A fantastic opportunity for land change science studies in the near immediate future is the growing utility of cloud computing geospatial analysis platforms such as Google Earth Engine. Combined with the ever-increasing availability of earth observation datasets, these kinds of technologies are expected to facilitate more regional- to global-scale analyses, as well as in-depth local-scale investigations, of land system changes.
The benefits of using Earth Engine is that it overcomes the computational and data storage limitations that kept remote sensing studies in the past from realising its full potential. Previously I had to download and make local copies of all the imagery that I required on my machine, and implement all my analyses using available software tools. But I always worried about computing and storage requirements, of how much disk space, memory, and time it would take to get these done. Now, Earth Engine takes care of all these through the cloud via its parallel computing server infrastucture.
Datasets such as the entire Landsat archive and the Sentinel missions are all freely and openly accessible through Earth Engine’s public data catalog . Accessing these and displaying them takes only a few lines of code without even downloading them. I’ve been able to complete landscape-scale analyses, which I would not have been able to do before given my own local resources. Now, I’ve decided Earth Engine should be one of the primary tools in my work.
Some examples of global-scale applications benefitting from Earth Engine include monitoring forest cover change  and surface water dynamics , and the global mapping of terrestrial ecoregions , among other examples. I invite you to read the paper by Gorelick et al. (2017) , which talks about Earth Engine in-depth, as well as a list of other noteworthy examples. And if you haven’t tried it yet, I encourage you to take Earth Engine for a spin. Trust me, you will not be disappointed.
 Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. & Moore, R. (2017) Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. doi:10.1016/j.rse.2017.06.031.
 Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., et al. (2013) High-resolution global maps of 21st-century forest cover change. Science, 342, 850–853. doi:10.1126/science.1244693.
 Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A.S. (2016) High-resolution mapping of global surface water and its long-term changes. Nature, 540, 418–422. doi:10.1038/nature20584.
 Dinerstein, E., Olson, D., Joshi, A., Vynne, C., Burgess, N.D., Wikramanayake, E., et al. (2017) An ecoregion-based approach to protecting half the terrestrial realm. BioScience, 67, 534–545. doi:10.1093/biosci/bix014.