Classification of Tundra Vegetation in the Krkonose Mts. National Park Using APEX, AISA Dual and Sentinel-2A Data
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Classification of Tundra Vegetation in the Krkonose Mts. National Park Using APEX, AISA Dual and Sentinel-2A Data
Original language description
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%.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10508 - Physical geography
Result continuities
Project
<a href="/en/project/LO1417" target="_blank" >LO1417: Centre of Experimental Plant Biology of CU</a><br>
Continuities
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
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Europen Journal of Remote Sensing [online]
ISSN
2279-7254
e-ISSN
—
Volume of the periodical
50
Issue of the periodical within the volume
1
Country of publishing house
IT - ITALY
Number of pages
18
Pages from-to
29-46
UT code for WoS article
000405204300003
EID of the result in the Scopus database
—