Plant Species Discrimination in a Tropical Wetland Using  In Situ Hyperspectral Data

Prospere, Kurt; McLaren, Kurt; Wilson, Byron S.

doi:10.3390/rs6098494

Cited by 70 publications

(55 citation statements)

References 74 publications

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“…Vegetation type identification accuracies with the training size = 25%. Considering RLR, 1 regularization, which controls the selection or the removal of variables, always underperforms 2 -regularization, which handles collinear variables [16]. Because of mixed plant species, it is difficult to remove variables that are not involved in the classification of all the vegetation types.…”

Section: Supervised Classificationmentioning

confidence: 99%

“…However, plant species have been successfully classified in estuarine [11], palustrine [12] and riparian habitats [13], as well in saltmarsh [5], in mangrove [14,15], in swamp [16] but not in peatlands, to our knowledge. Peatland mapping faces two great challenges at local and global scales due to their high environmental function (biodiversity hotspot, greenhouse gas fluxes, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, using different techniques hyperspectral measurements can be made thanks to a portable spectroradiometer (FieldSpec Pro FR, Analytical Spectral Devices-ASD) which ranges on the reflective domain ([350-2500 nm] with a spectral resolution of 3 nm in Visible and Near InfraRed (VNIR) and approximatively 10 nm in the ShortWave InfraRed (SWIR)) either on laboratory [14] or immediately after the leaf was cut using the leaf clip accessory [16]. This can be an indicator of the ability of discriminating plant species using specific wavelengths or evaluating the performance of a classifier.…”

Section: Introductionmentioning

confidence: 99%

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Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements

et al. 2017

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This study aims to evaluate three classes of methods to discriminate between 13 peatland vegetation types using reflectance data. These vegetation types were empirically defined according to their composition, strata and biodiversity richness. On one hand, it is assumed that the same vegetation type spectral signatures have similarities. Consequently, they can be compared to a reference spectral database. To catch those similarities, several similarities criteria (related to distances (Euclidean distance, Manhattan distance, Canberra distance) or spectral shapes (Spectral Angle Mapper) or probabilistic behaviour (Spectral Information Divergence)) and several mathematical transformations of spectral signatures enhancing absorption features (such as the first derivative or the second derivative, the normalized spectral signature, the continuum removal, the continuum removal derivative reflectance, the log transformation) were investigated. Furthermore, those similarity measures were applied on spectral ranges which characterize specific biophysical properties. On the other hand, we suppose that specific biophysical properties/components may help to discriminate between vegetation types applying supervised classification such as Random Forest (RF), Support Vector Machines (SVM), Regularized Logistic Regression (RLR), Partial Least Squares-Discriminant Analysis (PLS-DA). Biophysical components can be used in a local way considering vegetation spectral indices or in a global way considering spectral ranges and transformed spectral signatures, as explained above. RLR classifier applied on spectral vegetation indices (training size = 25%) was able to achieve 77.21% overall accuracy in discriminating peatland vegetation types. It was also able to discriminate between 83.95% vegetation types considering specific spectral range [350-1350 nm], first derivative of spectral signatures and training size = 25%. Conversely, similarity criterion was able to achieve 81.70% overall accuracy using the Canberra distance computed on the full spectral range [350-2500 nm]. The results of this study suggest that RLR classifier and similarity criteria are promising to map the different vegetation types with high ecological values despite vegetation heterogeneity and mixture.

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Section: Supervised Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements

et al. 2017

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“…(Prospere et al, 2014). Three spectral measurements were taken from individual S. plumosum plant and grass in close proximity.…”

Section: Study Areamentioning

confidence: 99%

Assessing the potential of remote sensing to discriminate invasive <i>Seriphium plumosum</i> from grass

Dubula¹,

Tefsamichael²,

Rampedi³

2016

SA J of Geomatics

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“…For instance, random forest, support vector machines and neural network were employed in numerous hyperspectral studies to discriminate vegetation communities [15], genera [16], and plant species (e.g., [17][18][19]). However, the high dimensionality in hyperspectral data is often problematic when the number of field samples is smaller than the number of spectral features [20].…”

Section: Introductionmentioning

confidence: 99%

The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping

et al. 2015

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Knowledge of the floral cycle and the spatial distribution and abundance of flowering plants is important for bee health studies to understand the relationship between landscape and bee hive productivity and honey flow. The key objective of this study was to show how AISA Eagle hyperspectral data and random forest (RF) can be optimally utilized to produce flowering and spatially explicit land use/land cover (LULC) maps for a study site in Kenya. AISA Eagle imagery was captured at the early flowering period (January 2014) and at the peak flowering season (February 2013). Data on white and yellow flowering trees as well as LULC classes in the study area were collected and used as ground-truth points. We utilized all 64 AISA Eagle bands and also used variable importance in RF to identify the most important bands in both AISA Eagle data sets. The results showed that flowering was most accurately mapped using the AISA Eagle data from the peak flowering period (85.71%-88.15% overall accuracy for the peak flowering OPEN ACCESSRemote Sens. 2015, 7 13299 season imagery versus 80.82%-83.67% for the early flowering season). The variable optimization (i.e., variable selection) analysis showed that less than half of the AISA bands (n = 26 for the February 2013 data and n = 21 for the January 2014 data) were important to attain relatively reliable classification accuracies. Our study is an important first step towards the development of operational flower mapping routines and for understanding the relationship between flowering and bees' foraging behavior.

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Plant Species Discrimination in a Tropical Wetland Using In Situ Hyperspectral Data

Cited by 70 publications

References 74 publications

Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements

Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements

Assessing the potential of remote sensing to discriminate invasive <i>Seriphium plumosum</i> from grass

The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping

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