Assessment of WorldView-2 images for aboveground seagrass carbon stock mapping in patchy and continuous seagrass meadows

Wicaksono, Pramaditya; Maishella, Amanda; Arjasakusuma, Sanjiwana; Lazuardi, Wahyu; Harahap, Setiawan Djody

doi:10.1080/01431161.2022.2074809

Cited by 5 publications

(6 citation statements)

References 78 publications

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“…However, for seagrass AGC mapping, our RMSE is notably higher than the work by Wicaksono et al (2022c) using Sentinel-2 in Labuan Bajo. Yet, it falls within the range of accuracy obtained by Wicaksono et al (2022a) study using WorldView-2 on Parang Island. In addition to the aforementioned issues, these variations in accuracy and RMSE can be attributed to the differing characteristics of seagrass meadows.…”

Section: Discussionsupporting

confidence: 73%

“…Additionally, water column correction was not necessary for our study due to the shallow reef flat nature of the seagrass meadows and minimal underwater topographic variations. Previous studies by Zhang et al (2013) and Wicaksono et al (2021b;2022a;2022b) have also emphasized the negligible impact of water column energy attenuation on this benthic habitat condition.…”

Section: Image Corectionmentioning

confidence: 76%

“…Several image transformations were applied to the WV2 SR bands in order to obtain derivative data that could enrich the mapping input during the mapping process. These transformations were selected based on their successful application in previous works focusing on benthic habitat and seagrass mapping, including Principle Component Analysis (PCA), cooccurrence texture analysis, and band ratios (Wicaksono, 2016;Wicaksono, et al, 2022a;Wicaksono et al, 2022b;Wicaksono et al, 2022c). For PCA, all six bands ranging from coastal to red-edge bands were used as input, and the resulting PC bands were subset up to 99% of the total eigenvalues, which translates to the first three PC bands.…”

Section: Worldview-2 Derivativesmentioning

confidence: 99%

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Mapping Seagrass Biodiversity Indicators of Pari Island using Multiple WorldView-2 Bands Derivatives

Wicaksono,

Harahap

2023

Geos. Ind.

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Comprehensive information on seagrass biodiversity indicators, such as species composition, percentage cover, and biomass carbon stock, remains limited across various regions globally. Mapping these indicators using remote sensing images requires extracting maximum information from the input images to achieve effective results. This study aims to map seagrass distribution, percent cover (PC), and aboveground carbon stock (AGC) as biodiversity indicators in the optically shallow waters surrounding Pari Island. We integrate WorldView-2 (WV2) derivatives, field seagrass data, and RF classification and regression algorithms to accomplish this objective. The WV2 image derivatives encompass surface reflectance bands, band ratios, mean and variance co-occurrence texture bands, and principle component bands. These inputs are used individually and collectively for mapping, employing a random forest algorithm trained with field seagrass data. Our results demonstrate that the most accurate benthic habitat map achieves an overall accuracy (OA) of 65.2%, with a user's accuracy of 65.2% and a producer's accuracy of 72.8% for the seagrass-dominated class. Seagrass PC mapping yields a root mean square error (RMSE) of 17.1%, with an average PC of 47.4 ± 9.9%. Seagrass AGC mapping achieves an RMSE of 5.0 g C m-2, with an average AGC range of 6.2 – 29.1 g C m-2, estimating the study area's aboveground biomass carbon stock at 27.9 tons C. Combined inputs produce the most accurate results for all biodiversity indicators, emphasizing the importance of utilizing combined bands from SR band derivatives to maximize information input for training mapping algorithms, instead of using derivative bands individually or as replacements for the initial SR bands. Keywords : Seagrass; Biodiversity; Mapping; WorldView-2; Pari Island Copyright (c) 2023 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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Section: Discussionsupporting

confidence: 73%

Section: Image Corectionmentioning

confidence: 76%

Section: Worldview-2 Derivativesmentioning

confidence: 99%

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Mapping Seagrass Biodiversity Indicators of Pari Island using Multiple WorldView-2 Bands Derivatives

Wicaksono,

Harahap

2023

Geos. Ind.

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“…Furthermore, information such as seagrass percent cover, which is considered as the key parameter for seagrass monitoring effort [7], is even more lacking. More importantly, seagrass percent cover can be used to estimate aboveground carbon stock [8] and sequestration [9] non-destructively.…”

Section: Introductionmentioning

confidence: 99%

Integrating Sentinel-2 and PlanetScope Image with Drone-based Seagrass Data for Seagrass Percent Cover Mapping

Wicaksono,

Hafizt,

Harahap

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Seagrass field data collection activities to train remote sensing images for seagrass percent cover mapping and assess its accuracy can be laborious, costly, and time-consuming, especially for vast seagrass meadows with high density variations. There is also a potential discrepancy in information between seagrass data collected in the field, which usually covers 0.25m2 or 1m2 ground area, and the spatial resolution of remote sensing image used. PlanetScope at 3m and Sentinel-2 at 10m are the currently frequently remote sensing images used to map seagrass. There is a considerable information gap between seagrass data collected in the field and their spatial resolution. The use of seagrass field data thus involves a generalization process and a set of assumptions to justify its integration with remote sensing image. An alternative is to use the drone-based aerial image (hereafter drone data), which captures seagrass meadows at very high spatial resolution, to interpret seagrass percent cover at a level of precision similar to the remote sensing data used. This research assessed the integration of drone-based seagrass data with PlanetScope and Sentinel-2 images to map seagrass percent cover. Seagrass percent cover was interpreted from drone data for each 9m2 and 100m2 ground size following the PlanetScope and Sentinel-2 grids, respectively. Stepwise, random forest, and support vector regression were employed to develop the seagrass’s percent cover mapping model. The accuracy assessment of the resulting seagrass percent cover map involves the calculation of RMS error and plot 1:1 and its derivative analyses. Our results showed that an unparalleled benefit of using drone data is the possibility to obtain SPC information that matches the spatial resolution of satellite imagery, where techniques such as photo-quadrat and photo-transect cannot match. Drone data is successfully integrated with PlanetScope and Sentinel-2 images to produce a high accuracy SPC map effectively and efficiently. Indeed, there are challenges in using drone data, mainly related to oceanographic and weather conditions, and the difficulties in interpreting SPC at the species level.

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“…This under-representation is due to a combination of fundamental uncertainties about the true extent and density of seagrass meadows, as well as the logistical and labor challenges involved in the ground-based mapping of submerged seagrass populations, many of which are located in remote coastal areas [2]. Remote sensing imagery obtained from air-and space-borne sensors can quantify seagrass and other benthic ecosystems, and the ability to remotely quantify seagrass meadows has been greatly enhanced by high-spatial-resolution (≤30 m) space-based sensors such as Landsat, IKONOS, Quickbird-2, RapidEye, WorldView-2 and -3, and Sentinel-2 [3][4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Impact of Atmospheric Correction on Classification and Quantification of Seagrass Density from WorldView-2 Imagery

Hill,

Zimmerman,

Bissett

et al. 2023

Remote Sensing

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Mapping the seagrass distribution and density in the underwater landscape can improve global Blue Carbon estimates. However, atmospheric absorption and scattering introduce errors in space-based sensors’ retrieval of sea surface reflectance, affecting seagrass presence, density, and above-ground carbon (AGCseagrass) estimates. This study assessed atmospheric correction’s impact on mapping seagrass using WorldView-2 satellite imagery from Saint Joseph Bay, Saint George Sound, and Keaton Beach in Florida, USA. Coincident in situ measurements of water-leaving radiance (Lw), optical properties, and seagrass leaf area index (LAI) were collected. Seagrass classification and the retrieval of LAI were compared after empirical line height (ELH) and dark-object subtraction (DOS) methods were used for atmospheric correction. DOS left residual brightness in the blue and green bands but had minimal impact on the seagrass classification accuracy. However, the brighter reflectance values reduced LAI retrievals by up to 50% compared to ELH-corrected images and ground-based observations. This study offers a potential correction for LAI underestimation due to incomplete atmospheric correction, enhancing the retrieval of seagrass density and above-ground Blue Carbon from WorldView-2 imagery without in situ observations for accurate atmospheric interference correction.

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Assessment of WorldView-2 images for aboveground seagrass carbon stock mapping in patchy and continuous seagrass meadows

Cited by 5 publications

References 78 publications

Mapping Seagrass Biodiversity Indicators of Pari Island using Multiple WorldView-2 Bands Derivatives

Mapping Seagrass Biodiversity Indicators of Pari Island using Multiple WorldView-2 Bands Derivatives

Integrating Sentinel-2 and PlanetScope Image with Drone-based Seagrass Data for Seagrass Percent Cover Mapping

Impact of Atmospheric Correction on Classification and Quantification of Seagrass Density from WorldView-2 Imagery

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