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

• Published in: European Journal of Remote Sensing
• Main Authors: Lucie Kupková, Lucie Červená, Renáta Suchá, Lucie Jakešová, Bogdan Zagajewski, Stanislav Březina, Jana Albrechtová
• Format: Article in Journal/Newspaper
• Language: English
• Published: Taylor & Francis Group 2017
• Subjects: Tundra vegetation; The Krkonoše Mountains; Per-pixel classification; Object-based classification; Hyperspectral data; Sentiel-2A; Oceanography; GC1-1581; Geology; QE1-996.5; Tundra
• Online Access: https://doi.org/10.1080/22797254.2017.1274573; https://doaj.org/article/9be87133fe9e4211b8b1c8410c7cc998

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 Krkonoše 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%.