Classification of floristic composition of mangrove forests using hyperspectral data: case study of Bhitarkanika National Park, India

Kumar, Tanumi; Panigrahy, Sushma; Kumar, Prasanna; Parihar, J. S.

doi:10.1007/s11852-012-0223-2

Cited by 35 publications

(22 citation statements)

References 19 publications

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“…SVM is an advanced supervised non-parametric classifier that has been extensively used for hyperspectral image classification [76,89,90], including mangrove species classification [51,52]. Based on statistical learning theory, SVM is designed to look for an optimal decision hyperplane in high-dimensional space, which produces an optimal separation of classes.…”

Section: Svmmentioning

confidence: 99%

“…Most studies on mangrove species classification were conducted using pixel-based methods such as spectral angle mapper (SAM) [45,46], maximum likelihood classification (MLC) [7,8,46], and spectral unmixing [47][48][49], or object-based methods, such as nearest neighbor (NN) [20,21], random forest (RF) [50], and support vector machine (SVM) [14,51,52]. Previous studies have shown that the object-based methods generally outperformed the pixel-based methods for mangrove species classification, particularly with high-resolution hyperspectral images [21,[53][54][55].…”

Section: Introductionmentioning

confidence: 99%

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Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

et al. 2018

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Mangroves are one of the most important coastal wetland ecosystems, and the compositions and distributions of mangrove species are essential for conservation and restoration efforts. Many studies have explored this topic using remote sensing images that were obtained by satellite-borne and airborne sensors, which are known to be efficient for monitoring the mangrove ecosystem. With improvements in carrier platforms and sensor technology, unmanned aerial vehicles (UAVs) with high-resolution hyperspectral images in both spectral and spatial domains have been used to monitor crops, forests, and other landscapes of interest. This study aims to classify mangrove species on Qi'ao Island using object-based image analysis techniques based on UAV hyperspectral images obtained from a commercial hyperspectral imaging sensor (UHD 185) onboard a UAV platform. First, the image objects were obtained by segmenting the UAV hyperspectral image and the UAV-derived digital surface model (DSM) data. Second, spectral features, textural features, and vegetation indices (VIs) were extracted from the UAV hyperspectral image, and the UAV-derived DSM data were used to extract height information. Third, the classification and regression tree (CART) method was used to selection bands, and the correlation-based feature selection (CFS) algorithm was employed for feature reduction. Finally, the objects were classified into different mangrove species and other land covers based on their spectral and spatial characteristic differences. The classification results showed that when considering the three features (spectral features, textural features, and hyperspectral VIs), the overall classification accuracies of the two classifiers used in this paper, i.e., k-nearest neighbor (KNN) and support vector machine (SVM), were 76.12% (Kappa = 0.73) and 82.39% (Kappa = 0.801), respectively. After incorporating tree height into the classification features, the accuracy of species classification increased, and the overall classification accuracies of KNN and SVM reached 82.09% (Kappa = 0.797) and 88.66% (Kappa = 0.871), respectively. It is clear that SVM outperformed KNN for mangrove species classification. These results also suggest that height information is effective for discriminating mangrove species with similar spectral signatures, but different heights. In addition, the classification accuracy and performance of SVM can be further improved by feature reduction. The overall results provided evidence for the effectiveness and potential of UAV hyperspectral data for mangrove species identification.

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Section: Svmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

et al. 2018

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“…They also concluded that the hyperspectral images are very useful in discriminating mangrove wetlands, and having a finer spectral and spatial resolution can be crucial in investigating fine details of ground features. Kumar et al [35] used the five most dominant classes of mangrove species present in Bhitarkanika as training sets to classify using SAM on Hyperion hyperspectral images, and archived an OA of 0.64. Ashokkumar and Shanmugam [36] demonstrated the influence of band selection in data fusion technique; they performed classification using support vector machine and observed that factor based ranking approach shown better results (R 2 of 0.85) in discriminating mangrove species than other statistical approaches.…”

Section: Introductionmentioning

confidence: 99%

Use of Hyperion for Mangrove Forest Carbon Stock Assessment in Bhitarkanika Forest Reserve: A Contribution Towards Blue Carbon Initiative

et al. 2020

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Mangrove forest coastal ecosystems contain significant amount of carbon stocks and contribute to approximately 15% of the total carbon sequestered in ocean sediments. The present study aims at exploring the ability of Earth Observation EO-1 Hyperion hyperspectral sensor in estimating aboveground carbon stocks in mangrove forests. Bhitarkanika mangrove forest has been used as case study, where field measurements of the biomass and carbon were acquired simultaneously with the satellite data. The spatial distribution of most dominant mangrove species was identified using the Spectral Angle Mapper (SAM) classifier, which was implemented using the spectral profiles extracted from the hyperspectral data. SAM performed well, identifying the total area that each of the major species covers (overall kappa = 0.81). From the hyperspectral images, the NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) were applied to assess the carbon stocks of the various species using machine learning (Linear, Polynomial, Logarithmic, Radial Basis Function (RBF), and Sigmoidal Function) models. NDVI and EVI is generated using covariance matrix based band selection algorithm. All the five machine learning models were tested between the carbon measured in the field sampling and the carbon estimated by the vegetation indices NDVI and EVI was satisfactory (Pearson correlation coefficient, R, of 86.98% for EVI and of 84.1% for NDVI), with the RBF model showing the best results in comparison to other models. As such, the aboveground carbon stocks for species-wise mangrove for the study area was estimated. Our study findings confirm that hyperspectral images such as those from Hyperion can be used to perform species-wise mangrove analysis and assess the carbon stocks with satisfactory accuracy.

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“…Spectral discrimination had been studied by utilizing field and lab reflectance data of various vegetation types such as agricultural crops (Song et al, 2011), Mediterranean species (Manevski et al, 2011) and also coastal vegetation including mangroves (Manjunath et al, 2013;Panigrahy et al, 2012;Schmidt and Skidmore, 2003;Vaiphasa et al, 2005). Apart from that both airborne and satellite based hyperspectral image data were used to discriminate mangrove species at finer levels (Held et al, 2003;Hirano et al, 2003;Koedsin and Vaiphasa, 2013;Kumar et al, 2013). Some studies were conducted based on the derivative spectral analysis of hyperspectral data such as conifer species identification using in-situ spectral data of range 350 nm to 1050 nm (Gong et al, 1997), optimal band selection for wetland species identification using second derivative spectra (Becker et al, 2005) and identifying plant stress caused due to gas leaks using derivative spectral ratios in red edge region (Smith et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Species Discrimination of Mangroves using Derivative Spectral Analysis

Prasad

Gnanappazham

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Mangroves are salt tolerant trees or shrubs commonly seen in mudflats of intertidal regions of tropical and subtropical coastlines. Recent advances in field spectroscopic techniques enabled the species level discrimination among closely related vegetation types. In this study we have analysed the laboratory spectroscopy data collected from eight species of Rhizophoraceaea family of mangroves. The spectral data ranges between the wavelength of 350 nm and 2500 nm at a very narrow bandwidth of 1 nm. Preprocessing techniques including smoothing were done on the spectra to remove the noise before compiling it to a spectral library. Derivative analysis of the spectra was done and its corresponding first and second derivatives were obtained. Statistical analysis such as parametric and non-parametric tests were implemented on the original processed spectra as well as their respective first and second order derivatives for the identification of significant bands for species discrimination. Results have shown that red edge region (680 nm -720 nm) and water vapour absorption region around 1150 nm and 1400 nm are optimal as they were consistent in discriminating species in reflectance spectra as well as in its first and second derivative spectra. C. decandra species is found to be discriminable from other species while reflectance and its derivative spectra were used. Non-parametric statistical analysis gave better results than that of parametric statistical analysis especially in SWIR 2 spectral region (1831 nm -2500 nm).

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Classification of floristic composition of mangrove forests using hyperspectral data: case study of Bhitarkanika National Park, India

Cited by 35 publications

References 19 publications

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

Use of Hyperion for Mangrove Forest Carbon Stock Assessment in Bhitarkanika Forest Reserve: A Contribution Towards Blue Carbon Initiative

Species Discrimination of Mangroves using Derivative Spectral Analysis

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