Evaluating the statistical performance of less applied algorithms in classification of worldview-3 imagery data in an urbanized landscape

Ranaie, Mehrdad; Soffianian, Alireza; Pourmanafi, Saeid; Mirghaffari, Nourollah; Tarkesh, Mostafa

doi:10.1016/j.asr.2018.01.004

Cited by 9 publications

(2 citation statements)

References 34 publications

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“…With the addition of the four additional bands, and the conventional blue, green, and near-infrared-1 wavelengths, WorldView-2 delivers a total of six bands, whereas four new VNIR Colors have been introduced to the VNIR Color palette: coastal (red edge), yellow (yellow edge), and Near-IR (near-IR). Around 617 kilometres above the Earth's surface, the Worldview-3 has a revisit time of less than a day [31][32][33]. With no cloud cover above our study location, a Worldview-3 image taken on 16 July 2016, was utilized to identify Chl-a, SDT, TP, and TN concentrations.…”

Section: Wordview-3mentioning

confidence: 99%

Eutrophic Status Assessment Based on Very High-Resolution Satellite Imagery in the Coastline Environment of Korea

et al. 2023

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Anthropoid activities are severely altering natural land cover and growing the transport of soil, organic and inorganic compounds, nutrients, toxic chemicals, and other pollutants to the water ecosystem. The eutrophication of the coastal water environment is one of the furthermost bitter consequences of human activities. In this research, we have used three different satellite images for efficient land-use land-cover (LULC) classification, comparison, and further coastal water quality assessment over the coastal zone of the Boseong County of South Korea. The results of LULC classification showed that Landsat-8, Sentinel-2, and WorldView-3 gave an overall accuracy of about 74%, 82%, and 96% with Kappa coefficient of 0.71, 0.78, and 0.91, respectively. By comparing, LULC accuracies and kappa coefficient, the very high-resolution Worldview-3 satellite imagery is considered one of the best-suited satellite imageries for water quality assessment. The study used recently developed algorithms for the calculation of the transparency of Secchi depth, concentration of Chlorophyll-a, Total Phosphorus, and Total Nitrogen; whereas the eutrophication status of the coastal water has been identified using the Carlson Trophic State Index (CTSI) method. The result show that the medium state of eutrophication occurred nearby agricultural regions and urban settlements. Overall, trophic status of the coastal water is ranged from 61.56 to 74.37 with a mean value of 65.63 (CTSI) and placed under the medium eutrophic state. The study analysed that the nutrient entrance from the surrounding land cover is high and needs proper water treatment before releasing into a coastal ecosystem. Hence, these investigations will assist the various local and international agencies in improving the reliability of the monitoring of eutrophication state, dynamics, and potential impacts.

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Section: Wordview-3mentioning

confidence: 99%

Eutrophic Status Assessment Based on Very High-Resolution Satellite Imagery in the Coastline Environment of Korea

et al. 2023

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“…These algorithms are divided into parametric and nonparametric groups. Nonparametric methods allow training data to more robustly participate in the process of image classification and have a higher potency than nonparametric ones (Mountrakis et al 2011;Ranaie et al 2018). According to the literature review (Fig.…”

Section: Introductionmentioning

confidence: 99%

Evaluating resampled and fused Sentinel-2 data and machine-learning algorithms for mangrove mapping in the northern coast of Qeshm island, Iran

Soffianian¹,

Toosi²,

Asgarian³

et al. 2023

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Mangrove forests, as an essential component of the coastal zones in tropical and subtropical areas, provide a wide range of goods and ecosystem services that play a vital role in ecology. Mangroves are globally threatened, disappearing, and degraded. Consequently, knowledge on mangroves distribution and change is important for effective conservation and making protection policies. Developing remote sensing data and classification methods have proven to be suitable tools for mapping mangrove forests over a regional scale. Here, we scrutinized and compared the performance of pixel-based and object-based methods under Support Vector Machine (SVM) and Random Forest (RF) algorithms in mapping a mangrove ecosystem into four main classes (Mangrove tree, mudflat, water, and sand spit) using resampled and fused Sentinel-2 images. Additionally, landscape metrics were used to identify the differences between spatial patterns obtained from different classification methods. Results showed that pixel-based classifications were influenced heavily by the effect of salt and pepper noise, whereas in object-based classifications, boundaries of land use land cover (LULC) polygons were smoother and visually more appealing. Object-based classifications, with an excellent level of kappa, distinguished mudflat and sand spit from each other and from mangrove better than the pixel-based classifications which obtained a fair-to-good level of kappa. RF and SVM performed differently under comparable circumstances. The results of landscape metrics comparison presented that the classification methods can be affected on quantifying area and size metrics. Although the results supported the idea that fused Sentinel images may provide better results in mangrove LULC classification, further research needs to develop and evaluate various image fusion approaches to make use of all Sentinel’s fine resolution images. Our results on the mapping of mangrove ecosystems can contribute to the improvement of management and conservation strategies for these ecosystems being impacted by human activities.

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A high-speed D-CART online fault diagnosis algorithm for rotor systems

Deng

Diao

et al. 2019

Appl Intell

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Evaluating the statistical performance of less applied algorithms in classification of worldview-3 imagery data in an urbanized landscape

Cited by 9 publications

References 34 publications

Eutrophic Status Assessment Based on Very High-Resolution Satellite Imagery in the Coastline Environment of Korea

Eutrophic Status Assessment Based on Very High-Resolution Satellite Imagery in the Coastline Environment of Korea

Evaluating resampled and fused Sentinel-2 data and machine-learning algorithms for mangrove mapping in the northern coast of Qeshm island, Iran

A high-speed D-CART online fault diagnosis algorithm for rotor systems

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Evaluating the statistical performance of less applied algorithms in classification of worldview-3 imagery data in an urbanized landscape

Cited by 9 publications

References 34 publications

Eutrophic Status Assessment Based on Very High-Resolution Satellite Imagery in the Coastline Environment of Korea

Eutrophic Status Assessment Based on Very High-Resolution Satellite Imagery in the Coastline Environment of Korea

﻿Evaluating resampled and fused Sentinel-2 data and machine-learning algorithms for mangrove mapping in the northern coast of Qeshm island, Iran

A high-speed D-CART online fault diagnosis algorithm for rotor systems

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Evaluating resampled and fused Sentinel-2 data and machine-learning algorithms for mangrove mapping in the northern coast of Qeshm island, Iran