Abstract
Detailed knowledge about tree species composition is of great importance for forest management. The two identical European Space Agency (ESA) Sentinel-2 (S2) satellites provide data with unprecedented spectral, spatial and temporal resolution. Here, we investigated the potential benefits of using high temporal resolution data for classification of five coniferous and seven broadleaved tree species in a diverse Central European Forest. To run the classification, 18 cloud-free S2 acquisitions were analyzed in a two-step approach. The available scenes were first used to stratify the study area into six broad land-cover classes. Subsequently, additional classification models were created separately for the coniferous and the broadleaved forest strata. To permit a deeper analytical insight in the benefits of multi-temporal datasets for species identification, classification models were developed taking into account all 262,143 possible permutations of the 18 S2 scenes. Each model was fine-tuned using a stepwise recursive feature reduction. The additional use of vegetation indices improved the model performances by around 5 percentage points. Individual mono-temporal tree species accuracies range from 48.1% (January 2017) to 78.6% (June 2017). Compared to the best mono-temporal results, the multi-temporal analysis approach improves the out-of-bag overall accuracy from 72.9% to 85.7% for the broadleaved and from 83.8% to 95.3% for the coniferous tree species, respectively. Remarkably, a combination of six–seven scenes achieves a model quality equally high as the model based on all data; images from April until August proved most important. The classes European Beech and European Larch attain the highest user’s accuracies of 96.3% and 95.9%, respectively. The most important spectral variables to distinguish between tree species are located in the Red (coniferous) and short wave infrared (SWIR) bands (broadleaved), respectively. Overall, the study highlights the high potential of multi-temporal S2 data for species-level classifications in Central European forests.
Highlights
The current Global Assessment Report on Biodiversity and Ecosystem Services again depicts an alarming picture of the Earth with accelerating rates of biodiversity loss [1]
The land cover classification based on all input data using the random forest modeling approach including the feature selection achieved an overall accuracy of 96%, and most class-specific accuracies were higher than 90% (Table 4)
The average improvement of the OA was around 5 percentage points: The highest OA of the best models improved from 82.1% to 85.9% for the broadleaf strata, from 90.4% to 95.3% for the coniferous strata and from 83.5% to 88.7% for all tree species pooled together
Summary
The current Global Assessment Report on Biodiversity and Ecosystem Services again depicts an alarming picture of the Earth with accelerating rates of biodiversity loss [1]. Earth observation (EO) has a high potential for biodiversity assessments, mainly for the description of vegetation habitats [2]. The synoptic view, and the delivery of detailed, objective and cost-efficient information over large areas, makes EO data one of the most useful tools for biodiversity assessments [3,4,5]. Spatial and temporal resolution of the EO data, various categorical and biophysical traits can be mapped [6,7]. In addition to the occurrences of tree species, information about the distribution and the spatial pattern of tree species within larger geographic extents is essential
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