Sammanfattning

The multitemporal acquisition of images from the Sentinel-1 satellites allows continuous monitoring of a forest. This study focuses on the use of multitemporal C-band synthetic aperture radar (SAR) data to assess the results for forest type (FTY), between coniferous and deciduous forest, and tree species (SPP) classification. We also investigated the temporal stability through the use of backscatter from multiple seasons and years of acquisition. SAR acquisitions were pre-processed, histogram-matched, smoothed, and temperature-corrected. The normalized average backscatter was extracted for interpreted plots and used to train Random Forest models. The classification results were then validated with field plots. A principal component analysis was tested to reduce the dimensionality of the explanatory variables, which generally improved the results. Overall, the FTY classifications were promising, with higher accuracies (OA of 0.94 and K = 0.86) than the SPP classification (OA of 0.66 and K = 0.54). The use of merely winter images (OA = 0.89) reached, on average, results that were almost as good as those using of images from the entire year. The use of images from a single winter season reached a similar result (OA = 0.87). We conclude that multiple Sentinel-1 images acquired in winter conditions are feasible to classify forest types in a hemi-boreal Swedish forest.

Nyckelord

SAR; backscatter; forest classification; C-band; Sentinel-1

Publicerad i

Remote Sensing
2021, Volym: 13, nummer: 16, artikelnummer: 3237
Utgivare: MDPI

SLU författare

• Persson, Henrik

  • Institutionen för skoglig resurshushållning, Sveriges lantbruksuniversitet
    

Associerade SLU-program

Remningstorp


UKÄ forskningsämne

Fjärranalysteknik


Publikationens identifierare

DOI: https://doi.org/10.3390/rs13163237

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/113468