Applying Spectral Unmixing to Determine Surface Water Parameters in a Mining Environment

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F14%3A10293500" target="_blank" >RIV/00216208:11310/14:10293500 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00025798:_____/14:00000249

Výsledek na webu

<a href="http://dx.doi.org/10.3390/rs61111204" target="_blank" >http://dx.doi.org/10.3390/rs61111204</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/rs61111204" target="_blank" >10.3390/rs61111204</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Applying Spectral Unmixing to Determine Surface Water Parameters in a Mining Environment

Popis výsledku v původním jazyce

Compared to natural waters, mine waters represent an extreme water type that is frequently heavily polluted. Although they have been traditionally monitored by in situ measurements of point samples taken at regular intervals, the emergence of a new generation of multispectral and hyperspectral (HS) sensors means that image spectroscopy has the potential to become a modern method for monitoring polluted surface waters. This paper describes an approach employing linear Spectral Unmixing (LSU) for analysisof hyperspectral image data to map the relative abundances of mine water components (dissolved Fe-Fediss, dissolved organic carbon-DOC, undissolved particles). The ground truth data (8 monitored ponds) were used to validate the results of spectral mapping. The same approach applied to HS data was tested using the image data resampled to WorldView2 (WV2) spectral resolution. A key aspect of the image data processing was to define the proper pure image end members for the fundamental wate

Název v anglickém jazyce

Applying Spectral Unmixing to Determine Surface Water Parameters in a Mining Environment

Popis výsledku anglicky

Compared to natural waters, mine waters represent an extreme water type that is frequently heavily polluted. Although they have been traditionally monitored by in situ measurements of point samples taken at regular intervals, the emergence of a new generation of multispectral and hyperspectral (HS) sensors means that image spectroscopy has the potential to become a modern method for monitoring polluted surface waters. This paper describes an approach employing linear Spectral Unmixing (LSU) for analysisof hyperspectral image data to map the relative abundances of mine water components (dissolved Fe-Fediss, dissolved organic carbon-DOC, undissolved particles). The ground truth data (8 monitored ponds) were used to validate the results of spectral mapping. The same approach applied to HS data was tested using the image data resampled to WorldView2 (WV2) spectral resolution. A key aspect of the image data processing was to define the proper pure image end members for the fundamental wate

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

DJ - Znečištění a kontrola vody

OECD FORD obor

—

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Remote Sensing

ISSN

2072-4292

e-ISSN

—

Svazek periodika

6

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

21

Strana od-do

11204-11224

Kód UT WoS článku

000345530700043

EID výsledku v databázi Scopus

2-s2.0-84912076874