass cover differences between grassland age-classes are detectable by spectral imaging. These techniques may potentially be used for mapping the spatial distribution of grassland habitats at different successional stages.

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Section: Pls-da Based Classification Of Grassland Age-classessupporting

confidence: 92%

Section: Pls-da Based Classification Of Grassland Age-classesmentioning

confidence: 99%

“…Spectral measurements near the green reflectance peak at 550 nm have previously been shown to be important for the classification of vegetation communities (e.g., [47]). …”

Section: Spectral Dissimilarities Between Grasslands Belonging To Difmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery

et al. 2014

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“…The identification of tree species through remote sensing provides an efficient and potentially cost-effective way to inventory, protect and manage forest resources [1][2][3][4][5]. Detailed and accurate forest maps are crucial for the preparation and monitoring of fire, drought and other forest disturbances caused by climate change [2,6,7].…”

Section: Introductionmentioning

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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Intraspecific genetic variation of a Fagus sylvatica population in a temperate forest derived from airborne imaging spectroscopy time series

Czyż

Escribà

Wulf

et al. 2020

Ecology and Evolution

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The growing pace of environmental change has increased the need for large‐scale monitoring of biodiversity. Declining intraspecific genetic variation is likely a critical factor in biodiversity loss, but is especially difficult to monitor: assessments of genetic variation are commonly based on measuring allele pools, which requires sampling of individuals and extensive sample processing, limiting spatial coverage. Alternatively, imaging spectroscopy data from remote platforms may hold the potential to reveal genetic structure of populations. In this study, we investigated how differences detected in an airborne imaging spectroscopy time series correspond to genetic variation within a population of Fagus sylvatica under natural conditions. We used multi‐annual APEX (Airborne Prism Experiment) imaging spectrometer data from a temperate forest located in the Swiss midlands (Laegern, 47°28'N, 8°21'E), along with microsatellite data from F. sylvatica individuals collected at the site. We identified variation in foliar reflectance independent of annual and seasonal changes which we hypothesize is more likely to correspond to stable genetic differences. We established a direct connection between the spectroscopy and genetics data by using partial least squares (PLS) regression to predict the probability of belonging to a genetic cluster from spectral data. We achieved the best genetic structure prediction by using derivatives of reflectance and a subset of wavebands rather than full‐analyzed spectra. Our model indicates that spectral regions related to leaf water content, phenols, pigments, and wax composition contribute most to the ability of this approach to predict genetic structure of F. sylvatica population in natural conditions. This study advances the use of airborne imaging spectroscopy to assess tree genetic diversity at canopy level under natural conditions, which could overcome current spatiotemporal limitations on monitoring, understanding, and preventing genetic biodiversity loss imposed by requirements for extensive in situ sampling.

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Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu–Natal, South Africa

Cited by 120 publications

References 52 publications

Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery

Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery

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

Intraspecific genetic variation of a Fagus sylvatica population in a temperate forest derived from airborne imaging spectroscopy time series

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