European Wide Forest Classification Based on Sentinel-1 Data

Abstract

The constellation of two Sentinel-1 satellites provides an unprecedented coverage of Synthetic Aperture Radar (SAR) data at high spatial (20 m) and temporal (2 to 6 days over Europe) resolution. The availability of dense time series enables the analysis of the SAR temporal signatures and exploitation of these signatures for classification purposes. Frequent backscatter observations allow derivation of temporally filtered time series that reinforce the effect of changes in vegetation phenology by limiting the influence of short-term changes related to environmental conditions. Recent studies have already shown the potential of multitemporal Sentinel-1 data for forest mapping, forest type classification (coniferous or broadleaved forest) as well as for derivation of phenological variables at local to national scales. In the present study, we tested the viability of a recently published multi-temporal SAR classification method for continental scale forest mapping by applying it over Europe and evaluating the derived forest type and tree cover density maps against the European-wide Copernicus High Resolution Layers (HRL) forest datasets and national-scale forest maps from twelve countries. The comparison with the Copernicus HRL datasets revealed high correspondence over the majority of the European continent with overall accuracies of 86.1% and 73.2% for the forest/non-forest and forest type maps, respectively, and a Pearson correlation coefficient of 0.83 for tree cover density map. Moreover, the evaluation of both datasets against the national forest maps showed that the obtained accuracies of Sentinel-1 forest maps are almost within range of the HRL datasets. The Sentinel-1 forest/non-forest and forest type maps obtained average overall accuracies of 88.2% and 82.7%, respectively, as compared to 90.0% and 87.2% obtained by the Copernicus HRL datasets. This result is especially promising due to the facts that these maps can be produced with a high degree of automation and that only a single year of Sentinel-1 data is required as opposed to the Copernicus HRL forest datasets that are updated every three years.

Investigation of Sentinel-1 Time Series for Sensitivity to Fern Vegetation in an European Temperate Forest

“…Dense time series can be used to identify small changes in backscatter over time to establish a stable pattern of difference between areas with no fern and areas with strong fern presence in the undergrowth vegetation. As an evergreen forest, the influence of seasonally-induced structural changes in the tree canopy should be minimal with predictable temporal radar signatures (Dostálová et al 2021). This also allows the extraction of inter-seasonal differences for further analysis using phenological information.…”

“…As the importance of the utilization of these dense time series becomes more apparent, research focuses on developing approaches using advanced temporal metrics, like multitemporal recurrence plots for deforestation mapping (Cremer et al 2020). Similarly, Dostálová et al (2021) applies temporal signatures of different vegetation types for a more advanced landcover classification on European scale. While some of these approaches utilize (qualitative) phenological data for comparative measures, they do not focus on analyzing the quantitative influence of external parameters such as moisture, VPD or VWC.…”

A Sentinel-1 Backscatter Datacube for Global Land Monitoring Applications

“…Our Sentinel-1 datacube system has already served many scientific investigations covering topics such as rice mapping [39], vegetation monitoring [40], soil moisture retrieval [41], forest type classification [6], and building height estimation [42]. For an ESA funded study, we used the first version of our worldwide Sentinel-1 datacube system to perform global-scale data aggregation and image mosaicking.…”

“…This high natural variability is both a hurdle to and a chance for using Sentinel-1 data in land cover classification and biogeophysical retrievals. As exemplified by many recent Sentinel-1 studies (e.g., [4][5][6]), the most promising approach is to use dense and long backscatter time series as the basis for the scientific analysis and the development of advanced algorithms. Unfortunately, implementing such an approach may be quite difficult for an individual user, particularly when one would like to work with several years of data over large regions.…”

Potential of Sentinel-1 C-Band Time Series to Derive Structural Parameters of Temperate Deciduous Forests

“…Potential predictors also encompass time series metrics, which were demonstrated to be valuable for the derivation of structure-related forest parameters, such as forest type [35,36,49] and woody cover [50].…”