Geomatics and Environmental Engineering, vol. 16, no. 4

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Vol. 16 No. 4 (2022): Geomatics and Environmental Engineering

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Submitted
May 10, 2022
Accepted
Jul 11, 2022
Published
Oct 12, 2022
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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

https://doi.org/10.7494/geom.2022.16.4.103
Ewa Glowienka
AGH University of Science and Technology, Faculty of Geo-Data Science, Geodesy, and Environmental Engineering, Krakow, Poland
https://orcid.org/0000-0001-7326-1592
Nicole Zembol
SATIM, Krakow, Poland

Corresponding Author(s) : Ewa Glowienka

eglo@agh.edu.pl

Geomatics and Environmental Engineering, Vol. 16 No. 4 (2022): Geomatics and Environmental Engineering

Abstract

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%.

Keywords

hyperspectral pre-processing multispectral Sentinel-2 CHRIS/PROBA machine learning
Glowienka, E., & Zembol, N. (2022). Forest Community Mapping Using Hyperspectral (CHRIS/PROBA) and Sentinel-2 Multispectral Images. Geomatics and Environmental Engineering, 16(4), 103–117. https://doi.org/10.7494/geom.2022.16.4.103
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