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

Chaube, Nilima Rani; Lele, Nikhil; Misra, Arundhati; Murthy, T. V. R.; Manna, Sudip; Hazra, Sugata; Panda, Mrutyunjaya; Samal, R. N.

doi:10.18520/cs/v116/i7/1136-1142

Cited by 30 publications

(10 citation statements)

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“…In recent studies, the use of canopy spectroscopy, such as hyperspectral images from AVIRIS and the Hyperspectral Infrared Imager (HyspIRI) and Surface Biology and Geology (SBG) missions, to predict vegetation composition, structure, and function has been shown to be effective in a variety of biomes [13][14][15][16][17][18]. However, similar work remains technically challenging in the arctic tundra as a result of low data availability and high spatial heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

“…The canopy properties of different vegetation types, including spectral reflectance, temperature, and structure, can strongly affect energy and water exchange through the atmosphere-plant-soil continuum [11,12], and are therefore important for understanding the consequences of rapid climate change. For example, spectral reflectance that describes the light-absorbing and -scattering properties of plant leaves and canopies has shown great effectiveness for retrieving vegetation parameters, such as albedo, leaf pigments, macronutrients, as well as vegetation fractional covers [13][14][15][16][17][18], that are important to inform Earth system models (ESMs). Similarly, vegetation structural details (e.g., canopy height) are essential for monitoring above-ground biomass and primary productivity [19][20][21] while canopy temperature can help detect plant water stress associated with hydrological events [22,23].…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

et al. 2020

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Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional and global climate feedbacks. In the Arctic, climate is warming twice as fast as compared to the global average (known as ‘Arctic amplification’), likely having stronger impacts on arctic tundra vegetation. In order to quantify these changes and assess their impacts on ecosystem structure and function, methods are needed to accurately characterize the canopy properties of tundra vegetation types. However, commonly used ground-based measurements are limited in spatial and temporal coverage, and differentiating low-lying tundra plant species is challenging with coarse-resolution satellite remote sensing. The collection and processing of multi-sensor data from unoccupied aerial systems (UASs) has the potential to fill the gap between ground-based and satellite observations. To address the critical need for such data in the Arctic, we developed a cost-effective multi-sensor UAS (the ‘Osprey’) using off-the-shelf instrumentation. The Osprey simultaneously produces high-resolution optical, thermal, and structural images, as well as collecting point-based hyperspectral measurements, over vegetation canopies. In this paper, we describe the setup and deployment of the Osprey system in the Arctic to a tundra study site located in the Seward Peninsula, Alaska. We present a case study demonstrating the processing and application of Osprey data products for characterizing the key biophysical properties of tundra vegetation canopies. In this study, plant functional types (PFTs) representative of arctic tundra ecosystems were mapped with an overall accuracy of 87.4%. The Osprey image products identified significant differences in canopy-scale greenness, canopy height, and surface temperature among PFTs, with deciduous low to tall shrubs having the lowest canopy temperatures while non-vascular lichens had the warmest. The analysis of our hyperspectral data showed that variation in the fractional cover of deciduous low to tall shrubs was effectively characterized by Osprey reflectance measurements across the range of visible to near-infrared wavelengths. Therefore, the development and deployment of the Osprey UAS, as a state-of-the-art methodology, has the potential to be widely used for characterizing tundra vegetation composition and canopy properties to improve our understanding of ecosystem dynamics in the Arctic, and to address scale issues between ground-based and airborne/satellite observations.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

et al. 2020

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“…For example, in-situ measurement from portal spectral radiometer were usually conducted for the assessment of spectral differences among mangrove species and for the validation of spectral information obtained via space-borne spectral radiometer [21][22][23][24][25], which cannot be applied over large scale. Airborne platform can overcome the limitation of large coverage to some extent when compared to in-situ measurement, such as CASI [26] and AVIRIS [27], but the shortage of battery endurance makes it difficult to monitor mangrove over larger areas. Moreover, the requirement of professional operators and application for airspace is another problem, especially for the administration boundary, like the Mai Po Nature Reserve (MPNR) over which the permission of airspace from mainland China and Hong Kong are necessary.…”

Section: Introductionmentioning

confidence: 99%

GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong

et al. 2020

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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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“…Previous work by the authors as well as other researchers has allowed assessing the biomass of the several mangrove plant species and has provided the biomass of species individually. Chaube et al [34] employed AVIRIS-NG (Airborne Visible InfraRed Imaging Spectrometer Next Generation) hyperspectral data to map mangrove species using a SAM (Spectral Angle Mapper) classifier. Authors identified 15 mangrove species over Bhitarkanika mangrove forest, reporting an overall accuracy (OA) of 0.78 (R 2 ).…”

Section: Introductionmentioning

confidence: 99%

“…In purview of the above, this study aimed at evaluating the net above ground carbon stocks present at Bhitarkanika mangrove forest ecosystem, particularly with relevant field inventory and remote sensing approaches. [34,43] 2013…”

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

Cited by 30 publications

References 0 publications

A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong

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

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