Using remote sensing to assess ecosystem resilience

Abstract

Vegetation ecosystems are increasingly under pressure from both direct human influence and indirect anthropogenically-driven climate change. Increasing amounts of data are made available from satellite systems which can image these ecosystems from afar. The work in this thesis provides several examples of the utility of remotely sensed data from satellites to assess the resilience of ecosystems. This notion of resilience is measured by considering the return rate following a perturbation, with statistical metrics such as AR(1) and variance providing an indication of system resilience and the proximity to a potential tipping point. The first focus of this work is on direct human environmental intervention through community-based agroforestry groups in Kenya. These results show that the efforts of these groups can be detected with satellite data as a greening trend which occurs both within designated tree planting groves and in the surrounding landscape. These groups provide a case study for the power of positive social tipping points to achieve environmental improvement. Following this, the potential of high-resolution satellite data from Sentinel-2 to quantify patterned vegetation in the Sahel is explored. These striking patterns have often been associated with vegetation resilience in drylands. No correlation is found between pattern morphology and resilience, contrary to a previously held hypothesis from the literature. Precipitation is also identified as a key driver of these patterns. Moving beyond drylands, satellite data is utilised at a global scale to assess the link between vegetation resilience and climatic variables across the world. There is a clear relationship between average resilience, as measured by AR(1), and precipitation, which is evident at three spatial scales; the local (pixel), ecoregion and biome. There is also a temperature component, with hotter, drier locations displaying lower levels of resilience. This thesis finishes with a discussion of the potential for a resilience sensing framework constructed by combining remote sensing data with new cloud computing technologies. This will enable the monitoring of resilience change across the world and the identification of regions which require further investigation and intervention.