Multispectral Resampling of Seagrass Species Spectra: WorldView-2, Quickbird, Sentinel-2A, ASTER VNIR, and Landsat 8 OLI

Wicaksono, Pramaditya; Kumara, Ignatius Salivian Wisnu; Kamal, Muhammad; Fauzan, Muhammad Afif; Zhafarina, Zhafirah; Nurswantoro, Dwi Agus; Yogyantoro, Rifka Noviaris

doi:10.1088/1755-1315/98/1/012039

Cited by 9 publications

(12 citation statements)

References 12 publications

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“…Finally, refining the classification scheme in order to understand whether the model scheme has satisfactorily answered various management needs, and if obtaining the variations of benthic habitats areas across different geographical areas are necessary. For instance, seagrass class in our study did not include Thalassodendrom ciliatum (Tc), which is uniquely abundant on Nusa Lembongan Island [42]. Furthermore, it is essential to identify the balance between the details of benthic habitat classification scheme and the ease of scheme adaption to different areas.…”

Section: Machine-learning Model Performance In Test Areamentioning

confidence: 99%

“…Seagrass class was detailed into species composition, given that information regarding species may represent their unique ability to provide shelter and food for marine biota, sequestering and burying carbon, coastal protection, and biodiversity measure. Moreover, mapping seagrass species is a difficult task [40][41][42], despite the spectral response variations [43][44][45]. Seagrass species commonly found in abundance in the study area were Enhalus acoroides (Ea), Thalassia hemprichii (Th), and Cymodocea rotundata (Cr).…”

Section: Classification Schemementioning

confidence: 99%

“…Brown algae class was mainly misclassified as mixed-algae and sand, which was expected, given that mixed-algae also contains brown algae, while mixed-algae was misclassified as coral reef classes due to similar pigmentation, resulting in similar reflectance. Ea was misclassified as brown algae since its reflectance covered by epiphyte resembles that of brown algae [42]. EaTh was misclassified as mixed-seagrass and Th, as the spectra of these classes are overlapping [42].…”

Section: Remote Sens 2019 11 X For Peer Review 12 Of 25mentioning

confidence: 99%

“…Ea was misclassified as brown algae since its reflectance covered by epiphyte resembles that of brown algae [42]. EaTh was misclassified as mixed-seagrass and Th, as the spectra of these classes are overlapping [42]. Ho was mainly misclassified as intermediate coral, Th as brown algae and EaTh, and ThCr as Th.…”

Section: Remote Sens 2019 11 X For Peer Review 12 Of 25mentioning

confidence: 99%

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Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms

2019

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This research was aimed at developing the mapping model of benthic habitat mapping using machine-learning classification algorithms and tested the applicability of the model in different areas. We integrated in situ benthic habitat data and image processing of WorldView-2 (WV2) image to parameterise the machine-learning algorithm, namely: Random Forest (RF), Classification Tree Analysis (CTA), and Support Vector Machine (SVM). The classification inputs are sunglint-free bands, water column corrected bands, Principle Component (PC) bands, bathymetry, and the slope of underwater topography. Kemujan Island was used in developing the model, while Karimunjawa, Menjangan Besar, and Menjangan Kecil Islands served as test areas. The results obtained indicated that RF was more accurate than any other classification algorithm based on the statistics and benthic habitats spatial distribution. The maximum accuracy of RF was 94.17% (4 classes) and 88.54% (14 classes). The accuracies from RF, CTA, and SVM were consistent across different input bands for each classification scheme. The application of RF model in the classification of benthic habitat in other areas revealed that it is recommended to make use of the more general classification scheme in order to avoid several issues regarding benthic habitat variations. The result also established the possibility of mapping a benthic habitat without the use of training areas.

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Section: Machine-learning Model Performance In Test Areamentioning

confidence: 99%

Section: Classification Schemementioning

confidence: 99%

Section: Remote Sens 2019 11 X For Peer Review 12 Of 25mentioning

confidence: 99%

Section: Remote Sens 2019 11 X For Peer Review 12 Of 25mentioning

confidence: 99%

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Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms

2019

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“…Satellite multispectral images are still the primary source of remote sensing data for seaweed mapping, but the limited number of spectral bands (less than ten) and the frequent occurrence of mixed pixel (also for medium-high resolution geometric satellites, in the range of 1÷5 m/pixel), limit the possibility of an accurate seaweed monitoring (Hossain et al 2015;Wicaksono et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Multispectral UAV monitoring of submerged seaweed in shallow water

et al. 2019

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The sudden algal bloom in shallow water may be a serious problem for sea coastal economy based on clams farming because it leads quickly to anoxia conditions with the consequent death of the molluscs. In order to detect the rise of algae, normally satellite remote sensing is used, exploiting the higher response in the near infrared wavelengths. A recent progress in monitoring this phenomenon derives from the availability of unmanned aerial vehicles (UAVs) equipped with lightweight multispectral cameras. Such technique makes it possible to acquire detailed spectral information with narrow bands attaining an assessment of the algal bloom at both high geometric and radiometric resolutions. In this work, we tested the MicaSense RedEdge-M multispectral camera mounted on a DJI Phantom 3 Professional aircraft to map submerged seaweeds and assess their evolution with particular regard to the importance of the radiometric calibration of raw imageries using a Downwelling Light Sensor (DLS) and a known reflectance panel. The case study is the lagoon of Goro (Northern Adriatic Sea, Italy), a crucial environment for the clams farming in the Emilia-Romagna region. Digital images acquired in two subsequent flights were processed with either Agisoft PhotoScan Professional and Pix4D Mapper Pro varying the calibration strategies. After a pre-analysis, we applied two different approaches for the seaweed detection: NDVI and maximum likelihood classification. All the tests performed in this study confirm that the monitoring over time with a multispectral lightweight camera mounted on a UAV is possible, but also that by applying proper radiometric corrections, most accurate and reliable results can be achieved.

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Salt Marsh Dynamics in a Period of Accelerated Sea Level Rise

et al. 2020

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Salt marshes are dynamic systems able to laterally expand, contract, and vertically accrete in response to sea level rise. Here, we present the grand challenges that need to be addressed to fully characterize marsh morphodynamics. The review focuses on physical processes and quantitative models. Without predictive models, it is impossible to determine the future marsh evolution under accelerated sea level rise. In these models, one of the challenges is to resolve both horizontal and vertical dynamics within the same framework. Vertically, the marsh has to accumulate enough material to contrast rising water levels. Horizontally, marsh erosion at the ocean side must be compensated by landward expansion in forests, lawns, and agricultural fields. The dynamics of the marsh‐upland boundary are still not fully understood and will require more research in the upcoming years. The complexity of marsh vegetation is seldom captured in predictive models of marsh evolution. More research is needed to understand the effects of each species or species assemblages on hydrodynamics and sediment transport. Here, we further advocate that a sediment budget resolving all sediment fluxes in a marsh complex is the most important metric of marsh resilience. Characterization of these fluxes will enable to connect salt marshes to other landforms and to unravel feedbacks controlling the evolution of the entire coastal system. Current models of marsh evolution rely on sparse data sets collected at few locations. Novel remote sensing techniques will provide high‐resolution spatial data that will inform a new generation of computer models.

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Multispectral Resampling of Seagrass Species Spectra: WorldView-2, Quickbird, Sentinel-2A, ASTER VNIR, and Landsat 8 OLI

Cited by 9 publications

References 12 publications

Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms

Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms

Multispectral UAV monitoring of submerged seaweed in shallow water

Salt Marsh Dynamics in a Period of Accelerated Sea Level Rise

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