Mapping temperate forest tree species using dense Sentinel-2 time series
Introduction
Accurate and current tree species maps are essential for natural resource management and conservation, including sustainable forest management and planning, biodiversity monitoring and the assessment of terrestrial carbon stocks and fluxes (Chiarucci and Piovesan, 2020; Wang and Gamon, 2019; Xiao et al., 2019). Forest inventories based on field visits and interpretation of very-high-resolution imagery provide the most accurate data source in many regions, but their availability is often dependent on accessibility, remoteness, and forest ownership (White et al., 2016; Young et al., 2017). Consequently, there is a great demand for satellite-based approaches that can be scaled easily over large areas and repeated frequently (Latifi and Heurich, 2019; Lister et al., 2020; Shifley et al., 2017).
Mapping tree species over large geographic areas with remote sensing data is still an active area of research (Breidenbach et al., 2021; Fassnacht et al., 2016). Research on tree species mapping has substantially increased over the last decades, e.g., by using and combining various types of remote sensing data such as radar, lidar, hyper-spectral, and multispectral data (Cao et al., 2016; Ghosh et al., 2014; Saatchi and Rignot, 1997; Walsh, 1980). However, most studies have been limited to relatively small areas of a few hundred to thousand square kilometers (Grabska et al., 2020). Exceptions are few studies that have mapped tree species at the regional to national scale in the boreal and temperate zone (Ohmann and Gregory, 2002; Tomppo et al., 2008). These studies combined extensive forest inventory data and Landsat data via imputation approaches. Hence, their methodological focus has been on the simultaneous mapping and estimation of multiple forest attributes rather than on optimizing the mapping of tree species. More recently, studies have utilized more advanced satellite-based approaches based on image composites (Breidenbach et al., 2021) and spectral-temporal features from Landsat (Thompson et al., 2015) and Sentinel-2 (Grabska et al., 2020).
The launch of the Sentinel-2 satellites in 2015 and 2017 represents a substantial advancement in terms of spatial and temporal resolution of freely available multispectral remote sensing data. The five-day revisit time and spatial resolution of 10–20 m have shown to improve the thematic detail of land cover such as crop type and yield (Fan et al., 2020; Hunt et al., 2019; Maponya et al., 2020) and tree species (Grabska et al., 2019; Immitzer et al., 2019; Zeug et al., 2019). A common finding of these studies is that classifications based on multiple images are superior to classifications based on single images. Especially the combination of images from distinct phenological stages at the beginning and the end of the vegetation period seem to be important (Grabska et al., 2019; Immitzer et al., 2019). The problem with image-based classification approaches (single and multi-date) is, however, that they are difficult to scale and repeat across larger geographic areas due to swift changes in phenology and the availability of cloud-free images (Sudmanns et al., 2020).
To overcome the limitations of image-based classification, several approaches have been developed with Landsat data and recently applied to Sentinel-2 data: 1) image compositing, 2) spectral temporal binning, and 3) time series methods. Image compositing essentially fills data gaps by selecting best-suited observations for a given pixel and target day based on certain quality characteristics such as temporal distance to target day and distance to clouds and cloud shadows (Franklin et al., 2015; Griffiths et al., 2013; Potapov et al., 2011). Image composites provide seamless, cloud-free images suitable for large-area land cover classification, but they discard a lot of observations that may also be useful to describe the temporal variability of land surfaces. Spectral temporal binning approaches use all available cloud-free observations within a given time period to describe the spectral-temporal distributions via statistical moments and percentiles (Dara et al., 2020; Pflugmacher et al., 2019; Rufin et al., 2019). Grabska et al. (2020) recently used this approach to classify tree species in the Polish Carpathians. Spectral-temporal binning can capture variations of the land surface signal at seasonal intervals (e.g., 2–3 months). In comparison, time series smoothing and interpolation methods preserve the temporal signal associated with phenology and abiotic factors (do Nascimento Bendini et al., 2019; Schwieder et al., 2018). By fitting time series models or filters high-frequency noise is reduced, producing gap-free time series at a weekly resolution and better (Schwieder et al., 2016; Zeng et al., 2020). Time series methods require relatively high input data densities, and therefore they may be particularly beneficial when using Sentinel-2 time series. Time series-based approaches have been used to classify land cover and crop types (do Nascimento Bendini et al., 2019; Schwieder et al., 2016), and forest types (Pasquarella et al., 2018), but there are only few case studies for tree species classification (Kollert et al., 2021; Sheeren et al., 2016).
Another challenge in satellite-based mapping of tree species is related to spectral variability at both regional and local scale. At the regional scale, spectral variability of a tree species increases with increasing variability in growing conditions. Tree species adapt their morphology and phenology to their environment. As a result, the spectral signature of a tree species may differ geographically, which can make a separation from other tree species using only satellite data more challenging (Abdollahnejad et al., 2017; Ziello et al., 2009). To account for this variability, studies typically add environmental variables as auxiliary predictors in classification models (Leckie et al., 2017; Ohmann and Gregory, 2002), whereas the level of detail and spatial resolution strongly varies with the size of the study area. Particularly, accurate information on soil-water-nutrient availability is often not available for larger areas, but soil conditions strongly influence tree species distributions (Walthert and Meier, 2017).
At the local scale, spectral variability is related to stand structure, i.e., stand density, crown sizes, and gap sizes. Even observations in direct proximity covering different parts of the same tree crown often differ spectrally (Clark, 2005; Ferreira et al., 2019). Most studies using texture metrics to predict forest attributes were based on very-high spatial resolution data (Johansen and Phinn, 2006; Mallinis et al., 2008), but texture metrics have also been used to map forest structure variables with Landsat (Wood et al., 2012) and Sentinel-2 (Lang et al., 2019). It has also been reported that texture metrics calculated from 10-m resolution data are more strongly associated with forest structure than texture metrics calculated from 30-m resolution data (Cohen and Spies, 1992; Farwell et al., 2021). Since stand structure can be an indirect indicator of species composition (White et al., 2010), including measures of local spectral variability may help with the discrimination of tree species. It remains to be tested whether contextual information from Sentinel-2 data improves classification of temperate forest tree species.
The objective of this study was to test the utility of time-series based classification of combined Sentinel-2A/B data for mapping a large number of tree species in a temperate forest region. Specifically, we wanted to address the following research questions:
- (i)
How well are the main and minor trees species mapped using dense Sentinel-2 time series?
- (ii)
How do environmental variables describing climate and soil conditions improve tree species classification based on Sentinel-2 data?
- (iii)
Does the incorporation of image texture metrics improve tree species classification?
Section snippets
Study area
Our study area is the German federal state Brandenburg, which spans over an area of thirty thousand km2 in Central Europe (Fig. 1). With 37% of the state area covered by forest, Brandenburg is one of the densely forested states of Germany. Forests are mainly managed (about 60% are privately owned) with a large proportion of even-aged Scots pine stands (Pinus sylvestris L.) on sandy or sandy-loamy soils (MLUL, 2015). The vast majority of Scots pine stands does not correspond to the potential
Sentinel-2 time series
In this study, we used all available Sentinel-2 scenes acquired between January 1st 2018 and December 31st 2019 with an estimated cloud cover of <75%. We downloaded images at processing Level-1C from the Copernicus Open Access Hub. Image pre-processing and analyses were conducted using the Framework for Operational Radiometric Correction for Environmental Monitoring (FORCE) (Frantz, 2019). Pre-processing included geometric correction following Rufin et al. (2020), cloud and cloud shadow
Classification accuracy and class confusions
To describe the accuracy of the tree species classification, we report the results of the model based on the spectral time series (Spec, Table 3). The Spec model had an overall accuracy of 96.0 ± 0.1%, which was slightly lower but not statistically significant compared to the models that included also environmental and texture metrics. First, we report the accuracies of the nine main tree species that each account for more than 0.5% of the considered single species area. Then, we report the
Discussion
In this study, we showed the utility of dense Sentinel-2 A/B time series for mapping tree species in a temperate forest region. Accuracy of the main tree species ranged between 98.9% for Scots pine and 66.8% for Douglas fir, while the overall accuracy of all 17 tree species was 96.0%. Although a direct comparison of accuracy estimates is not possible, our results seem to compare favorably with other studies. For example, Immitzer et al. (2019) achieved an accuracy of 89% when classifying 12
Conclusions
Mapping tree species over large areas with satellite data is still a research challenge. Time series from Sentinel-2 represent a new opportunity to improve on previous efforts. The constellation provides global observations of forest cover and phenology at an unprecedented combination of spatial and temporal detail. Using data from Sentinel-2A/B, this study demonstrated a time series approach to map tree species in a temperate forest region in Central Europe. Based on an ensemble filter that
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This research was funded by the Forest Climate Fund of the Federal Ministry of Food and Agriculture and the Federal Ministry of Environment Germany (FKZ: 2219WK51B4), and Humboldt-Universität zu Berlin. We thank the European Space Agency and the European Commission for making Sentinel-2 data freely available. We thank the Federal Forest Service of Brandenburg for the provision of the forest inventory data, particularly Bernd Rose and Dr. Olaf Rüffer who also provided guidance and comments to an
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