Mapping wetlands in Sweden using multi-source satellite data and random forest algorithm

Sammanfattning: Wetlands are valuable ecosystems, and assets for human life, that must be regularly monitored, starting with accurately mapping their location and extent. However, an updated national inventory of wetlands is needed. The availability of multi-source data, and advanced machine learning algorithms in Google Earth Engine (GEE) offers excellent opportunities to map wetlands on a country-wide scale. This study mapped wetlands in Sweden using optical, radar, and topographical data, with the Random Forest algorithm, and labels from digitized polygons within the boundaries of the latest national wetlands inventory of Sweden (VMI), completed in 2005. This study discriminates between three classes (non-wetlands, wetlands, and water). From the digitized polygons, 30,000 points were sampled per class in each county (1,890,000 in total). A single RF classifier was trained for each county of Sweden, and a new Swedish national wetlands inventory (RFWI) was generated. The accuracy assessment with testing samples showed that the country-wide overall accuracy (OA) of the classified validation set of points is 98.97%, with a kappa value of 0.985, where the counties with the best, and worst OA are Kronobergs (99.84%), and Norrbottens (97.40%), respectively. RFWI agrees with VMI to a large extent, thus, there are new wetlands mapped, and wetlands surveyed in VMI disappeared. The countrywide area classified as wetlands in RFWI is 30.8% bigger than VMI, as VMI does not incorporate many small wetlands. Nevertheless, the results between counties are mixed. Six out of the 21 counties are estimated to have suffered an overall loss of wetlands area, as big as 73%. RFWI wetlands coverage is higher than VMI’s in the remaining counties, lesser than 30% (small) in seven counties, between 38-64% (mild) in four, between 202-245% (high) in other three, while Stockholm presents a huge difference (450%). The decrease observed in some counties was corroborated with Google Earth imagery. The small, and mild differences are due to the incorporation of wetlands not present in VMI. While high, and huge differences found in four counties are overestimated due to label-related issues. Approximately 70% of the areal difference between RFWI & VMI is contained in Jämtlands county. Besides VMI, the developed RFWI was compared to other continental, and global wetlands products specifically LUCAS, ESA WorldCover, the Ramsar Database, MODIS LC1, and GLWD-3. LUCAS dataset was reclassified to match this study’s classes, and for extracting the class from RFWI, this procedure indicated good agreement between the products (87.88% OA). All the other large-scale inventories mentioned underestimated wetlands occurrence in Sweden to a large extent. The area of wetlands in RFWI could be considered as a realistic maximum in most counties except Stockholms, and Jämtlands. Limitations of this study are discussed and recommendations for future studies are given.