Recent advances in Earth Observation (EO) now allow for high-resolution assessments of glacier and land cover changes in deglaciating regions of the world. In Peru, where glaciers are rapidly melting in the face of climate warming, communities need a comprehensive understanding of these changes in order to plan for climate adaptation. However, global-scale products, such as Google’s Dynamic World, often struggle to capture the complexities of high-Andean ecosystems (Pizarro et al., 2022). Innovations in satellite sensors, the increasing accessibility of machine learning, and availability of satellite archives to the public have overcome the challenges of conducting EO studies in the mountain cryosphere (Taylor et al., 2021), allowing us to begin using these products to conduct thorough assessments of how deglaciating landscapes may evolve with future climate warming.

Communities living directly beneath diminishing ice reserves have observed important landscape changes in recent decades, such as a decline of bofedales (high altitude wetlands) and gradual vegetation succession into previously barren lands (Castro et al., 2022). This has a direct consequence for people’s livelihoods, as indigenous communities rely on these lands to graze animals and grow crops. Remote sensing and machine learning have been used in deglaciating regions to assess vegetation succession (e.g. Klaar et al., 2014), including complex high-Andean ecosystems (Pizarro et al., 2022). Similarly, advances in satellite processing techniques have allowed for new insights into the rate of deglaciation of the Peruvian mountain cryosphere (Taylor et al., 2022). Connecting these components together will facilitate a detailed analysis of how glacier recession drives ecosystem changes downstream, and thus the identification of hotspots where adaptation interventions are most needed.

This project will utilise a broad range of EO sensors to conduct an interdisciplinary evaluation of recent, and future, land cover changes in the Peruvian Andes (see Belward and Skøien, 2015, for an overview). This will include utilising a range of high-resolution missions with multi-year archives (e.g. Sentinel-1 (SAR) and Sentinel-2 (Optical), Planet, CryoSat-2, HISUI) and satellites that will be launched in the first-year of the PhD project (e.g. BIOMASS, FLEX, Planet Pelican), for which the successful candidate will be leading on new algorithm development and low-level processing as a pioneer user. Research will be conducted in the Peruvian Cordilleras, and the successful candidate should expect to have the opportunity to conduct fieldwork to ground-truth satellite data with a variety of field methods, such as photogrammetry or capturing hyperspectral imagery from a UAV.

The successful candidate will work closely with local partners in regional government, industry, and with community leaders to generate remote sensing datasets that can support indigenous populations at the face of climate change. Such datasets will likely include a quantification of recent past glacial mass balance, projections of future glacier retreat, land cover change maps, quantifications of land cover change, and availability of future water resource. Upscaling to a national (and even international) level will be achieved by using big data and cloud computing. During fieldwork in the Peruvian Cordilleras, the candidate should expect to work with local communities and partner organisations to disseminate PhD findings to a non-academic audience.

Belward, A.S and Skøien, J.O. (2015) Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites. ISPRS Journal of Photogrammetry and Remote Sensing 103, pp.115-128. https://doi.org/10.1016/j.isprsjprs.2014.03.009

Castro, J., Montoya, N., Quincey, D., and Potter, E. (2022) Characterising the multi-decadal evolution of highland ecosystems, Sibinacocha, Peru, using GoogleEarth Engine. EGU General Assembly 2022, Vienna, Austria. https://doi.org/10.5194/egusphere-egu22-8634

Klaar, M.J., Kidd, C., Malone, E. et al. (2014) Vegetation succession in deglaciated landscapes: implications for sediment and landscape stability. Earth Surface Processes and Landforms. 40(8), pp.1088-1100. https://doi.org/10.1002/esp.3691

Pizzaro, S.E., Pricope, N.G., Vargas-Machuca, D. et al. (2022) Mapping Land Cover Types for Highland Andean Ecosystems in Peru Using Google Earth Engine. Remote Sensing. 14(7). https://doi.org/10.3390/rs14071562

Taylor, L.S., Quincey, D.J., Smith, M.W. et al. (2021) Remote sensing of the mountain cryosphere: Current capabilities and future opportunities for research. Prog. Phys. Geogr. 45(6). pp.931-964. https://doi.org/10.1177/03091333211023690

Taylor, L.S., Quincey, D.J., Smith, M.W. et al. (2022) Multi-Decadal Glacier Area and Mass Balance Change in the Southern Peruvian Andes. Front. Earth. Sci. 10.  https://doi.org/10.3389/feart.2022.863933