preprints.org > environmental and earth sciences > remote sensing > doi: 10.20944/preprints202102.0338.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 13 February 2021 / Approved: 16 February 2021 / Online: 16 February 2021 (14:15:01 CET)

This paper presents ensemble learning of multi-source satellite sensors dataset to obtain better predictive performance of the forest biomass. Spectral, spectral-indices, and spectral-textural features were generated from two optical satellite sensors, Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI). In addition, two radar satellite sensors, Sentinel-1 C-band Synthetic Aperture Radar (CSAR), and Advanced Land Observing Satellite (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) were utilized to generate backscattering and backscattering-textural features. The plot-wise above ground biomass data available from five forests in New England region were utilized. Ensemble learning of multi-source satellite sensors dataset was carried out by employing four machine learning regressors namely, Support Vector Machines (SVM), Random Forests (RF), Gradient Boosting (GB), and Multilayer Perceptron (MLP). A five-fold cross-validation method was used to evaluate predictive performance of the multi-source satellite sensors. The integration of multi-source satellite features, comprising of spectral, spectral-indices, backscattering, spectral-textural, and backscattering-textural information, through ensemble learning and cross-validation approach implemented in the research showed promising results (R² = 0.81, RMSE = 46.2 Mg/ha) for the estimation of plots-level forest biomass in New England region.

Forests; biomass; ALOS-2 PALSAR-2; Sentinel-1 CSAR; Sentinel-2 MSI; Landsat 8 OLI; ensemble learning.

Environmental and Earth Sciences, Remote Sensing

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