Classification of selected species of vegetation in the Krkonoše Mountains tundra based on time series of PlanetScope imagery

Cílem práce bylo zjistit, do jaké míry lze s využitím dat PlanetScope rozlišit tři druhy travin - bezkolenec modrý (Molinia caerulea), třtina chloupkatá (Calamagrostis villosa) a smilka tuhá (Nardus stricta) v krkonošské tundře a zda multitemporální přístup může přispět ke zvýšení přesnosti klasifikace těchto druhů. Využito bylo dat PlanetScope s prostorovým rozlišením 3 m. Řízená pixelová klasifikace Maximum Likelihood, Support Vector Machine, Random Forest a objektová klasifikace SVM proběhly v programu ENVI 5.3 na základě terénních dat zaměřených GPS přístrojem v letech 2014 až 2018. Jednotlivé přesnosti klasifikace byly porovnány s výstupy řízené klasifikace v Kupková et al. (2017) a Marcinkowska-Ochytra et al. (2018a). Nejlepší výsledek klasifikace byl dosažen pro multitemporální kompozit metodou Random Forest. Celková přesnost klasifikace byla 80,67 %, což je lepší výsledek, než v případě klasifikace snímku z jednoho termínu (celková přesnost 76,06 %). Data PlanetScope byla porovnána s daty RapidEye a Apex. V rámci dat RapidEye dosáhl nejvyšší celkové přesnosti multitemporální kompozit klasifikovaný metodou Random Forest (74,75 %), v případě dat Apex (termín 10.9.2012) bylo dosaženo nejlepšího výsledku také metodou Random Forest (75,91 %). Klíčová slova: multitemporální klasifikace, vegetace,...The aim of this thesis was to test the suitability of PlanetScope imagery to differentiate and evaluate the possibility of multi-temporal approach to improve classification accuracy of selected vegetation species (Molinia caerulea, Calamagrostis villosa, Nardus stricta) in eastern tundra in the Krkonoše Mts. National Park. PlanetScope imagery - 4 spectral bands with spatial resolution 3 m - was used. Per-pixel classifications Maximum Likelihood, Support Vector Machine and Random Forest and object-based classification SVM were executed in software ENVI 5.3. based on GPS field data collected from 2014 till 2018. The best classification results were compared to classification results in Kupková et al. 2017 and Marcinkowska-Ochytra et al. (2018a). The overall accuracy of the best classification result (multitemporal composite using Random Forest classifier) was 80,67 %. It is better result than in the case of single image classification (overall accuracy was 76,06 %). PlanetScope data were compared to RapidEye and Apex data. The overall accuracy of the RapidEye best classification result (multitemporal composite using Random Forest classifier) was 74,75 %, the best overall accuracy of monotemporal classification of Apex data reached 75,91 %. Key words: multi-temporal classification, vegetation,...Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografiePřírodovědecká fakultaFaculty of Scienc

Classification of selected vegetation land cover categories in the Krkonoše Mts. tundra from Sentinel-2A imagery using multi-temporal data

Classification of selected vegetation land cover categories in the Krkonoše Mts. Tundra from Sentinel-2A imagery using multitemporal data Abstract The aim of this thesis was to evaluate the possibilities of multi-temporal approach to improve classification accuracy of vegetation cover in eastern tundra in the Krkonoše Mts. National Park. Sentinel-2A imagery - 10 spectral bands with spatial resolution 10 and 20 m - was used. The classification legend was created by a botanist of the national park. Maximum likelihood classification for 11 categories of vegetation land cover was executed in software ENVI 5.3. The overall accuracy of the best classification result was 53,4 % which is similar result as in the case of single image classification (overall accuracy was 51,2 %). Key words: multi-temporal classification, vegetation, spectral features, Sentinel-2A, tundra, The Krkonoše Mts. National Par

Classification of selected species of vegetation in the Krkonoše Mountains tundra based on time series of PlanetScope imagery

The aim of this thesis was to test the suitability of PlanetScope imagery to differentiate and evaluate the possibility of multi-temporal approach to improve classification accuracy of selected vegetation species (Molinia caerulea, Calamagrostis villosa, Nardus stricta) in eastern tundra in the Krkonoše Mts. National Park. PlanetScope imagery - 4 spectral bands with spatial resolution 3 m - was used. Per-pixel classifications Maximum Likelihood, Support Vector Machine and Random Forest and object-based classification SVM were executed in software ENVI 5.3. based on GPS field data collected from 2014 till 2018. The best classification results were compared to classification results in Kupková et al. 2017 and Marcinkowska-Ochytra et al. (2018a). The overall accuracy of the best classification result (multitemporal composite using Random Forest classifier) was 80,67 %. It is better result than in the case of single image classification (overall accuracy was 76,06 %). PlanetScope data were compared to RapidEye and Apex data. The overall accuracy of the RapidEye best classification result (multitemporal composite using Random Forest classifier) was 74,75 %, the best overall accuracy of monotemporal classification of Apex data reached 75,91 %. Key words: multi-temporal classification, vegetation,..