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<p>Five different data fusion techniques (multiple linear regression (MLR), high-pass filtering (HPF), intensity hue saturation (IHS), wavelet transformation (WT) and the hybrid method WT + IHS) have been applied to model the aboveground forest biomass (AGB) in this study. The RapidEye multispectral image and the PALSAR radar image were used in research as sources of remote sensing data. Five models for estimating forest AGB were built and analysed using data from test area in Chernihiv region (Ukrainian Polissya). Correlation and min–max accuracy have been calculated for each model to measure the model performance. Among all the data fusion approaches used in the study, the high-pass filtering method has shown the greatest efficiency.</p>
</abstract>

Five different data fusion techniques (multiple linear regression (MLR), high-pass filtering (HPF), intensity hue saturation (IHS), wavelet transformation (WT) and the hybrid method WT + IHS) have been applied to model the aboveground forest biomass (AGB) in this study. The RapidEye multispectral image and the PALSAR radar image were used in research as sources of remote sensing data. Five models for estimating forest AGB were built and analysed using data from test area in Chernihiv region (Ukrainian Polissya). Correlation and min–max accuracy have been calculated for each model to measure the model performance. Among all the data fusion approaches used in the study, the high-pass filtering method has shown the greatest efficiency.

Application of various approaches of multispectral and radar data fusion for modelling of aboveground forest biomass

Folia Forestalia Polonica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Five different data fusion techniques (multiple linear regression (MLR), high-pass filtering (HPF), intensity hue saturation (IHS), wavelet transformation (WT)...

Application of various approaches of multispectral and radar data fusion for modelling of aboveground forest biomass

Article Category: ORIGINAL ARTICLE

Publicado en línea: 12 jun 2023

Páginas: 55 - 67

Recibido: 12 dic 2022

Aceptado: 27 mar 2023

DOI: https://doi.org/10.2478/ffp-2023-0006

© 2023 Dmytro Movchan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.