Tropical mangrove species discrimination using hyperspectral data: A laboratory study

Hyperspectral data has been widely used in species discrimination of plants with rich spectral information in hundreds of spectral bands, while the availability of hyperspectral data has hindered its applications in many specific cases. The successful operation of the Chinese satellite, Gaofen-5 (GF-5), provides potentially promising new hyperspectral dataset with 330 spectral bands in visible and near infrared range. Therefore, there is much demand for assessing the effectiveness and superiority of GF-5 hyperspectral data in plants species mapping, particularly mangrove species mapping, to better support the efficient mangrove management. In this study, mangrove forest in Mai Po Nature Reserve (MPNR), Hong Kong was selected as the study area. Four dominant native mangrove species were investigated in this study according to the field surveys. Two machine learning methods, Random Forests and Support Vector Machines, were employed to classify mangrove species with Landsat 8, Simulated Hyperion and GF-5 data sets. The results showed that 97 more bands of GF-5 over Hyperion brought a higher over accuracy of 87.12%, in comparison with 86.82% from Hyperion and 73.89% from Landsat 8. The higher spectral resolution of 5 nm in GF-5 was identified as making the major contribution, especially for the mapping of Aegiceras corniculatum. Therefore, GF-5 is likely to improve the classification accuracy of mangrove species mapping via enhancing spectral resolution and thus has promising potential to improve mangrove monitoring at species level to support mangrove management.

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Application of unsupervised end member detection algorithms for spectral unmixing of hyperspectral data for mangrove species discrimination

2012 International Conference on Communications, Devices and Intelligent Systems (CODIS) ◽

10.1109/codis.2012.6422141 ◽

2012 ◽

Author(s):

Somdatta Chakravortty ◽

Arpita Saha Choudhury

Keyword(s):

Hyperspectral Data ◽

Spectral Unmixing ◽

Species Discrimination ◽

Mangrove Species ◽

Detection Algorithms

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DIFFERENT STATISTICAL METHODS FOR THE DISCRIMINATION OF TROPICAL MANGROVE SPECIES USING IN-SITU HYPERSPECTRAL DATA

Journal of Hyperspectral Remote Sensing ◽

10.29150/jhrs.v9.2.p49-67 ◽

2019 ◽

Vol 9 (2) ◽

pp. 49

Author(s):

Tanumi Kumar ◽

Dibyendu Dutta ◽

Diya Chatterjee ◽

K Chandrasekar ◽

Goru Srinivasa Rao ◽

...

Keyword(s):

Near Infrared ◽

Infrared Region ◽

Hyperspectral Data ◽

Feature Reduction ◽

Reflectance Spectra ◽

Stepwise Discriminant Analysis ◽

Species Discrimination ◽

Mangrove Species ◽

Domain Specific ◽

Continuum Removal

The study highlights the hyperspectral characteristics of canopies of 14 tropical mangrove species, belonging to nine families found in the tidal forests of the Indian Sundarbans. Hyperspectral observations were recorded using a field spectroradiometer, pre-processed and subjected to derivative analysis and continuum removal. Mann-Whitney U tests were applied on the spectral data in four spectral forms: (i) Reflectance Spectra (ii) First Derivative, (iii) Second Derivative and (iv) Continuum Removal Reflectance Spectra. Factor analysis was applied in each of the spectral forms for feature reduction and identification of the important wavelengths for species discrimination. Stepwise discriminant analysis was used on the feature reduced reflectance spectra to obtain optimal bands for computation of Jeffries–Matusita distance. The Mann-Whitney U test could be satisfactorily used for determining the significant (separable) bands for discriminating the species. In general, the red region, red edge domain, specific near infrared bands (including 759, 919, 934, 940, 948, 1152, 1156, 1159 and 1212 nm) and shortwave infrared region (1503–1766 nm) played major roles in spectral separability. Overall, hyperspectral data showed potential for discriminating between mangrove canopies of different species and the results of the study also indicated the usefulness of the applied statistical tools for discrimination.

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Mangrove Species Discrimination and Health Assessment using AVIRIS-NG Hyperspectral Data

Current Science ◽

10.18520/cs/v116/i7/1136-1142 ◽

2019 ◽

Vol 116 (7) ◽

pp. 1136 ◽

Cited By ~ 5

Author(s):

Nilima R. Chaube ◽

Nikhil Lele ◽

Arundhati Misra ◽

T. V. R. Murthy ◽

Sudip Manna ◽

...

Keyword(s):

Health Assessment ◽

Hyperspectral Data ◽

Species Discrimination ◽

Mangrove Species

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Application of Spectral Unmixing Algorithm on Hyperspectral Data for Mangrove Species Classification

Applied Algorithms - Lecture Notes in Computer Science ◽

10.1007/978-3-319-04126-1_19 ◽

2014 ◽

pp. 223-236 ◽

Cited By ~ 2

Author(s):

Somdatta Chakravortty ◽

Ekta Shah ◽

Arpita Saha Chowdhury

Keyword(s):

Hyperspectral Data ◽

Spectral Unmixing ◽

Species Classification ◽

Mangrove Species

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Continuous Wavelet Analysis of Leaf Reflectance Improves Classification Accuracy of Mangrove Species

Remote Sensing ◽

10.3390/rs11030254 ◽

2019 ◽

Vol 11 (3) ◽

pp. 254 ◽

Cited By ~ 3

Author(s):

Yi Xu ◽

Junjie Wang ◽

Anquan Xia ◽

Kangyong Zhang ◽

Xuanyan Dong ◽

...

Keyword(s):

Wavelet Analysis ◽

Classification Accuracy ◽

Vegetation Index ◽

Hyperspectral Data ◽

Continuous Wavelet ◽

Mangrove Forests ◽

Bruguiera Gymnorrhiza ◽

Species Classification ◽

Successive Projections Algorithm ◽

Mangrove Species

Due to continuous degradation of mangrove forests, the accurate monitoring of spatial distribution and species composition of mangroves is essential for restoration, conservation and management of coastal ecosystems. With leaf hyperspectral reflectance, this study aimed to explore the potential of continuous wavelet analysis (CWA) combined with different sample subset partition (stratified random sampling (STRAT), Kennard-Stone sampling algorithm (KS), and sample subset partition based on joint X-Y distances (SPXY)) and feature extraction methods (principal component analysis (PCA), successive projections algorithm (SPA), and vegetation index (VI)) in mangrove species classification. A total of 301 mangrove leaf samples with four species (Avicennia marina, Bruguiera gymnorrhiza, Kandelia obovate and Aegiceras corniculatum) were collected across six different regions. The smoothed reflectance (Smth) and first derivative reflectance (Der) spectra were subjected to CWA using different wavelet scales, and a total of 270 random forest classification models were established and compared. Among the 120 models with CWA of Smth, 88.3% of models increased the overall accuracy (OA) values with an improvement of 0.2–28.6% compared to the model with the Smth spectra; among the 120 models with CWA of Der, 25.8% of models increased the OA values with an improvement of 0.1–11.4% compared to the model with the Der spectra. The model with CWA of Der at the scale of 23 coupling with STRAT and SPA achieved the best classification result (OA = 98.0%), while the best model with Smth and Der alone had OA values of 86.3% and 93.0%, respectively. Moreover, the models using STRAT outperformed those using KS and SPXY, and the models using PCA and SPA had better performances than those using VIs. We have concluded that CWA with suitable scales holds great potential in improving the classification accuracy of mangrove species, and that STRAT combined with the PCA or SPA method is also recommended to improve classification performance. These results may lay the foundation for further studies with UAV-acquired or satellite hyperspectral data, and the encouraging performance of CWA for mangrove species classification can also be extended to other plant species.

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