Integrating global land cover datasets for deriving user-specific maps

Global scale land cover(LC)mapping has interested many researchers over the last two decades as it is an input data source for various applications.Current global land cover(GLC)maps often do not meet the accuracy and thematic requirements of specific users.This study aimed to create an improved GLC map by integrating available GLC maps and reference datasets.We also address the thematic requirements of multiple users by demonstrating a concept of producing GLC maps with user-specific legends.We used a regression kriging method to integrate Globcover-2009,LC-CCI-2010,MODIS-2010 and Globeland30 maps and several publicly available GLC reference datasets.Overall correspondence of the integrated GLC map with reference LC was 80%based on 10-fold crossvalidation using 24,681 sample sites.This is globally 10%and regionally 6–13%higher than the input map correspondences.Based on LC class presence probability maps,expected LC proportion maps at coarser resolution were created and used for characterizing mosaic classes for land system modelling and biodiversity assessments.Since more reference datasets are becoming freely accessible,GLC mapping can be further improved by using the pool of all available reference datasets.LC proportion information allow tuning LC products to specific user needs.

Producing consistent visually interpreted land cover reference data: learning from feedback

Reference data for large-scale land cover map are commonly acquired by visual interpretation of remotely sensed data.To assure consistency,multiple images are used,interpreters are trained,sites are interpreted by several individuals,or the procedure includes a review.But little is known about important factors influencing the quality of visually interpreted data.We assessed the effect of multiple variables on land cover class agreement between interpreters and reviewers.Our analyses concerned data collected for validation of a global land cover map within the Copernicus Global Land Service project.Four cycles of visual interpretation were conducted,each was followed by review and feedback.Each interpreted site element was labelled according to dominant land cover type.We assessed relationships between the number of interpretation updates following feedback and the variables grouped in personal,training,and environmental categories.Variable importance was assessed using random forest regression.Personal variable interpreter identifier and training variable timestamp were found the strongest predictors of update counts,while the environmental variables complexity and image availability had least impact.Feedback loops reduced updating and hence improved consistency of the interpretations.Implementing feedback loops into the visually interpreted data collection increases the consistency of acquired land cover reference data.