scholarly journals Impact of Coastal Infrastructure on Ocean Colour Remote Sensing: A Case Study in Jiaozhou Bay, China

2019 ◽  
Vol 11 (8) ◽  
pp. 946 ◽  
Author(s):  
Yuan Yuan ◽  
Isabel Jalón-Rojas ◽  
Xiao Hua Wang
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Jiaozhou Bay ◽  
Spectral Characteristics ◽  
Hangzhou Bay ◽  
Post Processing ◽  
Ocean Colour ◽  
Water Index ◽  
Ocean Colour Remote Sensing ◽  
The Impact

Spatial and temporal ocean colour data are increasingly accessible through remote sensing, which is a key tool for evaluating coastal biogeochemical and physical processes, and for monitoring water quality. Coastal infrastructure such as cross-sea bridges may impact ocean colour remote sensing due to the different spectral characteristics of asphalt and the seawater surface. However, this potential impact is typically ignored during data post-processing. In this study, we use Jiaozhou Bay (East China) and its cross-bay bridge to examine the impact of coastal infrastructure on water-quality remote-sensing products, in particular on chlorophyll-a concentration and total suspended sediment. The values of these products in the bridge area were significantly different to those in the adjacent water. Analysis of the remote-sensing reflectance and application of the Normalised Difference Water Index demonstrate that this phenomenon is caused by contamination of the signal by bridge pixels. The Moderate Resolution Imaging Spectroradiometer (MODIS) products helped estimate the pixel scale that could be influenced by contamination. Furthermore, we found similar pixel contamination at Hangzhou Bay Bridge, suggesting that the impact of large coastal infrastructure on ocean colour data is common, and must therefore be considered in data post-processing.

scholarly journals Impact of the cameras radiometric resolution on the accuracy of determining spectral reflectance coefficients

2014 ◽  
Vol XL-1 ◽  
pp. 347-349 ◽  
Author(s):  
A. Orych ◽  
P. Walczykowski ◽  
A. Jenerowicz ◽  
Z. Zdunek
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Spectral Reflectance ◽  
Spectral Characteristics ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Contaminants ◽  
Data Registration ◽  
Sensing Applications ◽  
The Impact

Nowadays remote sensing plays a very important role in many different study fields, i.e. environmental studies, hydrology, mineralogy, ecosystem studies, etc. One of the key areas of remote sensing applications is water quality monitoring. Understanding and monitoring of the water quality parameters and detecting different water contaminants is an important issue in water management and protection of whole environment and especially the water ecosystem. There are many remote sensing methods to monitor water quality and detect water pollutants. One of the most widely used method for substance detection with remote sensing techniques is based on usage of spectral reflectance coefficients. They are usually acquired using discrete methods such as spectrometric measurements. These however can be very time consuming, therefore image-based methods are used more and more often. In order to work out the proper methodology of obtaining spectral reflectance coefficients from hyperspectral and multispectral images, it is necessary to verify the impact of cameras radiometric resolution on the accuracy of determination of them. This paper presents laboratory experiments that were conducted using two monochromatic XEVA video sensors (400–1700 nm spectral data registration) with two different radiometric resolutions (12 and 14 bits). In view of determining spectral characteristics from images, the research team used set of interferometric filters. All data collected with multispectral digital video cameras were compared with spectral reflectance coefficients obtained with spectroradiometer. The objective of this research is to find the impact of cameras radiometric resolution on reflectance values in chosen wavelength. The main topic of this study is the analysis of accuracy of spectral coefficients from sensors with different radiometric resolution. By comparing values collected from images acquired with XEVA sensors and with the curves obtained with spectroradiometer it's possible to determine accuracy of imagebased spectral reflectance coefficients and decide which sensor will be more accurate to determine them for protection of water aquatic environment purpose.

Mariculture Zones Extraction Using NDWI and NDVI

2013 ◽  
Vol 659 ◽  
pp. 153-155 ◽  
Author(s):  
Hong Jun Pan ◽  
Xue Xian Li ◽  
Guang Wei Wang ◽  
Chong Song Qi
Keyword(s):  
Remote Sensing ◽  
Spectral Characteristics ◽  
Remote Sensing Images ◽  
Distribution Planning ◽  
Surface Features ◽  
Water Index ◽  
Normalized Difference Water Index

On the analysis of spectral characteristics of Aoshan remote sensing images, we find the spectral differences between mariculture zones and other surface features. This paper combines normalized difference water index with mariculture zones distribution planning to complete the extraction and the statistics of the mariculture zones, in order to effectively achieve the regulation of mariculture zones.

Ocean surface partitioning strategies using ocean colour remote Sensing: A review

2017 ◽  
Vol 155 ◽  
pp. 41-53 ◽  
Author(s):  
Lilian Anne Krug ◽  
Trevor Platt ◽  
Shubha Sathyendranath ◽  
Ana B. Barbosa
Keyword(s):  
Remote Sensing ◽  
Ocean Surface ◽  
Ocean Colour ◽  
Surface Partitioning ◽  
Ocean Colour Remote Sensing

scholarly journals Effects of Location and Season on Seaweed Spectral Signatures

2021 ◽  
Vol 9 ◽  
Author(s):  
Sadhvi Selvaraj ◽  
Bradley S. Case ◽  
W. Lindsey White
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Spectral Reflectance ◽  
Spectral Characteristics ◽  
Plant Diseases ◽  
Spectral Signature ◽  
Seasonal Effect ◽  
Spectral Variation ◽  
Seaweed Species ◽  
The Impact

Remote sensing is an effective tool for applications such as discriminating plant species, detecting plant diseases or drought, and mapping aquatic vegetation such as seagrasses and seaweeds. Each plant species has a unique spectral reflectance which can be used with remote sensing to map them. However, variations in season, illumination, phenological stages, turbidity or location may affect the spectral reflectance. The aim of this study is to understand the spatial and seasonal effect on two commonly found New Zealand native seaweed species, Ecklonia radiata (C. Agardh) J. Agardh. and Carpophyllum maschalocarpum (Turner) Grev. We collected hyperspectral data (using ASD Handheld2 Field spectrometer with wavelength range 325–1,075 nm) of the seaweed species from four locations across four seasons and used mixed effects modelling to determine the model that best described the spectral data of each seaweed species. The results showed some seasonal pattern across the four locations. In general, summer has an effect on both the species in all four locations; likely due to the higher rates of photosynthesis. However, location did not effect the spectral signature of either species in winter. This study shows the potential for analysis of other micro-and macro-environment factors of different species and provides an understanding of the degree of natural spectral variation in seaweeds enabling further assessment of the impact of anthropogenic activities and changing environment on their spectral characteristics and health. It also identifies a general trend for best season to collect data for better classification accuracy across larger areas.

scholarly journals DETERMINING SPECTRAL REFLECTANCE COEFFICIENTS FROM HYPERSPECTRAL IMAGES OBTAINED FROM LOW ALTITUDES

2016 ◽  
Vol XLI-B7 ◽  
pp. 107-110
Author(s):  
P. Walczykowski ◽  
A. Jenerowicz ◽  
A. Orych ◽  
K. Siok
Keyword(s):  
Remote Sensing ◽  
Spectral Reflectance ◽  
Spectral Characteristics ◽  
Research Work ◽  
Object Identification ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Post Processing ◽  
Ecosystem Studies ◽  
Processing Techniques

Remote Sensing plays very important role in many different study fields, like hydrology, crop management, environmental and ecosystem studies. For all mentioned areas of interest different remote sensing and image processing techniques, such as: image classification (object and pixel- based), object identification, change detection, etc. can be applied. Most of this techniques use spectral reflectance coefficients as the basis for the identification and distinction of different objects and materials, e.g. monitoring of vegetation stress, identification of water pollutants, yield identification, etc. Spectral characteristics are usually acquired using discrete methods such as spectrometric measurements in both laboratory and field conditions. Such measurements however can be very time consuming, which has led many international researchers to investigate the reliability and accuracy of using image-based methods. According to published and ongoing studies, in order to acquire these spectral characteristics from images, it is necessary to have hyperspectral data. The presented article describes a series of experiments conducted using the push-broom Headwall MicroHyperspec A-series VNIR. This hyperspectral scanner allows for registration of images with more than 300 spectral channels with a 1.9 nm spectral bandwidth in the 380- 1000 nm range. The aim of these experiments was to establish a methodology for acquiring spectral reflectance characteristics of different forms of land cover using such sensor. All research work was conducted in controlled conditions from low altitudes. Hyperspectral images obtained with this specific type of sensor requires a unique approach in terms of post-processing, especially radiometric correction. Large amounts of acquired imagery data allowed the authors to establish a new post- processing approach. The developed methodology allowed the authors to obtain spectral reflectance coefficients from a hyperspectral sensor mounted on an unmanned aerial vehicle, ensuring a high accuracy of obtained data.

scholarly journals Hyperspectral analysis of lithologies in the Arctic in areas with abundant lichen cover

2018 ◽  
pp. 51-55
Author(s):  
Sara Salehi
Keyword(s):  
Remote Sensing ◽  
Chemical Composition ◽  
Mineral Exploration ◽  
Spectral Characteristics ◽  
Image Data ◽  
Geological Mapping ◽  
The Arctic ◽  
Electromagnetic Spectrum ◽  
Rock Types ◽  
The Impact

Lithological mapping using remote sensing depends, in part, on the identification of rock types by their spectral characteristics. Chemical and physical properties of minerals and rocks determine their diagnostic spectral features throughout the electromagnetic spectrum. Shifts in the position and changes in the shape and depth of these features can be explained by variations in chemical composition of minerals. Detection of such variations is vital for discriminating minerals with similar chemical composition. Compared with multispectral image data, airborne or spaceborne hyperspectral imagery offers higher spectral resolution, which makes it possible to estimate the mineral composition of the rocks under study without direct contact. Arctic environments provide challenging ground for geological mapping and mineral exploration. Inaccessibility commonly complicates ground surveys, and the presence of ice, vegetation and rock-encrusting lichens hinders remote sensing surveys. This study addresses the following objectives: 1. Modelling the impact of lichen on the spectra of the rock substrate; 2. Identification of a robust lichen index for the deconvolution of lichen and rock mixtures and 3. Multiscale hyperspectral analysis of lithologies in areas with abundant lichens.

scholarly journals Application of Remote Sensing on El Niño Extreme Effect in Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)

2021 ◽  
Vol 6 (1) ◽  
pp. 46-56
Author(s):  
Ricky Anak Kemarau ◽  
Oliver Valentine Eboy
Keyword(s):  
Remote Sensing ◽  
El Niño ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Information ◽  
El Nino ◽  
Extreme Temperature ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
The Impact

The years 1997/1998 and 2015/2016 saw the worst El Niño occurrence in human history. The occurrence of El Niño causes extreme temperature events which are higher than usual, drought and prolonged drought. The incident caused a decline in the ability of plants in carrying out the process of photosynthesis. This causes the carbon dioxide content to be higher than normal. Studies on the effects of El Niño and its degree of strength are still under-studied especially by researchers in the tropics. This study uses remote sensing technology that can provide spatial information. The first step of remote sensing data needs to go through the pre-process before building the NDVI (Normalized Difference Vegetation Index) and Normalized Difference Water Index (NDWI) maps. Next this study will identify the relationship between Oceanic Nino Index (ONI) with Application Remote Sensing in The Study Of El Niño Extreme Effect 1997/1998 and 2015/2016 On Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)NDWI and NDWI landscape indices. Next will make a comparison, statistical and spatial information space between NDWI and NDVI for each year 1997/1998 and 2015/2016. This study is very important in providing spatial information to those responsible in preparing measures in reducing the impact of El Niño.

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