Abstract
The continued increase of anthropogenic pressure on the Earth’s ecosystems is degrading the natural environment and then decreasing the services it provides to humans. The type, quantity, and quality of many of those services are directly connected to land cover, yet competing demands for land continue to drive rapid land cover change, affecting ecosystem services. Accurate and updated land cover information is thus more important than ever, however, despite its importance, the needs of many users remain only partially attended. A key underlying reason for this is that user needs vary widely, since most current products – and there are many available – are produced for a specific type of end user, for example the climate modelling community. With this in mind we focus on the need for flexible, automated processing approaches that support on-demand, customized land cover products at various scales. Although land cover processing systems are gradually evolving in this direction there is much more to do and several important challenges must be addressed, including high quality reference data for training and validation and even better access to satellite data. Here, we 1) present a generic system architecture that we suggest land cover production systems evolve towards, 2) discuss the challenges involved, and 3) propose a step forward. Flexible systems that can generate on-demand products that match users’ specific needs would fundamentally change the relationship between users and land cover products – requiring more government support to make these systems a reality.
Highlights
Changes in land cover are one of the greatest and most immediate threats impacting the natural environment and affecting the ecosystem services they provide to humans (Maxwell et al, 2016; Newbold et al, 2016, 2015; Budroni et al, 2019)
The UNsponsored Global Climate Observing System has highlighted the need for improved land cover (LC) information (WMO, 2016), identifying a requirement for annual Earth Observation (EO)-based mapping of LC at 10–30 m resolution as part of the suite of observations called for by the United Nations (UN) Framework Convention on Climate Change (UNFCCC) (Table 2) and which the Paris Agreement has pledged to strengthen (UN-ESC, 2016; UN-FCCC, 2015)
Ground-based data infrastructures such as the United States’ Long Term Ecological Research (LTER) sites and the National Ecological Observatory Network (NEON) and Australia’s Terrestrial Ecosystem Research Network (TERN) are often not considered by LC producers as a source of reference data yet they represent an underexploited opportunity for integrating data from multiple sources such as these
Summary
Changes in land cover are one of the greatest and most immediate threats impacting the natural environment and affecting the ecosystem services they provide to humans (Maxwell et al, 2016; Newbold et al, 2016, 2015; Budroni et al, 2019). More and better LC information is available than ever before, the available products do not meet many users and applications’ needs (Herold et al, 2011; Bontemps et al, 2012; Tsendbazar et al, 2015, 2017) This results in a variety of important impacts, including unmet MEA reporting requirements, compromised ability to monitor and manage change in a timely manner, protect biodiversity, or combat desertification, and a reduction in the accuracy with which we can predict climate change and its impacts (Bontemps et al, 2012; Joppa et al, 2016; O’Connor et al, 2015). The current state of EO data repositories, platforms and research infrastructures
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