Global changes in landscapes between 1992 and 2015

<p>Land-cover change is a pervasive phenomenon caused by climate change, and, in recent decades, by the rapid population growth and accelerated industrialization.<br> Therefore, the assessment of land cover changes is of prime importance for planing and management of natural resources.<br> It provides necessary information for making decisions on trade-off between development and conversation.<br> We present the first comprehensive map and GIS-based database of global land cover during the 1992–2015 period.<br> The map is derived using a post-classification change detection algorithm applied to the new European Space Agency (ESA) global time series of land cover maps at 300m resolution (CCI-LC).<br> To smooth possible errors stemming from incorrect category assignments at individual pixels and obtain distinct changes on relevant geographical scale, we tessellate the entire landmass into local landscapes – 9km × 9km tiles of 900 CCI-LC pixels.<br> Change is detected by comparing the 2015 pattern of land cover categories within a tile with its 1992 pattern.<br> Only 22% of tiles registered a meaningful change in their patterns.<br> The collection of all changed tiles constitute a SQL-searchable spatial database to be used for analyzing land cover transitions and global mapping of change trajectories. <br> A global map of change trajectories provides a visualization of spatial distribution of all major changes and serves as a guide to a more focused use of the database.<br> Globally dominant CCI-LC transitions during the 1992-2015 period was forest → agriculture (19%).<br> The vegetation type that experiences the largest net loss was the trees at -559,124 km2 globally.<br> We concluded that post-classification change detection is well-suited for a fairly accurate estimation of the global forest area and a depiction of a geographical distribution of forest losses/gains, but, in comparison with estimates stemming from a forest-dedicated change detection method using high resolution images, it provides a low estimation of forest loss and a high estimation of forest gain.<br> For other vegetation types estimations of losses and gains are expected to be more accurate due to more homogeneous definitions of non-forested CCI-LC categories.