scholarly journals Development of Kd(490) Algorithm Using Medium Spatial Resolution Landsat 8 OLI Arround Shallow Waters In Panggang Island

2021 ◽  
Vol 5 (1) ◽  
pp. 413-423
Author(s):  
Budhi Agung Prasetyo ◽  
Wikanti Asriningrum ◽  
Vincentius Paulus Siregar
Keyword(s):  
Water Quality ◽  
Spatial Resolution ◽  
Spatial Data ◽  
Near Infrared ◽  
Regional Scale ◽  
Ocean Color ◽  
Data Availability ◽  
Shallow Waters ◽  
Landsat 8 ◽  
Color Data

The state of water quality around Panggang Island, Seribu Islands, in recent decades experienced degradation caused by human activities. The parameters of the diffuse attenuation coefficient (Kd) is an important optical property-related attenuation of light in the water column, and its brightness. Landsat 8 data has potential to map the value of Kd(490) in regional waters in Indonesia. Landsat 8 data could provide solutions to spatial data availability of Kd(490) values in addition to Ocean Color data. The purposes of this research was to developed empirical algorithm of Landsat 8 data to derive values of Kd(490) that can be use as tools for monitoring water quality optically on a regional scale which could not be done by Ocean Color data that has spatial resolution limitation. In-situ measurement of radiometric data was done by using TriOS-RAMSES hyperspectral spectroradiometer with a range of 320 – 890 nm and spectral sampling of 3.3 nm on shallow-waters around Panggang Island. The development of Kd(490) algortihm was done by simulation on ratio of Green and Near-infrared band has great determination values with Kd(490) empirically, which that empirical algorithm can be applied on Landsat 8 data to derive its values. In addition, it is noted that the shallow-waters around Panggang Island, dominant affected by absorption of chlorophyll-a rather than scattering by suspended solids.

scholarly journals Deriving VIIRS High-Spatial Resolution Water Property Data over Coastal and Inland Waters Using Deep Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1944
Author(s):  
Xiaoming Liu ◽  
Menghua Wang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Ocean Color ◽  
Super Resolution ◽  
Inland Waters ◽  
Coastal Oceans ◽  
Chl A ◽  
Color Data

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been a reliable source of ocean color data products, including five moderate (M) bands and one imagery (I) band normalized water-leaving radiance spectra nLw(λ). The spatial resolutions of the M-band and I-band nLw(λ) are 750 m and 375 m, respectively. With the technique of convolutional neural network (CNN), the M-band nLw(λ) imagery can be super-resolved from 750 m to 375 m spatial resolution by leveraging the high spatial resolution features of I1-band nLw(λ) data. However, it is also important to enhance the spatial resolution of VIIRS-derived chlorophyll-a (Chl-a) concentration and the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), as well as other biological and biogeochemical products. In this study, we describe our effort to derive high-resolution Kd(490) and Chl-a data based on super-resolved nLw(λ) images at the VIIRS five M-bands. To improve the network performance over extremely turbid coastal oceans and inland waters, the networks are retrained with a training dataset including ocean color data from the Bohai Sea, Baltic Sea, and La Plata River Estuary, covering water types from clear open oceans to moderately turbid and highly turbid waters. The evaluation results show that the super-resolved Kd(490) image is much sharper than the original one, and has more detailed fine spatial structures. A similar enhancement of finer structures is also found in the super-resolved Chl-a images. Chl-a filaments are much sharper and thinner in the super-resolved image, and some of the very fine spatial features that are not shown in the original images appear in the super-resolved Chl-a imageries. The networks are also applied to four other coastal and inland water regions. The results show that super-resolution occurs mainly on pixels of Chl-a and Kd(490) features, especially on the feature edges and locations with a large spatial gradient. The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd(490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images.

scholarly journals Improvement of Atmospheric Correction of Satellite Sentinel-3/OLCI Data for Oceanic Waters in Presence of Sargassum

2022 ◽  
Vol 14 (2) ◽  
pp. 386
Author(s):  
Léa Schamberger ◽  
Audrey Minghelli ◽  
Malik Chami ◽  
François Steinmetz
Keyword(s):  
Near Infrared ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Correction Algorithm ◽  
Infrared Band ◽  
Economic Issues ◽  
Optical Signature ◽  
Color Data ◽  
The Caribbean ◽  
Satellite Ocean Color

The invasive species of brown algae Sargassum gathers in large aggregations in the Caribbean Sea, and has done so especially over the last decade. These aggregations wash up on shores and decompose, leading to many socio-economic issues for the population and the coastal ecosystem. Satellite ocean color data sensors such as Sentinel-3/OLCI can be used to detect the presence of Sargassum and estimate its fractional coverage and biomass. The derivation of Sargassum presence and abundance from satellite ocean color data first requires atmospheric correction; however, the atmospheric correction procedure that is commonly used for oceanic waters needs to be adapted when dealing with the occurrence of Sargassum because the non-zero water reflectance in the near infrared band induced by Sargassum optical signature could lead to Sargassum being wrongly identified as aerosols. In this study, this difficulty is overcome by interpolating aerosol and sunglint reflectance between nearby Sargassum-free pixels. The proposed method relies on the local homogeneity of the aerosol reflectance between Sargassum and Sargassum-free areas. The performance of the adapted atmospheric correction algorithm over Sargassum areas is evaluated. The proposed method is demonstrated to result in more plausible aerosol and sunglint reflectances. A reduction of between 75% and 88% of pixels showing a negative water reflectance above 600 nm were noticed after the correction of the several images.

scholarly journals Evaluation of TsHARP Utility for Thermal Sharpening of Sentinel-3 Satellite Images Using Sentinel-2 Visual Imagery

2019 ◽  
Vol 11 (19) ◽  
pp. 2304 ◽  
Author(s):  
Hanna Huryna ◽  
Yafit Cohen ◽  
Arnon Karnieli ◽  
Natalya Panov ◽  
William P. Kustas ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Land Surface ◽  
Near Infrared ◽  
Landsat 8 ◽  
Large Area ◽  
Primary Methods ◽  
Thermal Images ◽  
Spatially Distributed ◽  
The Difference ◽  
Sentinel 2

A spatially distributed land surface temperature is important for many studies. The recent launch of the Sentinel satellite programs paves the way for an abundance of opportunities for both large area and long-term investigations. However, the spatial resolution of Sentinel-3 thermal images is not suitable for monitoring small fragmented fields. Thermal sharpening is one of the primary methods used to obtain thermal images at finer spatial resolution at a daily revisit time. In the current study, the utility of the TsHARP method to sharpen the low resolution of Sentinel-3 thermal data was examined using Sentinel-2 visible-near infrared imagery. Compared to Landsat 8 fine thermal images, the sharpening resulted in mean absolute errors of ~1 °C, with errors increasing as the difference between the native and the target resolutions increases. Part of the error is attributed to the discrepancy between the thermal images acquired by the two platforms. Further research is due to test additional sites and conditions, and potentially additional sharpening methods, applied to the Sentinel platforms.

scholarly journals Estimation of near-infrared water-leaving reflectance for satellite ocean color data processing

2010 ◽  
Vol 18 (7) ◽  
pp. 7521 ◽  
Author(s):  
Sean W. Bailey ◽  
Bryan A. Franz ◽  
P. Jeremy Werdell
Keyword(s):  
Data Processing ◽  
Near Infrared ◽  
Ocean Color ◽  
Color Data ◽  
Satellite Ocean Color

scholarly journals Optimal Spatiotemporal Scales to Aggregate Satellite Ocean Color Data for Nearshore Reefs and Tropical Coastal Waters: Two Case Studies

2021 ◽  
Vol 8 ◽  
Author(s):  
Erick F. Geiger ◽  
Scott F. Heron ◽  
William J. Hernández ◽  
Jamie M. Caldwell ◽  
Kim Falinski ◽  
...  
Keyword(s):  
Water Quality ◽  
Puerto Rico ◽  
Satellite Data ◽  
Ocean Color ◽  
Local Analysis ◽  
Small Scale ◽  
Space And Time ◽  
Data Gaps ◽  
Color Data

Remotely sensed ocean color data are useful for monitoring water quality in coastal environments. However, moderate resolution (hundreds of meters to a few kilometers) satellite data are underutilized in these environments because of frequent data gaps from cloud cover and algorithm complexities in shallow waters. Aggregating satellite data over larger space and time scales is a common method to reduce data gaps and generate a more complete time series, but potentially smooths out the small-scale, episodic changes in water quality that can have ecological influences. By comparing aggregated satellite estimates of Kd(490) with related in-water measurements, we can understand the extent to which aggregation methods are viable for filling gaps while being able to characterize ecologically relevant water quality conditions. In this study, we tested a combination of six spatial and seven temporal scales for aggregating data from the VIIRS instrument at several coral reef locations in Maui, Hawai‘i and Puerto Rico and compared these with in situ measurements of Kd(490) and turbidity. In Maui, we found that the median value of a 5-pixels, 7-days spatiotemporal cube of satellite data yielded a robust result capable of differentiating observations across small space and time domains and had the best correlation among spatiotemporal cubes when compared with in situ Kd(490) across 11 nearshore sites (R2 = 0.84). We also found long-term averages (i.e., chronic condition) of VIIRS data using this aggregation method follow a similar spatial pattern to onshore turbidity measurements along the Maui coast over a three-year period. In Puerto Rico, we found that the median of a 13-pixels, 13-days spatiotemporal cube of satellite data yielded the best overall result with an R2 = 0.54 when compared with in situ Kd(490) measurements for one nearshore site with measurement dates spanning 2016–2019. As spatiotemporal cubes of different dimensions yielded optimum results in the two locations, we recommend local analysis of spatial and temporal optima when applying this technique elsewhere. The use of satellite data and in situ water quality measurements provide complementary information, each enhancing understanding of the issues affecting coastal ecosystems, including coral reefs, and the success of management efforts.

scholarly journals Interpreting the progressive eutrophication behind the world’s largest macroalgal blooms with water quality and ocean color data

2015 ◽  
Vol 78 (1) ◽  
pp. 7-21 ◽  
Author(s):  
Qianguo Xing ◽  
Luigi Tosi ◽  
Federica Braga ◽  
Xuelu Gao ◽  
Meng Gao
Keyword(s):  
Water Quality ◽  
Ocean Color ◽  
Macroalgal Blooms ◽  
Color Data

scholarly journals Regional-Scale Forest Mapping over Fragmented Landscapes Using Global Forest Products and Landsat Time Series Classification

2020 ◽  
Vol 12 (1) ◽  
pp. 187 ◽  
Author(s):  
Viktor Myroniuk ◽  
Mykola Kutia ◽  
Arbi J. Sarkissian ◽  
Andrii Bilous ◽  
Shuguang Liu
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Forest Cover ◽  
Regional Scale ◽  
Forest Products ◽  
Google Earth ◽  
Tree Cover ◽  
Landsat 8 ◽  
Fragmented Landscapes ◽  
Forest Mapping

Satellite imagery of 25–30 m spatial resolution has been recognized as an effective tool for monitoring the spatial and temporal dynamics of forest cover at different scales. However, the precise mapping of forest cover over fragmented landscapes is complicated and requires special consideration. We have evaluated the performance of four global forest products of 25–30 m spatial resolution within three flatland subregions of Ukraine that have different forest cover patterns. We have explored the relationship between tree cover extracted from the global forest change (GFC) and relative stocking density of forest stands and justified the use of a 40% tree cover threshold for mapping forest in flatland Ukraine. In contrast, the canopy cover threshold for the analogous product Landsat tree cover continuous fields (LTCCF) is found to be 25%. Analysis of the global forest products, including discrete forest masks Global PALSAR-2/PALSAR Forest/Non-Forest Map (JAXA FNF) and GlobeLand30, has revealed a major misclassification of forested areas under severe fragmentation patterns of landscapes. The study also examined the effectiveness of forest mapping over fragmented landscapes using dense time series of Landsat images. We collected 1548 scenes of Landsat 8 Operational Land Imager (OLI) for the period 2014–2016 and composited them into cloudless mosaics for the following four seasons: yearly, summer, autumn, and April–October. The classification of images was performed in Google Earth Engine (GEE) Application Programming Interface (API) using random forest (RF) classifier. As a result, 30 m spatial resolution forest mask for flatland of Ukraine was created. The user’s and producer’s accuracy were estimated to be 0.910 ± 0.015 and 0.880 ± 0.018, respectively. The total forest area for the flatland Ukraine is 9440.5 ± 239.4 thousand hectares, which is 3% higher than official data. In general, we conclude that the Landsat-derived forest mask performs well over fragmented landscapes if forest cover of the territory is higher than 10–15%.

scholarly journals Evaluation of Landsat 8 OLI/TIRS Level-2 and Sentinel 2 Level-1C Fusion Techniques Intended for Image Segmentation of Archaeological Landscapes and Proxies

2020 ◽  
Vol 12 (3) ◽  
pp. 579
Author(s):  
Athos Agapiou
Keyword(s):  
Spatial Resolution ◽  
Near Infrared ◽  
Archaeological Site ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Object Based Image Analysis ◽  
Segmentation Process ◽  
Object Based ◽  
Level 2 ◽  
Sentinel 2

The use of medium resolution, open access, and freely distributed satellite images, such as those of Landsat, is still understudied in the domain of archaeological research, mainly due to restrictions of spatial resolution. This investigation aims to showcase how the synergistic use of Landsat and Sentinel optical sensors can efficiently support archaeological research through object-based image analysis (OBIA), a relatively new scientific trend, as highlighted in the relevant literature, in the domain of remote sensing archaeology. Initially, the fusion of a 30 m spatial resolution Landsat 8 OLI/TIRS Level-2 and a 10 m spatial resolution Sentinel 2 Level-1C optical images, over the archaeological site of “Nea Paphos” in Cyprus, are evaluated in order to improve the spatial resolution of the Landsat image. At this step, various known fusion models are implemented and evaluated, namely Gram–Schmidt, Brovey, principal component analysis (PCA), and hue-saturation-value (HSV) algorithms. In addition, all four 10 m available spectral bands of the Sentinel 2 sensor, namely the blue, green, red, and near-infrared bands (Bands 2 to 4 and Band 8, respectively) were assessed for each of the different fusion models. On the basis of these findings, the next step of the study, focused on the image segmentation process, through the evaluation of different scale factors. The segmentation process is an important step moving from pixel-based to object-based image analysis. The overall results show that the Gram–Schmidt fusion method based on the near-infrared band of the Sentinel 2 (Band 8) at a range of scale factor segmentation to 70 are the optimum parameters for the detection of standing visible monuments, monitoring excavated areas, and detecting buried archaeological remains, without any significant spectral distortion of the original Landsat image. The new 10 m fused Landsat 8 image provides further spatial details of the archaeological site and depicts, through the segmentation process, important details within the landscape under examination.

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