Abstract
Among the machine learning tools being used in recent years for environmental applications such as forestry, self-organizing maps (SOM) and the k-nearest neighbor (kNN) algorithm have been used successfully. We applied both methods for the mapping of organic carbon (Corg) in riparian forests due to their considerably high carbon storage capacity. Despite the importance of floodplains for carbon sequestration, a sufficient scientific foundation for creating large-scale maps showing the spatial Corg distribution is still missing. We estimated organic carbon in a test site in the Danube Floodplain based on RapidEye remote sensing data and additional geodata. Accordingly, carbon distribution maps of vegetation, soil, and total Corg stocks were derived. Results were compared and statistically evaluated with terrestrial survey data for outcomes with pure remote sensing data and for the combination with additional geodata using bias and the Root Mean Square Error (RMSE). Results show that SOM and kNN approaches enable us to reproduce spatial patterns of riparian forest Corg stocks. While vegetation Corg has very high RMSEs, outcomes for soil and total Corg stocks are less biased with a lower RMSE, especially when remote sensing and additional geodata are conjointly applied. SOMs show similar percentages of RMSE to kNN estimations.
Highlights
In recent decades, machine learning approaches have been introduced to manage the vast amount of data produced by various scientific disciplines, including environmental sciences such as forestry
The self-organizing maps (SOM) and the k-nearest neighbor (kNN) approach were used in the Danube Floodplain National Park
Terrestrial data were used as a basis for comparison of the statistical estimates obtained by the SOM- and kNN-approaches
Summary
Machine learning approaches have been introduced to manage the vast amount of data produced by various scientific disciplines, including environmental sciences such as forestry. A different approach to the spatial classification of data is the k-nearest neighbor (kNN) technique; this so-called instance-based, ‘lazy’ learning algorithm often serves as a benchmark for other methods [4]. It has been applied in a number of forest inventories, e.g., in Finland [5,6], New Zealand [7], Austria [8] or Ireland [9]. The majority of studies are based on the use of Landsat data, few of them used VHSR (very high spatial resolution) satellite data
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