Aboveground Biomass Estimation in Short Rotation Forest Plantations in Northern Greece Using ESA’s Sentinel Medium-High Resolution Multispectral and Radar Imaging Missions

Forests

Nikos Theofanous¹  Irene Chrysafis²  Giorgos Mallinis²  Sofia Siahalou²  Natalia Verde²  Christos Domakinis² 

[1] Forest Remote Sensing and Geospatial Analysis Laboratory, Democritus University of Thrace, 68200 Orestiada, Greece;Laboratory of Photogrammetry and Remote Sensing Unit (PERS Lab), School of Rural and Surveying Engineering, The Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;

关键词: optical;    SAR;    spectral indices;    AGB;    seasonal;    random forests;   

DOI  :  10.3390/f12070902

来源: DOAJ

【 摘 要 】

Plantations of fast-growing forest species such as black locust (Robinia Pseudoacacia) can contribute to energy transformation, mitigate industrial pollution, and restore degraded, marginal land. In this study, the synergistic use of Sentinel-2 and Sentinel-1 time series data is explored for modeling aboveground biomass (AGB) in black locust short-rotation plantations in northeastern Greece. Optimal modeling dates and EO sensor data are also identified through the analysis. Random forest (RF) models were originally developed using monthly Sentinel-2 spectral indices, while, progressively, monthly Sentinel-1 bands were incorporated in the statistical analysis. The highest accuracy was observed for the models generated using Sentinel-2 August composites (R² = 0.52). The inclusion of Sentinel-1 bands in the spectral indices’ models had a negligible effect on modeling accuracy during the leaf-on period. The correlation and comparative performance of the spectral indices in terms of pairwise correlation with AGB varied among the phenophases of the forest plantations. Overall, the field-measured AGB in the forest plantations plots presented a higher correlation with the optical Sentinel-2 images. The synergy of Sentinel-1 and Sentinel-2 data proved to be a non-efficient approach for improving forest biomass RF models throughout the year within the geographical and environmental context of our study.

【 授权许可】

Unknown