Addressing the need for improved land cover map products for policy support.

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.

Monitoring vegetation change and their potential drivers in Yangtze River Basin of China from 1982 to 2015.

Monitoring vegetation change and their potential drivers are important to environmental management. Previous studies on vegetation change detection and driver discrimination were two independent fields. Specifically, change detection methods focus on nonlinear and linear change behaviors, i.e., abrupt change (AC) and gradual change (GC). But driver discrimination studies mainly used linear coupling models which rarely concerned the nonlinear behaviors of vegetation. The two diagnoses need be treated as sequential flow because they have inner causality mechanisms. Furthermore, ACs concealed in time series may induce over/under-estimate contributions from human. We chose the Yangtze River Basin of China (YRB) as a study area, first separated ACs from GCs using breaks for additive and seasonal trend method, then discriminated drivers of GCs using optimized Restrend method. Results showed that (1) 2.83% of YRB were ACs with hotspots in 1998 (30.2%), 2003 (10.4%), and 2002 (7.6%); 66.7% of YRB experienced GC with 94.8% of which were positive; and (2) climate induced more area but less dramatic GCs than human activities. Further analysis showed that temperature was the main climate driver to GCs, while human-induced GCs were related to local eco-policies. The widely occurring ACs in 1998 were related to the flooding catastrophe, while the dramatic ACs in sub-basin 12 in 2003 may result from urbanization. This paper provides clear insights on the vegetation changes and their drivers at a relatively long perspective (i.e., 34 years). Sequential combination of specifying different vegetation behaviors with driver analysis could improve driver characterizations, which is key to environmental assessment and management in YRB.