Search

Questions: Field‐based ecosystem mapping is prone to observer bias, typically resulting in a mismatch between maps made by different mappers, that is, inconsistency. Experimental studies testing the influence of site, mapping scale, and differences in experience level on inconsistency in field‐based ecosystem mapping are lacking. Here, we study how inconsistencies in field‐based ecosystem maps depend on these factors. Location: Iškoras and Guollemuorsuolu, northeastern Norway, and Landsvik and Lygra, western Norway. Methods: In a balanced experiment, four sites were field‐mapped wall‐to‐wall to scales 1:5000 and 1:20,000 by 12 mappers, representing three experience levels. Thematic inconsistency was calculated by overlay analysis of map pairs from the same site, mapped to the same scale. We tested for significant differences between sites, scales, and experience‐level groups. Principal components analysis was used in an analysis of additional map inconsistencies and their relationships with site, scale and differences in experience level and time consumption were analysed with redundancy analysis. Results: On average, thematic inconsistency was 51%. The most important predictor for thematic inconsistency, and for all map inconsistencies, was site. Scale and its interaction with site predicted map inconsistencies, but only the latter were important for thematic inconsistency. The only experience‐level group that differed significantly from the mean thematic inconsistency was that of the most experienced mappers, with nine percentage points. Experience had no significant effect on map inconsistency as a whole. Conclusion: Thematic inconsistency was high for all but the dominant thematic units, with potentially adverse consequences for mapping ecosystems that are fragmented or have low coverage. Interactions between site and mapping system properties are considered the main reasons why no relationships between scale and thematic inconsistency were observed. More controlled experiments are needed to quantify the effect of other factors on inconsistency in field‐based mapping.Field‐based ecosystem maps often have high spatial uncertainty. We found that the area assigned to each ecosystem is considerably more reliable. Moreover, spatial uncertainty is highly influenced by site properties. In particular, mapping of ecosystems that are fragmented or have low coverage is associated with uncertainty. [ABSTRACT FROM AUTHOR]

Although generally given little attention in vegetation studies, ground-dwelling (terricolous) lichens are major contributors to overall carbon and nitrogen cycling, albedo, biodiversity and biomass in many high-latitude ecosystems. Changes in biomass of mat-forming pale lichens have the potential to affect vegetation, fauna, climate and human activities including reindeer husbandry. Lichens have a complex spectral signature and terricolous lichens have limited growth height, often growing in mixtures with taller vegetation. This has, so far, prevented the development of remote sensing techniques to accurately assess lichen biomass, which would be a powerful tool in ecosystem and ecological research and rangeland management. We present a Landsat based remote sensing model developed using deep neural networks, trained with 8914 field records of lichen volume collected for >20 years. In contrast to earlier proposed machine learning and regression methods for lichens, our model exploited the ability of neural networks to handle mixed spatial resolution input. We trained candidate models using input of 1 × 1 (30 × 30 m) and 3 × 3 Landsat pixels based on 7 reflective bands and 3 indices, combined with a 10 m spatial resolution digital elevation model. We normalised elevation data locally for each plot to remove the region-specific variation, while maintaining informative local variation in topography. The final model predicted lichen volume in an evaluation set (n = 159) reaching an R2 of 0.57. NDVI and elevation were the most important predictors, followed by the green band. Even with moderate tree cover density, the model was efficient, offering a considerable improvement compared to earlier methods based on specific reflectance. The model was in principle trained on data from Scandinavia, but when applied to sites in North America and Russia, the predictions of the model corresponded well with our visual interpretations of lichen abundance. We also accurately quantified a recent historic (35 years) change in lichen abundance in northern Norway. This new method enables further spatial and temporal studies of variation and changes in lichen biomass related to multiple research questions as well as rangeland management and economic and cultural ecosystem services. Combined with information on changes in drivers such as climate, land use and management, and air pollution, our model can be used to provide accurate estimates of ecosystem changes and to improve vegetation-climate models by including pale lichens. • We used AI to detect biomass of pale mat-forming terricolous lichens using Landsat. • Our final model reached a R2 of 0.57 (RMSE = 11.24). • NDVI, terrain and the green band were the most important predictors. • Though trained on European data, we got plausible results in North America. • Using historical satellite images we quantified spatiotemporal changes in lichens. [ABSTRACT FROM AUTHOR]