No abstract

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, utilizing 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. This article is part of the theme issue ‘Ecological complexity and the biosphere: the next 30 years’.

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.

Peatland resilience, defined here as the rate of recovery from perturbation, is crucial to our understanding of the impacts of climate change and land management on these unique ecosystems. Many peatland areas in the UK are managed as grouse moors using small burns (or increasingly, heather cutting) to encourage heather growth and limit fuel load. These small burns or cuts are distinct disturbance events which provide a useful means of assessing resilience. Until now, it has been difficult to monitor the area affected by management each season due to the remoteness and size of moorland sites. Newer satellite sensors such as those on Sentinel-2 are now collecting data at a spatial resolution that is fine enough to detect individual burns or cut areas, and at a temporal resolution which can be used to monitor occurrence and recovery each year. This study considered four areas of moorland; the North Pennines, Yorkshire Dales, North York Moors, and the Peak District. For each of these areas Sentinel-2 optical data was used to detect management areas using the dNBR (differenced Normalized Burn Ratio), and to monitor vegetation recovery using the NDVI (Normalised Difference Vegetation Index). Significant differences were found between the four selected sites in management repeat interval, with the North York Moors having the shortest repeat interval of 20 years on average (compared to 40–66 years across the other three study sites). Recovery times were found to be affected by burn size and severity, weather during the summer months, and altitude. This suggests that the interactions between peatland management and climate change may affect the future resilience of these areas, with hot, dry summers causing longer management recovery times.

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.