Classification of Tundra Vegetation in the Krkonose Mts. National Park Using APEX, AISA Dual and Sentinel-2A Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F17%3A10361541" target="_blank" >RIV/00216208:11310/17:10361541 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1080/22797254.2017.1274573" target="_blank" >http://dx.doi.org/10.1080/22797254.2017.1274573</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/22797254.2017.1274573" target="_blank" >10.1080/22797254.2017.1274573</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification of Tundra Vegetation in the Krkonose Mts. National Park Using APEX, AISA Dual and Sentinel-2A Data

Popis výsledku v původním jazyce

The aim of this study was to evaluate and compare suitability of aerial hyperspectral data (AISA Dual and APEX sensors) and Sentinel-2A data for classification of tundra vegetation cover in the Krkonose Mts. National Park. We compared classification results (accuracy, maps) of pixel-based (Maximum Likelihood, Suport Vector Machine and Neural Net) and object-based approaches. The best classification results (overall accuracy 84.3%, Kappa coefficient = 0.81) were achieved for AISA Dual data using per-pixel SVM classifier for 40 PCA bands. The best classification results of APEX though were only 1.7 percentage points lower. To get comparable results for Sentinel-2A classification legend had to be simplified. With the simplified legend the accuracy using MLC classifier reached 77.7%.

Název v anglickém jazyce

Classification of Tundra Vegetation in the Krkonose Mts. National Park Using APEX, AISA Dual and Sentinel-2A Data

Popis výsledku anglicky

The aim of this study was to evaluate and compare suitability of aerial hyperspectral data (AISA Dual and APEX sensors) and Sentinel-2A data for classification of tundra vegetation cover in the Krkonose Mts. National Park. We compared classification results (accuracy, maps) of pixel-based (Maximum Likelihood, Suport Vector Machine and Neural Net) and object-based approaches. The best classification results (overall accuracy 84.3%, Kappa coefficient = 0.81) were achieved for AISA Dual data using per-pixel SVM classifier for 40 PCA bands. The best classification results of APEX though were only 1.7 percentage points lower. To get comparable results for Sentinel-2A classification legend had to be simplified. With the simplified legend the accuracy using MLC classifier reached 77.7%.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10508 - Physical geography

Projekt

<a href="/cs/project/LO1417" target="_blank" >LO1417: Centrum experimentální biologie rostlin UK</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Europen Journal of Remote Sensing [online]

ISSN

2279-7254

e-ISSN

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Svazek periodika

50

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

IT - Italská republika

Počet stran výsledku

18

Strana od-do

29-46

Kód UT WoS článku

000405204300003

EID výsledku v databázi Scopus

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