We are in a climate and ecological emergency, where climate change and direct anthropogenic interference with the biosphere are risking abrupt and/or irreversible changes that threaten our life-support systems. Efforts are underway to increase the resilience of some ecosystems that are under threat, yet collective awareness and action are modest at best. Here we highlight the potential for a biosphere resilience sensing system to make it easier to see where things are going wrong, and to see whether deliberate efforts to make things better are working. We focus on global resilience sensing of the terrestrial biosphere at high spatial and temporal resolution through satellite remote sensing, utilising the generic mathematical behaviour of complex systems – loss of resilience corresponds to slower recovery from perturbations, gain of resilience equates to faster recovery. We consider what subset of biosphere resilience remote sensing can monitor, critically reviewing existing studies. Then we present illustrative, global results for vegetation resilience and trends in resilience over the last 20 years, from both satellite data and model simulations. We close by discussing how resilience sensing nested across global, biome-ecoregion, and local ecosystem scales, could aid management and governance at these different scales, and identify priorities for further work.

Abstract
. Nature-based solutions to climate change are growing policy priorities yet remain hard to quantify. Here we use remote sensing to quantify direct and indirect benefits from community-led agroforestry by the International Small group and Tree planting program (TIST) in Kenya. Since 2005, TIST-Kenya has incentivised smallholder farmers to plant trees for agricultural benefit and to sequester CO2. We use Landsat-7 satellite imagery to examine the effect on the historically deforested landscape around Mount Kenya. We identify positive greening trends in TIST groves during 2000-2019 relative to the wider landscape. These groves cover 27,198 hectares, and a further 27,750 hectares of neighbouring agricultural land is also positively influenced by TIST. This positive ‘spill-over’ impact of TIST activity occurs at up to 360m distance. TIST also benefits local forests, e.g. through reducing fuelwood and fodder extraction. Our results show that community-led initiatives can lead to successful landscape-scale regreening on decadal timescales.

<p><span xml:lang="EN-GB"><span>Through innovation and wider socio-economic processes, large sections of the economy have been known to rapidly (and often irreversibly) transition to alternative states. One such sector currently undergoing a transition is the automotive industry, which is moving from a state dominated by internal combustion engines to one characterised by low-emission vehicles. While much research has focused on early warning signals of climate and ecological tipping points, there is much to be done on assessing the applicability of these methods to social systems. Here we focus on the potential for tipping points to occur in the sale of electrical vehicles in various markets, including Norway and the UK. Early indicators that this new state is being approached are considered</span> <span>through the use of</span><span> novel data sources such as car sales, infrastructure announcements and online advert engagement. We </span><span>then</span> <span>map out the socio-technical feedback loops which may drive these tipping points. Consideration is also given to the resilience of the wider automotive industry to previous economic shocks.</span></span><span> </span></p>

Patterning of vegetation in drylands is a consequence of localized feedback mechanisms. Such feedbacks also determine ecosystem resilience-i.e. the ability to recover from perturbation. Hence, the patterning of vegetation has been hypothesized to be an indicator of resilience, that is, spots are less resilient than labyrinths. Previous studies have made this qualitative link and used models to quantitatively explore it, but few have quantitatively analysed available data to test the hypothesis. Here we provide methods for quantitatively monitoring the resilience of patterned vegetation, applied to 40 sites in the Sahel (a mix of previously identified and new ones). We show that an existing quantification of vegetation patterns in terms of a feature vector metric can effectively distinguish gaps, labyrinths, spots, and a novel category of spot-labyrinths at their maximum extent, whereas NDVI does not. The feature vector pattern metric correlates with mean precipitation. We then explored two approaches to measuring resilience. First we treated the rainy season as a perturbation and examined the subsequent rate of decay of patterns and NDVI as possible measures of resilience. This showed faster decay rates-conventionally interpreted as greater resilience-associated with wetter, more vegetated sites. Second we detrended the seasonal cycle and examined temporal autocorrelation and variance of the residuals as possible measures of resilience. Autocorrelation and variance of our pattern metric increase with declining mean precipitation, consistent with loss of resilience. Thus, drier sites appear less resilient, but we find no significant correlation between the mean or maximum value of the pattern metric (and associated morphological pattern types) and either of our measures of resilience.

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.

Nature-based solutions to climate change are growing policy priorities yet remain hard to quantify. Here we use remote sensing to quantify direct and indirect benefits from community-led agroforestry by the International Small group and Tree planting program (TIST) in Kenya. Since 2005, TIST-Kenya has incentivised smallholder farmers to plant trees for agricultural benefit and to sequester CO2. We use Landsat-7 satellite imagery to examine the effect on the historically deforested landscape around Mount Kenya. We identify positive greening trends in TIST groves during 2000-2019 relative to the wider landscape. These groves cover 27,198 ha, and a further 27,750 ha of neighbouring agricultural land is also positively influenced by TIST. This positive 'spill-over' impact of TIST activity occurs at up to 360 m distance. TIST also benefits local forests, e.g. through reducing fuelwood and fodder extraction. Our results show that community-led initiatives can lead to successful landscape-scale regreening on decadal timescales.