Forest Community Mapping Using Hyperspectral (CHRIS/PROBA) and Sentinel-2 Multispectral Images

• Autorzy: Głowienka Ewa , Zembol Nicole

Identyfikatory

DOI

10.7494/geom.2022.16.4.103

• Języki publikacji: EN

Abstrakty

EN

The possibility to use hyperspectral images (CHRIS/PROBA) and multispectral images (Sentinel-2) in the classification of forest communities is assessed in this article. The pre-processing of CHRIS/PROBA image included: noise reduction, radiometric correction, atmospheric correction, geometric correction. Due to MNF transformation the number of the hyperspectral image channels was reduced (to 10 channels) and smiling errors were removed. Sentinel-2 image (level 2A) did not require pre-processing. Three tree genera occurring in the study area were selected for the classification: pine (Pinus), alder (Alnus) and birch (Betula). Image classification was carried out with three methods: SAM (Spectral Angle Mapper ), MTMF (Mixture Tuned Matched Filtering), SVM (Support Vector Machine). For the CHRIS/PROBA image, the algorithm SVM turned out to be the best. Its overall accuracy (OA) was 72%. The poorest result (OA = 52%) was for the MTMF classifier. In the classification of Sentinel-2 multispectral image the best result was for the MTMF method: OA = 82%, kappa coefficient 0.7. For other methods, the overall accuracy exceeded 65%. Among the classified genera, the highest producer’s accuracy was obtained for pine (PA = 96%), and the broad-leaf genera: alder and birch had PA ranging from 42% to 85%.

Słowa kluczowe

EN

hyperspectral; pre-processing; multispectral; Sentinel-2; CHRIS/PROBA; machine learning

• Wydawca: AGH University of Science and Technology Press
• Czasopismo: Geomatics and Environmental Engineering
• Rocznik: 2022
• Tom: Vol. 16, no. 4
• Strony: 103--117
• Opis fizyczny: Bibliogr. 44 poz., fot., tab., wykr.

Twórcy

autor

Głowienka Ewa

• AGH University of Science and Technology, Faculty of Geo-Data Science, Geodesy, and Environmental Engineering, Krakow, Poland

• https://orcid.org/0000-0001-7326-1592

autor

Zembol Nicole

• SATIM, Krakow, Poland

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023)